OBJECTIVES/GOALS: The host immune response during sepsis is now recognized to have anti-inflammatory pathophysiology. We aim to determine whether mitochondrial dysfunction of leukocytes predicts which critically ill septic patients develop immune paralysis and to identify differences in cellular metabolites between patients with and without immune paralysis. METHODS/STUDY POPULATION: Critically ill septic and control adult patients were recruited from one of 6 ICUs in a single-center tertiary care academic hospital. After enrollment, peripheral blood mononuclear cells (PBMCs) were isolated from a tube of whole blood on day 0-1 after ICU admission. Flow cytometry to quantify monocyte HLA-DR was performed to determine whether patients were immune paralyzed or not. Mitochondrial functional assays of PBMCs were performed with inhibitors of the electron transport chain to assess for differences in oxidative phosphorylation and glycolysis utilization. Metabolic profiling of cell pellets was performed to evaluate for specific metabolites and pathways associated with immune paralyzed patients. RESULTS/ANTICIPATED RESULTS: A total of 101 patients were recruited, including 62 control and 39 septic patients. 81 patients had immune paralysis status available for analysis. 52% of all recruited subjects were immune paralyzed. Of these, 58% were controls and 75% were septic. Immune paralyzed septic and control patients showed features of reduced utilization of oxidative phosphorylation (ox phos) including reduced basal respiration, ATP production and maximal respiration compared with non-immune paralyzed septic and control patients. Immune paralyzed septic patients showed diminished glycolysis utilization compared with septic non-immune paralyzed patients. Finally, cellular kynurenine and quinolinate levels were low in both immune paralyzed control and septic patients compared with non-immune paralyzed patients. DISCUSSION/SIGNIFICANCE: The PBMCs of immune paralyzed septic patients show evidence of mitochondrial dysfunction, with reduced ox phos and glycolysis utilization. Low levels of kynurenine and quinolinate, metabolite precursors to NAD+, in immune paralyzed patients may signal key deficiencies and targetable therapeutic avenues for reversal of an immune paralyzed state.

ABSTRACT IMPACT: This work describes, for the first time, the methylome in patients with T-LGLL, focusing on the IL-15 promoter, and clearly demonstrates that 5-azacytidine decreases IL-15 production leading to T-LGLL cell death. These results form the basis a translational clinical trial in T-LGLL that will begin accrual in 2021 OBJECTIVES/GOALS: T-LGLL is an incurable leukemia with few treatment options driven by overexpression of IL-15. Our objective is to characterize the methylation status of the IL-15 promoter in T-LGLL patients and evaluate the potential use of 5-azacytidine (5-aza) in a translational trial by studying the effect of 5-aza in vitro on IL-15 levels, and the IL-15 promoter. METHODS/STUDY POPULATION: We sorted T-LGLL patient (n=3) and normal donor (ND) samples (n=3) for CD3+/CD8+/CD5-/dim for T-LGLL immunophenotype. We analyzed DNA methylation and gene expression profiling using reduced representation bisulfite and RNA sequencing and determined differential methylation and gene expression using 1-way ANOVA analysis. To determine the functional significance of differential methylation, we evaluated MOTN-1 T-LGLL cell viability in vitro with 5-aza at increasing concentrations. Next, we evaluated IL-15 gene expression in MOTN-1 cells treated with 5-Aza versus MOTN-1 with control using western immunoblot. Finally, we exposed MOTN-1 cells to a novel IL-15 inhibitor, IBI-15, and compared cell viability against MOTN-1 cells exposed to an inactive control. RESULTS/ANTICIPATED RESULTS: There was significant differential methylation (P= 0.0178) and expression (P =0.0059) in T-LGLL patients vs ND. These data revealed significant differential hypermethylation of gene promoters, including an increase in DNA methylation of the IL-15 promoter in T-LGLL cells vs ND. In MOTN-1 cells treated in vitro with 5-Aza at 24 and 48 hours, a dose-dependent decrease in the viability of T-LGLL cells was observed, from 100% to 49.5%, p=0.037. Further, a marked decrease in IL-15 expression was observed at all concentrations of 5-aza compared to control (p=0.0001). Finally, a decrease in cell viability was observed utilizing the IL-15 inhibitor IBI-15 vs control. These results confirm that 5-aza leads to decreased transcription of the IL-15 gene, possibly due to hypomethylation of the IL-15 promoter. DISCUSSION/SIGNIFICANCE OF FINDINGS: Hypermethylation of the IL-15 promoter and subsequent increase in IL-15 is critical to the pathogenesis of T-LGLL. Inhibition of the IL-15 promoter by 5-aza leads to down-regulation of the IL-15 gene transcript, which is sufficient to induce T-LGLL cell death. Based on these results, a phase I trial will be conducted using CC-486 (oral 5-Aza) in T-LGLL.

