Brigham Young University

NSF Awards · FY 2024 · 2024-10

Mathematical Opportunities in Student Thinking (MOSTs) are high-leverage instances of student mathematical thinking that emerge in whole-class discussions. The challenge for teachers is to build on these opportunities to help the whole class understand the mathematics underlying these student contributions. To help teachers learn how to build on MOSTs, there is a need for professional development resources and tools that facilitators can use. There is also a need for research about how teachers use what they learn in professional development in their teaching. This project is developing a teacher learning sequence that will support teachers in learning to productively use student thinking that surfaces in-the-moment during their instruction—that is, in learning to build on MOSTs. This project builds on prior work that developed a framework for recognizing MOSTs and conceptualized the building practice teachers use to effectively capitalize on MOSTs. The overarching research question for the project is: to what extent does the professional learning sequence help teachers understand and enact the teaching practice of building? As part of this investigation, the project also considers factors that might mitigate teachers’ learning, such as teacher attributes (knowledge, practices, or experiences) and contextual factors. The study uses a design research framework to document how teachers take up aspects of building on MOSTs from the professional development, the process of teachers’ learning, and changes in their classroom practice. The study relies on data from the professional development activities, teacher surveys and interviews, and classroom data. The project sites include secondary schools in urban and rural settings. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. 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.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY The COVID-19 pandemic created the greatest infectious threat to global health in 100 years, and monumental efforts have been made by the scientific community to combat the SARS-CoV- 2 virus. This proposal seeks to extend this effort by investigating a mechanism by which SARS- CoV-2 hijacks the host cell chaperone system to replicate itself. We have evidence that the SARS-CoV-2 RNA polymerase (RdRp) co-opts the cytosolic chaperonin containing TCP-1 (CCT, also called TRiC) to assemble the active polymerase complex. CCT is a large (1 MDa) protein-folding machine that plays a major role in the cellular chaperone network responsible for maintaining the proteome in good working condition. It uses ATP hydrolysis-driven conformational changes to assist cytosolic proteins with multiple domains, complex folding trajectories, or obligate binding partners to achieve their native state and assemble into complexes. In addition to folding cellular proteins, CCT has been shown to bind several viral proteins and contribute to viral replication of HIV, hepatitis C, influenza A, rabies, Zika and reovirus. These observations show that CCT is a common host chaperone used by diverse viruses to fold viral proteins, assemble viral complexes, and support viral replication. Based on these findings, we initiated an investigation of the role of CCT in SARS-CoV-2 replication. Here, we present robust preliminary evidence indicating that the SARS-CoV-2 non-structural protein 12 (Nsp12), the catalytic subunit of the RNA polymerase, is folded by CCT and that CCT contributes to RdRp complex formation and SARS-CoV-2 replication. In Aim 1, we propose to thoroughly test this hypothesis using multiple experimental approaches. In Aim 2, we propose to determine high-resolution structures of the complex between Nsp12 and CCT. We have isolated an Nsp12 folding intermediate bound to CCT and have determined preliminary structures of the complex by cryogenic electron microscopy (cryo-EM). Further cryo-EM analysis will yield a high- resolution structure of the Nsp12-CCT complex, which will be invaluable in guiding the design of therapeutics to block Nsp12 folding by CCT, inhibit formation of the RdRp complex, and disrupt viral replication.

NIH Research Projects · FY 2025 · 2024-09

There is a critical need for new protein crystallization methods that are more successful and require less labor, time, and resources. Lack of straightforward methods to successfully crystallize any protein of interest significantly hinders study of molecular disease mechanisms and the development of effective treatments. This lack of effective treatments for many diseases forces them to be addressed instead with costly symptom management programs. Over the past four years, we have investigated TELSAM, a novel polymer-forming protein crystallization chaperone. TELSAM carrier proteins can be genetically fused to disease proteins, drug targets, and bioengineered proteins. In low pH crystallization conditions, TELSAM-target protein fusions polymerize, and the resulting polymers zipper up to form crystals suitable for X-ray diffraction and atomic resolution structure determination. TELSAM fusion readily forms crystals of 90% of proteins of interest (a stark improvement over the 30% crystallization rate of traditional methods) and routinely at protein concentrations of 1 mg/mL (promising to enable the structure determination of proteins that can only be produced in minute quantities). TELSAM fusion crystallography thus has the potential to revolutionize the small molecule and biologic therapeutic industries by accelerating structure determination steps, currently a bottleneck. For TELSAM to realize this potential, academic and industrial structure biologists need 1) experimentally validated guiding principles for the use of TELSAM, 2) a sufficient number of successful use cases to demonstrate general efficacy, and 3) demonstration of the applications and limits of TELSAM fusion crystallization. Thus far we have rigorously investigated the guiding principles for TELSAM’s use and begun to demonstrate its usefulness with proteins relevant in human disease. Our goals for the next five years are to rigorously address the above three needs and broadly disseminate our findings to the structural biology community. The overall vision of our research program is focused on pushing the limits of protein engineering and structure determination fields while at the same time developing undergraduate and graduate students into excellent biochemists. We do this by putting them at the front lines of groundbreaking research. Both undergraduate and graduate students in our group participate in all parts of scientific inquiry, including reagent preparation, experiment design and execution, data collection and analysis, and manuscript preparation and presentation at national and international meetings. The proposed research is significant because it will accelerate the successful structure determination of a greater number and variety of biotechnology and disease-relevant proteins, drugs, and biologics, ultimately leading to new biotechnology tools, more effective disease treatments, and reduced healthcare costs.

