Chatterjee, Ranojoy

Crandall, Keith A.

Hourigan, Suchi

Kulkarni,, Sanika

Levy, Shira L.

Oke, Oluwadamilola(“Dami”)

Rahnavard, Ali

English

George Washington University: Health Sciences Research Commons (HSRC)

Characterizing the metabolite-microbiome dynamics between breastmilkand infant health outcomesSanika Kulkarni, Oluwadamilola “Dami” Oke, Ranojoy Chatterjee, Shira L. Levy, Keith A. Crandall, Suchi Hourigan, Ali RahnavardThe Rahnavard Lab, Computational Biology Institute, Department of Biostatistics and Bioinformatics,Milken Institute School of Public Health, George Washington UniversityRaw Read SequencesData InspectionFASTQC & MULTIQCQuality EvaluationQuality TrimmingTrimmomaticChoose appropriate parameter setMicrobial Strain IdentificationMetabolomic AnalysisIntroductionTime points, samples, & omicsYesNoMetagenomic Analysis RReferencesDiscussion / ConclusionAcknowledgements & Funding1. How does the breastmilk microbiome vary with prematurity and gestational age?2. How does the breastmilk microbiome vary between maternal and donor milk?3. How does caloric fortification change the breastmilk microbiome?4. How is the breastmilk microbiome associated with early childhood health outcomes (eczema)?Fungal Viral Fungal ViralQuality ok?Fungal Viral Fungal ViralInfant Metabolites (Birth Weight Category) Infants’ microbiome-metabolite interactionAnalysisSequencing by the GW Computational Biology Institute’s Genomics Core. In partnership with the Milken Institute School of Public Health and INOVA Health Services. Methods used are supported by NSF grant DEB 2028280 and the Bill & Melinda Gates Foundation grant INV-016930. - Microbes produce metabolites within the host’s microenvironment.- Metabolites may play significant biological roles in infant development- This project seeks to understand the metabolite-microbiome associations that exist between mothers and infants- Small differences between origin and content of breastmilk may have significant effects on infant development- Future analyses will be conducted on bacterial data- Future analyses will reveal the correlation between the breastmilk microbiome and the infant gut microbiomeAndrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina Sequence Data. Bioinformatics, btu170.Ewels, P., Magnusson, M., Lundin, S., Käller, M. (2016). MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics. doi: 10.1093/bioinformatics/btw354Hong, C., Manimaran, S., Shen, Y. et al. PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples. Microbiome 2, 33 (2014). https://doi.org/10.1186/2049-2618-2-33Source: Hong, C., Manimaran, S., Shen, Y. et al. PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples. Microbiome 2, 33 (2014). https://doi.org/10.1186/2049-2618-2-33https://www.rahnavard.org

Characterizing the metabolite-microbiome dynamics between breastmilk and infant health outcomes

https://hsrc.himmelfarb.gwu.edu/cgi/viewcontent.cgi?article=1006&context=gw_research_showcase

Characterizing the metabolite-microbiome dynamics between breastmilk and infant health outcomes

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