OBJECTIVES/GOALS: Measuring the area of skin involvement in chronic graft-versus-host disease (cGVHD) relies on costly, time-consuming manual assessment, with high disagreement among experts (>20%). Our published AI method, trained on labeled 3D photos, showed promise for delineating affected areas. We aim to improve its performance using unlabeled 2D photos. METHODS/STUDY POPULATION: Our published AI model (baseline) was trained on 360 labeled photos of 36 cGVHD patients,from a 3D camera with calibrated distance and lighting.Our gold standard labels were contours around affected skin, marked by a trained expert. A second unlabeledcohort of 974 standard 2D photos of 8 cGVHD patients was used to improve the baseline model. First the baseline model predicted affected areas on the unlabeled photos. Photos with good predictions were added to the training set with their AI-predicted labels. The model was then re-trained with the expanded labeled set. Models were successively trained with more AI labels until performance stopped improving. AI performance was assessed on a test set of 20 photos from 20 patients unseen during training, labeled by 4 experts to improve accuracy. RESULTS/ANTICIPATED RESULTS: Model performance was calculated by comparing against the gold standard labels on the test set. To quantify the spatial overlap of labeled areas the Dice coefficient was used (0 is no overlap, 1 is complete agreement), where higher values are better. To estimate clinical error we used surface area error (Error), where lower values are better. On the test set, the baseline model had a median Dice of 0.57 [interquartile range: 0.39 – 0.82] and Error of 57.6% [20.2 – 103.3%]. Re-training with additional AI-predicted labels from 8 new patients, the model yielded a median Dice of 0.60 [0.35 – 0.80] and Error of 50% [12.5 – 103.8%]. This approach is being expanded to a further 300 unlabeled patients, where we anticipate significant improvements to AI performance and consistency. DISCUSSION/SIGNIFICANCE: Evaluating AI models in standard photos could provide a consistent method of assessing and tracking cutaneous cGVHD and relieve the burden of costly expert assessment. A reliable automated AI tool would provide a meaningful improvement to the current standard of manual assessment and could be easily applied to large patient cohorts.

OBJECTIVES/GOALS: We study the association of adaptive decision-making, motor planning, and neuromuscular constraints, in children with hemiplegia. We examine how children scale motor decisions to body mechanics and the distance of a target while reaching in sitting/standing, and if they can recalibrate motor decisions to sudden changes in body mechanics. METHODS/STUDY POPULATION: Forty-five 6-12 year-olds with hemiplegia and 45 age/gender matched typically developing controls participate in clinical tests (i.e. balance, visual perceptual skills, etc.) and 3 experiments. Children “reach to tap” toward a target while sitting with both preferred and not preferred arms under three conditions: regular elbow extension siting and standing and elbow extension range reduced by 50% via a splint while sitting. Trials are easy, ambiguous, and difficult. Motor decisions are compared to abilities and motion sensors (IMUs) worn at wrist, arm, sternum and lumbar area, record biomechanical strategies children use under different decisions. Synchronized video analysis presents biomechanical strategies under different decisions. RESULTS/ANTICIPATED RESULTS: Data collection is still underway. A mixed models analysis is used to compare 2 (group: hemiplegic/typically developing) X 2 (arms: healthy/impaired & dominant/non dominant) X 3 (difficulty levels) the children’s decisions. Functional analysis is used to capture biomechanical strategies children use under different decisions and levels of difficulty. Exploration strategies are recorded relative to levels of difficulty. We will also compute correlations between affordance thresholds for all children and measures of sensation, range of motion, cognition and balance (in each posture). Lastly, a secondary analysis will compare behaviors of children with left/right hemisphere lesions, as they differ in spatial abilities. Preliminary results show that children with hemiplegia make errors with both their affected and unaffected side. DISCUSSION/SIGNIFICANCE OF IMPACT: Motor deficits in children with hemiplegia are the primary focus of treatments. Motor learning interventions focus on biomechanical deficits. Results from these studies expand the focus to planning and cognitive control issues underlying motor deficits.

