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Beattie GA, Bayliss KL, Jacobson DA, Broglie R, Burkett-Cadena M, Sessitsch A, Kankanala P, Stein J, Eversole K, Lichens-Park A. From Microbes to Microbiomes: Applications for Plant Health and Sustainable Agriculture. PHYTOPATHOLOGY 2024; 114:1742-1752. [PMID: 38776137 DOI: 10.1094/phyto-02-24-0054-kc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2024]
Abstract
Plant-microbe interaction research has had a transformative trajectory, from individual microbial isolate studies to comprehensive analyses of plant microbiomes within the broader phytobiome framework. Acknowledging the indispensable role of plant microbiomes in shaping plant health, agriculture, and ecosystem resilience, we underscore the urgent need for sustainable crop production strategies in the face of contemporary challenges. We discuss how the synergies between advancements in 'omics technologies and artificial intelligence can help advance the profound potential of plant microbiomes. Furthermore, we propose a multifaceted approach encompassing translational considerations, transdisciplinary research initiatives, public-private partnerships, regulatory policy development, and pragmatic expectations for the practical application of plant microbiome knowledge across diverse agricultural landscapes. We advocate for strategic collaboration and intentional transdisciplinary efforts to unlock the benefits offered by plant microbiomes and address pressing global issues in food security. By emphasizing a nuanced understanding of plant microbiome complexities and fostering realistic expectations, we encourage the scientific community to navigate the transformative journey from discoveries in the laboratory to field applications. As companies specializing in agricultural microbes and microbiomes undergo shifts, we highlight the necessity of understanding how to approach sustainable agriculture with site-specific management solutions. While cautioning against overpromising, we underscore the excitement of exploring the many impacts of microbiome-plant interactions. We emphasize the importance of collaborative endeavors with societal partners to accelerate our collective capacity to harness the diverse and yet-to-be-discovered beneficial activities of plant microbiomes.
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Affiliation(s)
- Gwyn A Beattie
- International Alliance for Phytobiomes Research, Eau Claire, WI 54701, U.S.A
- Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA 50014, U.S.A
| | - Kirsty L Bayliss
- Food Futures Institute, Murdoch University, Murdoch, Western Australia 6150, Australia
| | - Daniel A Jacobson
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN 37830, U.S.A
| | - Richard Broglie
- International Alliance for Phytobiomes Research, Eau Claire, WI 54701, U.S.A
| | | | - Angela Sessitsch
- International Alliance for Phytobiomes Research, Eau Claire, WI 54701, U.S.A
- Bioresources Unit, AIT Austrian Institute of Technology, 3430 Tulln, Austria
| | | | - Joshua Stein
- International Alliance for Phytobiomes Research, Eau Claire, WI 54701, U.S.A
- Eversole Associates, Arlington, MA 02476, U.S.A
| | - Kellye Eversole
- International Alliance for Phytobiomes Research, Eau Claire, WI 54701, U.S.A
- Eversole Associates, Arlington, MA 02476, U.S.A
| | - Ann Lichens-Park
- International Alliance for Phytobiomes Research, Eau Claire, WI 54701, U.S.A
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2
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Behr M, Kumbier K, Cordova-Palomera A, Aguirre M, Ronen O, Ye C, Ashley E, Butte AJ, Arnaout R, Brown B, Priest J, Yu B. Learning epistatic polygenic phenotypes with Boolean interactions. PLoS One 2024; 19:e0298906. [PMID: 38625909 PMCID: PMC11020961 DOI: 10.1371/journal.pone.0298906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 01/31/2024] [Indexed: 04/18/2024] Open
Abstract
Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.
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Affiliation(s)
- Merle Behr
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany
| | - Karl Kumbier
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States of America
| | | | - Matthew Aguirre
- Department of Pediatrics, Stanford Medicine, Stanford, CA, United States of America
- Department of Biomedical Data Science, Stanford Medicine, Stanford, CA, United States of America
| | - Omer Ronen
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
| | - Chengzhong Ye
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
| | - Euan Ashley
- Division of Cardiovascular Medicine, Stanford Medicine, Stanford, CA, United States of America
| | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States of America
| | - Rima Arnaout
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States of America
- Division of Cardiology, Department of Medicine, University of California, San Francisco, San Francisco, CA, United States of America
| | - Ben Brown
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
- Biosciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - James Priest
- Department of Pediatrics, Stanford Medicine, Stanford, CA, United States of America
| | - Bin Yu
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
- Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California at Berkeley, Berkeley, CA, United States of America
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3
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Desai RI, Kangas BD, Luc OT, Solakidou E, Smith EC, Dawes MH, Ma X, Makriyannis A, Chatterjee S, Dayeh MA, Muñoz-Jaramillo A, Desai MI, Limoli CL. Complex 33-beam simulated galactic cosmic radiation exposure impacts cognitive function and prefrontal cortex neurotransmitter networks in male mice. Nat Commun 2023; 14:7779. [PMID: 38012180 PMCID: PMC10682413 DOI: 10.1038/s41467-023-42173-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 09/28/2023] [Indexed: 11/29/2023] Open
Abstract
Astronauts will encounter extended exposure to galactic cosmic radiation (GCR) during deep space exploration, which could impair brain function. Here, we report that in male mice, acute or chronic GCR exposure did not modify reward sensitivity but did adversely affect attentional processes and increased reaction times. Potassium (K+)-stimulation in the prefrontal cortex (PFC) elevated dopamine (DA) but abolished temporal DA responsiveness after acute and chronic GCR exposure. Unlike acute GCR, chronic GCR increased levels of all other neurotransmitters, with differences evident between groups after higher K+-stimulation. Correlational and machine learning analysis showed that acute and chronic GCR exposure differentially reorganized the connection strength and causation of DA and other PFC neurotransmitter networks compared to controls which may explain space radiation-induced neurocognitive deficits.
