This paper is part of the Environmental Scan of Adaptive Capacity and Preparedness of Clinical and Translational Science Award (CTSA) hubs, illuminating challenges, practices, and lessons learned related to CTSA hubs’ efforts of engaging community partners to reduce the spread of the virus, address barriers to COVID-19 testing, identify treatments to improve health outcomes, and advance community participation in research. CTSA researchers, staff, and community partners collaborated to develop evidence-based, inclusive, accessible, and culturally appropriate strategies and resources helping community members stay healthy, informed, and connected during the pandemic. CTSA institutions have used various mechanisms to advance co-learning and co-sharing of knowledge, resources, tools, and experiences between academic professionals, patients, community partners, and other stakeholders. Forward-looking and adaptive decision-making structures are those that prioritize sustained relationships, mutual trust and commitment, ongoing communication, proactive identification of community concerns and needs, shared goals and decision making, as well as ample appreciation of community members and their contributions to translational research. There is a strong need for further community-engaged research and workforce training on how to build our collective and individual adaptive capacity to sustain and improve processes and outcomes of engagement with and by communities—in all aspects of translational science.

OBJECTIVES/GOALS: Prostate cancer is the 2nd most common cancer among men. 1/3 of these men have a slow-growing disease that can be managed without intervention. Instead of treatment, they can enter an active surveillance program. The goal of this study is to examine if cumulative cancer location can predict one’s disease progression and be used as a clinical marker. METHODS/STUDY POPULATION: This is a retrospective cohort study consisting of men with Gleason Grade 1 prostate cancer enrolled in the Active Surveillance Program at Johns Hopkins. The cohort includes men who were enrolled in the program from 2007 to 2015 before prostate biopsies incorporated multiparametric MRI as of the prostate. We will assess if cumulative cancer location (CCLO), a sum of the total number of histological cancer-positive locations on diagnostic and confirmatory biopsy, can predict grade progression, adverse findings on radical prostatectomy findings, or protocol-based discontinuation. Kaplan Meier survival analyses and multivariable Cox regression will be used to determine if stratifying by CCLO can predict these outcomes. RESULTS/ANTICIPATED RESULTS: We included 1298 men in this study. The study will analyze variables that will be used in multivariable regression. Some variables of interest include age at diagnosis, PSA, PSA density, race/ethnicity, and number of positive cores. We expect that greater variability of tumor location, a higher CCLO score, will lead to more grade progression, protocol-based discontinuation, shorter time on active surveillance and adverse findings after radical prostatectomy. This hypothesis is based on a 2018 study that determined cancer location as a significant predictor of progression at the time of biopsy. Results will be discussed in full at the conference. DISCUSSION/SIGNIFICANCE: Finding a predictive marker of progression at the time of biopsy is clinically significant and can lead to adjusted patient observation and testing while on active surveillance. This will better stratify men on active surveillance, determine who would benefit from genetic testing, and better counsel patients as to how long they will be on surveillance.