OBJECTIVES/GOALS: An age-dependent restitution defect in our neonatal pig intestinal ischemia model is rescued by unknown factors within homogenized mucosa of weaned pigs. A postnatally maturing network of enteric glia regulates the epithelial barrier, so we aim to show rescue is due to replacement of glial factors. METHODS/STUDY POPULATION: Jejunal tissues from suckling or weaned pigs were assessed by RNAseq and processed for immunofluorescent histology and 3-D volume imaging. Jejunal ischemia was surgically induced in weaned pigs and injured mucosa was recovered ex vivo with or without the glial inhibitor fluoroacetate (FA) while monitoring transepithelial electrical resistance (TER). RESULTS/ANTICIPATED RESULTS: Ingenuity Pathways Analysis of RNAseq data revealed significant suppression of numerous pathways critical for epithelial wound healing in suckling pigs (Z-score <−2 for of nine key pathways). Volume imaging studies confirmed lower density (P≤0.05) and complexity of the subepithelial glial network in suckling pigs. Treatment with FA inhibited TER recovery (P<0.0001) and restitution (P<0.05) in weaned pigs, mimicking the suckling pig phenotype and supporting glia as an important regulator of restitution in our model. DISCUSSION/SIGNIFICANCE OF IMPACT: These findings provide important evidence that a developing glial network may be critical to the postnatal development of intestinal barrier repair mechanisms. Ongoing work will explore glial-epithelial interactions in vitro to further define postnatal development of barrier repair.

OBJECTIVES/GOALS: This pilot study investigated psilocybin-induced changes in neural reactivity to alcohol and emotional cues in patients with alcohol use disorder (AUD). METHODS/STUDY POPULATION: Participants were recruited from a phase II, randomized, double-blind, placebo-controlled clinical trial investigating psilocybin-assisted therapy (PAT) for the treatment of AUD (NCT02061293). Eleven adult patients completed task-based blood oxygen dependent functional magnetic resonance imaging (fMRI) approximately 3 days before and 2 days after receiving 25 mg of psilocybin (n = 5) or 50 mg of diphenhydramine (n = 6). Visual alcohol and emotionally valanced (positive, negative, or neutral) stimuli were presented in block design. RESULTS/ANTICIPATED RESULTS: Across both alcohol and emotional cues, psilocybin increased activity in the medial and lateral prefrontal cortex (PFC) and left caudate, and decreased activity in the insular, motor, temporal, parietal, and occipital cortices, and cerebellum. Unique to negative cues, psilocybin increased supramarginal gyrus activity; unique to positive cues, psilocybin increased right hippocampus activity and decreased left hippocampus activity. DISCUSSION/SIGNIFICANCE: Greater PFC and caudate engagement and concomitant insula, motor, and cerebellar disengagement suggests enhanced goal-directed action, improved emotional regulation, and diminished craving. The robust changes in brain activity observed in this pilot study warrant larger neuroimaging studies to elucidate neural mechanisms of PAT.