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Affiliation(s)
- Rajeev I Desai
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA.
- Behavioral Biology Program, McLean Hospital, Belmont, MA, 02478, USA.
- Center for Drug Discovery, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, 02115, USA.
| | - Brian D Kangas
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
- Behavioral Biology Program, McLean Hospital, Belmont, MA, 02478, USA
| | - Oanh T Luc
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
- Behavioral Biology Program, McLean Hospital, Belmont, MA, 02478, USA
| | - Eleana Solakidou
- Center for Drug Discovery, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, 02115, USA
- Medical School, University of Crete, Heraklion, Greece
| | - Evan C Smith
- Center for Drug Discovery, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, 02115, USA
| | - Monica H Dawes
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
- Behavioral Biology Program, McLean Hospital, Belmont, MA, 02478, USA
| | - Xiaoyu Ma
- Center for Drug Discovery, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, 02115, USA
| | - Alexandros Makriyannis
- Center for Drug Discovery, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, 02115, USA
| | | | - Maher A Dayeh
- Southwest Research Institute, San Antonio, TX, 78238, USA
- University of San Antonio, San Antonio, TX, 78249, USA
| | | | - Mihir I Desai
- Southwest Research Institute, San Antonio, TX, 78238, USA
- University of San Antonio, San Antonio, TX, 78249, USA
| | - Charles L Limoli
- Department of Radiation Oncology, University of California, Irvine, Orange, CA, 92697, USA
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4
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Noshay J, Walker T, Alexander W, Klingeman D, Romero J, Walker A, Prates E, Eckert C, Irle S, Kainer D, Jacobson D. Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering. Nucleic Acids Res 2023; 51:10147-10161. [PMID: 37738140 PMCID: PMC10602897 DOI: 10.1093/nar/gkad736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/07/2023] [Accepted: 08/29/2023] [Indexed: 09/24/2023] Open
Abstract
CRISPR-Cas9 tools have transformed genetic manipulation capabilities in the laboratory. Empirical rules-of-thumb have been developed for only a narrow range of model organisms, and mechanistic underpinnings for sgRNA efficiency remain poorly understood. This work establishes a novel feature set and new public resource, produced with quantum chemical tensors, for interpreting and predicting sgRNA efficiency. Feature engineering for sgRNA efficiency is performed using an explainable-artificial intelligence model: iterative Random Forest (iRF). By encoding quantitative attributes of position-specific sequences for Escherichia coli sgRNAs, we identify important traits for sgRNA design in bacterial species. Additionally, we show that expanding positional encoding to quantum descriptors of base-pair, dimer, trimer, and tetramer sequences captures intricate interactions in local and neighboring nucleotides of the target DNA. These features highlight variation in CRISPR-Cas9 sgRNA dynamics between E. coli and H. sapiens genomes. These novel encodings of sgRNAs enhance our understanding of the elaborate quantum biological processes involved in CRISPR-Cas9 machinery.
