# Qualifying Exam Aims **Hypothesis**: Systemic antibiotics perturb the composition of infant's microbiota at different mucosal sites leading to an altered immune response to respiratory tract infection [toc] ## Introduction ### Importance of healthy human respiratory microbiome Disruption of alveologenesis is associated with severe pediatric lung disorders,including bronchopulmonary dysplasia(BPD). Infants are commonly administered antibiotics which promotes and influx of ILC3 [Gray et al., 2017](https://stm.sciencemag.org/content/scitransmed/9/376/eaaf9412.full.pdf) In recent years emerging evidence implicates the respiratory microbiome and its health as a key regulator in controlling asthma, influencing lung development, and modulating the immune response. The lung microbiota protects the lung by [stimulating immune maturation](https://www.nature.com/articles/nm.3568) and [promoting epithelial integrity](https://science.sciencemag.org/content/sci/336/6086/1268.full.pdf). The composition of the respiratory microbiome is thought to arise through micro-aspiration which aids in seeding the lung with commensal microbes early in life. Epidemiological findings demonstrate 1) that the infant respiratory microbiome is distinct from aged individuals ([Odamaki et al., 2016](https://bmcmicrobiol.biomedcentral.com/track/pdf/10.1186/s12866-016-0708-5)) and 2) that the infant lung microbiome is both volitile and prone to alterations from external factors. Due to these findings and other relivant data it is warrented to further study and characterize the respiratory microbiome. The primary function of the respiratory tract is for gaseous exchange of oxygen and carbon dioxide. This function makes the lung distinct from the more readally studied gastrointestinal (GI) tract. Despite the plethora of studies on the GI microbiome there is little research awarded to characterizing the effects of the GI microbiome on the respiratory microbiome. It is well known that bacterial, antigens, and a host of other factors are connected to the both the circulatory and lymphatic systems via the portal vein. This relationship between the lung and gut lung microbiomes is commonly referred to as the gut-lung axis. The axis supports the notion that all mucosal surfaces are interconnected and that perturbation at one site will most definentely lead to dysbiosis at other mucosal sites. 1. Stimulate immune maturation A.[Gollwitzer et al., 2014](https://www.nature.com/articles/nm.3568) B.[Olszak et al., 2012](https://science.sciencemag.org/content/sci/336/6080/489.full.pdf) 3. Promotes epitheial integrity A.[Hooper et al., 2012](https://science.sciencemag.org/content/sci/336/6086/1268.full.pdf) ### Study design Rhesus monkeys are a good model for human physiological proccesses given their 98% genomic similarity with humans. Rhesus monekys age 3 times faster than humans so the 6 month window in our current study design can be compaired to 1.5-2 years of age for human infants ([Simmons et al., 2018](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5909965/pdf/nihms954841.pdf)).Dispite their increased aging relative to humans, rhesus monkeys go through the same developmental processes as humans do such as puberty (2.5-4.5 years) and menopause (~26 years). Our time frame captures the juncture in which bulk aleovolargensis occurs. This process is characterized by an exponential rise in alveoli due to secondary septation contributes to an increase in lung volume. ## Aim 1: Charcteriw the microbiome at different mucosal sites during infnacy and determine if systemic antibiotic treatmetn alters composition **Rational:** We recently reported a decrease in