NSF Awards · FY 2024 · 2024-09

Non-technical Description: Quantum information technologies rely on quantum entanglement, or the intrinsic linking of one quantum object to another. An important research objective is to gain a fundamental understanding of many-body quantum entanglement involving large numbers of quantum objects. Certain magnetic materials known as geometrically frustrated magnets provide a valuable platform for this topic of study because they may exhibit many-body entanglement at low temperature. This project advances the search for promising quantum-entangled frustrated magnets through a systematic investigation of the role of atomic-scale disorder in promoting or hindering many-body entanglement. The results illuminate strategies for utilizing disorder to promote quantum-entangled ground states and contribute to a deeper understanding of many-body quantum entanglement in general. These research activities are integrated into education and outreach efforts including intensive undergraduate mentoring, summer research internships for diverse students, and a new organization called the Physics Breakfast Club that supports regional high-school physics teachers by building community and providing teaching resources. Technical Description: Recent work suggests that disorder in certain types of frustrated magnets can stabilize entangled magnetic states such as a quantum spin liquid. This project explores that idea in the context of rare-earth pyrochlore compounds with mixed atomic species on the nonmagnetic metal/metalloid site. The level of random disorder can be controlled by the size mismatch of the different atomic species, allowing a systematic investigation of the influence of disorder on the formation of a quantum spin liquid or a related phase in disordered pyrochlore compounds. The goals are to develop guiding principles for utilizing disorder as a tool for stabilizing entangled magnetic states and evaluate the potential of disordered pyrochlores for achieving these states. The magnetic and structural properties of the materials are characterized by state-of-the-art techniques including x-ray and neutron total scattering, muon spin spectroscopy, and inelastic neutron scattering. This multi-modal methodological approach is ideally suited to gaining a comprehensive understanding of the local disorder and its effect on the magnetism in pyrochlore compounds, while also providing a template for similar studies on other materials in the future. 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.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Biological tissues exhibit a high degree of phenotypic heterogeneity and plasticity, comprising many different subpopulations of cells in various states. Quantifying this heterogeneity at the single-cell level and with molecular depth across large numbers of cells and multiple classes of molecules provides information that cannot be obtained at the bulk scale and will ultimately lead to improved diagnostics and more effective treatments. While single-cell nucleic acid sequencing approaches are having a significant impact on biomedical research, proteins, lipids and metabolites mediate the bulk of cellular function and measurement of their expression provides more direct insight into cellular phenotype. There is thus an urgent need to develop new technologies for large-scale direct proteome, lipidome and metabolome profiling at the single-cell level. To fill this gap, mass spectrometry (MS)-based profiling of protein expression in single cells has recently been demonstrated through the implementation of more efficient sample processing workflows, novel experimental designs and improved instrument sensitivity. Label-free MS-based proteomics can now quantify >3,000 protein groups per cell across >4 orders of magnitude of dynamic range. Here we propose to apply mass spectrometry to study biomolecular expression at the single-cell level beyond the global proteome. We will develop global and targeted approaches to profile posttranslational modifications in single cells, beginning with phosphorylation. We will also extend nanoflow liquid chromatography-MS capabilities for in-depth single-cell lipid profiling. Ultimately, we will develop novel means of generating complex LC gradients that utilize more than two mobile phases to efficiently profile multiple classes of biomolecules (e.g., proteome and lipidome) from the same single cell. These research directions will, in combination with mature nucleic acid sequencing strategies, provide an unprecedented view of cellular regulation from genotype to phenotype at the single-cell level.

NSF Awards · FY 2024 · 2024-09

The mathematical field of dynamical systems concerns the long-term behavior of systems which evolve in time according to specified rules. Dynamical systems arise naturally in many areas of science and engineering, including statistical mechanics, neurophysiology, and climate science. This project will focus on the dynamics of certain systems – known as weakly hyperbolic systems -- that display chaotic behavior, in which small perturbations of initial conditions can lead to widely varying trajectories for the system. Because these systems are inherently difficult to predict, they are often studied from a statistical point of view, that is, one analyzes the properties of the system that are expressed through various types of average. This is the focus of ergodic theory, a subfield of dynamical systems, and the conceptual framework for this project, in which the PI will investigate the statistical properties of systems with different types of hyperbolicity. The project will also contribute to education and training, through mentorship of graduate students and the development of new seminars on the topics studied. The project has three distinct parts. Previously, the PI used measure rigidity results to identify new open sets of dynamical systems with a unique physical measure. The first part of the project aims to address questions related to the utilization of quantified non-joint integrability in establishing the existence and finiteness of physical measures, as well as to understand how often these conditions occur in the partially hyperbolic setting. In the second part of the project, the PI and his co-authors aim to understand different types of transversality to obtain absolute continuity of stationary measures for certain types of random products of surface diffeomorphisms. One goal of this part is to obtain a Benoist-Quint type of result in a non-homogeneous setting. The third part of the project focuses on applying coding techniques to study measures of maximal entropy for non-invertible systems possibly having singularities. Some of the goals include understanding conditions that guarantee existence and finiteness of measures of maximal entropy in these settings, and understanding new examples of such maps. 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.

NSF Awards · FY 2024 · 2024-09

This award supports research that looks to design and manufacture 3D printed parts that absorb considerably more vibration than existing metals, thereby promoting the progress of science, and advancing prosperity and welfare. Additive manufacturing has recently gained popularity for producing metal parts. Using this process, parts are created one layer at a time from a bed of metal powder by using a laser to melt and fuse the metal at certain locations. Any powder that is not fused is typically washed from the finished parts. However, metal materials that can be used for additive manufacturing have very low vibration damping. This limits the performance that can be achieved when dynamic loads or acoustic performance is important. This project will solve this challenge by designing parts such that they retain pockets of trapped metal powder, which can be designed to increase the parts ability to absorb vibration, reducing stresses and the noise that they generate. This can dramatically increase the life of parts used in automotive, aerospace or consumer applications, improving safety for passengers and end users. The ability to tailor damping on demand could also enable engineers to design systems with unprecedented acoustic performance, improving the competitiveness of domestic products. Beyond technology advancement, this method is expected to be readily adopted by industry through the offering of short courses to practicing engineers. This research aims to make fundamental contributions to expand our understanding of the ability of trapped powders to dissipate energy within additively manufactured parts. The work includes both an experimental component and a modeling component. In the experimental component, various parts will be created and tested to understand what shapes produce the most vibration absorption and the conditions under which they absorb vibration. Both linear and nonlinear dynamic testing methods will be used to characterize the linear modal characteristics of the parts as well as nonlinear behaviors that change the apparent stiffness and damping of the various modes. In the modeling component, a multi-faceted campaign will be conducted to identify a modeling framework for metal powders and methods to determine the effective material properties. The powders of interest contain billions of particles that are governed by complicated and unknown interaction laws, and hence modeling them using first principles is not currently feasible. This work plans to derive an equivalent, homogenized model for metal powders, so they can be treated as elastic or plastic solids within a finite element model of the part of interest, with a focus on capturing the effective stiffness and damping of the powders. This simplifies the material model and makes it feasible to deduce the properties of the powder from simple test coupons that exercise powder pockets in elongation and shear in multiple directions. Measurements of the vibration amplitude-dependent stiffness and damping of the test coupons will be correlated with finite element models that include either linear viscoelastic or nonlinear plastic powder material behavior. Computations will be dramatically accelerated by using quasi-static modal analysis, which allows for dynamic properties to be inferred from a few carefully chosen nonlinear static load-displacement curves. This project is jointly funded by the Dynamics, Control and Systems Diagnostics (DCSD) program, and the Advanced Manufacturing (AM) program. 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.