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Affiliation(s)
- Jaclyn M Noshay
- Computational and Predictive Biology, Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Tyler Walker
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee-Knoxville, Knoxville, TN, USA
| | - William G Alexander
- Synthetic Biology, Biosciences,Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Dawn M Klingeman
- Synthetic Biology, Biosciences,Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jonathon Romero
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee-Knoxville, Knoxville, TN, USA
| | - Angelica M Walker
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee-Knoxville, Knoxville, TN, USA
| | - Erica Prates
- Computational and Predictive Biology, Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Carrie Eckert
- Synthetic Biology, Biosciences,Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Stephan Irle
- Computational Sciences and Engineering, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - David Kainer
- Computational and Predictive Biology, Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Daniel A Jacobson
- Computational and Predictive Biology, Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
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5
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Pavicic M, Walker AM, Sullivan KA, Lagergren J, Cliff A, Romero J, Streich J, Garvin MR, Pestian J, McMahon B, Oslin DW, Beckham JC, Kimbrel NA, Jacobson DA. Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts. Front Psychiatry 2023; 14:1178633. [PMID: 37599888 PMCID: PMC10433206 DOI: 10.3389/fpsyt.2023.1178633] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/21/2023] [Indexed: 08/22/2023] Open
Abstract
Introduction Despite a recent global decrease in suicide rates, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts to combat this growing epidemic. In this study, we aim to identify potential risk factors of suicide attempt using geospatial features in an Artificial intelligence framework. Methods We use iterative Random Forest, an explainable artificial intelligence method, to predict suicide attempts using data from the Million Veteran Program. This cohort incorporated 405,540 patients with 391,409 controls and 14,131 attempts. Our predictive model incorporates multiple climatic features at ZIP-code-level geospatial resolution. We additionally consider demographic features from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. In total 1,784 features were included in the predictive model. Results Our results show that geographic areas with higher concentrations of married males living with spouses are predictive of lower rates of suicide attempts, whereas geographic areas where males are more likely to live alone and to rent housing are predictive of higher rates of suicide attempts. We also identified climatic features that were associated with suicide attempt risk by age group. Additionally, we observed that firearms and alcohol vendors were associated with increased risk for suicide attempts irrespective of the age group examined, but that their effects were small in comparison to the top features. Discussion Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans.
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Affiliation(s)
- Mirko Pavicic
- Oak Ridge National Laboratory, Computational and Predictive Biology, Oak Ridge, TN, United States
| | - Angelica M. Walker
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, United States
| | - Kyle A. Sullivan
- Oak Ridge National Laboratory, Computational and Predictive Biology, Oak Ridge, TN, United States
| | - John Lagergren
- Oak Ridge National Laboratory, Computational and Predictive Biology, Oak Ridge, TN, United States
| | - Ashley Cliff
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, United States
| | - Jonathon Romero
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, United States
| | - Jared Streich
- Oak Ridge National Laboratory, Computational and Predictive Biology, Oak Ridge, TN, United States
| | - Michael R. Garvin
- Oak Ridge National Laboratory, Computational and Predictive Biology, Oak Ridge, TN, United States
| | - John Pestian
- Oak Ridge National Laboratory, Computational and Predictive Biology, Oak Ridge, TN, United States
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, United States
| | - Benjamin McMahon
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - David W. Oslin
- VISN 4 Mental Illness Research, Education, and Clinical Center, Center of Excellence, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jean C. Beckham
- Durham Veterans Affairs Health Care System, Durham, NC, United States
- VA Mid-Atlantic Mental Illness, Research, Education, and Clinical Center, Seattle, WA, United States
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
| | - Nathan A. Kimbrel
- Durham Veterans Affairs Health Care System, Durham, NC, United States
- VA Mid-Atlantic Mental Illness, Research, Education, and Clinical Center, Seattle, WA, United States
- Duke University School of Medicine, Duke University, Durham, NC, United States
- VA Health Services Research and Development Center of Innovation to Accelerate Discovery and Practice Transformation, Durham, NC, United States
| | - Daniel A. Jacobson
- Oak Ridge National Laboratory, Computational and Predictive Biology, Oak Ridge, TN, United States
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6
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Harfouche AL, Nakhle F, Harfouche AH, Sardella OG, Dart E, Jacobson D. A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey. TRENDS IN PLANT SCIENCE 2023; 28:154-184. [PMID: 36167648 DOI: 10.1016/j.tplants.2022.08.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. This primer is accompanied by an interactive online tutorial.
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Affiliation(s)
- Antoine L Harfouche
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy.
| | - Farid Nakhle
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Antoine H Harfouche
- Unité de Formation et de Recherche en Sciences Économiques, Gestion, Mathématiques, et Informatique, Université Paris Nanterre, 92001 Nanterre, France
| | - Orlando G Sardella
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Eli Dart
- Energy Sciences Network (ESnet), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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7
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Levin MG, Huffman JE, Verma A, Sullivan KA, Rodriguez AA, Kainer D, Garvin MR, Lane M, Cashman M, Miller JI, Won H, Li B, Luo Y, Jarvik GP, Hakonarson H, Jasper EA, Bick AG, Tsao PS, Ritchie MD, Jacobson DA, Madduri RK, Damrauer SM. Genetics of varicose veins reveals polygenic architecture and genetic overlap with arterial and venous disease. NATURE CARDIOVASCULAR RESEARCH 2023; 2:44-57. [PMID: 39196206 DOI: 10.1038/s44161-022-00196-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 11/23/2022] [Indexed: 08/29/2024]
Abstract
Varicose veins represent a common cause of cardiovascular morbidity, with limited available medical therapies. Although varicose veins are heritable and epidemiologic studies have identified several candidate varicose vein risk factors, the molecular and genetic basis remains uncertain. Here we analyzed the contribution of common genetic variants to varicose veins using data from the Veterans Affairs Million Veteran Program and four other large biobanks. Among 49,765 individuals with varicose veins and 1,334,301 disease-free controls, we identified 139 risk loci. We identified genetic overlap between varicose veins, other vascular diseases and dozens of anthropometric factors. Using Mendelian randomization, we prioritized therapeutic targets via integration of proteomic and transcriptomic data. Finally, topological enrichment analyses confirmed the biologic roles of endothelial shear flow disruption, inflammation, vascular remodeling and angiogenesis. These findings may facilitate future efforts to develop nonsurgical therapies for varicose veins.