ABSTRACT IMPACT: This study characterizes interactions between human limbic circuitry and ventral temporal cortex using single pulse electrical stimulation, which may inform emerging stimulation therapies for epilepsy. OBJECTIVES/GOALS: The goal of electrical brain stimulation treatment is to modulate brain network function. However, stimulation inputs to different brain sites alter the network in a variety of ways. This study examines that variability by characterizing responses in a target region while stimulating multiple other brain sites. METHODS/STUDY POPULATION: We measured voltages in intracranial EEG in 6 patients who had electrodes implanted for epilepsy monitoring. We stimulated pairs of electrodes at multiple sites in the brain with a single pulse every 5 to 7 s and measured the resulting corticocortical evoked potential (CCEP) responses in the ventral temporal cortex (VTC). Using a novel clustering method, we uncovered sets of distinct canonical response shapes from the 20 to 500 ms post-stimulation period. This allowed us to group stimulation sites that evoked similar responses. We then related each group to high frequency, broadband, changes in spectral power as a reflection of local neuronal activity. RESULTS/ANTICIPATED RESULTS: We found that the VTC receives strong inputs specifically from the amygdala and hippocampus, both in terms of amplitude and broadband spectral power change. However, inputs from the hippocampus produced a different canonical shape than those from the amygdala. We also observed that VTC responses to inputs from the insula clustered in shape with those from the amygdala. These clustering patterns were consistent across subjects, although the actual shapes of the clusters showed variability. We further observed that some shapes were more associated with increases in overall neuronal activity than others, as reflected by broadband spectral power change. DISCUSSION/SIGNIFICANCE OF FINDINGS: Stimulation of connected sites may drive excitability at the target region in ways that are described by sets of full-time-course responses. By capturing their shapes, we can begin to decipher canonical input types at the circuit level. This approach might identify how stimulation inputs can be tailored to therapy while mitigating adverse effects.

OBJECTIVES/SPECIFIC AIMS: Facilitate relationships and partnership development to address the opioid crisis in Detroit and Wayne County Contribute to real-time conversations on opioid epidemic policy and practice to identify and build consensus on research questions Apply findings from each learning community session to policy briefs to better inform policymakers, providers and consumers; and advocate for institutional responsiveness METHODS/STUDY POPULATION: The study population utilizes a purposive sampling approach to intentionally organize relationships and partnership development. For example, participants registered for the December 2018 session, “Detroit/Wayne County Opioid Crisis Learning Community Series: Data Session,” include representation from school-based health clinics, community and faith-based organizations, health systems, city and county level public health, addiction/recovery organizations, law enforcement, academia and citizens. The team feels this approach ensures and builds diverse, team science perspectives and regional collaboration. The Detroit Area Mental Health Leadership Team formed in 2015 at a retreat held by the University of Michigan’s Clinical and Translational Science Initiative attended by nearly 100 community-academic partners. Mental health, stigma and suicide were identified as community priorities by participants who attended the summit. A mental health workgroup formed and later expanded its membership to strengthen diverse perspectives. The team immediately designed and administered a survey amongst its partners creating the following priorities and focus: substance abuse interventions, healthcare access, and consumer awareness of mental health issues/available resources. Since data, policy and service are common threads to design interventions, the partnership decided to facilitate dialogue and discussion from the community on special topics related to the crisis, and share the community’s recommendations on how to address them. The learning community series was designed as a bi-lingual format for sharing and expression. Deliberative democracy encourages inclusion of voices, interests and opinions often not heard or included in decision-making processes; driving the project’s purposive sampling approach. Institutional responsiveness and advocacy for adoption of the community’s recommendations will occur through strategic policy briefs summarizing each learning community session and the entire series. A dissemination plan will be utilized to encourage the policy briefs reach appropriate audiences for capacity building and institutional responsiveness. The learning community series will provide 5 sessions on data (impacting adolescents, emerging adults, and 20-mid 30 year-old adults), recovery/law enforcement, prescribing, and marijuana. The session topics arose from earlier assessment conducted by the Detroit Area Mental Health Leadership Team. RESULTS/ANTICIPATED RESULTS: A response to the opioid crisis should address community priorities identified through data, research and community input. Community providers should have access to real-time data and research to develop appropriate interventions and institutional responsiveness. Equally important is the need for legislators and others impacting resource allocation to hear from the community on priorities they feel should be addressed, and to better understand the need for new types of data and information to drive service delivery, policy and resources to address the crisis. The learning community series will focus on describing the epidemic and building infrastructure to collaborate, and share data and information to strengthen advocacy and responsiveness to address the crisis. We feel this will enable more efficient programming to strengthen service delivery that captures life experiences from those who directly interface with individuals impacted by the crisis. DISCUSSION/SIGNIFICANCE OF IMPACT: There is limited knowledge and consensus on types of data and information to effectively describe the opioid crisis. For example, data and information connecting gateway drugs such as marijuana with more hardcore drugs (i.e., opioids and heroin) is not available; community-based providers have limited access to what research says about the crisis; and local public and community providers are dependent upon the state for surveillance data. Individuals dealing with addiction and recovery often need immediate attention. A gap in access to services exists depending on types of insurance. For example, Medicaid and some HMOs require an assessment before clients can seek treatment, resulting in uncompensated care among providers to immediately address patients need. Access to healthcare is a longstanding issue in medically underserved communities. The impact of the crisis varies geographically in communities and regions due to cultural and ethnic differences, yet data and information on these differences is not readily available. Cultural competency and sensitivity is often an issue in medically underserved areas because stakeholders may feel professionals providing services do not relate to them effectively. Finally, the community does not understand the economic impact of the crisis. These issues make it difficult for community advocates and providers to work with elected officials, providers and others on the opioid crisis because they do not have the data and informed required to effectively flush out a hypothesis and form solutions. Information captured in the learning community series (i.e., presentations by experts, facilitated discussion and personal testimony) will be summarized in a policy brief after each session and the entire series. Recommendations and priorities from the community will be shared with providers, policymakers, the business community, consumers and others to provide community input on problem solving approaches, new interventions, types of data not currently available that should be captured, and other important strategies and information to address the crisis. This information will also encourage designing research questions to guide developing new community engaged and community based participatory research to address the crisis. Finally, utilizing a purposive approach in participant recruitment will encourage partnership development from a team science and capacity building perspective.