OBJECTIVES/GOALS: Exposure to arsenic, cadmium, manganese, and lead have been linked to adverse neurocognitive outcomes in adults/children, but effects in adolescents are not fully characterized. This study aims to examine the association between exposure to a mixture of metals (As, Cd, Mn, Pb, Se) and cognitive function in adolescents. METHODS/STUDY POPULATION: The Metals, Arsenic, & Nutrition in Adolescents Study (MANAS) is a cross-sectional study of 572 Bangladeshi adolescents. Blood levels of As, Cd, Mn, Pb, and Se were measured via ICP-MS. An abbreviated Cambridge Neuropsychological Test Automated Battery (CANTAB) was administered, with subtests assessing cognitive function and executive function tasks. Linear regression and Bayesian kernel machine regression (BKMR) were used to examine associations between individual metals, the overall mixture of metals, and cognitive function as measured by the CANTAB. RESULTS/ANTICIPATED RESULTS: Linear regression showed that As (B=−2.40) and Mn (B=−5.31) were negatively associated with Spatial Working Memory (p<0.05). Negative associations were also observed between Cd and Spatial Recognition Memory (SRM) (B=−2.77, p<0.05), and between Pb and Delayed Match to Sample (DMS), a measure of visual recognition and memory (B=−3.67, p<0.05). Se and Spatial Span Length (B=0.92, p<0.05) were seen to be positively associated. BKMR showed no overall effect of the mixture but indicated that Pb was negatively associated with DMS, and that Cd was negatively associated with SRM. Se was positively associated with Planning, Reaction Time, and Spatial Span. Posterior inclusion probability consistently rated Se as the most influential mixture component. DISCUSSION/SIGNIFICANCE: Se was positively associated with cognition, while Mn and As were linked to poorer working memory, and Cd and Pb were associated with poorer visual recognition and memory. We saw agreement between linear regression and BKMR in analyzing metal mixture exposures. Findings suggest interventions aimed at adolescents might influence lifelong cognition.

OBJECTIVES/GOALS: Decoding the origins of cell-free DNA (cfDNA) released from dying cells in a liquid biopsy sample (e.g. blood) offers the potential to provide insight into the dynamic, organism-wide changes reflective of health and disease. Thus, making cfDNA an ideal target for serial, minimally invasive monitoring of disease-related changes. METHODS/STUDY POPULATION: We develop a probabilistic method that leverages the co-regulation of neighboring CpG sites on individual methylome-wide sequencing (WGBS) reads to more flexibly model cell-specific methylation compared to prior methods that focus on the methylation rate of a single CpG site. We then extend our cross-sectional model to account for sequential sampling within the same subject. The increased sampling frequency is critical to identifying the evolutionary dynamics of disease progression influencing treatment response and resistance, and disease recurrence. We utilize Bayesian inference techniques to model patient-specific longitudinal profiles of cell-type turnover in simulated serial samples. RESULTS/ANTICIPATED RESULTS: We found our model more effective at capturing a range of methylation patterns on cfDNA fragments with lower Root Mean Square Error across simulations compared to a single CpG model. We apply our model to detect significant (p < 0.05, Friedman’s test) increases in cellular contributions from lung and cardiac tissue in breast cancer patients (n=15) undergoing radiation therapy compared to baseline. We also identify signals of radiation induced toxicity to the liver in right-sided breast cancer patients (n=8) receiving radiation treatment compared to left-sided breast cancer patients (n=7). Finally, we show our extended model results in more efficient estimates of simulated cell-type turnover profiles compared to analyzing serial samples cross-sectionally, ignoring the longitudinal nature of the data. DISCUSSION/SIGNIFICANCE: Here we address an unmet need in developing novel statistical methodologies to decode the origins of methylated cfDNA obtained from liquid biopsy samples. We demonstrate the far-ranging clinical utility of serial liquid biopsy sampling to complement and advance the standards of clinical care in oncology and other pathologies.