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Affiliation(s)
- Michael G Levin
- Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Anurag Verma
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kyle A Sullivan
- Computational and Predictive Biology, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Alexis A Rodriguez
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
| | - David Kainer
- Computational and Predictive Biology, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Michael R Garvin
- Computational and Predictive Biology, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Matthew Lane
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, USA
| | - Mikaela Cashman
- Computational and Predictive Biology, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - J Izaak Miller
- Computational and Predictive Biology, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Hyejung Won
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Yuan Luo
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gail P Jarvik
- Departments of Medicine (Division of Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, WA, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Elizabeth A Jasper
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alexander G Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Philip S Tsao
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Daniel A Jacobson
- Computational and Predictive Biology, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Ravi K Madduri
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
| | - Scott M Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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8
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Abstract
High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state-of-the-art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines.
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Affiliation(s)
- David S Watson
- Department of Statistical Science, University College London, London, UK.
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9
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Qi C, Huang B, Wu M, Wang K, Yang S, Li G. Concrete Strength Prediction Using Different Machine Learning Processes: Effect of Slag, Fly Ash and Superplasticizer. MATERIALS 2022; 15:ma15155369. [PMID: 35955301 PMCID: PMC9370044 DOI: 10.3390/ma15155369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 07/31/2022] [Accepted: 08/01/2022] [Indexed: 11/16/2022]
Abstract
Blast furnace slag (BFS) and fly ash (FA), as mining-associated solid wastes with good pozzolanic effects, can be combined with superplasticizer to prepare concrete with less cement utilization. Considering the important influence of strength on concrete design, random forest (RF) and particle swarm optimization (PSO) methods were combined to construct a prediction model and carry out hyper-parameter tuning in this study. Principal component analysis (PCA) was used to reduce the dimension of input features. The correlation coefficient (R), the explanatory variance score (EVS), the mean absolute error (MAE) and the mean square error (MSE) were used to evaluate the performance of the model. R = 0.954, EVS = 0.901, MAE = 3.746, and MSE = 27.535 of the optimal RF-PSO model on the testing set indicated the high generalization ability. After PCA dimensionality reduction, the R value decreased from 0.954 to 0.88, which was not necessary for the current dataset. Sensitivity analysis showed that cement was the most important feature, followed by water, superplasticizer, fine aggregate, BFS, coarse aggregate and FA, which was beneficial to the design of concrete schemes in practical projects. The method proposed in this study for estimation of the compressive strength of BFS-FA-superplasticizer concrete fills the research gap and has potential engineering application value.
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Affiliation(s)
- Chongchong Qi
- China State Key Laboratory of Strata Intelligent Control and Green Mining Co-Founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China
| | - Binhan Huang
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China
| | - Mengting Wu
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China
| | - Kun Wang
- China State Key Laboratory of Strata Intelligent Control and Green Mining Co-Founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China
| | - Shan Yang
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China
| | - Guichen Li
- School of Mines, China University of Mining and Technology, Xuzhou 221116, China
- Correspondence:
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10
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Walker AM, Cliff A, Romero J, Shah MB, Jones P, Felipe Machado Gazolla JG, Jacobson DA, Kainer D. Evaluating the Performance of Random Forest and Iterative Random Forest Based Methods when Applied to Gene Expression Data. Comput Struct Biotechnol J 2022; 20:3372-3386. [PMID: 35832622 PMCID: PMC9260260 DOI: 10.1016/j.csbj.2022.06.037] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Random Forest can be replaced in this process by iterative Random Forest (iRF), which performs variable selection and boosting. Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3 (RF-LOOP). We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.
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Affiliation(s)
- Angelica M. Walker
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, 821 Volunteer Blvd, Knoxville 37996, TN, USA
| | - Ashley Cliff
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, 821 Volunteer Blvd, Knoxville 37996, TN, USA
| | - Jonathon Romero
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, 821 Volunteer Blvd, Knoxville 37996, TN, USA
| | - Manesh B. Shah
- Computational and Predictive Biology, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge 37830, TN, USA
| | - Piet Jones
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, 821 Volunteer Blvd, Knoxville 37996, TN, USA
| | | | - Daniel A Jacobson
- Computational and Predictive Biology, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge 37830, TN, USA
- Corresponding authors.
| | - David Kainer
- Computational and Predictive Biology, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge 37830, TN, USA
- Corresponding authors.