NSF Awards · FY 2024 · 2024-09

Number theory sits at a busy intersection in mathematics. The effect of this is twofold. First, it means number theory provides tools to solve seemingly unrelated mathematical problems in other areas. Second, it means problems in number theory can be studied using tools from many realms of mathematics. This project concerns number-theoretic problems in two general categories: rational points on curves and related Diophantine problems, and the study of special functions known as L-functions. Problems in the first category, though usually simple to state, frequently require sophisticated mathematical technology in their resolution. The second category of problems concerns L-functions, special mathematical functions that combine large amounts of arithmetic data in a single package. L-functions are important, still poorly understood, and the subject of far-reaching conjectures. In both categories, the investigator draws inspiration from branches of mathematics outside of number theory. In addition to breaking new theoretical ground, the investigator will mentor students in research projects. The project is structured to provide the students with opportunities and means for collaboration. There is a focus on recruiting students from historically excluded or underrepresented groups. On a technical level, the investigator will study a deep conjecture of Sander, which predicts the rational points on a certain infinite family of curves, known as Erdos-Selfridge curves. In general, it is a difficult problem to find all the rational points on a curve of large genus. The investigator will develop a novel "mass increment argument" to study rational points on these curves. This argument is loosely inspired by various increment arguments in additive combinatorics, such as those used to prove Roth’s or Szemeredi’s theorems on arithmetic progressions in sets. This requires intricate combinatorics and a quantitative version of Faltings's celebrated theorem on rational points on curves of genus at least two. In related problems, Chabauty-type arguments make an appearance. Additive combinatorics also has connections to the investigator's recent collaborative work on new methods of detecting zeros of the Riemann zeta function. The project will explore further the potential of these methods. Furthermore, to facilitate work on the Riemann zeta function requiring explicit results, the investigator will obtain sharper bounds on the zeta function in important regions and derive new zero-density estimates. 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.

NSF Awards · FY 2024 · 2024-09

To adequately warn citizens of impending natural disasters and other events, one needs accurate predictions of weather, but also of wildfire spread, the melting of ice sheets, and even economic growth. Incorporating observable data into a model that is based on physical (or economical) principles is referred to as data assimilation. Unlike some artificial intelligence (AI) algorithms popularized today that are built exclusively from data, data assimilation uses data and scientific knowledge about how the underlying phenomena behaves, resulting in predictions that are interpretable. This grant will support students and faculty researching two data assimilation methods to generate comparisons between the two approaches, and to see what physical circumstances are best modeled by each. Specifically, the investigators are interested in testing these data assimilation methods in the modeling of wildfires. In carrying out this research, the team will involve students, which will help prepare them for careers involving high performance computing and scientific modeling. This funding will also support the creation of two graduate level courses in data assimilation and the mathematical foundations of deep learning. These courses will help students understand the theoretical foundations of data science and to prepare for interdisciplinary careers in which they harness the data revolution. The investigators are developing these courses in collaboration with faculty at the Institute for Foundations of Data Science (IDFS), an NSF-TRIPODS institute which will greatly enhance the experience of the students at Brigham Young University. The primary contribution of the project is an in-depth comparison between the continuous data assimilation (CDA) method developed for partial differential equations, and the conditionally Gaussian Kalman filtering (CGKF) approach. Anecdotally these two methods are applicable on exactly the same physical phenomena, but at the same time fail for the same set of models as well. Using recently developed insights into CDA which tie CDA into an optimization framework reminiscent of a machine learning context, the research team will provide a rigorous comparison between these two methods and identify how the CGKF approach makes use of noise in the system and the observations. These scientific questions will directly complement the development of the courses mentioned above, which will focus on a survey of data assimilation methods, and the optimization routines that determine the identification of deep neural networks that generalize well. The funds will support graduate students who will both work on the active research questions in optimization and data assimilation, and who will assist in the development of the curriculum for both courses. 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.

NIH Research Projects · FY 2024 · 2024-08

Abstract The transformative breakthrough of Google DeepMind’s AlphaFold2 on the reliability of sequence to protein structure prediction, demonstrated the power of machine learning approaches in advancing the study and engineering of proteins. Currently a number of inverse protein folding neural network models employ different objective functions in the design of proteins which come with trade-offs and can lead to adversarial sequence predictions. This project seeks to apply a different objective function to overcome limitations of current inverse protein folding models with the specific goal of predicting mutations that will increase the stability of therapeutic and diagnostic proteins. Additionally, AI- and Physics-based Simulation filters are integrated to enable the prediction of sequences that increase stability and retain function. It is hypothesized that by combining these AI tools with the experimental cell-free protein synthesis and stability/activity assays, rapid design-build-test-learn cycles can be performed to create AI models tuned specifically for the target protein. This technology is directly applied to the highly sensitive diagnostic reporter protein NanoLuc and the promising cancer therapeutic Onconase to expand their utility through enhanced stability.

NSF Awards · FY 2024 · 2024-07

There are very few climate reconstructions from tree ring records from Africa as a whole, so there is a lack of knowledge about the nature of past climate variability to put current climate change in context. This project will use tree species from Zambia that have been shown to have promise for reconstruction of past climate. Samples will be collected by participants of annual field schools which train Zambian students and researchers in field, lab and data analysis techniques. The resulting data will be used to create a gridded precipitation reconstruction from the region, which will be analyzed to identify the primary drivers of climate variability. The Broader Impacts of the project is the capacity building and international collaboration associated with the annual field school. The goals of this project are measure radiocarbon, tree ring width and quantitative wood anatomy from the dominant tree species in Zambia to develop multi-century records. These data will be used to create a gridded reconstruction of precipitation from the region, and to identify primary climate drivers of climate variability. The project will evaluate correlations with the El Niño-Southern Oscillation, the Indian Ocean Dipole and the Southern Annular Mode, track the movement of the Intertropical Convergence Zone (ITCZ), and evaluate if and how the ITCZ extent and intensity has changed through time. The Broader Impacts of the project are to continue the African Dedrochronological Field School (ADF), which will also be the mechanism to collect samples from the study area. 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.