ABSTRACT IMPACT: MiaA has a human homolog known as TRIT1. Mutations in TRIT1 have been associated with rare diseases such as MELAS and MERRF syndromes. These diseases are associated with mitochondrial disfunction.Understanding the mechanisms of bacterial sRNAs, and the miRNAs associated with these diseases could potentially afford the insight into effective cures. OBJECTIVES/GOALS: The aim is to investigate the regulation and function of tRNA isopentyladenine transferase enzyme in Escherichia coli. We aimed to execute screens for the identification of small RNA regulators of MiaA. The study will also investigate if i6A tRNA modification is necessary for the expression of major heat shock and mitochondrial proteins. METHODS/STUDY POPULATION: We constructed a chromosomal miaA-lacZ translational fusion driven by the arabinose responsive PBAD promoter and used it to screen against an Escherichia coli small RNA library. Using CsrB, one of our candidate sRNA regulators from our genetic screen, we measured the steady state levels of MiaA by Northern Blot in a PBAD-miaA2(P2HS)-lacZ translational fusion strain whereby pBR-pLac-csrB, pBR-pLac-csrA and the pBR-pLac vector are over-expressed, and under the control of an IPTG inducible promoter. Additionally, and in the same PBAD-miaA2(P2HS)-lacZ translational fusion strain background, we measured the steady state levels of MiaA in the wild type, csrA:zeo mutant strain, and csrA:zeo pBR-pLac-csrA complementation strain to determine if a combination of the pair would restore the wild-type genotype. RESULTS/ANTICIPATED RESULTS: Upon measuring the effect of small RNAs on miaA expression using quantitative b-galactosidase assays, we saw a 5-fold decrease in the expression of MiaA in the miaA-lacZ translational fusion containing sRNA CsrB, suggesting that this sRNA may play a role in the regulation of post-transcriptional expression of MiaA.From our northern blotting analysis, we observed a 6-fold decrease in MiaA expression in the absence of csrA, suggesting that csrA is essential for MiaA expression. DISCUSSION/SIGNIFICANCE OF FINDINGS: Identifying, mapping and characterizing how MiaA is regulated post-transcriptionally will give us an increased understanding in the maintenance and regulation of the normal function of E.coli to conserve homeostasis and translation fidelity.

OBJECTIVES/SPECIFIC AIMS: The soluble adenylyl cyclase (sAC) is a noncanonical source of cAMP in mammalian cells. sAC is an ATP/bicarbonate ion sensor, whose activity responds to intracellular signals such as pH changes and metabolism. Unlike the more traditionally studied transmembrane adenylyl cyclase, sAC is not tethered to the cell membrane and instead is found in subcellular microdomains like the mitochondria and nucleus. In particular, sAC localization in the mitochondria has been implicated in oxidative phosphorylation and mitochondrial metabolism. Specific changes in sAC microdomain localization have diagnostic utility in a wide variety of cancers, namely melanoma. We have recently found that loss of sAC leads to tumorigenesis and a Warburg/cancer-like metabolic phenotype, consisting of an activated flux through glycolysis, increased lactate production, and dependence on glucose metabolism. In addition, computational analysis of the metabolomics profile of sAC null cells suggests an increased flux through serine synthetic pathways. We hypothesized that specific sAC microdomains are responsible for this cancer-like metabolic state. METHODS/STUDY POPULATION: We have established oncogenic SV40 large T antigen and HPV16-E6 expressing mouse embryonic fibroblasts lacking sAC expression (SV40 KO and E6 KO, respectively). Using these parental lines, we reintroduced sAC by targeting the protein to specific microdomains. sAC was either driven into the mitochondria (mito-sAC) or was driven into all possible microdomains (WT sAC). Single clones were generated and sAC expression was confirmed by Western analysis. Stable cell lines were evaluated for mitochondrial metabolism, glucose sensitivity, and serine sensitivity. RESULTS/ANTICIPATED RESULTS: We found that reintroduction of WT sAC into sAC null cells rescued sensitivity to glycolytic inhibition compared with control cells (p<0.01). The effect was not dependent on the method of immortalization as it was seen in both SV40 and E6 KO cell lines. sAC activity was not directly proportional to expression suggesting that additional regulatory pathways exist. Interestingly, targeted delivery of sAC to the mitochondria was not as effective in rescuing glucose sensitivity as untargeted delivery of sAC into all possible microdomains. Therefore, even though mitochondrial sAC is known to influence metabolism, our data suggests that the nonmitochondrial isoform is most important for cancer cell metabolism. Although metabolomics analysis suggested that serine synthetic pathways are activated in sAC null cells, there is no evidence to suggest that serine is required for sAC null cell growth. Neither inhibition of serine synthesis nor serine starvation differentially affected the growth of sAC null cells compared with WT sAC. DISCUSSION/SIGNIFICANCE OF IMPACT: These data suggest that the Warburg metabolic phenotype in sAC null cells is regulated by specific sAC microdomains. By targeting sAC to specific microdomains, we can further distinguish the role of sAC localization in cellular metabolism. Cancer cells have been shown to exhibit altered metabolic circuitry of pathways like glycolysis, which allow them to adapt to increased metabolic demands of cellular proliferation and waning environmental resources. Beyond helping us improve the use of sAC immunolocalization as a cancer diagnostic, a better understanding of sAC microdomains in transformed cells will help us understand how this signaling pathway is important in cancer. Pharmacologic manipulation of sAC signaling may represent a new cancer therapeutic strategy.

