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Title: Systems Biology for Organotypic Cell Cultures

Abstract

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss thismore » ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data. This consensus report summarizes the discussions held.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [4];  [1];  [7];  [4];  [1];  [1];  [1];  [8];  [9];  [10];  [4];  [11];  [12];  [4] more »;  [1];  [13] « less
  1. RTI International, Research Triangle Park, NC (United States)
  2. Texas A & M Univ., College Station, TX (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  4. National Inst. of Environmental Health Sciences, Research Triangle Park, NC (United States)
  5. GlaxoSmithKline, Research Triangle Park, NC (United States)
  6. Translational Genomics Research Inst., Phoenix, AZ (United States)
  7. HemoShear Therapeutics, Charlottesville, VA (United States)
  8. Johns Hopkins Univ., Baltimore, MD (United States). Center for Alternatives to Animal Testing
  9. IPQ Analytics, Kennett Square, PA (United States)
  10. Phillip Morris International, Neuchatel (Switzerland)
  11. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  12. Defense Threat Reduction Agency, Ft. Belvoir, VA (United States)
  13. The Hamner Inst. for Health Sciences, Research Triangle Park, NC (United States); ScitoVation, Research Triangle Park, NC (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
Contributing Org.:
National Inst. of Environmental Health Sciences, Research Triangle Park, NC (United States); Johns Hopkins Univ., Baltimore, MD (United States); RTI International, Research Triangle Park, NC (United States); Texas A & M Univ., College Station, TX (United States)
OSTI Identifier:
1313549
Report Number(s):
LLNL-TR-699637
DOE Contract Number:
AC52-07NA27344; AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Grego, Sonia, Dougherty, Edward R., Alexander, Francis J., Auerbach, Scott S., Berridge, Brian R., Bittner, Michael L., Casey, Warren, Cooley, Philip C., Dash, Ajit, Ferguson, Stephen S., Fennell, Timothy R., Hawkins, Brian T., Hickey, Anthony J., Kleensang, Andre, Liebman, Michael N., Martin, Florian, Maull, Elizabeth A., Paragas, Jason, Qiao, Guilin, Ramaiahgari, Sreenivasa, Sumner, Susan J., and Yoon, Miyoung. Systems Biology for Organotypic Cell Cultures. United States: N. p., 2016. Web. doi:10.2172/1313549.
Grego, Sonia, Dougherty, Edward R., Alexander, Francis J., Auerbach, Scott S., Berridge, Brian R., Bittner, Michael L., Casey, Warren, Cooley, Philip C., Dash, Ajit, Ferguson, Stephen S., Fennell, Timothy R., Hawkins, Brian T., Hickey, Anthony J., Kleensang, Andre, Liebman, Michael N., Martin, Florian, Maull, Elizabeth A., Paragas, Jason, Qiao, Guilin, Ramaiahgari, Sreenivasa, Sumner, Susan J., & Yoon, Miyoung. Systems Biology for Organotypic Cell Cultures. United States. doi:10.2172/1313549.
Grego, Sonia, Dougherty, Edward R., Alexander, Francis J., Auerbach, Scott S., Berridge, Brian R., Bittner, Michael L., Casey, Warren, Cooley, Philip C., Dash, Ajit, Ferguson, Stephen S., Fennell, Timothy R., Hawkins, Brian T., Hickey, Anthony J., Kleensang, Andre, Liebman, Michael N., Martin, Florian, Maull, Elizabeth A., Paragas, Jason, Qiao, Guilin, Ramaiahgari, Sreenivasa, Sumner, Susan J., and Yoon, Miyoung. Thu . "Systems Biology for Organotypic Cell Cultures". United States. doi:10.2172/1313549. https://www.osti.gov/servlets/purl/1313549.
@article{osti_1313549,
title = {Systems Biology for Organotypic Cell Cultures},
author = {Grego, Sonia and Dougherty, Edward R. and Alexander, Francis J. and Auerbach, Scott S. and Berridge, Brian R. and Bittner, Michael L. and Casey, Warren and Cooley, Philip C. and Dash, Ajit and Ferguson, Stephen S. and Fennell, Timothy R. and Hawkins, Brian T. and Hickey, Anthony J. and Kleensang, Andre and Liebman, Michael N. and Martin, Florian and Maull, Elizabeth A. and Paragas, Jason and Qiao, Guilin and Ramaiahgari, Sreenivasa and Sumner, Susan J. and Yoon, Miyoung},
abstractNote = {Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data. This consensus report summarizes the discussions held.},
doi = {10.2172/1313549},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Aug 04 00:00:00 EDT 2016},
month = {Thu Aug 04 00:00:00 EDT 2016}
}

Technical Report:

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  • Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.« less
  • First results of this feasibility study showed that evaluation of the stored material of the chronically irradiated dogs with modern molecular biological techniques proved to be successful and extremely promising. Therefore an in deep analysis of at least part of the huge amount of remaining material is of outmost interest. The methods applied in this feasibility study were pathological evaluation with different staining methods, protein analysis by means of immunohistochemistry, strand break analysis with the TdT-assay, DNA- and RNA-analysis as well as genomic examination by gene array. Overall more than 50% of the investigated material could be used. In particularmore » the results of an increased stimulation of the immune system within the dogs of the 3mSv group as both compared to the control and higher dose groups gives implications for the in depth study of the cellular events occurring in context with low dose radiation. Based on the findings of this study a further evaluation and statistically analysis of more material can help to identify promising biomarkers for low dose radiation. A systematic evaluation of a correlation of dose rates and strand breaks within the dog tissue might moreover help to explain mechanisms of tolerance to IR. One central problem is that most sequences for dog specific primers are not known yet. The discovery of the dog genome is still under progress. In this study the isolation of RNA within the dog tissue was successful. But up to now there are no gene arrays or gene chips commercially available, tested and adapted for canine tissue. The uncritical use of untested genomic test systems for canine tissue seems to be ineffective at the moment, time consuming and ineffective. Next steps in the investigation of genomic changes after IR within the stored dog tissue should be limited to quantitative RT-PCR of tested primer sequences for the dog. A collaboration with institutions working in the field of the discovery of the dog genome could have synergistic effects.« less
  • Replacement of petroleum with advanced biofuels is critical for environmental protection needs, sustainable and secure energy demands, and economic development. Bacteria, yeasts, and fungi can naturally synthesize fatty acids, isoprenoids, or polyalkanoates for energy storage, and therefore are currently explored for hydrocarbon fuel production. Oleaginous yeasts can accumulate high levels of lipids in the form of triacylglycerols (TAGs) when encountering stress conditions or imbalanced growth (e.g., growing under excess carbon sources and limited nitrogen conditions). Advantages of using oleaginous yeast as cell factories include short duplication time (< 1 hour), high yield of intracellular droplets, and easy scale-up for industrialmore » production. Currently, various oleaginous yeasts (e.g., Yarrowia, Candida, Rhodotorulla, Rhodosporidium, Cryptococcus, Trichosporon, and Lipomyces) have been developed as potential advanced biofuel producers. Oleaginous yeast lipid production has two phases: 1) growth phase, where cells utilize the carbon and nitrogen source to build up biomass. And 2) lipid accumulation phase, where they convert carbon source in media into the storage lipid body. (i.e. a high carbon to nitrogen ratio leads to high lipid production). The lipid production varies dramatically when different sugar, e.g. glucose, xylose is used as carbon source. The efficient utilization of all monomeric sugars of hexoses and pentoses from various lignocellulosic biomass processing approaches is the key for economic lignocellulosic biofuel production. In this project, we explored lipid production in oleaginous yeast under different nitrogen and sugar conditions at the single-cell level. To understand the lipid production mechanism and identify genetic features responsive to lipid accumulation in the presence of pentose and nitrogen, we developed an automated chemical imaging and single-cell transcriptomics method to correlate the lipid accumulation with the transcriptional profiles at the single-cell level, as follows: 1) We developed hyperspectral Stimulated Raman Scattering (hsSRS) microscope tailored for fast chemical imaging in complex biological systems. It features high numerical aperture of 1.4 for better spatial resolution and reduced cross-phase modulation. The femtosecond infrared laser was selected for deeper penetration and less photodamage for longer in-situ imaging time. hsSRS was achieved through spectral focusing where two femtosecond laser beams were linearly chirped to create overlapping pulses in time domain. We achieved transformed limited spectral resolution of ~15cm for superior clarity in identifying different types of lipids. A set of sophisticated algorithms based on Multivariate Curve Resolution (MCR) was developed to analyze yeast images and track droplets. 2) Lipid production was carried out on selected oleaginous yeast strains. Lipid accumulation was studied in R. glutinis and Y. lipolytica grown in regular and nitrogen starvation media. hsSRS imaging of lipid droplets showed considerable variation among individual Y. lipolytica cells regarding volume and composition, while the R. glutins lipid droplet showed similar Raman response representing more homogenous lipid production. hsSRS analysis revealed the spatial distribution of two major lipids-TAG and sterol ester (SE)- where TAG formed cores were surrounded by SE shells. Significantly elevated level of SE/TAG was observed at cellular and sub-cellular levels which could be attributed to epigenetic variation in cell development. 3) Yeast growth conditions were studied based on their lipid droplets formation. Optimal condition was found in two-stage grown media where the yeast was grown in regular YEPD and transferred to nitrogen starvation media after maturation. The volume and composition of lipid droplets vary significantly depending on the availability of nitrogen source. Large number of cells have been fast screened and analyzed by custom-designed microfluidic systems for statistical significance. 4) For transcriptome profiling, we developed a single-cell whole-transcriptome amplification assay that offers the highest sensitivity so far. Because the mRNA content of a single yeast is minuscule (< 5% of a human cell), the importance of high sensitivity is critical for sequencing single yeast transcriptome. Finally, to correlate the lipidomic profile to transcriptomic data for the same yeast cells, we designed a microfluidic platform for isolating single yeast cells, imaging each cell with hsSRS microscopy for lipidomic quantification, and collecting each cell for whole-transcriptome amplification and sequencing. These recently developed methods are to probe lipogenesis in relation to transcriptome changes in oleaginous yeasts for sustainable biofuel production.« less
  • Biology has entered a systems-science era with the goal to establish a predictive understanding of the mechanisms of cellular function and the interactions of biological systems with their environment and with each other. Vast amounts of data on the composition, physiology, and function of complex biological systems and their natural environments are emerging from new analytical technologies. Effectively exploiting these data requires developing a new generation of capabilities for analyzing and managing the information. By revealing the core principles and processes conserved in collective genomes across all biology and by enabling insights into the interplay between an organism's genotype andmore » its environment, systems biology will allow scientific breakthroughs in our ability to project behaviors of natural systems and to manipulate and engineer managed systems. These breakthroughs will benefit Department of Energy (DOE) missions in energy security, climate protection, and environmental remediation.« less