NIH Research Projects · FY 2024 · 2024-07

SUMMARY/ABSTRACT The National Cancer Institute estimates ~40% of persons in the United States will be diagnosed with cancer at some point in their lives (https://www.cancer.gov). While 5-year survival rates continue to increase due to improvements in clinical care, the Centers for Disease Control and Prevention reports cancer is still the second leading cause of death in the United States (www.cdc.gov). Innovative measures and larger datasets are required to continue these improving trends in clinical care(Rahib et al. 2021). The last 15 years of cancer research has benefited tremendously from the advent of next-generation sequence technologies. Ever present in this genomics revolution is The Cancer Genome Atlas (TCGA). For over a decade, TCGA led the way to molecularly characterize over 10,000 tumors from 33 different cancer types(Ellrott et al. 2018; Ding et al. 2018; Bailey et al. 2018). From these efforts arose a common theme that all tumors are unique, but many share prognostic and diagnostic drivers of disease. Among these biomarkers are cancer predisposition or germline mutations contributing to cancer development(K.-L. Huang et al. 2018). Despite this large effort, TCGA is a case set heavily biased toward cancer type selection and post-cancer data collection, thus making it difficult to identify predictive or preventive disease models. To address this issue, and many others concerning human health, the National Institutes of Health has united to produce the All of Us Research Program(Ramirez et al. 2022). This phenomenal program currently has over 400,000 participants who have agreed to share their electronic health records (EHR) and genetic information(Doerr et al. 2021). This number is expected to grow to one million by its conclusion. Participant selection is disease agnostic, and recruitment has focused on underrepresented minorities, with almost 50% of participants reporting non-White. Preliminary analysis of the insurance billing codes suggests the All of Us collection will be a fruitful dataset to study cancer. We found 35% (34,849 of 98,553, version 6 release) have (or had) reported neoplasms. Furthermore, this 35% makes up ~80% of all billing code occurrences shared in the electronic health record, again highlighting the All of Us dataset will be a rewarding environment to study cancer. Here, we propose two ambitious aims run by two teams of undergraduate students that will achieve our overall objective to characterize and quantify the impact of known predisposition cancer mutations and develop models for cancer misdiagnosis in the All of Us Research Program. Separated by genotype and environment, these aims seek to i) identify and assess the genetic intersection of cancer predisposition databases with the All of Us genomics cohort and ii) discover computational algorithms and features that can predict cancer misdiagnosis. Collectively, these aims encompass doable tasks for well-trained undergraduates in bioinformatics. We look forward to advancing the cancer research community beyond tumor-specific phenotypes by exploring the whole individual to find novel links to comorbidities and cancer triggers to help elucidate the missing heritability in cancer.

NSF Awards · FY 2024 · 2024-06

This project explores a proof-of-concept and feasibility evaluation to inform the future development of a centralized data repository to support the privacy research community. The repository will enable tracking and systematic study of privacy harms. Current incident reporting systems are designed to track the occurrence of large-scale data breaches, but there is currently no centralized reporting system to effectively track other types of privacy violations (e.g., online harassment, cyber abuse) that negatively impact end-users. Without access to this information, it is difficult to quantify / qualify how and to what extent different online platforms propagate privacy breaches, as well as how to redesign such systems to be more secure and trustworthy. Therefore, this planning effort aims to (1) solicit the opinions of privacy experts on the design of the repository; (2) prototype the repository and solicit feedback from experts piloting it; and (3) build on these learnings to develop a plan to develop a centralized privacy incident repository. This will ultimately enable researchers to work together to (1) identify and prioritize privacy harms and the factors associated with the incidents; (2) understand how various populations are impacted by these harms; and (3) develop and evaluate potential interventions. This repository is envisioned to support the protection of vulnerable end-users who are disproportionately threatened and harmed by digital privacy violations, addressing the recent R&D budget priority from the White House and the Office of Science and Technology Policy focused on reducing inequities. By identifying evolving privacy risks, we also work towards two other budget priorities -- advancing trustworthy AI technology and maintaining global security and stability. 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.

NIH Research Projects · FY 2025 · 2024-06

PROJECT SUMMARY Due to mandates from funding agencies and publishers, high-throughput, molecular data from Down syndrome individuals and controls (mostly humans and mice) are available in public repositories. Researchers can use such data to corroborate their own findings and pose new research questions. Doing so would help to leverage prior investments and complement efforts by the INCLUDE Data Coordinating Center (DCC) to generate data for new cohorts. Our proposal focuses specifically on mRNA expression and DNA methylation data. These data types shed light on how genes are regulated, how molecular aberrations lead to medical conditions, and how medical outcomes can be predicted, potentially leading to improved diagnostics, treatments, and insights into human health and disease. However, many data-generation platforms are used for these data types, and researchers use a wide range of techniques for normalizing the data, checking data quality (if they check at all), and mapping to gene annotations. To reuse the data most effectively, the data must be reprocessed from its original form; normalized and quality checked consistently; and mapped to current annotations. Agencies who manage public repositories lack resources and expertise to perform these steps. In our first aim, we will address this problem using a data-curation approach. We have identified 148 datasets specific to Down Syndrome that we believe should be prioritized for reuse. Using our expertise in molecular-data processing and bioinformatics, we will re-normalize, quality-check, summarize, and annotate the data using an approach that maximizes consistency for all of the datasets. Additionally, we will map the metadata to biomedical-ontology terms in collaboration with the INCLUDE DCC. We expect that these efforts will reduce barriers for researchers in the Down syndrome community to reuse the data and accelerate progress in the field. Our second aim focuses on interoperability. For many research questions, a single dataset is insufficient. Sample sizes may be small and/or a single dataset may not represent the range of phenotypes or other factors necessary to answer a given question. Therefore, it is often crucial to integrate datasets from multiple sources. However, systematic differences between datasets are inevitable due to differences in populations, laboratory conditions, and environmental factors. Failing to adjust for these differences will likely lead to biased conclusions. We will evaluate the feasibility of using generative neural networks, a type of algorithm that is highly configurable and is behind many of the most influential artificial-intelligence advances of the past decade. We will apply these algorithms in the context of studying medical conditions that co-occur with DS, such as autoimmune conditions, dementia-related disease, congenital heart defects, and leukemias. Our algorithms will search for systematic patterns that differ between datasets and generate a modified version of the data in which those differences have been minimized yet the biologically relevant patterns have been retained.

