Tin Nguyen - US grants

The ThYme database includes most enzymes involved in the formation of fatty acids and polyketides. These are ultimately converted into valuable products. Such products include cosmetics, detergents, insecticides, fungicides, antibiotics, and other medicinal compounds. The updated ThYme database will provide vital support to research related to these products. Metabolic engineers, plant biologists, natural products and medicinal chemists will all benefit from improved access to enzyme structure and function. High school students from underrepresented populations will be recruited and trained in various aspects of coding. They will then have the opportunity to work with graduate students and postdocs to contribute to the improvement of the platform. This should ultimately strengthen the STEM workforce in Nevada, and nationally.

The ThYme database contains most known sequences and structures of enzymes that act on thioesters, classified by sequence similarity into families. The advantage of classifying enzymes by sequence similarity is that one can infer that all enzymes in a family will have very similar structures and nearly identical catalytic residues and mechanisms. The goal of this project is to launch a new and updated ThYme database by identifying the current families of thioester active enzymes, developing a new approach to identify representative sequences, improving the database management scheme, and modernizing the online user interface. We will develop an efficient method, using submodular functions, to select the representative sequence(s) of an enzyme family among sequences experimentally verified to have a particular enzymatic activity. The database will be disseminated by a website, where every enzyme family will have its own webpage including relevant knowledge and an open query field where users will be able to search by organism, sequence accession code, function, name, sequence, or crystal structure. With input from the user community, the new website will have features to make the content more interactive and allow automated data querying. The user community will be engaged and supported with a forum page to pose questions and begin discussions to which both developers and users can contribute, as well as with tutorials of useful website functionalities.

This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the ivision of Chemical, Bioengineering, Environmental and Transport Systems within the NSF Directorate of Engineering.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PROJECT SUMMARY Household socioeconomic status (SES) is a major contributor to child brain and cognitive growth, and related to their future educational and occupational outcomes. Lower-SES adversities hinder children's executive functions (EF) and language, which impede their classroom performance. Lower-SES children also have lower literacy and language due to impoverished home literacy and language environment (HLE). Lower SES and HLE exacerbate children's classroom difficulties. Yet, despite lower-SES disadvantages, some families are attuned to and foster their children's academic goals and actively enrich HLE. When lower-SES families have access to HLE enrichment programs, such experiences can scaffold gains in literacy and language outcomes. That is, enriched HLE mitigates the adverse impacts of lower SES on child development. Given the critical role of early cognitive skills in classroom performance, understanding the ways household SES versus HLE affects child development, especially brain growth, provides key implications for policymaking and interventions. Lower SES negatively impacts brain structure and function, centrally within the brain networks that support EF and language. After accounting for SES effect, recent studies report that HLE taps brain networks linked to literacy and language, suggesting a protective mechanism. Interestingly, our preliminary results revealed that HLE also overlaps with SES in explaining differences in brain regions subserving EF. Given the role of EF in reading and language, this suggests a potential additive mechanism. But, these studies focus exclusively on how frequently parents and children read together, which thereby narrows the multifaceted nature of HLE. To unpack child socioeconomic circumstances, we examine how brain networks link to specific dimensions of a multifaceted HLE (m-HLE). We drew upon prior behavioral literature, and compiled a comprehensive measure for m-HLE. Not only do we account for child-initiated HLE, we also capture the quantity and quality of parent-initiated HLE. We hypothesize that the ways dimensions of m-HLE interact with different brain networks contribute to mediate and/or moderate the impact of SES on child outcomes. Our first aim focuses on how m- HLE versus SES affect brain, and in turn child outcomes. While SES will predict brain regions subserving EF and language, we anticipate that m-HLE will contribute to brain regions that support language and literacy. Though parents are not always available, such that children likely recruit self-regulation and voluntarily initiate HLE, which may also tap EF brain regions. Our second aim examines whether and how m-HLE interacts with the brain to mitigate lower-SES adversity. We speculate that, despite lower SES, children with enriched m-HLE will exhibit similar interaction between brain networks as their higher-SES peers to scaffold positive outcomes. Results from this proposal will fill critical gaps in understanding how household environment affects child development, using a brain network framework. Also, the findings will inform policymaking and intervention strategies for children at disadvantaged socioeconomic backgrounds and/or with learning difficulties.

Identifying impacted pathways and changes in biological processes is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway-analysis methods have been developed thus far. However, as all these methods are biased toward well-studied conditions, such as cancer diseases, the accuracy of pathway-analysis methods is severely compromised, especially when investigating non-cancer diseases and phenotypes. More importantly, existing methods are limited to the analysis of a single cohort or data type, making them sensitive to biological heterogeneity and unable to analyze complex diseases that involve multiple molecular levels. This project aims to bridge these gaps by designing an analysis pipeline that will tackle bias correction and data integration in one computational framework. This will have a great impact in many research and public health areas by facilitating the identification of putative molecular causes of disease, as well as the identification of potential therapeutic interventions and their possible side effects. This project also includes a systematic outreach program involving Primarily Undergraduate Institutions (PUI) across Nevada, especially minority- and Hispanic-serving institutions: College of Southern Nevada and Nevada State College. The applications presented here will enable students to conduct exciting scientific research without the need to perform wet-lab experiments. Other planned outreach activities involve summer workshops for K-12 local schools from Washoe County School District.

Most pathway-annotation databases have important limitations. Some of these limitations are related to the domain (e.g., focused on cancer alone), others to the types of data included (e.g., only expression data), and still others to the level of detail chosen to describe the phenomenon. Further, pathway-analysis methods are subject to systematic bias due to unrealistic assumptions and overfitting. Another pain point is the inability to easily include multiple cohorts and multiple types of -omics data in the same analysis. This project will provide a framework that allows researchers to retain their preferred pathway methods while correcting for method bias and integrating multiple data types and datasets. The goal of this project will be achieved by two thrusts: 1) design a methodology for bias correction and consensus analysis of pathway methods, and 2) develop a novel approach for the flexible integration of multi-cohort and multi-omics data. The framework will be designed so that it can be applied in conjunction with any existing pathway-analysis method to correct for bias, incorporate knowledge from different databases and integrate data of different types. The project also includes a systematic evaluation plan of the proposed methodologies using more than 100 datasets with known mechanisms. The research team will deliver an implementation that supports several widely used methods for many model organisms.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.