lung function following antibiotic administration in males relative to females. Whether the alterations in lung function are attributed to the microbiome is unknown however, we hypothesize that the difference we observed in lung function can be attributed to the composition of some or are both of the mucosal sites we studied in aims 1 and 2. [Ma et al., 2019](https://onlinelibrary.wiley.com/doi/epdf/10.1002/advs.201902054) found evidence of sex dependent differences in the microbiome by reanalyzing Human Microbiome Projoct (HMP) data. This study did not look at the intestinal microbiome in infants therefore we aim to elucide potenial differences in the composition of difference samples site. We previously showed that monkeys administered an antibiotic cocktail consisting of Vancomycin, Neomycin, and Ampicilin have a reduction in bacterial diversity compared to their untreated counter parts. However, whether these alternations persist later in development or if these alterations are observed in other mucosal site is understudied. Using [longitudinal data](https://docs.qiime2.org/2019.10/tutorials/longitudinal/) consisting of fecal, BAL (broncheolar lavage), and nasalpharyngeal samples collected at 1 month intervals for 5 months following systemic antibiotic administration we will elucidate whether there are lasting effects to intestinal microbiome. In order to quantify our longitudinal findings we will look at the alpha diversity of the bacterial composition though out the study. We will measure alpha diversity in our samples by using three different metrics, Chao1, Shannon index, and relative abundance. First, [choa 1 index](https://www.researchgate.net/post/How_to_interpret_Chao1_and_Chao2_values) will allow to indentification of rare populations of microbes relative to our samples. Second, we will determine how the microbes are balanced to each other and whether we have species evenness (similar abundance level) or if some species dominate others by looking at the Shannon index. Third, we will determine the relative species richness by performing as ASV (amplicon sequencing variants) count. In addition to our alpha diversity metrics we will use [Multivariate Association with Linear Models (MaAsLin)](https://huttenhower.sph.harvard.edu/maaslin) to as a supplement to our diversity and richness metric to determine if there is an association between the antibiotic treatment and microbial composition at different mucosal sites. Beta diversity metrics will be used to determine how different the microbial composition in one group is compared to another. Beta diversity will be measured by Bray–Curtis dissimilarity and UniFrac. ## Aim 2: Determine how the degree of the innate immmune response to respiratory tract infection (RTI) is altered during systemic induced dysbiosis It is known that the microbiota stimulates the expression of pattern recognitition receptors ( PRRs) IL-22 primarily targets non-hematopoietic epithelial and stromal cells where it can promote proliferation and play a role in tissue regeneration. In addition, IL-22 regulates host defense at barrier surfaces. However, IL-22 has also been linked to several conditions involving inflammatory tissue pathology ([Dudakov et al., 2015](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4407497/pdf/nihms681768.pdf)). **Rational:** The nasopharynx is a common route of exposure to bacteria, some of which could potentially seed the microbiome of the airways. In a clinical study [Bosch et al., 2017](https://www.atsjournals.org/doi/pdf/10.1164/rccm.201703-0554OC) followed the development of the nasopharynx during the first year of development and through computational