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11
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Provable Boolean interaction recovery from tree ensemble obtained via random forests. Proc Natl Acad Sci U S A 2022; 119:e2118636119. [PMID: 35609192 PMCID: PMC9295780 DOI: 10.1073/pnas.2118636119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
SignificanceRandom Forests (RFs) are among the most successful machine-learning algorithms in terms of prediction accuracy. In many domain problems, however, the primary goal is not prediction, but to understand the data-generation process-in particular, finding important features and feature interactions. There exists strong empirical evidence that RF-based methods-in particular, iterative RF (iRF)-are very successful in terms of detecting feature interactions. In this work, we propose a biologically motivated, Boolean interaction model. Using this model, we complement the existing empirical evidence with theoretical evidence for the ability of iRF-type methods to select desirable interactions. Our theoretical analysis also yields deeper insights into the general interaction selection mechanism of decision-tree algorithms and the importance of feature subsampling.
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12
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You X, Dadwal UC, Lenburg ME, Kacena MA, Charles JF. Murine Gut Microbiome Meta-analysis Reveals Alterations in Carbohydrate Metabolism in Response to Aging. mSystems 2022; 7:e0124821. [PMID: 35400171 PMCID: PMC9040766 DOI: 10.1128/msystems.01248-21] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 03/28/2022] [Indexed: 11/23/2022] Open
Abstract
Compositional and functional alterations to the gut microbiota during aging are hypothesized to potentially impact our health. Thus, determining aging-specific gut microbiome alterations is critical for developing microbiome-based strategies to improve health and promote longevity in the elderly. In this study, we performed a meta-analysis of publicly available 16S rRNA gene sequencing data from studies investigating the effect of aging on the gut microbiome in mice. Aging reproducibly increased gut microbial alpha diversity and shifted the microbial community structure in mice. We applied the bioinformatic tool PICRUSt2 to predict microbial metagenome function and established a random forest classifier to differentiate between microbial communities from young and old hosts and to identify aging-specific metabolic features. In independent validation data sets, this classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.75 to 0.97 in differentiating microbiomes from young and old hosts. We found that 50% of the most important predicted aging-specific metabolic features were involved in carbohydrate metabolism. Furthermore, fecal short-chain fatty acid (SCFA) concentrations were significantly decreased in old mice, and the expression of the SCFA receptor Gpr41 in the colon was significantly correlated with the relative abundances of gut microbes and microbial carbohydrate metabolic pathways. In conclusion, this study identified aging-specific alterations in the composition and function of the gut microbiome and revealed a potential relationship between aging, microbial carbohydrate metabolism, fecal SCFA, and colonic Gpr41 expression. IMPORTANCE Aging-associated microbial alteration is hypothesized to play an important role in host health and longevity. However, investigations regarding specific gut microbes or microbial functional alterations associated with aging have had inconsistent results. We performed a meta-analysis across 5 independent studies to investigate the effect of aging on the gut microbiome in mice. Our analysis revealed that aging increased gut microbial alpha diversity and shifted the microbial community structure. To determine if we could reliably differentiate the gut microbiomes from young and old hosts, we established a random forest classifier based on predicted metagenome function and validated its performance against independent data sets. Alterations in microbial carbohydrate metabolism and decreased fecal short-chain fatty acid (SCFA) concentrations were key features of aging and correlated with host colonic expression of the SCFA receptor Gpr41. This study advances our understanding of the impact of aging on the gut microbiome and proposes a hypothesis that alterations in gut microbiota-derived SCFA-host GPR41 signaling are a feature of aging.
