Biology subjects

Dean, D. A.

Publications and source records attributed to Dean, D. A..

10 recordsLinked to original sources

bioRxiv2025

Anticodon Edited Transfer RNAs (ACE-tRNAs) Encoded as Therapeutic Nonviral Minimal DNA Vectors

Nonsense mutations, resulting from a premature termination codon (PTC), make up [~]11% of all genetic lesions causing disease, affecting millions of people worldwide. Nonsense suppressor anticodon-edited tRNAs (ACE-tRNAs) have emerged as a therapeutic modality for the rescue of PTCs. Delivery of ACE-tRNAs in vivo has been achieved by adeno-associated viral vector and RNA-lipid nanoparticle, however due to drawbacks associated with these approaches, DNA delivery remains an attractive approach. DNA-based approaches afford ease of manufacturing at a relatively low cost and exhibit improved therapeutic durability and safety as compared to viral vector-or RNA-based approaches. Due to the small size of human tRNA genes employed as ACE-tRNAs, in principle, DNA vectors <200 base pairs (bp) in size (minivectors) could be utilized for delivery of actively transcribed ACE-tRNAs. Here we demonstrate that linear DNA ACE-tRNA vectors as small as 200 bp effectively suppress several nonsense mutations in CFTR and REP1, and that ACE-tRNA minivectors display significantly improved bioavailability, reduced innate immune burden, and superior biostability as compared to conventional plasmid DNA vectors.

molecular biology↗

bioRxiv2023

Persistent biofluid small molecule alterations induced by Trypanosoma cruzi infection are not restored by antiparasitic treatment

Table of contents graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/543565v1_figu1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@10c3a0aorg.highwire.dtl.DTLVardef@1b69d6dorg.highwire.dtl.DTLVardef@199867org.highwire.dtl.DTLVardef@64b20b_HPS_FORMAT_FIGEXP M_FIG C_FIG Chagas Disease (CD), caused by Trypanosoma cruzi (T. cruzi) protozoa, is a complicated parasitic illness with inadequate medical measures for diagnosing infection and monitoring treatment success. To address this gap, we analyzed changes in the metabolome of T. cruzi-infected mice via liquid chromatography tandem mass spectrometry analysis of clinically-accessible biofluids: saliva, urine, and plasma. Urine was the most indicative of infection status, across mouse and parasite genotypes. Metabolites perturbed by infection in the urine include kynurenate, acylcarnitines, and threonylcarbamoyladenosine. Based on these results, we sought to implement urine as a tool for assessment of CD treatment success. Strikingly, it was found that mice with parasite clearance following benznidazole antiparasitic treatment had comparable overall urine metabolome to mice that failed to clear parasites. These results match with clinical trial data in which benznidazole treatment did not improve patient outcomes in late-stage disease. Overall, this study provides insights into new small molecule-based CD diagnostic methods and a new approach to assess functional treatment response.

microbiology↗

bioRxiv2021

Spatial metabolomics reveals localized impact of influenza virus infection on the lung tissue metabolome

The influenza virus (IAV) is a major cause of respiratory disease, with significant infection increases in pandemic years. Vaccines are a mainstay of IAV prevention, but are complicated by consideration of IAVs vast strain diversity, manufacturing and vaccine uptake limitations. While antivirals may be used for treatment of IAV, they are most effective in early stages of the infection and several virus strains have become drug resistant. Therefore, there is a need for advances in IAV treatment, especially host-directed, personalized therapeutics.Given the spatial dynamics of IAV infection and the relationship between viral spatial distribution and disease severity, a spatial approach is necessary to expand our understanding of IAV pathogenesis. We used spatial metabolomics to address this issue. Spatial metabolomics combines liquid chromatography-tandem mass spectrometry of metabolites extracted from systematic organ sections, 3D models and computational techniques, to develop spatial models of metabolite location and their role in organ function and disease pathogenesis. In this project, we analyzed plasma and systematically sectioned lung tissue samples from uninfected or infected mice. Spatial mapping of sites of metabolic perturbations revealed significantly lower metabolic perturbation in the trachea compared to other lung tissue sites. Using random forest machine learning, we identified metabolites that responded differently in each lung position based on infection, including specific amino acids, lipids and lipid-like molecules, and nucleosides. These results support the implementation of spatial metabolomics to understand metabolic changes upon IAV infection and to identify candidate pathways to be targeted for IAV therapeutics. ImportanceThe influenza virus is a major health concern. Over 1 billion people become infected annually despite the wide distribution of vaccines, and antiviral agents are insufficient to address current clinical needs. In this study, we used spatial metabolomics to understand changes in the lung and plasma metabolome of mice infected with influenza A virus, compared to uninfected controls. We determined metabolites altered by infection in specific lung tissue sites and distinguished metabolites perturbed by infection between lung tissue and plasma samples. Our findings highlight the importance of a spatial approach to understanding the intersection between lung metabolome, viral infection and disease severity. Ultimately, this approach will expand our understanding of respiratory disease pathogenesis and guide the development of novel host-directed therapeutics.

