   NSF Award Search: Award # 1419282 - CAREER:  Developing Biochemoinformatics Tools for Large-Scale Metabolomics Applications        
        
 
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    Award Abstract # 1419282 
CAREER:  Developing Biochemoinformatics Tools for Large-Scale Metabolomics Applications

   NSF Org:  DBI Div Of Biological Infrastructure     Awardee:   UNIVERSITY OF KENTUCKY RESEARCH FOUNDATION, THE     Initial Amendment Date: February 4, 2014   Latest Amendment Date: May 30, 2017   Award Number: 1419282   Award Instrument: Continuing Grant   Program Manager:  Jennifer Weller DBI  Div Of Biological Infrastructure BIO  Direct For Biological Sciences    Start Date: November 1, 2013   End Date: December 31, 2018 (Estimated)   Total Intended Award Amount: $1,053,735.00   Total Awarded Amount to Date: $1,053,735.00   Funds Obligated to Date:   FY 2013 = $166,817.00   FY 2014 = $218,939.00   FY 2015 = $223,240.00   FY 2016 = $219,264.00   FY 2017 = $225,475.00     History of Investigator:    Hunter  Moseley    (Principal Investigator)  hunter.moseley@gmail.com         Awardee Sponsored Research Office:    University of Kentucky Research Foundation
 109 Kinkead Hall
 Lexington
 KY  US  40526-0001 
(859)257-9420      Sponsor Congressional District:   06     Primary Place of Performance:   University of Kentucky Research Foundation
 KY  US  40526-0001     Primary Place of PerformanceCongressional District:   06     DUNS ID:   939017877     Parent DUNS ID:   119108470     NSF Program(s):  ADVANCES IN BIO INFORMATICS    Primary Program Source:   040100 NSF RESEARCH & RELATED ACTIVIT   040100 NSF RESEARCH & RELATED ACTIVIT   040100 NSF RESEARCH & RELATED ACTIVIT   040100 NSF RESEARCH & RELATED ACTIVIT   040100 NSF RESEARCH & RELATED ACTIVIT     Program Reference Code(s):   1045,   9150     Program Element Code(s):   1165     Award Agency Code:  4900    Fund Agency Code:  4900    Assistance Listing Number(s):  47.074                    
   ABSTRACT  Research: Recent advances in stable isotope-resolved metabolomics (SIRM) are enabling orders-of-magnitude increase in the number of observable metabolic traits (a metabolic phenotype) for a given organism or community of organisms.  Analytical experiments that take only a few minutes to perform can detect stable isotope-labeled variants of thousands of metabolites.  Thus, unique metabolic phenotypes may be observable for almost all significant biological states, biological processes, and perturbations.  Currently, the major bottleneck is the lack of data analysis that can properly organize and interpret this mountain of phenotypic data as insightful biochemical and biological information.  The research goals are to develop systems-level biochemical tools as part of an integrated data analysis pipeline that will alleviate this limitation, enabling a broad application of SIRM from the discovery of specific metabolic phenotypes representing biological states of interest to a mechanism-based understanding of a wide range of biological processes with particular metabolic phenotypes.  The major specific intellectual merits are developing:- Novel methods for detection and identification of metabolites that utilize the combined advantages from stable isotope labeling, chemoselective probes, ultra-high resolution/accurate MS, and NMR.  Since unidentified metabolites make up the majority of detected features in current metabolomics datasets, identification of metabolites is a key focus. - Key error analyses that allow: i) rigorous quantitative evaluation of detected isotopologue intensities and their errors; ii) evaluation of error propagation through subsequent analyses; and iii) development of quality control measures derived from the detected errors. - New algorithms for isotopic non-steady state conditions of SIRM experiments, especially deconvolution methods that will aid relative flux interpretation and metabolic flux analysis.  - New methods that integrate and cross-validate metabolomics with genomics, transcriptomics, and proteomics via mutually-identified metabolic, gene expression, and signaling pathways.Education: Simultaneous trends of declining student effort and declining graduation rates in STEM disciplines do not bode well for the successful education of the next generation of scientists. A more expedient approach to improving student outcomes may be to increase the effectiveness of students? effort.  Using a design-based research approach, this project integrates multiple advanced teaching-learning methods into content-rich college science courses.  Statistical analysis of these methods shows large effect sizes for the use of scaffolded explicit revision to improve the effectiveness of student effort and indicates a path for significant refinement of these methods, which will be pursued and implemented. The proposed research will create computational tools that analyze and derive unique mechanistic information from large datasets available from cutting-edge metabolomics technologies which track atomic level changes in the production and utilization of thousands of molecules (metabolites) inside the cells of organisms.  These novel computational tools will be tested and refined in the Center for Regulatory and Environmental Analytical Metabolomics (CREAM), which provides state-of-the-art analytical services and expertise for national and international stable isotope-resolved metabolomics (SIRM) research efforts.  Once these computational tools reach production-quality, they will be disseminated to the broader scientific community for a wide variety of scientific applications involving biological processes with changes in cellular metabolism.  Also, these methods will integrate metabolomics datasets with genomics and other omics-level datasets, allowing new systems-level metabolic insights into a wide range of biological processes.  During the execution of this proposed research, significant numbers of high school, undergraduate, and graduate students from a wide variety of STEM (Science, Technology, Engineering, & Math) disciplines will be exposed to and trained with multidisciplinary bioinformatics research projects using interdisciplinary approaches to research.  In addition, the principal investigator has developed an integrated set of advanced teaching-learning methods amenable to content-rich college science courses that have statistically significant impacts on student effort and outcomes.  These advanced teaching-learning methods focus students' efforts at correcting and learning from prior mistakes on assignments, quizzes, and exam questions via a series of explicit revision steps that span different levels of learning. 
   PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH    Note:                             When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).                            Some links on this page may take you to non-federal websites. Their policies may differ from this site.                         
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(Showing: 1 - 35 of 35)
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   William J. Carreer, Robert M. Flight, and Hunter N.B. Moseley. "A computational framework for high-throughput isotopic natural abundance correction of omics-level ultra-high resolution FT-MS datasets" Metabolites , v.3 , 2013 , p.853 
  Joshua M. Mitchell, Teresa W-.M. Fan, Andrew N. Lane, and Hunter N.B. Moseley "Development of Large-Scale Metabolite Identification Methods for Metabolomics" Frontiers in Genetics - Systems Biology , v.5 , 2014 , p.237  10.3389/fgene.2014.00237  
  Richard M. Higashi, Teresa W-M. Fan, Pawel K. Lorkiewicz, Hunter N.B. Moseley, Andrew N. Lane "Stable Isotope Labeled Tracers for Metabolic Pathway Elucidation by GC-MS and FT-MS" Mass Spectrometry Methods in Metabolomics , v.53 , 2015 , p.337  10.1002/mrc.4199  
  Nalabothula Narasimharao, Taha Al-jumaily, Robert Flight, Shao Xiaorong, Hunter N.B. Moseley, F. Lisa Barcellos, and Yvonne Fondufe-Mittendorf "Genome-wide profiling of PARP1 reveals an interplay with gene regulatory regions and DNA methylation" PLOS ONE , v.10 , 2015 , p.e0135410 
  Sen Yao, Robert M. Flight, Eric C. Rouchka, and Hunter N.B. Moseley. "A less biased analysis of metalloproteins reveals novel zinc coordination geometries" Proteins: Structure, Function, and Bioinformatics , v.83 , 2015 , p.1470  10.1002/prot.24834  
  Andrew N. Lane, Sengodagounder Arumugam, Pawel K. Lorkiewicz, Richard M. Higashi, Sébastien Laulhé, Michael H. Nantz, Hunter N.B. Moseley, and Teresa W.-M. Fan. "Chemoselective detection and discrimination of carbonyl-containing compounds in metabolite mixtures by 1H-detected 15N NMR" Magnetic Resonance in Chemistry , v.53 , 2015 , p.337 
  Andrey Smelter, Morgan Astra, Hunter N.B. Moseley. "A Fast and Efficient Python Library for Interfacing with the Biological Magnetic Resonance Data Bank" BMC Bioinformatics , v.18 , 2017 , p.175  http://dx.doi.org/10.1186/s12859-017-1580-5  
  Sen Yao, Robert M. Flight, Eric C. Rouchka, Hunter N.B. Moseley. "Aberrant coordination geometries discovered in the most abundant metalloproteins" Proteins: Structure, Function, and Bioinformatics , v.85 , 2017 , p.885  10.1002/prot.25257  
  Sen Yao, Robert M. Flight, Eric C. Rouchka, Hunter N.B. Moseley. "Cover Image of Volume 85, Issue 5" Proteins: Structure, Function, and Bioinformatics , v.85 , 2017 , p.cover  10.1002/prot.25126  
  Sen Yao, Robert M. Flight, Eric C. Rouchka, Hunter N.B. Moseley. "Perspectives and Expectations in Structural Bioinformatics of Metalloproteins" Proteins: Structure, Function, and Bioinformatics , v.85 , 2017 , p.938  10.1002/prot.25263  
  Stacy Webb, Tamas Nagy, Hunter N.B. Moseley, Michael Fried, Rebecca E. Dutch. "Hendra virus fusion protein transmembrane domain contributes to pre-fusion protein stability" Journal of Biological Chemistry , v.292 , 2017 , p.5685  http://dx.doi.org/10.1074/jbc.M117.777235  
  Andrey Smelter and Hunter N.B. Moseley "A Python library for FAIRer access and deposition to the Metabolomics Workbench Data Repository" Metabolomics , v.14 , 2018 , p.64 
  Andrey Smelter, Eric C. Rouchka, and Hunter N.B. Moseley "Detecting and accounting for multiple sources of positional variance in peak list registration and spin system grouping" Journal of Biomolecular NMR , v.68 , 2017 , p.281 