techniques showed that the microbiota forms the mediator between early-life environmental risk factors for and susceptibility to RTIs over the first year of life. Reasent literature showed that influenza is capable of infecting cells lining the trachea. We suspect that RTI may effect the respirtory tract microbial communities in different ways given they are somewhat distinct from one another. To the best of our knowledge, there is one study that longitudinally explored the nasal microbiome of preterm infants following antibiotic treatment. In the study, antibiotic use in human neonates increased bacterial diversity shortly after administration (7 days) and this trend persisted for the duration of the study (6 months)([Chonmaitree et al., 2017](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5510840/pdf/pone.0180630.pdf)). Furthermore, this study focused on upper respiratory tract infections (URT) as whole and did not interrogate the effects of an already perturbed microbiome. We therefore, in a highly controlled study, aim to understand how RTI can further effect the nasal microbiome and potentially lung function. After determining the bacterial diversity and richness in our samples we will perform a [PERMANOVA test](https://www.researchgate.net/post/What_is_the_purpose_of_a_Permanova_test_specifically_in_terms_of_the_gut_microbiota) to determine if the differences we observed in the nasalpharyngeal and oralpharyngeal microbiomes following infection by a common respiratory pathogen are modulated by the existing microbiota. As a sub aim we will determine the severity of infection between our control and test groups by measuring the amount of common innate inflammatory markers in the lung. Influenza in capable of inducing a innate immune response defined by the release of the cyotokines IL-6, IL-15, IL-12, TNF-alpha, IL-17 and the chemokines IL-8, IP-10, and MCP-1 ([Bermejo-Martin et al., 2009](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507467/pdf/fimmu-06-00361.pdf)). **Cytokines and chemokines detected in serum or lung tissue samples of human subjects with severe disease infected by IAV** Cytokines: IL-6 ([Bermejo-Martin et al., 2009](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507467/pdf/fimmu-06-00361.pdf));[Hagau et al. 2010](https://ccforum.biomedcentral.com/track/pdf/10.1186/cc9324)), IL-9 ([Bermejo-Martin et al., 2009](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507467/pdf/fimmu-06-00361.pdf)), IL-15 ([Bermejo-Martin et al., 2009](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507467/pdf/fimmu-06-00361.pdf); [Hagau et al. 2010](https://ccforum.biomedcentral.com/track/pdf/10.1186/cc9324)), IL-12 ([Bermejo-Martin et al., 2009](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507467/pdf/fimmu-06-00361.pdf)), IL-17 ([Bermejo-Martin et al., 2009](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507467/pdf/fimmu-06-00361.pdf)), TNFα ([Bermejo-Martin et al., 2009](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507467/pdf/fimmu-06-00361.pdf); [Hagau et al. 2010](https://ccforum.biomedcentral.com/track/pdf/10.1186/cc9324)) Chemokines: IL-8 ([Bermejo-Martin et al., 2009](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507467/pdf/fimmu-06-00361.pdf); [Kelvin et al., 2010](https://watermark.silverchair.com/50-6-850.