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Affiliation(s)
- Xiaomeng You
- Department of Orthopaedic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ushashi C. Dadwal
- Orthopaedic Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Marc E. Lenburg
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Melissa A. Kacena
- Orthopaedic Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Julia F. Charles
- Department of Orthopaedic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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13
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Ko KI, Merlet JJ, DerGarabedian BP, Zhen H, Suzuki-Horiuchi Y, Hedberg ML, Hu E, Nguyen AT, Prouty S, Alawi F, Walsh MC, Choi Y, Millar SE, Cliff A, Romero J, Garvin MR, Seykora JT, Jacobson D, Graves DT. NF-κB perturbation reveals unique immunomodulatory functions in Prx1 + fibroblasts that promote development of atopic dermatitis. Sci Transl Med 2022; 14:eabj0324. [PMID: 35108061 DOI: 10.1126/scitranslmed.abj0324] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Skin is composed of diverse cell populations that cooperatively maintain homeostasis. Up-regulation of the nuclear factor κB (NF-κB) pathway may lead to the development of chronic inflammatory disorders of the skin, but its role during the early events remains unclear. Through analysis of single-cell RNA sequencing data via iterative random forest leave one out prediction, an explainable artificial intelligence method, we identified an immunoregulatory role for a unique paired related homeobox-1 (Prx1)+ fibroblast subpopulation. Disruption of Ikkb-NF-κB under homeostatic conditions in these fibroblasts paradoxically induced skin inflammation due to the overexpression of C-C motif chemokine ligand 11 (CCL11; or eotaxin-1) characterized by eosinophil infiltration and a subsequent TH2 immune response. Because the inflammatory phenotype resembled that seen in human atopic dermatitis (AD), we examined human AD skin samples and found that human AD fibroblasts also overexpressed CCL11 and that perturbation of Ikkb-NF-κB in primary human dermal fibroblasts up-regulated CCL11. Monoclonal antibody treatment against CCL11 was effective in reducing the eosinophilia and TH2 inflammation in a mouse model. Together, the murine model and human AD specimens point to dysregulated Prx1+ fibroblasts as a previously unrecognized etiologic factor that may contribute to the pathogenesis of AD and suggest that targeting CCL11 may be a way to treat AD-like skin lesions.
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Affiliation(s)
- Kang I Ko
- Department of Periodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jean J Merlet
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Brett P DerGarabedian
- Department of Periodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Huang Zhen
- Department of Periodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Periodontology, Peking University School and Hospital of Stomatology, Haidian District, Beijing 100081, China
| | - Yoko Suzuki-Horiuchi
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew L Hedberg
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eileen Hu
- Department of Periodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anh T Nguyen
- Department of Periodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stephen Prouty
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Faizan Alawi
- Department of Basic and Translational Sciences, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew C Walsh
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yongwon Choi
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah E Millar
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ashley Cliff
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Jonathon Romero
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Michael R Garvin
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - John T Seykora
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Dana T Graves
- Department of Periodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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14
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Prates ET, Garvin MR, Jones P, Miller JI, Sullivan KA, Cliff A, Gazolla JGFM, Shah MB, Walker AM, Lane M, Rentsch CT, Justice A, Pavicic M, Romero J, Jacobson D. Antiviral Strategies Against SARS-CoV-2: A Systems Biology Approach. Methods Mol Biol 2022; 2452:317-351. [PMID: 35554915 DOI: 10.1007/978-1-0716-2111-0_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions.
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Affiliation(s)
- Erica T Prates
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Michael R Garvin
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Piet Jones
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - J Izaak Miller
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Kyle A Sullivan
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Ashley Cliff
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Joao Gabriel Felipe Machado Gazolla
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Manesh B Shah
- Genome Science and Technology, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Angelica M Walker
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Matthew Lane
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Christopher T Rentsch
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- VA Connecticut Healthcare/General Internal Medicine, West Haven, CT, USA
| | - Amy Justice
- VA Connecticut Healthcare/General Internal Medicine, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Mirko Pavicic
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Jonathon Romero
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Daniel Jacobson
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA.
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA.
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA.
- Genome Science and Technology, University of Tennessee Knoxville, Knoxville, TN, USA.
- Department of Psychology, NeuroNet Research Center, University of Tennessee Knoxville, Knoxville, TN, USA.
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15
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Huber R, Jacobson DA. The Phylogenetic Roots of Addiction: Compulsive Drug Seeking, Natural and Drug-Sensitive Reward, and the Acquisition of Learned Habits. BRAIN, BEHAVIOR AND EVOLUTION 2021; 95:217-221. [PMID: 34082419 DOI: 10.1159/000517121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 05/06/2021] [Indexed: 11/19/2022]
Affiliation(s)
- Robert Huber
- Department of Biological Sciences, Bowling Green State University, Bowling Green, Ohio, USA
| | - Daniel A Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.,Department of Psychology, University of Tennessee-Knoxville, Knoxville, Tennessee, USA
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16
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Palmer RHC, Johnson EC, Won H, Polimanti R, Kapoor M, Chitre A, Bogue MA, Benca‐Bachman CE, Parker CC, Verma A, Reynolds T, Ernst J, Bray M, Kwon SB, Lai D, Quach BC, Gaddis NC, Saba L, Chen H, Hawrylycz M, Zhang S, Zhou Y, Mahaffey S, Fischer C, Sanchez‐Roige S, Bandrowski A, Lu Q, Shen L, Philip V, Gelernter J, Bierut LJ, Hancock DB, Edenberg HJ, Johnson EO, Nestler EJ, Barr PB, Prins P, Smith DJ, Akbarian S, Thorgeirsson T, Walton D, Baker E, Jacobson D, Palmer AA, Miles M, Chesler EJ, Emerson J, Agrawal A, Martone M, Williams RW. Integration of evidence across human and model organism studies: A meeting report. GENES, BRAIN, AND BEHAVIOR 2021; 20:e12738. [PMID: 33893716 PMCID: PMC8365690 DOI: 10.1111/gbb.12738] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 04/11/2021] [Accepted: 04/21/2021] [Indexed: 12/13/2022]
Abstract
The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.
