Lessons from the microbiome in health and in death
dc.contributor.author | Nieciecki, Victoria, author | |
dc.contributor.author | Metcalf, Jessica, advisor | |
dc.contributor.author | Wrighton, Kelly, committee member | |
dc.contributor.author | Abdo, Zaid, committee member | |
dc.contributor.author | Lana, Susan, committee member | |
dc.date.accessioned | 2025-06-02T15:21:20Z | |
dc.date.available | 2025-06-02T15:21:20Z | |
dc.date.issued | 2025 | |
dc.description | Zip file contains appendixes A and B spreadsheets. | |
dc.description.abstract | The work presented in this dissertation explores two very different microbial ecosystems found within the broad field of microbiome science–tumors and cadavers. In health, recently described low-biomass tumor-associated microbial communities are implicated in disease, and therefore microbial intervention may represent a therapeutic target. In death, the microbial decomposers of human remains show potential as a novel forensic tool that could help solve homicide cases and provide relief to the families of victims. In Chapter 1, I give a brief review of each of my research areas and summarize current knowledge gaps that exist within these fields to help provide additional context for my work in Chapters 2–4. In Chapter 2, I use 16S rRNA gene amplicon sequencing to characterize the microbiome of mucin-secreting Pseudomyxoma peritonei human tumors, focusing on microbial contamination and data reproducibility. Moving away from human health and the tumor microbiome, Chapter 3 investigates the spatial and temporal responses of microbial communities found in soil near decomposing cadavers. Finally, in Chapter 4, I explore the effects of enclosed shelter on the cadaver microbiome during human decomposition and develop microbiome-based models to estimate the postmortem interval, or time since death. In summary, my dissertation presents new and valuable insights into microbial community structure and assembly in health and in death and provides new tools for assessing environmental contamination that afflict low-biomass samples and for estimating time since death using microbial-based machine learning models. | |
dc.format.medium | born digital | |
dc.format.medium | doctoral dissertations | |
dc.format.medium | ZIP | |
dc.format.medium | XLSX | |
dc.identifier | Nieciecki_colostate_0053A_18884.pdf | |
dc.identifier.uri | https://hdl.handle.net/10217/241059 | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Colorado State University. Libraries | |
dc.relation.ispartof | 2020- | |
dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
dc.subject | cancer | |
dc.subject | machine learning | |
dc.subject | tumor microbiome | |
dc.subject | forensics | |
dc.subject | cadaver microbiome | |
dc.subject | reproducibility | |
dc.title | Lessons from the microbiome in health and in death | |
dc.type | Text | |
dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
thesis.degree.discipline | Cell and Molecular Biology | |
thesis.degree.grantor | Colorado State University | |
thesis.degree.level | Doctoral | |
thesis.degree.name | Doctor of Philosophy (Ph.D.) |