NIH Research Projects · FY 2025 · 2023-08

PROJECT SUMMARY/ABSTRACT Cancer tissues exhibit a high degree of phenotypic heterogeneity and plasticity and contain numerous subpopulations of cells in various states. Quantifying this heterogeneity at the single-cell level and with molecular depth across large numbers of cells provides information that cannot be obtained at the bulk scale and will ultimately lead to improved diagnostics and more effective treatments. While single-cell nucleic acid sequencing approaches are having a significant impact on cancer research, proteins mediate the bulk of cellular function and are the targets of most therapeutics. There is thus an urgent need to develop new technologies for large- scale direct proteome profiling at the single-cell level. To fill this gap, mass spectrometry (MS)-based profiling of protein expression in single cells has recently been demonstrated through the implementation of more efficient sample processing workflows, novel experimental designs and improved instrument sensitivity. Label-free MS- based proteomics can now quantify >2,000 protein groups per cell across >4 orders of magnitude of dynamic range, but efforts to profile more than a few dozen cells per day have resulted in significantly reduced proteome coverage. This low throughput is insufficient for the large-scale statistically powered studies required to characterize heterogeneity in cancer cell populations. To increase measurement throughput, multiplexed workflows based on isobaric tandem mass tags (TMTs) enable up to 18 single cells to be measured in an LC- MS analysis, but these have still been limited to ~100 cells/day and, as generally implemented, suffer from a large proportion of missing values and other issues affecting quantitative performance. Our overall objective is to develop a platform that combines simplified pipette-free high-throughput sample preparation with rapid, multicolumn liquid chromatography separations and ‘greedy’ data-dependent acquisition to profile >2000 proteins per cell with a measurement throughput of >1000 single cells per day. We hypothesize that the advanced sample preparation and separation, combined with a far more efficient MS acquisition workflow, will achieve in-depth SCP with a 10× throughput gain, thus providing a capability for direct, in-depth and large-scale protein quantification that is analogous to single-cell RNA-seq. Studies in Aim 1 will focus on developing massively parallel centrifugal nanoliter dispensing to prepare >10,000 single-cells per day at a total reagent and consumables cost of <$0.40/cell. In Aim 2, we will develop rapid, robust and high-peak-capacity 20-min nanoLC separations with 100% duty cycle. In Aim 3, we will develop a novel ‘greedy’ data acquisition strategy in which only proteotypic peptides are selected for fragmentation, and with custom automatic gain control settings and fragmentation energy for each peptide, providing an unprecedented combination of sensitivity and throughput. With this next-generation platform, we will profile >10,000 cells to study acquired resistance to autophagy inhibitors in the context of autophagy-dependent triple negative breast cancer, thus establishing an innovative platform for advancing biomedical research and individualizing therapy.

NIH Research Projects · FY 2025 · 2022-09

Project Summary/Abstract Fatal or impairing neurological diseases, including movement disorders, brain cancers, psychological disorders, epilepsies, malformations, and memory disorders, impose heavy burdens on both individuals and society at large. Transcranial magnetic resonance guided focused ultrasound surgery (tMRgFUS) is an extremely promising, minimally invasive treatment modality for neurological diseases whereby sound waves are focused to a specific region of the brain. Because it is noninvasive, the efficacy of tMRgFUS procedure heavily relies on the accuracy and information content of the guidance technology. This study proposes to improve the treatment efficacy of nearly all tMRgFUS surgeries by eliminating a ubiquitous impediment to accurate and information-rich guidance MRI: the acoustic coupling medium. Interactions between the coupling media and guidance imaging impede tMRgFUS efficacy and translation. For example, while FDA-approved tMRgFUS treatments for essential tremor and Parkinson’s disease can rely on real-time patient feedback to compensate for errors in guidance MR imaging, other tMRgFUS indications cannot access patient feedback because either the patient is unconscious, or the consequences of treatment errors appear only days later. In these cases, guidance imaging errors imposed by the coupling bath cannot be compensated and degrade treatment efficacy To meet this need, our study proposes a dilute, iron-based coupling media (IBCM) that will eliminate coupling- media-induced errors in MRI guidance imaging while maintaining the coupling and cooling functionality critical to acoustic transmission. The specific aims of the study are as follows. Aim 1: Develop novel surface–modified iron oxide nanoparticles for an IBCM. Dilute, aqueous, surface- modified iron oxide nanoparticles can accelerate MRI signal decay such that, during image acquisition, a coupling medium will contribute negligible effects to guidance imaging. However, aqueous nanoparticles also agglomerate and seed treatment-impeding cavitation nucleation in the prefocal acoustic field. This aim will develop novel surface-modified particles that, upon suspension, accelerate MRI signal decay without promoting prefocal nucleation. Aim 2: Investigate the effects of IBCM suspension fluid properties on cavitation nucleation. Fluid properties play a critical role in particle suspension, acoustic coupling, subject cooling, and cavitation nucleation. This aim will investigate cavitation nucleation within the IBCM and how suspension fluid properties, such as pH, temperature, gas content, and flow state, can modify or suppress the nucleation process while maintaining suspension, coupling, and cooling capabilities. Aim 3: Enhance MRI guidance for tMRgFUS through the use of an IBCM. This aim will quantify the value of the IBCM designed in Aims 1 and 2 for tMRgFUS by measuring image quality metrics derived from guidance MRI scans of human subjects. This aim will also develop novel MRI guidance techniques that were previously rendered impossible due to severe image corruptions imposed by the acoustic coupling medium. The resulting IBCM will improve image quality for nearly all guidance techniques employed during, or undergoing development for, tMRgFUS, by rendering the acoustic coupling medium invisible to the MRI scanner without sacrificing necessary acoustic coupling and cooling functionality.

NIH Research Projects · FY 2025 · 2022-09

Single cell proteomics (SCP) is rapidly emerging and can quantify > 1000 proteins per cell. Significant advances in instrumentation and sample preparation are making SCP more broadly accessible. Yet technical advances in data acquisition have not been paired with advances to computational tools. Algorithms for proteomics were designed and optimized on data from bulk proteomics, and are ill-suited for SCP data. Our preliminary research shows that data from SCP lack many features that are critical for current proteomics algorithms. We will dramatically improve accuracy and coverage of the single cell proteome through creation of the first-ever dedicated SCP search software. This will be coupled with an initiative to improve SCP peptide and protein quantification. These algorithmic improvements will be informed from a large corpus of SCP data, gathered and centralized into the first SCP data archive.

