Genetic Engineering and Society Center

Research Thrust 1 (RT1) combines multi-omic investigations to determine the mechanisms of microbial colonization and aims to develop sensor and tracking technologies for diagnosing built environment health at varying resolutions (i.e., personal, room, and building level).

RT1 researchers will develop tracking tools that combine phylogenetic and functional aspects through the integration of personal and environmental microbiome data with microbial dark matter characteristics. These tools will include genome-enabled approaches that can target uncultivated microbial taxa and increase our understanding of microbial diversity, phylogenetic relationships, metabolic capabilities, and interactions in the built environment as well as functional approaches via meta-omics. In combination, these approaches will deepen our databases enabling the identification of key molecules and the development of sensors for health assessment of the built environment.

Projects in RT1 will apply and expand fundamental knowledge in microbiome monitoring. We will begin by developing approaches for monitoring and connecting the personal and the environmental microbiome as well as determine functional signatures that can diagnose built environment health. RT1 data will provide the early building blocks for monitoring the built environment microbiome as well that of its occupants, identifying the biomarkers that signal a healthy built environment, and inform PreMiEr’s future sensor development work.

Projects in Research Thrust 2 (RT2) will build the toolbox needed for targeting the delivery/removal of desired genetic features or vectors in the built environment as well as enabling functional modulation in an established microbial community.

RT2 researchers will use their knowledge of delivery systems and nanoparticle transport in complex environments to develop the requisite toolbox for microbiome engineering in the built environment. Initial projects will focus on limiting the spread of animicrobial resistance (AMR) in built microbiomes as well as targeting the built environment water microbiome as there is a critical body of work linking the microbiome of premise plumbing to adverse effects in the built environment. Later efforts will transition to engineering solutions to limiting pathogens and bioaerosols as PreMiEr’s research matures.

Projects in Research Thrust 3 (RT3) will develop predictive models that incorporate spatiotemporal methods, generative modeling concepts, and machine learning approaches to analyze built environment microbiomes.

RT3 projects will focus on the development of predictive models that identify factors that contribute to microbiome compositional variations, and microbiome signatures that associate with specific health outcomes, which in turn will inform built environment health signature identification.

Initial projects will focus on various facets of the predictive models using existing large datasets and then incorporate PreMiEr datasets from Research Thrust 1 as those are generated. Spatial-temporal statistical models for microbiome compositions will be constructed that can characterize the personalized equilibrium of an individual’s microbiome compositions and detect anomalies, or deviation from the normal equilibrium. This information will be crucial in linking to the environmental microbiome. Another project will integrate functional information to decipher the functional meanings of the signals identified in the predictive models. Finally, improvements in the bioinformatic preprocessing pipelines using machine learning approaches will enhance the sensitivity and specificity of the predictive models.

In Research Core A, PreMiEr’s microbiome tracking devices (personal and environmental sensors), targeted delivery tools and predictive microbiome modeling framework will be integrated to measure, predict and improve the health of the built environment microbiome in six model testbed environments:

1. Environmental Chambers
2. Artificial Gut
3. Tiny House
4. Duke University Smart Home
5. Hospitals, and
6. Bolivian Homes

The PreMiEr Data Analytics Core is an integral part of the success of the ERC by supporting the hardware and software needs of all other research thrusts. Its goals can be broken down into three main areas.

1. A seamless, central repository with core services providing

• Scientific data processing and analysis support
• Report and figure creation services
• Seamless and automated ability to start and modify processes and analyses

2. Ensuring transparency and reproducibility

• Eliminate “silos” of data
• Allow teams to easily work together
• Publish results that fully expose the processing pipeline and publish virtualized containers
• Support version control

3. Being “hardware agnostic”

• Allow any PreMiEr researcher (or other interested scientist) to faithfully reproduce analyses regardless of hardware
• Provide virtualized containers for on-campus and other systems, including a primary system at UNC-Charlotte, the ViCAR system at North Carolina A&T, and the cloud

All research and activities within PreMiEr are guided and done in collaboration with the Societal and Ethical Implications (SEI) Core.

PreMiEr’s work evokes a range of SEI issues at the intersection of health and environmental risk, medical ethics, research ethics, environmental release of GMOs, public trust and perceptions, privacy and regulation, and responsible governance.

PreMiEr provides a unique opportunity to engage researchers, engineers, stakeholders, and publics in emerging conversations about engineered microbiomes in built environments

The ERC also enables novel and ground-breaking scholarly examination of SEI aspects of microbiome engineering for built environments