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Affiliation(s)
- Rohan H. C. Palmer
- Behavioral Genetics of Addiction Laboratory, Department of PsychologyEmory UniversityAtlantaGeorgiaUSA
| | - Emma C. Johnson
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Hyejung Won
- Department of Genetics and Neuroscience CenterUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Renato Polimanti
- Department of PsychiatryYale University School of MedicineWest HavenConnecticutUSA
| | - Manav Kapoor
- Nash Family Department of Neuroscience and Friedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Apurva Chitre
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | | | - Chelsie E. Benca‐Bachman
- Behavioral Genetics of Addiction Laboratory, Department of PsychologyEmory UniversityAtlantaGeorgiaUSA
| | - Clarissa C. Parker
- Department of Psychology and Program in NeuroscienceMiddlebury CollegeMiddleburyVermontUSA
| | - Anurag Verma
- Biomedical and Translational Informatics LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Jason Ernst
- Department of Biological ChemistryUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Michael Bray
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Soo Bin Kwon
- Department of Biological ChemistryUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Dongbing Lai
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Bryan C. Quach
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Nathan C. Gaddis
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Laura Saba
- Department of Pharmaceutical SciencesUniversity of Colorado, Anschutz Medical CampusAuroraColoradoUSA
| | - Hao Chen
- Department of Pharmacology, Addiction Science, and ToxicologyUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
| | | | - Shan Zhang
- Department of Statistics and ProbabilityMichigan State UniversityEast LansingMichiganUSA
| | - Yuan Zhou
- Department of Department of BiostatisticsUniversity of FloridaGainesvilleFloridaUSA
| | - Spencer Mahaffey
- Department of Pharmaceutical Sciences, School of PharmacyUniversity of Colorado DenverAuroraColoradoUSA
| | - Christian Fischer
- Department of Genetics, Genomics and InformaticsUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
| | - Sandra Sanchez‐Roige
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Anita Bandrowski
- Department of NeuroscienceUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Qing Lu
- Department of Department of BiostatisticsUniversity of FloridaGainesvilleFloridaUSA
| | - Li Shen
- Nash Family Department of Neuroscience and Friedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | | | - Joel Gelernter
- Department of PsychiatryYale University School of MedicineWest HavenConnecticutUSA
| | - Laura J. Bierut
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Dana B. Hancock
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Howard J. Edenberg
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Biochemistry and Molecular BiologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Eric O. Johnson
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Eric J. Nestler
- Nash Family Department of Neuroscience and Friedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Peter B. Barr
- Department of PsychologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Pjotr Prins
- Department of Genetics, Genomics and InformaticsUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
| | - Desmond J. Smith
- Department of Molecular and Medical PharmacologyDavid Geffen School of Medicine, UCLALos AngelesCaliforniaUSA
| | - Schahram Akbarian
- Friedman Brain Institute and Departments of Psychiatry and NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | | | | | - Erich Baker
- Department of Computer ScienceBaylor UniversityWacoTexasUSA
| | - Daniel Jacobson
- Computational and Predictive Biology, BiosciencesOak Ridge National LaboratoryOak RidgeTennesseeUSA
- Department of PsychologyUniversity of Tennessee KnoxvilleKnoxvilleTennesseeUSA
| | - Abraham A. Palmer
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Institute for Genomic Medicine, University of California San DiegoLa JollaCaliforniaUSA
| | - Michael Miles
- Department of Pharmacology and ToxicologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | | | | | - Arpana Agrawal
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Maryann Martone
- Department of NeuroscienceUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Robert W. Williams
- Department of Genetics, Genomics and InformaticsUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
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17
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Garvin MR, T Prates E, Pavicic M, Jones P, Amos BK, Geiger A, Shah MB, Streich J, Felipe Machado Gazolla JG, Kainer D, Cliff A, Romero J, Keith N, Brown JB, Jacobson D. Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models. Genome Biol 2020; 21:304. [PMID: 33357233 PMCID: PMC7756312 DOI: 10.1186/s13059-020-02191-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 10/29/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. RESULTS Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp614Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro323Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. CONCLUSIONS These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.