NIH Research Projects · FY 2024 · 2022-09

PROJECT SUMMARY Cancer tissues exhibit a high degree of phenotypic heterogeneity and plasticity, with cancerous tissues comprising many different subpopulations of cells in various states. Quantifying this heterogeneity at the single- cell level and with molecular depth across large numbers of cells provides information that cannot be obtained from bulk studies and that will ultimately lead to improved diagnostics and more effective treatments. While single-cell sequencing approaches are having a significant impact on cancer research, proteins mediate the bulk of cellular function and are the targets of most therapeutics. Given that a compelling body of literature has shown that the correlation between RNA and protein abundance is at best poor to moderate, there is an urgent need to develop new technologies for large-scale unbiased direct proteome profiling at the single-cell level. To fill this gap, mass spectrometry (MS)-based profiling of protein expression in single cells has very recently become a reality due to more efficient sample processing workflows, novel experimental designs and improved instrument sensitivity. Label-free MS-based proteomics can currently quantify up to 1500 protein groups per cell across >4 orders of magnitude of dynamic range, but throughput has been limited to ~24 samples per day. This low throughput is inadequate to perform the large-scale statistically powered studies required to characterize heterogeneity in cancer cell populations. To increase measurement throughput, multiplexed workflows have been developed based on isobaric tandem mass tags (TMTs) that enable >10 single cells to be measured in an LC-MS analysis, but these suffer from a number of significant drawbacks including isotopic contamination, degraded quantitative accuracy when employing a carrier channel, precursor coisolation with concomitant ratio compression, chemical noise resulting from cross-reactivities of TMT reagents with contaminants, etc. The overall objective is to develop a platform that exceeds the throughput of current TMT-based workflows while preserving the depth of coverage and dynamic range of label-free workflows. We hypothesize that a robust multicolumn ultra-high-performance nanoLC system with a 5-minute peptide elution window and a 100% duty cycle, combined with novel MS1-level protein identification and quantification, will enable label-free profiling of >2000 protein groups per cell at a throughput of up to 288 samples per day, thus providing a providing a capability for direct, in-depth and large-scale protein quantification that is analogous to single-cell RNA-seq. Studies in Aim 1 will focus on developing high-peak-capacity fast nanoLC separations, as well as a novel sorbent-coated sample-loop providing desalting and debris removal for robust long-term operation. In Aim 2 we will develop a 4-column LC platform based on these rapid separations and a primarily MS1-based acquisition workflow to increate duty cycle to 100% and maximize coverage in these rapid analyses. We will apply this technology to CD138+ single cells isolated from multiple myeloma patients to predict response to immunomodulatory imide drugs (IMiDs). This project will establish an innovative measurement capability for individualizing cancer therapy.

NIH Research Projects · FY 2024 · 2022-05

There is a critical need for new protein crystallization methods that require less labor, time, and resources. Pre- viously, crystals of 10 out of 11 target proteins were readily generated by fusing them to TELSAM, a polymer- forming crystallization chaperone. There is great need for continued investigation of TELSAM due to its potential as a general-use protein crystallization chaperone. Lack of straightforward methods to successfully crystallize any protein of interest significantly hinders study of molecular disease mechanisms and the development of effective treatments. The lack of effective treatments for many diseases forces them to be addressed instead with costly symptom management programs. The long-term goal of this project is to develop protein crystalliza- tion methods that can result in well-ordered protein crystals on a time scale of less than a month, cost as little as $1000 per structure, and are successful for greater than 70% of proteins of interest. The overall objective of this proposal is to convincingly demonstrate the benefits of using TELSAM as a protein crystallization chaperone and to clearly define the requirements for doing so. The central hypothesis is that TELSAM will accelerate the speed and success rate of crystallization across a wide range of proteins of interest and that flexible fusion of target proteins to the 1TEL variant will be optimal. The rationale is that TELSAM has shown great promise in preliminary studies and has the potential to 1) decrease the cost of determining an atomic-scale protein structure, 2) accel- erate the rate that protein structures can be determined, and 3) increase the success rate of crystallization, expanding the range of proteins that can be structurally characterized in this way. The central hypothesis will be tested, and the overall objective achieved by executing 2 specific aims: 1) Compare the ease of obtaining well- ordered crystals across a range of proteins of interest with and without fusion to TELSAM. 2) Establish best practices for successfully using TELSAM. In Aim 1, a panel of target proteins or protein complexes of varying sizes will be crystallized alone or as flexible fusions to TELSAM. In Aim 2, selected target proteins will be flexibly or rigidly fused to TELSAM with varying degrees of target protein loading along the polymer. Longer linker lengths and unusually low protein concentrations in crystallization experiments will also be investigated. The proposed research is innovative, in the applicant’s opinion, because it proposes: 1) Systematic investigation of the factors required by TELSAM-target fusions to reliably form well-ordered crystals, 2) Investigation of 1TEL, which pre- sents 6 copies of the target protein per turn of the TELSAM polymer and precludes any direct inter-TELSAM contacts, 3) Investigation of semi-rigid fusions of target proteins to TELSAM, 4) Testing the limits of TELSAM- mediated protein crystallization with target protein complexes and ligand-bound targets. The proposed research is significant because it will enable the successful crystallization and structure determination of a greater number and variety of biotechnology and disease-relevant proteins, ultimately leading to new biotechnology tools, more effective disease treatments, and reduced healthcare costs.