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAnIwggJuBgkqhkiG9w0BBwagggJfMIICWwIBADCCAlQGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMf08jDvRcrIEm11hjAgEQgIICJUsBp3FhlPTuOOCMu19CEHTqyKe4pYiGF0cgD9Tc4pru-7B-NiqnH6bQIIVg-8likPUgNjbJJJcRWNt-2EyT8sXBJK0-QBkjukkpsUzGW7NkDxFYNqNgYeMzqY38OstfrM4_nyTQGhcRrspEsrY1Si1A69IpFC7ohF87OsGZ_YYMGtnNxLCUDOLCcU_IA_TNhrmuVp4U24WsZaFK9WDKVCdufqIk4UyZ9BrWBS1qo6xUMM9M87xS4MgKp1NDSNyGaJHuRJ-JRDO6fz3HPsWhHAbvrbXJi3vUyKeuZ465492GKgB8axKZxMhus_7ky_Hx8e5yqLUfoPezAog7N0-p_VexxdDu5vMynN1sfPn8TrLdEu1N_-gPjd39Cg3hmqvIb-ivMxqoMg4bLQfEWlEAUDMqqgjvNZfS0SFDdZ4EWg2_sMQ7-TfyIvP7l53wzh-eUncbPPJK5osId8KwBHsOC7kLfRVCO-4_i3kagpcTnJV-j6waD21frfI_GGTu4JHbAcUMYmT6_UmLumFeMwyYmJPXKTVTB-bBiEeaCMVaDLjLobUjSeL0Y6xJpXwxBGtbL2h9-eD6ONJNG9WwXfWTEvR5f-lxmNfp9ABXujNtiGrs5rT8NYbZDl6iXRc42GRj8HlkoRjVe6CPIcQ7o4US-hMzEl5Br-Bh_-2WO7MyND8hqZEIo4ubsbsYC0ilhu0uXMeDxXFnPQwpGJ4s5GXLf28t43yVvA)), IP-10 ([Kelvin et al., 2010](https://watermark.silverchair.com/50-6-850.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAnIwggJuBgkqhkiG9w0BBwagggJfMIICWwIBADCCAlQGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMf08jDvRcrIEm11hjAgEQgIICJUsBp3FhlPTuOOCMu19CEHTqyKe4pYiGF0cgD9Tc4pru-7B-NiqnH6bQIIVg-8likPUgNjbJJJcRWNt-2EyT8sXBJK0-QBkjukkpsUzGW7NkDxFYNqNgYeMzqY38OstfrM4_nyTQGhcRrspEsrY1Si1A69IpFC7ohF87OsGZ_YYMGtnNxLCUDOLCcU_IA_TNhrmuVp4U24WsZaFK9WDKVCdufqIk4UyZ9BrWBS1qo6xUMM9M87xS4MgKp1NDSNyGaJHuRJ-JRDO6fz3HPsWhHAbvrbXJi3vUyKeuZ465492GKgB8axKZxMhus_7ky_Hx8e5yqLUfoPezAog7N0-p_VexxdDu5vMynN1sfPn8TrLdEu1N_-gPjd39Cg3hmqvIb-ivMxqoMg4bLQfEWlEAUDMqqgjvNZfS0SFDdZ4EWg2_sMQ7-TfyIvP7l53wzh-eUncbPPJK5osId8KwBHsOC7kLfRVCO-4_i3kagpcTnJV-j6waD21frfI_GGTu4JHbAcUMYmT6_UmLumFeMwyYmJPXKTVTB-bBiEeaCMVaDLjLobUjSeL0Y6xJpXwxBGtbL2h9-eD6ONJNG9WwXfWTEvR5f-lxmNfp9ABXujNtiGrs5rT8NYbZDl6iXRc42GRj8HlkoRjVe6CPIcQ7o4US-hMzEl5Br-Bh_-2WO7MyND8hqZEIo4ubsbsYC0ilhu0uXMeDxXFnPQwpGJ4s5GXLf28t43yVvA)), MCP1 ([Kelvin et al., 2010](https://watermark.silverchair.com/50-6-850.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAnIwggJuBgkqhkiG9w0BBwagggJfMIICWwIBADCCAlQGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMf08jDvRcrIEm11hjAgEQgIICJUsBp3FhlPTuOOCMu19CEHTqyKe4pYiGF0cgD9Tc4pru-7B-NiqnH6bQIIVg-8likPUgNjbJJJcRWNt-2EyT8sXBJK0-QBkjukkpsUzGW7NkDxFYNqNgYeMzqY38OstfrM4_nyTQGhcRrspEsrY1Si1A69IpFC7ohF87OsGZ_YYMGtnNxLCUDOLCcU_IA_TNhrmuVp4U24WsZaFK9WDKVCdufqIk4UyZ9BrWBS1qo6xUMM9M87xS4MgKp1NDSNyGaJHuRJ-JRDO6fz3HPsWhHAbvrbXJi3vUyKeuZ465492GKgB8axKZxMhus_7ky_Hx8e5yqLUfoPezAog7N0-p_VexxdDu5vMynN1sfPn8TrLdEu1N_-gPjd39Cg3hmqvIb-ivMxqoMg4bLQfEWlEAUDMqqgjvNZfS0SFDdZ4EWg2_sMQ7-TfyIvP7l53wzh-eUncbPPJK5osId8KwBHsOC7kLfRVCO-4_i3kagpcTnJV-j6waD21frfI_GGTu4JHbAcUMYmT6_UmLumFeMwyYmJPXKTVTB-bBiEeaCMVaDLjLobUjSeL0Y6xJpXwxBGtbL2h9-eD6ONJNG9WwXfWTEvR5f-lxmNfp9ABXujNtiGrs5rT8NYbZDl6iXRc42GRj8HlkoRjVe6CPIcQ7o4US-hMzEl5Br-Bh_-2WO7MyND8hqZEIo4ubsbsYC0ilhu0uXMeDxXFnPQwpGJ4s5GXLf28t43yVvA)) ### Methods DNA isolated using the Qiagen PowerSoil DNA Isolation Kit according to the manufacturer’s instructions and a nested PCR will be used to amplify and barcode the V4 domain of the 16S rRNA gene. 16S rRNA amplicons will be sequenced using the Illumina MiSeq system. Base calling is automatically accomplished on the computer running the MiSeq system in real time. Sequences containing uncalled bases, incorrect primer sequences, or runs of greater than or equal to 12 identical nucleotides will be removed. The sequences will subsequently be demultiplexed and trimmed using Quantitative Insights into Microbial Ecology (QIIME) (Caporaso et al., 2010). Chimeras will be removed using Chimera Slayer. Downstream sequence alignment of QIIME processed sequences, identification of OTUs, clustering, phylogenetic and taxonomic analysis will be conducted using the Phylosift open source software package (https://phylosift.wordpress.com). All representative set sequences will be run through BLAST with an output of the top species based on sequence identity to quality check family- and genus-level OTU calls. The top hits based on percent