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Characterizing the Microbiome of Neonates and Infants to explore associations with Health and Disease

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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.

Published in: Health & Medicine

Characterizing the Microbiome of Neonates and Infants to explore associations with Health and Disease

  1. 1. Christopher J. Stewart, Ph.D. Post Doctoral Associate Baylor College of Medicine, Petrosino Lab
  2. 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. 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. 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. 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. 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. 7. Role of the microbiome in humans Laukens et al., 2015. FEMS
  8. 8. Factors influencing the microbiome Aagaard, Stewart, Chu. 2016. EMBO Reports
  9. 9. Microbiome development from birth Bokulich et al. Sci Trans Med (2016) Yassour et al. Sci Trans Med (2016)
  10. 10. Birth mode differences in year 1? Yassour et al. Sci Trans Med (2016) Bokulich et al. Sci Trans Med (2016)
  11. 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. 12. CS increases later life disease risk Sevelsted et al., Pediatrics (2015)
  13. 13. Pannaraj et al. 2017. JAMA Breast feeding slows maturation of the microbiome
  14. 14. Dogaru et al., Am J Epidemiology (2013) 117 study meta-analysis Breast milk reduces risk of asthma
  15. 15. Breast milk reduces risk of obesity Davis et al., Diabetes Care (2006) 15,253 children age 9-14 years old
  16. 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. 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. 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. 19. Birth Mode Cohort Stewart, CJ. et al. 2017. FronDers in Microbiology
  20. 20. Comparable microbiome profiles based on weighted UniFrac Stewart, CJ. et al. 2017. FronDers in Microbiology ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● −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 ● ● CS V P−Value: 0.925; R−Squared: 0.0114; F−Statistic: 0.346 ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● −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 ● ● CS V P−Value: 0.646; R−Squared: 0.0137; F−Statistic: 0.556 ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● −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 ● ● CS V P−Value: 0.795; R−Squared: 0.016; F−Statistic: 0.584 ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●●● ● −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 ● ● CS V P−Value: 0.344; R−Squared: 0.0365; F−Statistic: 1.1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −0.4 −0.2 0.0 0.2 0.4 −0.6 −0.3 0.0 0.3 PC1 (41.9% variation explained) PC2(21.9%variationexplained) Deliverymode_simple ● ● 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. 21. No difference in longitudinal alpha- and beta- diversity Stewart, CJ. et al. 2017. FronDers in Microbiology 0 10 20 30 40 0 25 50 75 100 Age in Days ObservedOTUs Deliverymode_simple CS V 0.0 0.5 1.0 1.5 2.0 2.5 0 25 50 75 100 Age in Days ShannonDiversity Deliverymode_simple CS V 0.0 0.2 0.4 0.6 0.8 0 25 50 75 100 Age in Days WeightedUniFrac Deliverymode_simple CS V 0.0 0.2 0.4 0.6 0.8 0 25 50 75 100 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 0 10 20 30 40 0 25 50 75 100 Age in Days ObservedOTUs Deliverymode_simple CS V 0 10 20 30 40 0 25 50 75 100 Age in Days ObservedOTUs Deliverymode_simple CS V Cesarean Vaginal
  22. 22. Vaginal infants ‘kept’ more OTUs Stewart, CJ. et al. 2017. FronDers in Microbiology Age in days Age in days 0 5 10 15 0 25 50 75 100 Age in Days OTUsKept Deliverymode_simple CS V 0 10 20 0 25 50 75 100 Age in Days OTUsLost Deliverymode_simple CS V 0 3 6 9 0 25 50 75 100 Age in Days OTUsRegained Deliverymode_simple CS V 0 5 10 15 20 0 25 50 75 100 Age in Days NewOTUsGained Deliverymode_simple CS V C DOTUs Regained New OTUs Gained A BOTUs Kept OTUs Lost 0 10 20 30 40 0 25 50 75 Age in Days ObservedOTUs Deliverymode_simple CS V 0 10 20 30 40 0 25 50 75 Age in Days ObservedOTUs Deliverymode_simple CS V Cesarean Vaginal
  23. 23. Comparable temporal development of abundant taxa Stewart, CJ. et al. 2017. FronDers in Microbiology
  24. 24. Post Discharge Cohort Stewart, CJ. et al. 2016. Nature ScienDfic Reports
  25. 25. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 0.5 1.0 1.5 2.0 0 25 50 75 100 DOL AlphaDiversity Disease ● ● ● Control LOS NEC DOL Shannon Diversity 4.0 PD 1 – 3 Yr Stewart, CJ. et al., Nature ScienDfic Reports (2016) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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. 26. Diversity increased post discharge NICU NICU Stewart, CJ. et al. 2016. Nature ScienDfic Reports
  27. 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. 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. 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. 30. Bacterial load comparable Abdulkadir, B. et al. 2016. Early Human Development
  31. 31. NEC/LOS vs Control Cohort Stewart, CJ. et al. 2016. Microbiome Stewart, CJ. et al. 2017. Microbiome (In Press)
  32. 32. Exis=ng data Results
  33. 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. 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. 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. 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. 37. Increased stability in controls 1 2 3 4 5 6 PGCT Vazquez-Baeza Y, et al (2013), Gigascience
  38. 38. Metabolic pathways associated with NEC Stewart, CJ. et al. 2016. Microbiome A B C D E F Control NEC
  39. 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. 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. 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. 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/

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