OBJECTIVES/SPECIFIC AIMS: To share the experience gained and lessons learned from a cross CTSA collaborative effort to improve the review process for Pilot Studies awards by exchanging external reviewers. METHODS/STUDY POPULATION: The CEREC process is managed by a web-based tracking system that enables all participating members to view at any time the status of reviewer invitations. This online tracking system is supplemented by monthly conference calls during which new calls for proposals are announced and best practices are identified. Each CTSA hub customized the CEREC model based on their individual pilot program needs and review process. Some hubs have supplemented their internal reviews by only posting proposals on CEREC that lack reviewers with significant expertise within their institutions. Other hubs have requested 1–3 external reviewers for each of their proposals or a selection of most promising proposals. In anticipation of potential scoring discrepancies, several hubs added a self-assessment of reviewer expertise and confidence at the end of each review. If a proposal is on the cusp of fundability, then the reviewers’ self-assessment may be taken into account. In addition to the tracking data collected by the online system, a survey of CEREC reviewers was conducted using Qualtrics. RESULTS/ANTICIPATED RESULTS: Across the 144 proposals submitted for reviews, CEREC members issued a total of 396 email invitations to potential reviewers. The number of invitations required to yield a reviewer ranged from 1 to 17. A total of 224 invitations were accepted, for a response rate of 56%. An external reviewer was unable to be located for 5 proposals (3%). Ultimately, 196 completed reviews were submitted, for a completion rate of 87%. The most common reasons for non-completion after acceptance of an invitation included reviewer illness and discovery of a conflict of interest. CEREC members found the process extremely useful for locating qualified reviewers who were not in conflict with the proposal being reviewed and for identifying reviewers for proposals related to highly specialized topics. The survey of CEREC reviewers found that they generally found the process easy to navigate and intellectually rewarding. Most would be willing to review additional CEREC proposals in the future. External reviewer comments and scores were generally in agreement with internal reviewer comments and scores. Thus, hubs could factor in external reviewer scores equally to internal reviewer scores, without feeling compelled to calibrate external reviewer scores. Overall, through CEREC external reviewers, mainly due to the stronger matching of scientific expertise and reduction of potential bias, the quality of reviews appear to be higher and more pertinent. DISCUSSION/SIGNIFICANCE OF IMPACT: Some aspects of the process emerged that will be addressed in the future to make the system more efficient. One issue that arose was the burden on the system during multiple simultaneous calls for proposals. Future plans call for harmonizing review cycles to avoid these overlaps. Efficiency also will be improved by optimizing the timing of reviewer invitations to minimize the probability of obtaining more reviews than requested. In addition to the original objective of CEREC, the collaboration has led to additional exchange of information regarding methods and processes related to running the Pilot Funding programs. For example, one site developed a method using REDCap to manage their reviewer database; an innovation that is being shared with the other CEREC partners. Another site has a well-developed process for integrating community reviewers into their review process and is sharing their training materials with the remaining CEREC partners.