NIH Research Projects · FY 2026 · 2022-04

Project Summary/Abstract. Reversible loss of consciousness is a crucial part of two major medical fields: general anesthesia and sleep. General anesthetics and non-rapid-eye-movement (NREM) sleep both induce slow waves (0.1-4 Hz) in the cortical electroencephalogram (EEG). It is unknown whether slow waves generated with different anesthetic agents and during NREM sleep are generated with the same neural circuit activity. Dr. Melonakos’ preliminary data suggests that anesthetic agents with different molecular targets have distinct slow wave mechanisms (Aim 1 Hypothesis). In addition, although dexmedetomidine anesthesia shares neural circuits with NREM sleep, it may also have distinct direct cortical effects, possibly leading to different slow wave activity (Aim 2 Hypothesis). The purpose of this research is to test these hypotheses by mapping cortical neural activity with respect to the EEG slow waves of both anesthesia and NREM sleep. In order to do this, Dr. Melonakos will learn how to perform calcium imaging experiments in freely behaving rodents. He will then record calcium images from Ca2+/calmodulin-dependent protein kinase IIa-positive (CaMKIIa+), parvalbumin-positive (PV+), somatostatin-positive (SST+), and vasoactive intestinal peptide-positive (VIP+) cortical neurons during anesthesia- and sleep-induced slow waves. Propofol, ketamine, and dexmedetomidine anesthesia will be tested. Dr. Melonakos will then compare the neural activity between the anesthetics and between general anesthesia and sleep. Finally, he will identify the role of SST+ neurons in slow waves (Aim 3 Hypothesis) by (1) looking at the activity of cortical neurons following disruption of slow waves by stimulation of the parabrachial nucleus, an arousal area in the brainstem, and (2) inhibiting SST+ neurons during anesthesia- and sleep-induced slow waves. During the K99 phase of this project, Dr. Melonakos will be mentored by Drs. Christa Nehs and Emery Brown, experts in anesthesia and sleep neurocircuitry and faculty at Harvard Medical School, Massachusetts General Hospital (MGH), and Massachusetts Institute of Technology (MIT). Dr. Melonakos will also collaborate with Drs. Michael Hasselmo (Boston University), Nancy Kopell (Boston University), and Daniel Aharoni (University of California, Los Angeles). He will be trained in calcium imaging by Drs. Hasselmo and Aharoni, and statistical analysis by Dr. Brown. Dr. Kopell will guide Dr. Melonakos as he orients his findings within hypothesized slow wave mechanisms from the field of computational neuroscience. Dr. Melonakos will also learn optogenetics stimulation techniques from Dr. Nehs and in a course at MIT. The mentors, collaborators, and other members of the MGH community will also provide him with professional guidance as he nears independence, including training in grant writing, peer review, teaching, and the faculty job search. The scientific and professional training Dr. Melonakos receives will enable him to develop an independent research program to study anesthetics’ direct vs. indirect effects. The resulting understanding of slow wave mechanisms has potential to improve the protocols used to monitor general anesthesia and treat sleep disorders, thus benefiting patient safety and health.

NIH Research Projects · FY 2024 · 2020-09

Project Summary: Over 22 million people need treatment for illicit drug/alcohol abuse in the U.S. (SAMHSA survey), costing the government $468 billion per year in related expenses. After alcohol and smoking, opiates are the leading cause for those admitted to substance abuse treatment programs. Currently, there is an opiate crisis throughout the US. Opiate abuse resulted in an exponential increase in synthetic opiate-caused deaths from ~3,000 in 2012 to almost 30,000 in 2017 (NIDA website: Aug. 2018). However, individuals attempting to overcome opiate as well as other addictions often relapse. Therefore, a better understanding of the reward circuit, in addition to a complete understanding of opiate targets in the reward circuit is essential. Here we propose to investigate the impact of morphine on a novel form of synaptic plasticity of inhibitory inputs as well as excitatory inputs onto inhibitory GABA cells in the ventral tegmental area (VTA), the brain’s reward center. Within the VTA, dopamine-containing cells are involved in motivation and reward. Reward is an essential component of survival, mediated by increased dopamine release from the VTA. Drugs of abuse dramatically enhance dopamine levels beyond normal rewarding behaviors, but also cause synaptic modifications on VTA cells, leading to the diseased state of addiction. While known that illicit drugs cause modifications to dopamine cell synapses, neither normal synaptic plasticity of GABA neurons nor how opiates alter GABA neuron activity is completely known. This, despite the fact that GABA neurons are involved in vivo in both the perception and associative learning of reward. Therefore, this role in reward makes VTA GABA cells nearly as important as DA cells to investigate. As opiates mediate non-pain related actions in the VTA, our findings will paint a clearer picture of neurocircuit adaptations caused by opiates and provide a basis for examining the effect of other drugs of abuse on GABA plasticity. The long- term goal is to understand normal physiology and morphine-induced modification of inputs to VTA GABA cells. We hope examining opiate-induced VTA neuroadaptations provides a more comprehensive solution in our efforts to reverse addiction. We hypothesize that unique VTA GABA cells that express distinct forms of plasticity that will correlate differentially to VTA GABA neurons that are either projecting or interneurons. We further hypothesize that excitatory and inhibitory forms of GABA cell plasticity will be maladaptively altered by chronic, but not acute, morphine exposure. We also will examine the potential impact of VTA GABA cell plasticity based on the cells unique projections and impact on DA cells. Collectively, this data will provide a better framework of GABAergic circuit role in reward, and morphine effect on synaptic modifications. We will examine this hypothesis using single cell electrophysiology, Retrobeads, rabies virus, optogenetics, and single cell PCR, etc. As currently there are no good treatments to address opiate or other forms of drug-dependence, the identification of a novel target for drugs of abuse could lead to potential new avenues of treatment. 1

NIH Research Projects · FY 2026 · 2020-09

The increasing appreciation that associated microorganisms (‘microbiota’) are fundamentally tied to the physiology of virtually all organisms on the planet necessitates that descriptions of biological processes account for microbial influences. One such model is the understanding of how animals adapt in response to selection. Traditional descriptions of spatially and temporally varying selection describe how variable environmental pressures select for favorable animal phenotypic outcomes with space and time, respectively. Recent evidence, including from previously funded work, links the microbiota to spatial and temporal selection by identifying species that both influence host traits and differ in abundance in their hosts with space and time. A major gap is that whereas seasonal changes in animal genotypes are well-documented, seasonal variation in animal- associated microbiomes are not. In this renewal application, the fruit fly Drosophila melanogaster and its microbiota will be used as a model to close this gap by determining how seasonal variation in the microbiota influences the flies’ seasonal adaptation; and by defining seasonal variation in the genomes of individual members of the flies’ microbiota. The microbial influences on host adaptation will be defined by comparing fly population sizes and axenic (bacteria-free) phenotypes after the flies are reared in outdoor mesocosms over a summer-to-fall season. The mesocosms enable researchers to track fly seasonal evolution by preventing fly migration so that changes in fly phenotypes over time result from selection or drift. It will also be tested if bacterial heat production influences seasonal adaptation of the flies by modifying the diet temperatures of flies in these mesocosms using physical (rheostat and temperature controller) or genetic (bacterial mutants that produce different diet-surface temperatures) means. Seasonal bacterial genomic variants will be defined by whole genome sequencing of bacterial strain pairs isolated from experimental outdoor mesocosms at the beginning and at the end of the summer-to-fall selection. Preliminary evidence confirms the likelihood of success by showing that the same bacterial strains are readily recovered at the beginning and end of a seasonal selection experiment in these outdoor mescososms. Additional experiments will measure fitness influences of the bacterial genomic changes on the bacteria and the host to determine if bacterial adaptations are driving fitness of host, microbe, or both. Together, these approaches will reveal how seasonal variation in microbiota contributes to seasonal adaptation of D. melanogaster. Relative to AREA-specific goals, the previously funding supported 67 mentored students who made 59 conference presentations and published 5 research articles (+ 2 submitted for review); 5 with undergraduates as first-authors and collectively 25 undergraduate co-authors. Of the 36 who graduated, most are training in Ph.D. (11), Masters (3), or professional school (4) programs; working in their fields (6); taking gap years (3); or seeking in-field work (4).

