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At least 235 records · Page 13

The landscape of regulatory element evolution in a C4 perennial grass

Gene regulatory evolution is a well-known source of phenotypic diversity and adaptive evolution. Although cis-regulatory elements (CREs) play a vital role in gene expression evolution, the molecular evolution of CREs remains mostly unknown due to the difficulty in identifying and characterizing these functional elements. Comparative genomic analyses of noncoding DNA can be leveraged to identify conserved noncoding sequences (CNS), many of which may harbor functional CREs conserved by purifying selection. However, purely computational inference of CREs from putative CNS can be erroneous due to the complex genomic architecture in plants. One promising experimental approach to identify CREs is by profiling accessible chromatin regions (ACRs) that are often associated with the location of CREs. In this study, we use comparative genomics along with the profiling of ACRs to study the molecular evolution of putative functional noncoding regulatory regions in Panicoid grasses. We identified sets of CNS that varied in relationship to the degree of evolutionary divergence among the studied taxa, including identifying core-Panicoid-CNS. We augmented this analysis by profiling ACRs in Panicum hallii ecotypes using ATAC-seq. ACRs had low SNP density at the summit, harbored a high frequency of core-Panicoid-CNS, and were enriched with expression QTL. These data help to annotate the P. hallii genome for putative functional elements and suggest that a large proportion of these ACRs are evolving under purifying selection. Turnover in CNS and ACR between ecotypes of P. hallii identifies a small set of putatively divergent CREs that may underlie differences in gene regulation between genotypes from inland and coastal habitats. In summary, we profiled ACRs in Panicoid grasses and integrated this data with our putative CNS prediction framework, which provides unique insight into patterns of polymorphism and divergence in CREs in C4 perennial grasses.

59 BASIC BIOLOGICAL SCIENCES↗

African Swine Fever Virus Protein–Protein Interaction Prediction

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein–protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein–protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein–protein interactions, further validating their relevance. Two protein–protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.

59 BASIC BIOLOGICAL SCIENCES↗

Cytochemical studies of planetary microorganisms - Explorations in exobiology

Analytical methodology using gas chromatography and mass spectrography for improved physiological monitoring of astronauts is developed. Reported research covers the following topics: (1) Chlorination of DNA bases; (2) mass fragmentography; (3) mass spectrometry; (4) urine analysis for metabolic constituents; (5) analysis of natural products by mass spectrometry; (6) computer identification of unknown molecular compounds; (7) fluorescent sorter for cell separation; (8) Mariner Mars 1971 orbital photography; and (9) Viking Lander imagery.

Lederberg, J.↗

NASA Models of Space Radiation Induced Cancer, Circulatory Disease, and Central Nervous System Effects

The risks of late effects from galactic cosmic rays (GCR) and solar particle events (SPE) are potentially a limitation to long-term space travel. The late effects of highest concern have significant lethality including cancer, effects to the central nervous system (CNS), and circulatory diseases (CD). For cancer and CD the use of age and gender specific models with uncertainty assessments based on human epidemiology data for low LET radiation combined with relative biological effectiveness factors (RBEs) and dose- and dose-rate reduction effectiveness factors (DDREF) to extrapolate these results to space radiation exposures is considered the current "state-of-the-art". The revised NASA Space Risk Model (NSRM-2014) is based on recent radio-epidemiology data for cancer and CD, however a key feature of the NSRM-2014 is the formulation of particle fluence and track structure based radiation quality factors for solid cancer and leukemia risk estimates, which are distinct from the ICRP quality factors, and shown to lead to smaller uncertainties in risk estimates. Many persons exposed to radiation on earth as well as astronauts are life-time never-smokers, which is estimated to significantly modify radiation cancer and CD risk estimates. A key feature of the NASA radiation protection model is the classification of radiation workers by smoking history in setting dose limits. Possible qualitative differences between GCR and low LET radiation increase uncertainties and are not included in previous risk estimates. Two important qualitative differences are emerging from research studies. The first is the increased lethality of tumors observed in animal models compared to low LET radiation or background tumors. The second are Non- Targeted Effects (NTE), which include bystander effects and genomic instability, which has been observed in cell and animal models of cancer risks. NTE's could lead to significant changes in RBE and DDREF estimates for GCR particles, and the potential effectiveness of radiation mitigator's. The NSRM- 2014 approaches to model radiation quality dependent lethality and NTE's will be described. CNS effects include both early changes that may occur during long space missions and late effects such as Alzheimer's disease (AD). AD effects 50% of the population above age 80-yr, is a degenerative disease that worsens with time after initial onset leading to death, and has no known cure. AD is difficult to detect at early stages and the small number of low LET epidemiology studies undertaken have not identified an association with low dose radiation. However experimental studies in mice suggest GCR may lead to early onset AD. We discuss modeling approaches to consider mechanisms whereby radiation would lead to earlier onset of occurrence of AD. Biomarkers of AD include amyloid beta (A(Beta)) plaques, and neurofibrillary tangles (NFT) made up of aggregates of the hyperphosphorylated form of the micro-tubule associated, tau protein. Related markers include synaptic degeneration, dentritic spine loss, and neuronal cell loss through apoptosis. Radiation may affect these processes by causing oxidative stress, aberrant signaling following DNA damage, and chronic neuroinflammation. Cell types to be considered in multi-scale models are neurons, astrocytes, and microglia. We developed biochemical and cell kinetics models of DNA damage signaling related to glycogen synthase kinase-3(Beta) (GSK3(Beta)) and neuroinflammation, and considered multi-scale modeling approaches to develop computer simulations of cell interactions and their relationships to A(Beta) plaques and NFTs. Comparison of model results to experimental data for the age specific development of A(Beta) plaques in transgenic mice will be discussed.