Objectives/Goals: To guide UCSF in adopting a justice-based, patient-informed model of health data sharing that moves beyond traditional regulatory frameworks that focus on individual privacy and do not address public good and health equity; to create a roadmap for UCSF to implement transparent, community-engaged data governance that is both responsible and effective. Methods/Study Population: We conducted 24 hours of observation at outpatient clinics and 75 in-depth interviews with multiple stakeholder groups: patients, community advisors, and UCSF faculty and staff involved in managing and governing health and research-generated data. We used an ethnographic approach to investigate health data sharing policies and practices and explore perspectives on data governance. We were particularly interested in how data sharing is explained to patients, and what patients and community members know and think about how patient data may be used other than for clinical care. We explored faculty, staff, patient, and community member perspectives about the potential involvement of patients/communities in data governance. We also reviewed UCSF websites with information about data use and sharing policies. Results/Anticipated Results: Policy Awareness: Clear communication and improved guidelines for data use policies will enhance awareness among patients, the public, and researchers, boosting UCSF’s credibility as a data steward. Risks/Benefits: Data sharing drives scientific and clinical progress but raises concerns about privacy, profiteering, and unequal benefit distribution. Improving the Data Ecosystem: Safe data sharing and public benefit can be strengthened through centralized governance and better communication. Key growth areas include deidentification, external sharing criteria, and leakage prevention. Shared Governance: Patients and community advisors favor shared governance with public involvement, while UCSF informants were equivocal. All are concerned about equitable representation and technical training challenges. Discussion/Significance of Impact: Our recommendations to University leadership: Improve communication of data sharing policies to patients and the public. Involve patients and the public in data governance. Support investigators to ensure understanding and compliance with data use policies. Hold UCSF and collaborators accountable for transparency, equity, and public benefit.

OBJECTIVES/GOALS: To develop a two-staged convolutional neural network to identify the ovary and antral follicles within ovarian ultrasound images and determine its reliability and feasibility compared to conventional techniques in 2D and 3D ultrasonography image analysis. METHODS/STUDY POPULATION: De-identified and archived ultrasonographic images of women across the reproductive spectrum (N=500) will be used in the study. These ultrasound images will be labeled by experienced raters to train a two-staged convolutional neural network (CU-Net). CU-Net will first separate the entire ovary from the background and subsequently identify all antral follicles within the ovary. Following training, the CU-Net will evaluate a second set of independent images (N=100) to determine performance accuracy. Three specialized raters will establish the reliability and feasibility of CU-Net compared to conventional 2D and 3D ovarian ultrasound image analysis methods. RESULTS/ANTICIPATED RESULTS: The labeled training dataset of ovarian ultrasound images is expected to successfully train the CU-Net and allow for accurate identification of the ovary and the total number of antral follicles in the second testing set of ultrasound images. When compared to conventional 2D and 3D ultrasound image analysis methods, CU-Net is expected to have similar accuracy when compared to the gold-standard method (2D-Offline with Grid) and outperform other approaches, such as 2D-Real Time and 3D volume software (VOCAL and Sono-AVC). However, CU-Net is anticipated to be the fastest and most reliable method across users, supporting its clinical feasibility. DISCUSSION/SIGNIFICANCE: This study will immediately translate to providing a standardized platform that can improve the accuracy, reliability, and time demand required for the evaluation of ovarian ultrasounds across users and clinical and research settings.