  Dries Verdegem, Hunter N.B. Moseley, Wesley Vermaelen, Abel Acosta, Sanchez, and Bart Ghesquière "MAIMS: a GAIMS alternative for the deconvolution of UDP-N-acetyl-D-glucosamine 13C mass isotopologue profiles" Metabolomics , v.13 , 2017 , p.123 
  Rashmie Abeysinghe, Eugene W. Hinderer III, Hunter N.B. Moseley, and Licong Cui "Auditing Subtype Inconsistencies among Gene Ontology Concepts" The 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017) -- Bioinformatics and Biomedicine (BIBM), 2017 IEEE International Conference , 2017 , p.1242 
  Yekaterina Y. Zaytseva, Piotr G. Rychahou, Anh-Thu Le, Timothy L. Scott, Robert M. Flight, Ji Tae Kim, Jennifer Harris, Jinpeng Liu, Chi Wang, Andrew J. Morris, Sivakumaran Theru Arumugam, Teresa Fan, Hunter N.B. Moseley, Tianyan Gao, Eun Y. Lee, Heidi L. "Activation of Akt pathway and autophagy promotes resistance to fatty acid synthase inhibition in colorectal cancer" Oncotarget , v.9 , 2018 , p.24787 
  Abdallah M. Eteleeb, Hunter N.B. Moseley, and Eric C. Rouchka "A Comparison of Combined P-value Methods for Gene Differential Expression Using RNA-Seq Data" BCB'14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology and Health Informatics , 2014 , p.417  http://dx.doi.org/10.1145/2649387.2649421  
  Andrew N. Lane, Sengodagounder Arumugam, Pawel K. Lorkiewicz, Richard M. Higashi, Sébastien Laulhé, Michael H. Nantz, Hunter N.B. Moseley, and Teresa W.-M. Fan "Chemoselective detection and discrimination of carbonyl-containing compounds in metabolite mixtures by 1H-detected 15N NMR" Magnetic Resonance in Chemistry , v.53 , 2015 , p.337  https://doi.org/10.1002/mrc.4199  
  Andrey Smelter and Hunter N.B. Moseley "A Python library for FAIRer access and deposition to the Metabolomics Workbench Data Repository" Metabolomics , v.14 , 2018 , p.64  https://doi.org/10.1007/s11306-018-1356-6  
  Andrey Smelter, Eric C. Rouchka, and Hunter N.B. Moseley "Detecting and accounting for multiple sources of positional variance in peak list registration and spin system grouping" Journal of Biomolecular NMR , v.68 , 2017 , p.281  http://dx.doi.org/10.1007/s10858-017-0126-5  
  Andrey Smelter, Morgan Astra, Hunter N.B. Moseley "A Fast and Efficient Python Library for Interfacing with the Biological Magnetic Resonance Data Bank" BMC Bioinformatics , v.18 , 2017 , p.175  http://dx.doi.org/10.1186/s12859-017-1580-5  
  Dries Verdegem, Hunter N.B. Moseley, Wesley Vermaelen, Abel Acosta, Sanchez, and Bart Ghesquière "MAIMS: a GAIMS alternative for the deconvolution of UDP-N-acetyl-D-glucosamine 13C mass isotopologue profiles" Metabolomics , v.13 , 2017 , p.123  https://doi.org/10.1007/s11306-017-1250-7  
  Jinpeng Liu*, Thilakam Murali*, Chunming Liu, Tianxin Yu, Sivakumaran Theru Arumugam, Hunter N.B. Moseley, Igor B. Jouline, Heidi L. Weiss, Eric B. Durbin, Sally R. Ellingson, Jinze Liu, Brent J. Hallahan, Craig M. Horbinski, Nathan L. Vanderford, Dave W. "Characterization of Squamous Cell Lung Cancers from Appalachian Kentucky" Cancer Epidemiology, Biomarkers and Prevention , v.online , 2018  DOI: 10.1158/1055-9965.EPI-17-0984  
  Joshua M. Mitchell, Robert M. Flight, Qing Jun Wang, Richard M Higashi, Teresa W-M Fan, Andrew N. Lane, and Hunter N.B. Moseley "New Methods to Identify High Peak Density Artifacts in Fourier Transform Mass Spectra and to Mitigate Their Effects on High-Throughput Metabolomic Data Analysis" Metabolomics , v.14 , 2018 , p.125  https://doi.org/10.1007/s11306-018-1426-9  
  Joshua M. Mitchell, Teresa W. M. Fan, Andrew N. Lane, and Hunter N.B. Moseley "Development and In silico Evaluation of Large-Scale Metabolite Identification Methods using Functional Group Detection for Metabolomics." Frontiers in Genetics - Systems Biology , v.5 , 2014 , p.237  DOI: 10.3389/fgene.2014.00237  