OBJECTIVES/SPECIFIC AIMS: The long-term goal of this project is to develop a cost effective 3D printed multipurpose intravaginal ring (IVR) to prevent against unintended pregnancies and infectious diseases.Our goal is to develop a female-controlled method for prevention using innovative IVRs. METHODS/STUDY POPULATION: In vitro and in vivo characterization. RESULTS/ANTICIPATED RESULTS: Controlled and fine-tuned release kinetics 100% drug release from 3D printed IVRs compared with 10%–15% with traditional injection molded IVRs cost-effective engineering of multipurpose IVR with CLIP 3D printing technology. DISCUSSION/SIGNIFICANCE OF IMPACT: If successful, this project will revolutionize the engineering of IVRs and will have a global impact on human health. Not only we will help save millions of women around the world but also millions of children that are infected by their HIV-positive mothers through gestation or breast feeding.

OBJECTIVES/GOALS: Lack of blood vessels remains a major obstacle in tissue regeneration. Angiopoietin 1 and 2 modulate angiogenesis through the Tie2 receptor tyrosine kinase. Ang1 activates pAKT to promote endothelial cell survival while Ang2 antagonizes these effects. We aim to dissect the Ang/Tie2 pathway to uncover the molecular basis for these opposing effects. METHODS/STUDY POPULATION: Ang1 and Ang2 bind Tie2 via nearly identical F-domains (Fd). To investigate the molecular basis regulating the Tie2 pathway, we generated a series of computationally designed self-assembling protein scaffolds presenting F-domains in a wide range of valencies and geometries using Rosette Molecular Modeling Suite. We examined the protein kinase activation, cell migration, and blood vessel formation produced by the designed proteins in human umbilical vein endothelial cells. RESULTS/ANTICIPATED RESULTS: Two phenotypic classes were demonstrated by the number of presented F domains: scaffolds presenting 3 or 4 Fd have Ang2 like activity, upregulating pFAK and pERK but not pAKT and failing to induce cell migration and tube formation; scaffolds presenting more than 6 Fd have Ang1 like activity, upregulating the three signaling branches and enhancing cell migration and tube formation. Scaffolds with 8 or more Fd show superagonist activity, producing significantly stronger phenotypes than Ang1. These results suggest that Fd valency largely determines Ang1 vs Ang2 signaling outcomes, and our designed superagonists can outperform Ang1 in vascularization and wound healing. In in vivo experiments, nanoparticles displaying 60 copies of Fd produce significant revascularization in hemorrhagic brains. DISCUSSION/SIGNIFICANCE OF IMPACT: Targeting the Tie2 pathway is a new paradigm in regenerative medicine. Our designed constructs will enable us to generate high-affinity Tie2 agonists and antagonists as drugs to control angiogenesis, enabling tissue regeneration that recapitulates the biological architecture of the native tissue physiology, improving organ transplant outcome.

ABSTRACT IMPACT: My work is on the development of a novel tumor immunotherapy to treat various types of cancer OBJECTIVES/GOALS: As iNKT cells can have direct and indirect killing effects on tumor cells, we propose a novel strategy for activating iNKT cells, via a PLGA nanoparticle delivery platform, to promote anti-tumor immune responses. METHODS/STUDY POPULATION: Poly-lactic-co-glycolic acid (PLGA) nanoparticles can be reproducibly loaded with an iNKT cell glycolipid agonist, alpha-galactosylceramide (αGalCer), and a tumor associated antigen, ovalbumin (OVA). We then test our nanoP prophylactically and therapeutically against a murine model of melanoma, B16F10-OVA. RESULTS/ANTICIPATED RESULTS: These dual-loaded PLGA nanoparticles rapidly activate iNKT cells in vivo to produce IFNgamma. Furthermore, in an in vivo model of melanoma, using B16F10-OVA cells, both prophylactic and therapeutic administration of nanoparticles containing αGalCer and OVA led to decreased tumor cell growth and increased survival. We also show our nanoparticle therapy has synergistic potential with clinically used immune checkpoint blockade (ICB) therapies, anti-PD-1 and anti-CTLA-4, indicated by the significance increase in survival and lower tumor growth rate of ICB +nanoP treated mice compared to either ICB or nanoP alone. DISCUSSION/SIGNIFICANCE OF FINDINGS: This novel delivery system provides a platform with tremendous potential to harness iNKT cells for cancer immunotherapy purposes against many cancer types.