The Stanford Population Health Sciences Data Ecosystem was created to facilitate the use of large datasets containing health records from hundreds of millions of individuals. This necessitated technical solutions optimized for an academic medical center to manage and share high-risk data at scale. Through collaboration with internal and external partners, we have built a Data Ecosystem to host, curate, and share data with hundreds of users in a secure and compliant manner. This platform has enabled us to host unique data assets and serve the needs of researchers across Stanford University, and the technology and approach were designed to be replicable and portable to other institutions. We have found, however, that though these technological advances are necessary, they are not sufficient. Challenges around making data Findable, Accessible, Interoperable, and Reusable remain. Our experience has demonstrated that there is a high demand for access to real-world data, and that if the appropriate tools and structures are in place, translational research can be advanced considerably. Together, technological solutions, management structures, and education to support researcher, data science, and community collaborations offer more impactful processes over the long-term for supporting translational research with real-world data.

Biospecimen repositories play a vital role in enabling investigation of biologic mechanisms, identification of disease-related biomarkers, advances in diagnostic assays, recognition of microbial evolution, and characterization of new therapeutic targets for intervention. They rely on the complex integration of scientific need, regulatory oversight, quality control in collection, processing and tracking, and linkage to robust phenotype information. The COVID-19 pandemic amplified many of these considerations and illuminated new challenges, all while academic health centers were trying to adapt to unprecedented clinical demands and heightened research constraints not witnessed in over 100 years. The outbreak demanded rapid understanding of SARS-CoV-2 to develop diagnostics and therapeutics, prompting the immediate need for access to high quality, well-characterized COVID-19-associated biospecimens. We surveyed 60 Clinical and Translational Science Award (CTSA) hubs to better understand the strategies and barriers encountered in biobanking before and in response to the COVID-19 pandemic. Feedback revealed a major shift in biorepository model, specimen-acquisition and consent process from a combination of investigator-initiated and institutional protocols to an enterprise-serving strategy. CTSA hubs were well equipped to leverage established capacities and expertise to quickly respond to the scientific needs of this crisis through support of institutional approaches in biorepository management.

Objectives/Goals: • To examine current barriers to research translation that collide with funder imperatives to share research results broadly. • To create community-driven guidance on broadening dissemination of research findings to communities to enhance health literacy and trustworthiness in the research process. Methods/Study Population: The Penn State Community Health Equity & Engagement in Research team, part of our Clinical and Translational Science Institute, completed six semi-structured focus groups in the Fall of 2023 (N = 46, including geographically diverse communities with and without research experience). Focus groups included presentation of evidence-based and novel approaches to lay results dissemination, including lay briefs and data walks, to elucidate experiences with and preferences for receiving research study results. Qualitative data were analyzed using MAXQDA software, with successful (>0.70) kappa coefficient achieved for interrater reliability. Codes and themes were developed inductively. Results/Anticipated Results: Focus group characteristics included N = 39 identifying as women, with a mean age of 56 years old and 10% identifying as Black/African American. Geographic breakdown included 49% rural, 44% suburban, and 7% urban. Thematic analyses demonstrated a perceived lack of trustworthiness and representation in the research process, with several cultural and geographic barriers to research accessibility and results dissemination. The majority of participants did not receive research results from prior studies and identified the importance of trusted community messengers to share findings broadly. Participants prefer receiving lay briefs, with health literate infographics, over published study manuscripts. Data walks featuring key findings presented by researchers within communities were highly desired. Discussion/Significance of Impact: Improving translation of study results in underrepresented communities is a catalyst for increasing engagement, demonstrating trustworthiness, and improving health literacy. The development of evidence-based lay dissemination methodologies can increase translation and bolster efforts to support informed, research-ready communities.