  Nalabothula Narasimharao, Taha Aljumaily, Robert Flight, Shao Xiaorong, Hunter N.B. Moseley, F. Lisa Barcellos, and Yvonne FondufeMittendorf "Genomewide profiling of PARP1 reveals an interplay with gene regulatory regions and DNA methylation" PLOS ONE , v.10 , 2015 , p.e0135410  https://doi.org/10.1371/journal.pone.0135410  
  Rashmie Abeysinghe, Eugene W. Hinderer III, Hunter N.B. Moseley, and Licong Cui "Auditing Subtype Inconsistencies among Gene Ontology Concepts" The 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017) -- Bioinformatics and Biomedicine (BIBM), 2017 IEEE International Conference , 2017 , p.1242  https://doi.org/10.1109/BIBM.2017.8217835  
  Sen Yao, Robert M. Flight, Eric C. Rouchka, and Hunter N.B. Moseley "(). A less biased analysis of metalloproteins reveals novel zinc coordination geometries" Proteins: Structure, Function, and Bioinformatics , v.83 , 2015 , p.1470  DOI: 10.1002/prot.24834  
  Sen Yao, Robert M. Flight, Eric C. Rouchka, and Hunter N.B. Moseley "A less biased analysis of metalloproteins reveals novel zinc coordination geometries" BCB '15 Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics , 2015 , p.493  https://doi.org/10.1145/2808719.2811424  
  Sen Yao, Robert M. Flight, Eric C. Rouchka, Hunter N.B. Moseley "Aberrant coordination geometries discovered in the most abundant metalloproteins" Proteins: Structure, Function, and Bioinformatics , v.85 , 2017 , p.885  DOI: 10.1002/prot.25257  
  Sen Yao, Robert M. Flight, Eric C. Rouchka, Hunter N.B. Moseley "Perspectives and Expectations in Structural Bioinformatics of Metalloproteins" Proteins: Structure, Function, and Bioinformatics , v.85 , 2017 , p.938  DOI: 10.1002/prot.25263  
  Stacy Webb, Tamas Nagy, Hunter N.B. Moseley, Michael Fried, Rebecca E. Dutch "Hendra virus fusion protein transmembrane domain contributes to pre-fusion protein stability" Journal of Biological Chemistry , v.292 , 2017 , p.5685  http://dx.doi.org/10.1074/jbc.M117.777235  
  William J. Carreer, Robert M. Flight, and Hunter N.B. Moseley. "). A computational framework for high-throughput isotopic natural abundance correction of omics-level ultra-high resolution FT-MS datasets." Metabolites , v.3 , 2013 , p.853  https://doi.org/10.3390/metabo3040853  
  Xi Chen, Andrey Smelter, and Hunter N.B. Moseley "Automatic 13C Chemical Shift Reference Correction for Unassigned Protein NMR Spectra" Journal of Biomolecular NMR , v.72 , 2018 , p.11  https://doi.org/10.1007/s10858-018-0202-5  
  Yekaterina Y. Zaytseva, Piotr G. Rychahou, Anh-Thu Le, Timothy L. Scott, Robert M. Flight, Ji Tae Kim, Jennifer Harris, Jinpeng Liu, Chi Wang, Andrew J. Morris, Sivakumaran Theru Arumugam, Teresa Fan, Hunter N.B. Moseley, Tianyan Gao, Eun Y. Lee, Heidi L. "Activation of Akt pathway and autophagy promotes resistance to fatty acid synthase inhibition in colorectal cancer" Oncotarget , v.9 , 2018 , p.24787  https://doi.org/10.18632/oncotarget.25361  
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(Showing: 1 - 35 of 35)
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    PROJECT OUTCOMES REPORT   Disclaimer This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
 The 21st century has brought a wealth of new data of many different types for studying life, especially the composition of life at the level of different organs and tissues, individual cells, individual biomolecules, and even the atoms that comprise these biomolecules.  Central to this study of life is understanding metabolism or the chemical processes that produce, convert, and consume the biomolecules that comprise life.