Cucinotta, Francis A.↗

Role of Translational Entropy in Spatially Inhomogeneous, Coarse-Grained Models

Coarse-grained models of polymer and biomolecular systems have enabled the computational study of cooperative phenomena, e.g., self-assembly, by lumping multiple atomistic degrees of freedom along the backbone of a polymer, lipid, or DNA molecule into one effective coarse-grained interaction center. Such a coarse-graining strategy leaves the number of molecules unaltered. In order to treat the surrounding solvent or counterions on the same coarse-grained level of description, one can also stochastically group several of those small molecules into an effective, coarse-grained solvent bead or "fluid element." Such a procedure reduces the number of molecules, and we discuss how to compensate the concomitant loss of translational entropy by density-dependent interactions in spatially inhomogeneous systems.

36 MATERIALS SCIENCE↗

The Study of Microbial Environmental Processes Related to the Natural Attenuation of Uranium at the Rifle Site using Systems-level Biology

Microbes exist in communities in the environment where they are fundamental drivers of global carbon, nutrient and metal cycles. In subsurface environments, they possess significant metabolic potential to affect these global cycles including the transformation of radionuclides. This study examined the influence of microbial communities in sediment zones undergoing biogeochemical cycling of carbon, nutrients and metals including natural attenuation of uranium. This study examined the relationship of both the microbiota (taxonomy) and their metabolic capacity (function) in driving carbon, nutrient and metal cycles including uranium reduction at the Department of Energy (DOE) Rifle Integrated Field Research Challenge (RIFRC). Objectives of this project were: 1) to apply systems-level biology through application of ‘metaomics’ approaches (collective analyses of whole microbial community DNA, RNA and protein) to the study of microbial environmental processes and their relationship to C, N and metals including the influence of microbial communities on uranium contaminant mobility in subsurface settings undergoing natural attenuation, 2) improve methodologies for data generation using metaomics (collectively metagenomics, metatranscriptomics and proteomics) technologies and analysis and interpretation of that data and 3) use the data generated from these studies towards microbial community-scale metabolic modeling. The strategy for examining these subsurface microbial communities was to generate sequence reads from microbial community DNA (metagenomics or whole genome shotgun sequencing (WGS)) and RNA (metatranscriptomcs or RNAseq) and protein information using proteomics. Results were analyzed independently and through computational modeling. Overall, the community model generated information on the microbial community structure that was observed using metaomic approaches at RIFRC sites and thus provides an important framework for continued community modeling development. The model as created is capable of predicting the response of the community structure in changing environments such as anoxic/oxic conditions or limitations by carbon or nutrients. The ability to more accurately model these responses is critical to understanding carbon and energy flows in an ecosystem is critical towards improving our ability to make predictions that can be used to design more efficient remediation and management strategies, and better understand the implications of environmental perturbations on these ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗

Developing a Genetic Variant Calling Pipeline for Quantifying the Complex Mutagenic Load Accumulated in BioNutrients-1 Production Pack Samples

Microorganisms hold great promise for on demand production of labile nutrients and pharmaceuticals as well recycling and in situ resource utilization. The utilization of microorganisms for such tasks on space missions is hindered by the limited data on how microbes respond to spaceflight. For example, the genetic stability of microorganisms, and the genomic engineered traits added to deliver desired functions, over long-term storage in the spacecraft environment is poorly understood. The BioNutrients-1 (BN-1) mission conducted a 5-year study of desiccated storage in Low Earth Orbit (LEO) to evaluate the suitability of eight synthetic biology chassis organisms for long-duration space missions. We are employing high-depth, whole genome sequencing (WGS) to determine the mutagenic load that accumulated during long-term storage. Mutation analysis pipelines are well established for homogenous culture grown from a single colony, but the mutational landscape of the BN-1 samples present a unique analysis challenge, as every cell in the BN-1 samples had a unique genetic journey of DNA damage and repair. Consequently, sequence variants are expected at low allele frequency within samples. To address this genetic complexity, we apply two distinct computational approaches to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO. For reference genome free mutation detection, we utilized DiscoSNP++, which is a de Bruijn graph approach. For reference genome-based mutation detection we utilize GATK for Microbes, which is a Bayesian probabilistic approach. We will benchmark these approaches against the mutations originally identified using CRISP, a method optimized for pooled samples. Ultimately, quantifying the mutation load imposed by storage or growth on the ISS will help identify chassis organisms with both high levels of genome stability and viability, which are desirable traits for implementation of bioproduction in long-duration missions.

SNP↗

A polymer, random walk model for the size-distribution of large DNA fragments after high linear energy transfer radiation

DNA double-strand breaks (DSBs) produced by densely ionizing radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. Stochastic DSB clustering at large scales, from > 100 Mbp down to < 0.01 Mbp, is modeled using computer simulations and analytic equations. A random-walk, coarse-grained polymer model for chromatin is combined with a simple track structure model in Monte Carlo software called DNAbreak and is applied to data on alpha-particle irradiation of V-79 cells. The chromatin model neglects molecular details but systematically incorporates an increase in average spatial separation between two DNA loci as the number of base-pairs between the loci increases. Fragment-size distributions obtained using DNAbreak match data on large fragments about as well as distributions previously obtained with a less mechanistic approach. Dose-response relations, linear at small doses of high linear energy transfer (LET) radiation, are obtained. They are found to be non-linear when the dose becomes so large that there is a significant probability of overlapping or close juxtaposition, along one chromosome, for different DSB clusters from different tracks. The non-linearity is more evident for large fragments than for small. The DNAbreak results furnish an example of the RLC (randomly located clusters) analytic formalism, which generalizes the broken-stick fragment-size distribution of the random-breakage model that is often applied to low-LET data.

DNA/radiation effects↗

Large-Area, Highly Crystalline DNA-Assembled Metasurfaces Exhibiting Widely Tunable Epsilon-Near-Zero Behavior

Metasurfaces prepared via bottom-up nanoparticle assembly enable the deliberate manipulation of light in the optical regime, resulting in media with unprecedented optical responses. Here, we report a scalable method to grow highly crystalline 2D metasurfaces comprised of colloidal gold nanocubes, over macroscopic areas, using DNA-mediated assembly under equilibrium conditions. Using an effective medium description, we predict that these plasmonic metasurfaces behave as dielectric media with high refractive indices that can be dynamically tuned by tuning DNA length. Furthermore, we predict that when coupled with an underlying thin gold film, the real permittivity of these metasurfaces exhibits a crossover region between positive and negative values, known as the epsilon-near-zero (ENZ) condition, which can be tuned between 1.5 - 2.6 μm by changing DNA length. Optical characterization performed on the DNA-assembled metasurfaces reveals that the predicted optical properties agree well with measured response. Overall, we propose an efficient method for realizing large-area plasmonic metasurfaces that enable dynamic control over unique optical characteristics. High-index and ENZ metasurfaces operating in the telecommunications regime could have significant implications in high-speed optical computing, optical communications, optical imaging, and other areas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Genetic Correlation with the DNA Repair Assay in Mice Exposed to High-LET