OBJECTIVES/SPECIFIC AIMS: Polyglutamine (polyQ) neurodegenerative diseases, associated with the unstable expansion of polyQ tracts, are devastating diseases for which no treatments exist. Moreover, most drug discovery attempts have been hindered by the lack of understanding on the relevant pathogenic mechanisms. Here, using previously reported 3D protein predicted structures of ataxin-2 and ataxin-3, we analyze the effect of polyQ enlargement on hydrogen bonding and water accessibility patterns as a possible mechanism for pathogenesis thought enhanced protein aggregation. METHODS/STUDY POPULATION: Using the I-TASSER predicted structures of ataxin-2 and ataxin-3 with different numbers of glutamine repeats representing polyQ lengths characteristic of both normal and pathological tracts (Journal of Biomolecular Structure and Dynamics, 2016: 1–16), we identified hydrogen bonds (HBs, UCSF Chimera FindHBond module) and calculated solvent-accessible surface areas (SASA, DSSP program) for the polyQ tracts available in the 3D structures. RESULTS/ANTICIPATED RESULTS: The identified HBs were analyzed as the function of the number of glutamines in the polyQ tracts and characterized as those intra-polyQ and exter-polyQ, respectively. The SASA of the polyQ region was also studied as the function of the polyQ tract length. DISCUSSION/SIGNIFICANCE OF IMPACT: The results obtained here indicate that polyQ regions increasingly prefer self-interactions, which consistently can lead to more compact polyQ structures. The results strongly support the notion that the expansion of the polyQ region can be an intrinsic force leading to self-aggregation of polyglutamine proteins and suggest that the modulation of solvent-polyQ interactions could be a possible therapeutic strategy for polyQ diseases.

OBJECTIVES/SPECIFIC AIMS: To examine and compare the claims in Bzdok, Altman, and Brzywinski under a broader set of conditions by using unbiased methods of comparison. To explore how to accurately use various machine learning and traditional statistical methods in large-scale translational research by estimating their accuracy statistics. Then we will identify the methods with the best performance characteristics. METHODS/STUDY POPULATION: We conducted a simulation study with a microarray of gene expression data. We maintained the original structure proposed by Bzdok, Altman, and Brzywinski. The structure for gene expression data includes a total of 40 genes from 20 people, in which 10 people are phenotype positive and 10 are phenotype negative. In order to find a statistical difference 25% of the genes were set to be dysregulated across phenotype. This dysregulation forced the positive and negative phenotypes to have different mean population expressions. Additional variance was included to simulate genetic variation across the population. We also allowed for within person correlation across genes, which was not done in the original simulations. The following methods were used to determine the number of dysregulated genes in simulated data set: unadjusted p-values, Benjamini-Hochberg adjusted p-values, Bonferroni adjusted p-values, random forest importance levels, neural net prediction weights, and second-generation p-values. RESULTS/ANTICIPATED RESULTS: Results vary depending on whether a pre-specified significance level is used or the top 10 ranked values are taken. When all methods are given the same prior information of 10 dysregulated genes, the Benjamini-Hochberg adjusted p-values and the second-generation p-values generally outperform all other methods. We were not able to reproduce or validate the finding that random forest importance levels via a machine learning algorithm outperform classical methods. Almost uniformly, the machine learning methods did not yield improved accuracy statistics and they depend heavily on the a priori chosen number of dysregulated genes. DISCUSSION/SIGNIFICANCE OF IMPACT: In this context, machine learning methods do not outperform standard methods. Because of this and their additional complexity, machine learning approaches would not be preferable. Of all the approaches the second-generation p-value appears to offer significant benefit for the cost of a priori defining a region of trivially null effect sizes. The choice of an analysis method for large-scale translational data is critical to the success of any statistical investigation, and our simulations clearly highlight the various tradeoffs among the available methods.