The research supported by this grant has developed new methods and computer software to analyze many types of biological data being generated, especially from high-throughput “omics” instruments.  Omics technologies systematically characterize certain molecular features of a living system.  For instance, genomics is a systematic characterization of the genetic material in a sample derived from a cell, tissue, or even a collection of organisms.
This supported research has focused on developing methods for analyzing metabolomics datasets, which is the systematic detection, quantification, classification, and identification of small biomolecules present in living systems that can include cells, tissues from organisms, or even environmental samples produced by organisms.  We have developed many new methods and associated software packages:
 for the analysis of metabolomics datasets generated from high-end Fourier-transform mass spectrometers and nuclear magnetic resonance spectrometers; for the integration of multi-omics datasets, especially metabolomics with other omics datasets; and for the integration of data with knowledge, especially omics-level datasets with organized scientific knowledge in public knowledgebases.  Here are two examples of the types of analyses enabled by our new software.  Figure 1 represents different category of lipids with different abundances in cancer vs non-cancer tissues.  These lipids were assigned from a metabolomics dataset using a new method that derives the chemical formula just from the data itself, without having to know what the molecule is.  Therefore, we can analyze these datasets without a database of known or theoretical lipids.  This allows us to discover new molecules present in a sample, with no prior knowledge about the existence of the molecule. 
Figure 2 shows the analysis of gene expression (transcriptomics) in horse connective tissue development and the integration of this data with known knowledge from the Gene Ontology and the STRING molecular interactions database.  Using software developed from this research, the data itself identifies the concept “cartilage development” and the interactions between the genes involved in “cartilage development”. The more information we can derive directly from the data itself, the less biased our interpretations will be.
During the course of the supported research, we have trained over 50 students/trainees from the high school to postdoctoral level in research activities.  We have also developed multiple teaching-learning instruments, all of which have been tested within a classroom environment.  In particular, we have developed the use of explicit revision within the context of content-rich science courses.   Explicit revision, defined as the requirement to amend or alter performance after assessment for the purpose of improvement, focuses on a cycle of feedback provided to learners and subsequently interpreted and applied during revision.  While explicit revision falls under the broader definition of formative assessment, a key difference is that students are “required” to interpret and use the feedback, whereas formative assessment does not require the student to use the feedback.  We tested the use of different forms of explicit revision within college biochemistry courses at two different universities and statistically evaluated their effectiveness to improve student learning outcomes. 
We have disseminated this research via 100 poster and 20 oral presentations at scientific conferences, the publication of 21 peer-reviewed articles, 2 book chapters, 4 preprints, 1 journal cover,  and multiple software packages, many of which are available on open source code repositories on GitHub (https://github.com/MoseleyBioinformaticsLab).  This includes presentation of effective teaching-learning methods at teaching-focused conferences.  In addition, a patent application was submitted to the United States Patent Office for new methods for the identification of metabolites (US20180011990A1).  Also, we have roughly 10 more manuscripts in preparation for publication at the time this outcomes report was submitted.  However, the full impact of this research will come from the use of the new data analysis methods and software by the scientific community on a wide range of future research projects.  Also, many students developed multidisciplinary research skills while working on the supported research projects, providing a strong start to their scientific research careers that includes authorship on publications.  It cannot be overstated the importance of this early exposure and development of strong multidisciplinary skills sets that span biochemical, mathematical, and computation disciplines, especially for the pursuit of an interdisciplinary research career.
            Last Modified: 01/15/2019
      Modified by: Hunter N Moseley

             Images (1 of )           


 


     Figure 1: SMIRFE assignment results. Log2 fold-changes for differential lipids by class in unlabeled human cancer vs non-cancer tissue.
Hunter N.B. Moseley
Copyrighted
Hunter N Moseley
Figure 1: SMIRFE assignment results.

    Figure 2. Equine RNAseq, Gene Ontology, STRING data-knowledge integration. A) PCA plot of equine RNAseq datasets. B) Organized groups of enriched GO-terms for PC1. C) STRING interactions between high PC1 loading gene(-product)s annotated with group G1 GO terms (cartilage development).
Hunter N.B. Moseley
Copyrighted
Hunter N Moseley
Figure 2. Equine RNAseq, Gene Ontology, STRING data-knowledge integration.

  



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