OBJECTIVES/GOALS: MiaA is a highly conserved prenyl transferase that catalyzes synthesis of the i6A37 tRNA modification in E. coli. While transcriptional regulation of MiaA is well characterized, there is no information on the MiaA post-transcriptional regulation. The aim of this study is to characterize the post-transcriptional regulation of the MiaA gene in E. coli. METHODS/STUDY POPULATION: To characterize the post-transcriptional regulation of miaA, we executed a targeted genetic screen of an E. coli small RNA library on a miaA-lacZ translational reporter fusion strain to identify small RNAs (sRNAs) that modulate MiaA translation or transcription termination. We also measured MiaA mRNA levels and miaA-lacZ activity in the absence or over-expression of candidate sRNA regulators of MiaA. We also measured MiaA mRNA levels in the absence of RNaseE and PNPase, two enzymes involved in mRNA turnover. Finally, we measured the ability of purified recombinant CsrA to bind to the MiaA mRNA transcript in vitro. RESULTS/ANTICIPATED RESULTS: We identified the carbon sensing sRNA CsrB and its cognate protein interaction partner CsrA, as potential post-transcriptional regulators of MiaA. Over-expression of CsrB fully repressed miaA-lacZ activity and MiaA mRNA levels. The absence of CsrA resulted in a defective miaA-lacZ activity and a 10-fold decrease in MiaA mRNA levels. We also identified an increase in the MiaA mRNA half-life particularly in the absence of RNaseE. Our results demonstrate an additional layer of regulation for the miaA operon by the CsrA/CsrB protein-sRNA system. DISCUSSION/SIGNIFICANCE: MiaA is a highly conserved bacterial protein. Our data may represent phenomena in an array of bacteria that could be targeted by novel antibiotics. The human MiaA homologue, TRIT1, plays a role in mitochondrial disorders. We anticipate that information garnered from MiaA studies will elucidate TRIT1 function and its role in mitochondrial disorders.

ABSTRACT IMPACT: A single seismocardiography (SCG) parameter has been shown to accurately classify aortic valve disease (AVD) status in healthy controls and AVD patients. This could support development of SCG as a quick, inexpensive screening tool to better tailor MRI examination to patients’ needs. OBJECTIVES/GOALS: MRI is used commonly for monitoring of aortic valve disease (AVD), but it has high costs. We hypothesize that energy in seismocardiograms (SCG)’‘ signals from chest surface vibrations’‘ is different between healthy controls and AVD patients, and we evaluate potential efficacy of using SCG to recommend MRI only for patients with flow abnormalities. METHODS/STUDY POPULATION: With IRB approval, 45 healthy control subjects (47 ±18years, 18 female) and 9 patients (63 ±16years, 2 female) with aortic valve disease history and known flow abnormalities were recruited. SCG signals were acquired supine, immediately prior to MRI of thoracic aortic blood flow at 1.5T with a time-resolved phase contrast (4D Flow) sequence.

The SCG was processed to calculate late-systole high-frequency (120-240Hz) RMS energy. MR velocity images were analyzed to measure peak velocity and trace pathlines of flow.

Screening efficacy of the SCG energy metric was assessed, with hypothesis testing for differences in energy level distributions between controls and patients, and receiver-operator characteristic (ROC) analysis was used to calculate rates of correct/incorrect classification of disease. RESULTS/ANTICIPATED RESULTS: Healthy subjects had coherent flow pathlines through the aortic arch and mid-ascending aorta peak velocities of 106 ±21cm/s (cohort mean ±standard deviation). All valve disease subjects had flow abnormalities, such as jetting flow near the valve or swirling through the arch, as visualized by pathlines. Patients’ peak mid-ascending aorta velocities were 167 ±69cm/s. The SCG energy for healthy controls was significantly different than that of valve patients (-5.6 ±0.3dBmm/s/s vs. -4.0 ±1.2dBmm/s/s respectively; p<0.001). Thresholding SCG energy to distinguish patients from controls correctly classifies subjects with a high true-positive rate and low false-positive rate. The ROC for this classification has area-under-curve 0.956. DISCUSSION/SIGNIFICANCE OF FINDINGS: A high potential screening efficacy was observed using a single, linear SCG metric to identify AVD patients with flow abnormalities. If used to complement MRI surveillance protocols for AVD, this method has potential to serve as a quick, inexpensive tool for better tailoring MRI exams to patient needs.