We hypothesize that DNA damage induced by high local energy deposition, occurring when cells are traversed by high-LET (Linear Energy Transfer) particles, can be experimentally modeled by exposing cells to high doses of low-LET. In this work, we validate such hypothesis by characterizing and correlating the time dependence of 53BP1 radiation-induced foci (RIF) for various doses and LET across 72 primary skin fibroblast from mice. This genetically diverse population allows us to understand how genetic may modulate the dose and LET relationship. The cohort was made on average from 3 males and 3 females belonging to 15 different strains of mice with various genetic backgrounds, including the collaborative cross (CC) genetic model (10 strains) and 5 reference mice strains. Cells were exposed to two fluences of three HZE (High Atomic Energy) particles (Si 350 megaelectronvolts per nucleon, Ar 350 megaelectronvolts per nucleon and Fe 600 megaelectronvolts per nucleon) and to 0.1, 1 and 4 grays from a 160 kilovolt X-ray. Individual radiation sensitivity was investigated by high throughput measurements of DNA repair kinetics for different doses of each radiation type. The 53BP1 RIF dose response to high-LET particles showed a linear dependency that matched the expected number of tracks per cell, clearly illustrating the fact that close-by DNA double strand breaks along tracks cluster within one single RIF. By comparing the slope of the high-LET dose curve to the expected number of tracks per cell we computed the number of remaining unrepaired tracks as a function of time post-irradiation. Results show that the percentage of unrepaired track over a 48 hours follow-up is higher as the LET increases across all strains. We also observe a strong correlation between the high dose repair kinetics following exposure to 160 kilovolts X-ray and the repair kinetics of high-LET tracks, with higher correlation with higher LET. At the in-vivo level for the 10-CC strains, we observe that drops in the number of T-cells and B-cells found in the blood of mice 24 hours after exposure to 0.1 gray of 320 kilovolts X-ray correlate well with slower DNA repair kinetics in skin cells exposed to X-ray. Overall, our results suggest that repair kinetics found in skin is a surrogate marker for in-vivo radiation sensitivity in other tissue, such as blood cells, and that such response is modulated by genetic variability.

cosmic radiation↗

A genomic regulatory network for development

Development of the body plan is controlled by large networks of regulatory genes. A gene regulatory network that controls the specification of endoderm and mesoderm in the sea urchin embryo is summarized here. The network was derived from large-scale perturbation analyses, in combination with computational methodologies, genomic data, cis-regulatory analysis, and molecular embryology. The network contains over 40 genes at present, and each node can be directly verified at the DNA sequence level by cis-regulatory analysis. Its architecture reveals specific and general aspects of development, such as how given cells generate their ordained fates in the embryo and why the process moves inexorably forward in developmental time.

Non-NASA Center↗

Programmable Dynamic Self-Assembly of DNA Nanostructures (Final Technical Report)

This technical report summarizes our research toward the synthesis of active biomolecular materials that take advantage of the biological properties of DNA and RNA, but can operate in non-biological contexts. Our approach takes inspiration from the dynamic assembly and disassembly of cytoskeletal filaments inside cells, and focuses on the generation of artificial filaments with comparable adaptation and responsiveness. We report the construction of DNA nanotube systems that assemble and disassemble reversibly in response to a variety of molecular stimuli including other nucleic acids (DNA and RNA), enzymes, and pH, as well as our progress on developing a reaction network framework to regulate self-assembly. Our DNA polymers are active in that they rely on the programmable energetics of hybridization and on the kinetics of enzymatic reactions to perform assembly and disassembly. Results include experiments and computational models. The research described here was performed between June 15, 2016 and June 15, 2019 at the University of California at Riverside with the support of the collaborative award DE-SC0010595 to PI Elisa Franco at UC Riverside and PI Rebecca Schulman at Johns Hopkins. The report focuses on the research performed by the team of PI Franco. The report also details work performed during a no-cost extension (June 15, 2019 - December 15, 2019) awarded to PI Guillermo Aguilar and PI Franco.