microbiology↗

bioRxiv2021

PDXNet Portal: Patient-Derived Xenograft model, data, workflow, and tool discovery

We created the PDX Network (PDXNet) Portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources (i.e., PDX models, sequencing data, treatment response data, and bioinformatics workflows), to facilitate collaboration among researchers, and to make resources easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols, and training materials for processing PDX data. The initial portal release highlights PDXNet model and data resources, including 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCIs Patient-Derived Model Repository (PDMR) for public access. These models have 2,822 associated sequencing files from 873 samples across 307 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal also includes results from standardized analysis workflows on PDXNet sequencing files and PDMR data (2,594 samples from 463 patients across 78 disease types). These 15 analysis workflows for whole-exome and RNA-Seq data are freely available, robust, validated, and standardized. The model and data lists will grow substantially over the next two years and will be continuously updated as new data are available. PDXNet models support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials. The PDXNet portal is a centralized location for these data and resources, which we expect to be of significant utility for the cancer research community.

cancer biology↗

bioRxiv2021

Optimized extraction method enables quantitative analysis of surface metabolite recovery for exposomics and behavioral studies

Workplace chemical exposures are a major source of occupational injury. Although over half of these are skin exposures, exposomics research often focuses on chemical levels in the air or in worker biofluids such as blood and urine. Until now, one limitation has been the lack of methods to quantitatively measure surface chemical transfer. Outside the realm of harmful chemicals, the small molecules we leave behind on surfaces can also reveal important aspects of human behavior. In this study, we developed a swab-based quantitative approach to determine small molecule concentrations across common surfaces. We demonstrate its utility using one drug, cyclobenzaprine, and two human-derived metabolites, carnitine and phenylacetylglutamine, on four common surfaces: linoleum flooring, plastified laboratory workbench, metal and Plexiglass. This approach enabled linear small molecule recovery and quantification of molecule abundance on workplace built environment surfaces. Overall, this method paves the way for future quantitative exposomics studies.

biochemistry↗

bioRxiv2020

Strengthening the BioCompute Standard by Crowdsourcing on PrecisionFDA

BackgroundThe field of bioinformatics has grown at such a rapid pace that a gap in standardization exists when reporting an analysis. In response, the BioCompute project was created to standardize the type and method of information communicated when describing a bioinformatic analysis. Once the project became established, its goals shifted to broadening awareness and usage of BioCompute, and soliciting feedback from a larger audience. To address these goals, the BioCompute project collaborated with precisionFDA on a crowdsourced challenge that ran from May 2019 to October 2019. This challenge had a beginner track where participants submitted BCOs based on a pipeline of their choosing, and an advanced track where participants submitted applications supporting the creation of a BCO and verification of BCO conformance to specifications. ResultsIn total, there were 28 submissions to the beginner track (including submissions from a bioinformatics masters class at George Washington University) and three submissions to the advanced track. Three top performers were selected from the beginner track, while a single top performer was selected for the advanced track. In the beginner track, top performers differentiated themselves by submitting BCOs that included more than the minimally compliant content. Advanced track submissions were very impressive. They included a complete web application, a command line tool that produced a static result, and a dockerized container that automatically created the BCO as the tool was run. The ability to harmonize the correct function, a simple user experience, and the aesthetics of the tool interface differentiated the tools. ConclusionsDespite being new to the concept, most beginner track scores were high, indicating that most users understood the fundamental concepts of the BCO specification. Novice bioinformatics students were an ideal cohort for this Challenge because of their lack of familiarity with BioCompute, broad diversity of research interests, and motivation to submit high-quality work. This challenge was successful in introducing the BCO to a wider audience, obtaining feedback from that audience, and resulting in a tool novices may use for BCO creation and conformance. In addition, the BCO specification itself was improved based on feedback illustrating the utility of a "wisdom of the crowd" approach to standards development.