sequence identity for each OTU will also be reported. Lung lavage and buccal microbiomes will be characterized by Illumina MiSeq using primers targeted at L the V4 region of 16S rRNA. Reads will be clustered into operational taxonomic units from buccal swabs and lung lavage. ### Preliminary Results Preliminary studies: We conducted 16S rRNA sequencing analysis of buccal swabs in our animal cohort. Eight indoor-housed and breast-fed infant rhesus macaques were selected for analysis of lung lavage and buccal swabs. Infant Rhesus monkeys evaluated with tracheobronchial epithelial (TBE) cultures were 6 months (range 3-6.5 months, n= 5) and evaluated adults were 67 months (range 36-158 months, n=5). Lung lavage and buccal microbiomes were characterized by Illumina MiSeq using primers targeted at L the V4 region of 16S rRNA. A total of 435,008 and 651,400 high-quality reads were obtained with an average of 29,000 and 54,283 reads per animal for lung lavage and buccal swabs respectively. The oral microbiome was dominated by the phyla Proteobacteria, Firmicutes, Bacteriodetes, and Fusobacteria while the lung microbiome was dominated by the phyla Actinobacteria, Proteobacteria, Firmicutes, and Bacteriodetes (Figure 1). The most dominant genera above 2% average community abundance for the infant oral microbiome were: Actinobacillus 26.82% (+/-0.15), Aggregatibacter 15.29% (+/-0.06), Streptococcus 11.63 (+/0.09), Porphyromonas 9.49% (+/-0.06), Fusobacterium 6.7% (+/-0.05), Leptotrichia 4.98% (+/-0.07), Moraxella 4.9% (+/-0.07), Veillonella 4.87% (+/-0.02), Prevotella 2.6% (+/-0. 04), and Neisseria 2.21% (+/-0.02). For the lung lavage microbiome, the most dominant genera above 2% average relative abundance were: Tropheryma 56.38% (+/-0.32), Streptococcus 4.02% (+0.05), Actinobacillus 2.9% (+/-0.04), Campylobacter 2.69% (+/0.03), Flavobacterium 2.53% (+/-0.04), Acinetobacter 2.51% (+/-0.09), and Aggregatibacter 2.14% (+/-0.03 ). ABX exposure not only reduced the total number of intestinal commensal bacteria but also modified the composition of the intestinal microbiota in infant monkeys (Figure 2 A, B). We predict analysis following antibiotic treatment and/or influenza infection will demonstrate a shift towards  * mouse and human lung development # References 1. [Smith et al., 2010](https://reader.elsevier.com/reader/sd/pii/S1526054209001006?token=04BA6D8BD4E03A77437B47177C2BD6AAAC44681A4C52DE936248264FC9BF549BD470F62BED2BBFC499FF7A8A685AA6BB) 2. [Odamaki et al., 2016](https://bmcmicrobiol.biomedcentral.com/track/pdf/10.1186/s12866-016-0708-5) 3. [Gasparrini et al., 2019](https://www.nature.com/articles/s41564-019-0550-2) 4. [Bosch et al., 2017](https://www.atsjournals.org/doi/pdf/10.1164/rccm.201703-0554OC) 5. [Peterson et al., 2016](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4809513/pdf/pone.0152493.pdf) 6. [Ma et al., 2019](https://onlinelibrary.wiley.com/doi/epdf/10.1002/advs.201902054) 7. [Ramos at al., 2015](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507467/pdf/fimmu-06-00361.pdf) 8. [Ren et al., 2019](https://www.atsjournals.org/doi/pdf/10.1164/rccm.201812-2312OC) 9. [Simmons et al.,2018](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5909965/pdf/nihms954841.pdf) 10.[Li et al., 2020](https://www.ncbi.nlm.nih.gov/pubmed/31930784) ## READ!!! 9. [Backhed et al., 2015](https://reader.elsevier.com/reader/sd/pii/S1931312815001626?token=BF7A0965857092B998AF65D36C3539802432E3597177256FF23A08466AB7EBA7A1F9B6642396F269FBE818AD35AFDEBD) 10. [Bokulich et al., 2016](https://stm.sciencemag.org/content/scitransmed/8/343/343ra82.full.pdf) 11. [Philpott et al.,2014](https://www.nature.com/articles/nri3565.pdf)