NIH Research Projects · FY 2025 · 2020-07

Project Abstract Elucidating molecular pathways that coordinate inflammatory responses and histopathology is of paramount importance in the study of lung disease and possible treatments. Our current R15 led to novel discoveries related to roles for RAGE in coordinating cellular responses to tobacco smoke exposure. The use of electronic cigarettes (eCigs) is a phenomenon that has emerged in the United States during the last few decades. Flavored eCig vaping is steadily rising and troubling trends for eCig use suggest an astonishing 1 out of 10 adolescents vape regularly. eCigs involves exposure to aerosols that are generated when heating diverse flavorings, propylene glycol and glycerine. The resulting aerosols are believed to be less toxic, notwithstanding the accumulation of harmful molecules including acrolein and formaldehyde. Our preliminary studies revealed RAGE-mediated responses to eCig exposure and foreshadow the need for renewed research of this receptor. SAGEs are semi-synthetic glycosaminoglycan ethers that are potent modulators of inflammation in numerous animal models of human disease, and are in preclinical development for periodontitis, oral mucositis, and bladder inflammation. Importantly, SAGEs significantly inhibit interactions between RAGE and its many ligands necessary for signaling. The present proposal is the first to thoroughly assess the biology of RAGE signaling in the context of eCig exposure. A key innovation of this proposal is a collection of animal models that tightly control RAGE expression including RAGE knock out mice, tissue-specific transgenic mice that up-regulate RAGE, and mice harboring phosphorylation deficient RAGE alleles. The central hypothesis is that exposure to eCig aerosols adversely impacts pulmonary health, culminating in significant inflammation and parenchymal cell death. We also hypothesize that abrogation of RAGE with SAGEs diminishes the myriad inflammatory events in the exposed lung. Two specific aims are proposed, and each uses advanced molecular methodologies employed by undergraduate students to test these hypotheses. The studies outlined in this proposal will validate RAGE signaling as a target pathway for the prevention or attenuation of lung diseases in individuals unable or unwilling to remove eCig exposure but may also help to clarify RAGE-mediated pathogenesis in a number of physiological processes.

NIH Research Projects · FY 2024 · 2019-08

Abstract One of the best methods for identifying novel genes and mutations affecting embryonic development is forward genetic screening. However, these screens require a large number of animals to be screened to identify a single novel mutant due to the inefficiency of chemical mutagenesis. Our current R15 finalized a method for enzymatic generation of CRISPR libraries and used the method to generate sgRNAs targeting all of, and only, the genes expressed in the developing zebrafish heart. The sgRNA templates were then divided into eight groups of ten, transcribed, and injected into single-cell zebrafish embryos. Amazingly, all eight pools showed clear morphological heart phenotypes. Thus, we are applying for this renewal grant to continue work on the screen. We have identified a gene from the first round of the screen, sytl5. We will describe this gene’s role in heart development, identify other mutated genes in our screen, and characterize their phenotypes in detail. We also propose to optimize screen parameters to maximize our efficiency and to apply our CRISPR library to a tissue-specific gene editing screen. In addition to the novel mutants identified in the screen, this method will be able to be adapted to any tissue in any species, reducing the resources needed to conduct forward genetic screens and increase the speed of gene discovery in a number of species and developmental processes.

NIH Research Projects · FY 2026 · 2016-09

Project Summary Pathogenic bacteria employ specialized secretion systems to identify and interact with host cells and to exchange genetic information through horizontal gene transfer. These machines are attractive drug targets because they are surface-exposed, widely conserved, and specific for pathogenicity. Unfortunately, however, the structures of many of these critical systems remain poorly understood. Here we describe how we will continue to use electron cryotomography (cryoET) to dissect the structures and functions of pathogenic nanomachines. CryoET is a revolutionary imaging technique with the power to reveal native structures inside intact cells in 3D with macromolecular (2-5 nm) resolution. Subtomogram averaging of identical structures from one or more cryotomograms can push this resolution to better than 1 nm in the most favorable cases, enabling components to be placed in their context in the complete machine. My group has pioneered the development of this revolutionary imaging technology, and in just under four years of our first award period, we have used cryoET to produce tens of new structures of pathogenic secretion systems and build architectural models of key systems belonging to the type IV pilus (T4P), type VI secretion system (T6SS) and type IV secretion system (T4SS) families, producing a flood of new mechanistic insights. By exploiting new cryoET technologies we have just developed in the past couple years, here we propose to extend our work in the next award period to different functional states of these complexes, key related systems, and a new target: the pathogenic type IX secretion system (T9SS). In addition, we will push the whole body of work to higher resolution. For each target, we will image the entire, intact structure in situ. In most cases, this will be the first high-resolution imaging of these structures. We will then combine subtomogram averaging with difference analysis of mutants in which individual components are knocked out or tagged with additional density in order to produce architectural models of the complexes. In cases where atomic models of components (or homologs) are available, we will dock them into our maps to produce pseudo-atomic models of each machine. By comparing these structures with those of non-pathogenic relatives (solved previously or in the proposed work), we aim to identify adaptations underlying virulence functions. We will also apply state-of-the-art cryogenic correlated light and electron microscopy (cryo-CLEM) to guide cryogenic focused ion beam (FIB) milling to enable us to image pathogenic secretion systems in action: in bacterial cells infecting eukaryotic hosts. This will provide the first such images of critical human pathogens, which we expect to provide invaluable insights into the operation of their virulence machinery in vivo. Together, we expect this project to produce a detailed mechanistic picture of the T4SS, T4P, and T9SS nanomachines that mediate pathogenesis, an important first step in identifying therapeutic targets in the future.