bioinformatics↗

bioRxiv2020

Spatial metabolomics identifies localized chemical changes in heart tissue during chronic cardiac Chagas disease

Chagas disease (CD) is one of thirteen neglected tropical diseases caused by the parasite Trypanosoma cruzi. CD is a vector-borne disease transmitted by triatomines but CD can also be transmitted through blood transfusions, organ transplants and congenital transmission. While endemic to Latin America, T. cruzi infects 7-8 million people worldwide and can induce severe cardiac symptoms including apical aneurysms, thromboembolisms and arrhythmias during the chronic stage of CD. However, these cardiac clinical manifestations and CD disease pathogenesis are not fully understood. Using spatial metabolomics (chemical cartography), we sought to understand the localized impact of infection on the cardiac metabolome of mice chronically infected with two divergent T. cruzi strains. Our data showed chemical differences in localized cardiac regions upon chronic T. cruzi infection, indicating that parasite infection changes the host metabolome at select sites in chronic CD. These sites were distinct from the sites of highest parasite burden. In addition, we identified acylcarnitines and phosphocholines as discriminatory chemical families within each heart region, comparing infected and uninfected samples. Overall, our study indicated overall and positional metabolic differences common to infection with different T. cruzi strains, and identified select infection-modulated pathways. These results provide further insight into CD pathogenesis and demonstrate the advantage of a spatial perspective to understand infectious disease tropism.Author Summary Chagas disease (CD) is a tropical disease caused by the parasite Trypanosoma cruzi. CD originated in South America; however, there are now 7-8 million people infected worldwide due to population movements. CD is transmitted through a triatomine vector, organ transplants, blood transfusions and congenital transmission. It occurs in two stages, an acute stage (usually asymptomatic) and the chronic stage. Chronic stage CD presents with severe cardiac symptoms such as heart failure, localized aneurysms and cardiomyopathy. Unfortunately, what causes severe cardiac symptoms in some individuals in chronic CD is not fully understood. Therefore, we used liquid chromatography-tandem mass spectrometry to analyze the heart tissue of chronically T. cruzi-infected and uninfected mice, to understand the impact of infection on the tissue metabolome. We identified discriminatory small molecules related to T. cruzi infection. We also determined that regions with the highest parasite burden are distinct from the regions with the largest changes in overall metabolite profile; these locations of high metabolic perturbation provide a molecular mechanism to why localized cardiac symptoms occur in CD. Overall, our work gives insight to chronic cardiac CD symptom development and shapes a framework for novel treatment and biomarker development.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗

bioRxiv2019

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, impacting the accuracy of PDX modeling of human cancer. Here we exhaustively analyze copy number alterations (CNAs) in 1451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multi-region samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

cancer biology↗

bioRxiv2019

Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis

Patient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.

cancer biology↗

bioRxiv2017

Enabling Precision Medicine via standard communication of NGS provenance, analysis, and results

A personalized approach based on a patients or pathogens unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to FAIR guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet lab procedures to computational methods. The BioCompute framework (https://osf.io/zm97b/) enables standardized reporting of genomic sequence data provenance, including provenance domain, usability domain, execution domain, verification kit, and error domain. This framework facilitates communication and promotes interoperability. Bioinformatics computation instances that employ the BioCompute framework are easily relayed, repeated if needed and compared by scientists, regulators, test developers, and clinicians. Easing the burden of performing the aforementioned tasks greatly extends the range of practical application. Large clinical trials, precision medicine, and regulatory submissions require a set of agreed upon standards that ensures efficient communication and documentation of genomic analyses. The BioCompute paradigm and the resulting BioCompute Objects (BCO) offer that standard, and are freely accessible as a GitHub organization (https://github.com/biocompute-objects) following the \"Open-Stand.org principles for collaborative open standards development\". By communication of high-throughput sequencing studies using a BCO, regulatory agencies (e.g., FDA), diagnostic test developers, researchers, and clinicians can expand collaboration to drive innovation in precision medicine, potentially decreasing the time and cost associated with next generation sequencing workflow exchange, reporting, and regulatory reviews.

scientific communication and education↗