OBJECTIVES/GOALS: The 'field effect' is a concept in pathology that pre-malignant tissue changes forecast health. Spatial transcriptomics could detect these changes earlier than histopathology, suggesting new early cancer screening methods. Knowing how normal tissue damage relates to cancer’s origin and progression may improve long-term outcomes. METHODS/STUDY POPULATION: We trained DEGAS, our machine learning framework, with prostate cancer data, combining both general cancer patterns and in-depth genetic information from individual tumors. The Tumor Cancer Genome Atlas (TCGA) shows how gene patterns in tumors relate to patient outcomes, emphasizing the differences between tumors from different patients (intertumor). On the other hand, spatial transcriptomics (ST) shows the genetic variety within a single tumor (intratumor) but has limited samples, making it hard to know which genetic differences are important for treatment. DEGAS bridges these areas by finding tissue sections that resemble those in TCGA profiles and are key indicators of patient survival. DEGAS serves as a valuable tool for generating clinically-important hypotheses. RESULTS/ANTICIPATED RESULTS: DEGAS identified benign-appearing glands in a normal prostate as being highly associated with poor progression-free survival. These glands have transcriptional signatures similar to high-grade prostate cancer. We confirmed this finding in a separate prostate cancer ST dataset. By integrating single cell (SC) data we demonstrated that cells annotated as cancerous in the SC data map to regions of benign glands in the ST dataset. We pinpoint several genes, chiefly Microseminoprotein-β (MSMB, PSP94), where reduced expression is highly correlated with poor progression-free survival. Cell type specific differential expression analysis further revealed that loss of MSMB expression associated with poor outcomes occurs specifically in luminal epithelia, the putative progenitor of prostate cancer. DISCUSSION/SIGNIFICANCE: DEGAS reveals that normal-appearing tissue can be highly-associated with tumor progression and underscores the importance of the 'field effect' in cancer research. Traditional analysis may miss such nuance, hiding key transitional cell states. Validating gene markers could boost early cancer detection and understanding of metastasis.

OBJECTIVES/GOALS: Many providers share patients resulting in networks where clinical information is exchanged, and which can impact the quality and efficiency of care. Here we analyzed the network properties of a primary care service area (PCSA) in Harris County TX, motivating our ongoing analysis of how they are associated with disparities in cancer screening. METHODS/STUDY POPULATION: Data.All providers (n=731, Medicare 2018) from the PCSA with the most providers in Harris County TX, with gender, specialty, and the number of shared patients. Method. Modeled the data as a network consisting of provider nodes, connected in pairs by edges if they shared >11 patients (an empirically-determined threshold). Analyzed the network structure using (1) modularity maximization and its significance to identify densely-connected communities; (2) degree centralization to measure whether a few providers shared many patients, and betweenness centralization to measure whether a few providers connected densely-connected communities; and (3) chi-squared to measure if pairs of connected providers tended to be of the same gender compared to disconnected provider pairs. RESULTS/ANTICIPATED RESULTS: The results (Fig. 1, http://www.skbhavnani.com/DIVA/Images/Fig-1-SNA-Network.jpg [http://www.skbhavnani.com/DIVA/Images/Fig-1-SNA-Network.jpg]) revealed a fragmented network with 120 small components (connected subnetworks, not part of any larger connected subnetwork), and 1 large component. The large component (n=244) had strong and significant modularity (Q=0.73, z=53.13, P<.001) with communities of providers that shared more patients than expected by chance; low degree centralization (dc=0.11) suggesting that no provider dominated patient sharing, in addition to high and significant betweenness centralization (bc=0.5, P<.01) suggesting that a few providers were responsible for connecting the densely-connected communities; and a significant gender bias (X2=10.05, df=1, P< .01) among those that shared patients, versus those that did not. DISCUSSION/SIGNIFICANCE: The analysis revealed a specific type of vulnerability (betweenness) for network fragmentation, and a gender bias in how patients were shared. These results motivated our ongoing analysis on how the network properties are associated with disparity in cancer screening within PCSAs across Texas.