36 MATERIALS SCIENCE↗

Discovery of Macrocyclic Inhibitors of Apurinic/Apyrimidinic Endonuclease 1

Apurinic/apyrimidinic endonuclease 1 (APE1) is an essential base excision repair enzyme that is upregulated in a number of cancers, contributes to resistance of tumors treated with DNA-alkylating or -oxidizing agents, and has recently been identified as an important therapeutic target. Here in this work, we identified hot spots for binding of small organic molecules experimentally in high resolution crystal structures of APE1 and computationally through the use of FTMAP analysis (http://ftmap.bu.edu/). Guided by these hot spots, a library of drug-like macrocycles was docked and then screened for inhibition of APE1 endonuclease activity. In an iterative process, hot-spot-guided docking, characterization of inhibition of APE1 endonuclease, and cytotoxicity of cancer cells were used to design next generation macrocycles. To assess target selectivity in cells, selected macrocycles were analyzed for modulation of DNA damage. Taken together, our studies suggest that macrocycles represent a promising class of compounds for inhibition of APE1 in cancer cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Navigating the maze: the impact of configurability in bioinformatics software

The bioinformatics software domain contains thousands of applications for automating tasks such as the pairwise alignment of DNA sequences, building and reasoning about metabolic models or simulating growth of an organism. Its end users range from sophisticated developers to those with little computational experience. In response to their needs, developers provide many options to customize the way their algorithms are tuned. Yet there is little or no automated help for the user in determining the consequences or impact of the options they choose. In this paper we describe our experience working with configurable bioinformatics tools. We find limited documentation and help for combining and selecting options along with variation in both functionality and performance. We also find previously undetected faults. We summarize our findings with a set of lessons learned, and present a roadmap for creating automated techniques to interact with bioinformatics software. We believe these will generalize to other types of scientific software.

McDevitt, Mikaela Cashman↗

An approach to learn regulation to maximize growth and entropy production rates in metabolism

Elucidating cell regulation remains a challenging task due to the complexity of metabolism and the difficulty of experimental measurements. Here we present a method for prediction of cell regulation to maximize cell growth rate while maintaining the solvent capacity of the cell. Prediction is formulated as an optimization problem using a thermodynamic framework that can leverage experimental data. We develop a formulation and variable initialization procedure that allows for computing solutions of the optimization with an interior point method. The approach is applied to photoheterotrophic growth of Rhodospirilium rubrum using ethanol as a carbon source, which has applications to biosynthesis of ethylene production. Growth is captured as the rate of synthesis of amino acids into proteins, and synthesis of nucleotide triphoshaptes into RNA and DNA. The method predicts regulation that produces a high rate of protein and RNA synthesis while DNA synthesis is reduced close to zero in agreement with production of DNA being turned off for much of the cell cycle.

59 BASIC BIOLOGICAL SCIENCES↗

Functional Gene Array-Based Ultrasensitive and Quantitative Detection of Microbial Populations in Complex Communities

ABSTRACT While functional gene arrays (FGAs) have greatly expanded our understanding of complex microbial systems, specificity, sensitivity, and quantitation challenges remain. We developed a new generation of FGA, GeoChip 5.0, using the Agilent platform. Two formats were created, a smaller format (GeoChip 5.0S), primarily covering carbon-, nitrogen-, sulfur-, and phosphorus-cycling genes and others providing ecological services, and a larger format (GeoChip 5.0M) containing the functional categories involved in biogeochemical cycling of C, N, S, and P and various metals, stress response, microbial defense, electron transport, plant growth promotion, virulence, gyrB , and fungus-, protozoan-, and virus-specific genes. GeoChip 5.0M contains 161,961 oligonucleotide probes covering >365,000 genes of 1,447 gene families from broad, functionally divergent taxonomic groups, including bacteria (2,721 genera), archaea (101 genera), fungi (297 genera), protists (219 genera), and viruses (167 genera), mainly phages. Computational and experimental evaluation indicated that designed probes were highly specific and could detect as little as 0.05 ng of pure culture DNAs within a background of 1 μg community DNA (equivalent to 0.005% of the population). Additionally, strong quantitative linear relationships were observed between signal intensity and amount of pure genomic (∼99% of probes detected; r > 0.9) or soil (∼97%; r > 0.9) DNAs. Application of the GeoChip to a contaminated groundwater microbial community indicated that environmental contaminants (primarily heavy metals) had significant impacts on the biodiversity of the communities. This is the most comprehensive FGA to date, capable of directly linking microbial genes/populations to ecosystem functions. IMPORTANCE The rapid development of metagenomic technologies, including microarrays, over the past decade has greatly expanded our understanding of complex microbial systems. However, because of the ever-expanding number of novel microbial sequences discovered each year, developing a microarray that is representative of real microbial communities, is specific and sensitive, and provides quantitative information remains a challenge. The newly developed GeoChip 5.0 is the most comprehensive microarray available to date for examining the functional capabilities of microbial communities important to biogeochemistry, ecology, environmental sciences, and human health. The GeoChip 5 is highly specific, sensitive, and quantitative based on both computational and experimental assays. Use of the array on a contaminated groundwater sample provided novel insights on the impacts of environmental contaminants on groundwater microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗

Structural and compositional complexities of hierarchical self-assembly: A hypergraph approach

Programmable self-assembly enables the construction of complex molecular, supramolecular, and crystalline architectures from well-designed building blocks. In this work, we introduce a hypergraph-based formalism, Blocks & Bonds (B&B), which generalizes classical chemical graph theory by incorporating directed and multicolored interactions, internal symmetries, and hierarchical organization. Within this framework, we develop the Structure Code (SC), a compact and versatile language for describing self-assembled architectures. We define a Kolmogorov-style structural complexity as the total information content of SC, obtained through its tokenization and Shannon information assignment. Complementing this encoding-based measure, we introduce a much simpler quantity, the compositional complexity, which depends only on the number and cumulative usage of block and bond types in the construction set. A central result of this work is a strong empirical correlation between the token-based structural complexity and the compositional complexity across all examined systems. Owing to this agreement, the compositional complexity emerges as the most practical and broadly applicable measure: it is easy to compute, requires no explicit encoding, and yet closely tracks the actual information content of structurally diverse architectures. Applications to molecular systems (ethylene glycol and glucose), DNA-origami lattices, and crystalline assemblies show that B&B hypergraphs provide a unified, scalable, and information-efficient representation of structural organization, naturally capturing symmetry, modularity, and stereochemistry. This framework establishes a quantitative foundation for complexity-aware classification and inverse design of programmable matter.

36 MATERIALS SCIENCE↗

SU-F-J-07: Evaluating the Adequacy of Biopsy Specimens for Genetic Signature Assessment by Measuring the Metabolic Activity in Specimens Obtained Under 18F-FDG PET/CT Guidance

Purpose: Genetic profiling of biopsied tissue is the basis for personalized cancer therapy. However biopsied materials may not contain sufficient amounts of DNA needed for analysis. We propose a method to determine the adequacy of specimens for performing genetic profiling by quantifying metabolic activity. Methods: We measured the response of two radiation detectors to the activity contained in the minimum amount of tumor cells needed for genetic profiling in biopsy specimens obtained under 2-deoxy-2-({sup 18}F)fluoro-D-glucose ({sup 18}F-FDG) PET/CT guidance. The expected tumor cell concentration in biopsy specimens was evaluated from the amount of DNA needed (∼100 µg) and the number of pathology sections typically used for the analysis. The average {sup 18}F-FDG uptake per cell was measured by incubating KPC-4662 pancreatic tumor cells and HT-29 colorectal adenocarcinoma tumor cells in {sup 18}F-FDG containing solution (activity concentrations between 0.0122 and 1.51 MBq/mL and glucose concentrations of 3.1 and 1 g/L) for 1 to 1.75 hours and then measuring the activity of a known number of cells. Measurements of surrogate specimens obtained using 18G needle biopsies of gels containing these cells in expected concentrations (∼10{sup 4} µL{sup −1}) were performed using an autoradiography CCD based device (up to 20 min exposure) and a scintillation well counter (∼1 min measurements) about 3 and 5 hours after the end of incubation respectively. Results: At start of autoradiography there were between 0.16 and 1.5 {sup 18}F-FDG molecules/cell and between 1.14 and 5.43×10{sup 7} {sup 18}F-FDG molecules/mL. For the scintillation well counter, sample to minimum-detectable-count rate ratios were greater than 7 and the counting error was less than 25% for ≤80 s measurement times. Images of the samples were identifiable on the autoradiograph for ∼10 min and longer exposure times. Conclusion: Scintillation well counter measurements and CCD based autoradiography have adequate sensitivity to detect the tumor burden needed for genetic profiling in 18G core needle biopsies. Supported in part through the NIH/NCI Cancer Center Support Grant P30 CA008748 and by a sponsored research agreement with Biospace Lab S.A.

60 APPLIED LIFE SCIENCES↗