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Affiliation(s)
- Michael R Garvin
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA
| | - Erica T Prates
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA
| | - Mirko Pavicic
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA
| | - Piet Jones
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - B Kirtley Amos
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA
- Department of Horticulture, N-318 Ag Sciences Center, University of Kentucky, Lexington, KY, USA
| | - Armin Geiger
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Manesh B Shah
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA
| | - Jared Streich
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA
| | | | - David Kainer
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA
| | - Ashley Cliff
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Jonathon Romero
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Nathan Keith
- Lawrence Berkeley National Laboratory, Environmental Genomics & Systems Biology, Berkeley, CA, USA
| | - James B Brown
- Lawrence Berkeley National Laboratory, Environmental Genomics & Systems Biology, Berkeley, CA, USA
| | - Daniel Jacobson
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, TN, USA.
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA.
- Department of Psychology, University of Tennessee Knoxville, Knoxville, TN, USA.
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18
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Zhang X, Baer AG, Price JM, Jones PC, Garcia BJ, Romero J, Cliff AM, Mi W, Brown JB, Jacobson DA, Lydic R, Baghdoyan HA. Neurotransmitter networks in mouse prefrontal cortex are reconfigured by isoflurane anesthesia. J Neurophysiol 2020; 123:2285-2296. [PMID: 32347157 PMCID: PMC7311717 DOI: 10.1152/jn.00092.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 04/13/2020] [Accepted: 04/23/2020] [Indexed: 12/17/2022] Open
Abstract
This study quantified eight small-molecule neurotransmitters collected simultaneously from prefrontal cortex of C57BL/6J mice (n = 23) during wakefulness and during isoflurane anesthesia (1.3%). Using isoflurane anesthesia as an independent variable enabled evaluation of the hypothesis that isoflurane anesthesia differentially alters concentrations of multiple neurotransmitters and their interactions. Machine learning was applied to reveal higher order interactions among neurotransmitters. Using a between-subjects design, microdialysis was performed during wakefulness and during anesthesia. Concentrations (nM) of acetylcholine, adenosine, dopamine, GABA, glutamate, histamine, norepinephrine, and serotonin in the dialysis samples are reported (means ± SD). Relative to wakefulness, acetylcholine concentration was lower during isoflurane anesthesia (1.254 ± 1.118 vs. 0.401 ± 0.134, P = 0.009), and concentrations of adenosine (29.456 ± 29.756 vs. 101.321 ± 38.603, P < 0.001), dopamine (0.0578 ± 0.0384 vs. 0.113 ± 0.084, P = 0.036), and norepinephrine (0.126 ± 0.080 vs. 0.219 ± 0.066, P = 0.010) were higher during anesthesia. Isoflurane reconfigured neurotransmitter interactions in prefrontal cortex, and the state of isoflurane anesthesia was reliably predicted by prefrontal cortex concentrations of adenosine, norepinephrine, and acetylcholine. A novel finding to emerge from machine learning analyses is that neurotransmitter concentration profiles in mouse prefrontal cortex undergo functional reconfiguration during isoflurane anesthesia. Adenosine, norepinephrine, and acetylcholine showed high feature importance, supporting the interpretation that interactions among these three transmitters may play a key role in modulating levels of cortical and behavioral arousal.NEW & NOTEWORTHY This study discovered that interactions between neurotransmitters in mouse prefrontal cortex were altered during isoflurane anesthesia relative to wakefulness. Machine learning further demonstrated that, relative to wakefulness, higher order interactions among neurotransmitters were disrupted during isoflurane administration. These findings extend to the neurochemical domain the concept that anesthetic-induced loss of wakefulness results from a disruption of neural network connectivity.
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Affiliation(s)
- Xiaoying Zhang
- Department of Anesthesiology, University of Tennessee Medical Center, Knoxville, Tennessee
- Department of Psychology, University of Tennessee, Knoxville, Tennessee
- Anesthesia and Operation Center, Chinese PLA General Hospital, Beijing, China
| | - Aaron G Baer
- Department of Anesthesiology, University of Tennessee Medical Center, Knoxville, Tennessee
| | - Joshua M Price
- Office of Information Technology, University of Tennessee, Knoxville, Tennessee
| | - Piet C Jones
- Oak Ridge National Laboratory, Oak Ridge, Tennessee
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Tennessee
| | | | - Jonathon Romero
- Oak Ridge National Laboratory, Oak Ridge, Tennessee
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Tennessee
| | - Ashley M Cliff
- Oak Ridge National Laboratory, Oak Ridge, Tennessee
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Tennessee
| | - Weidong Mi
- Anesthesia and Operation Center, Chinese PLA General Hospital, Beijing, China
| | - James B Brown
- Molecular Ecosystems Biology Department, Lawrence Berkeley National Laboratory, Berkeley, California
| | | | - Ralph Lydic
- Department of Anesthesiology, University of Tennessee Medical Center, Knoxville, Tennessee
- Department of Psychology, University of Tennessee, Knoxville, Tennessee
- Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Helen A Baghdoyan
- Department of Anesthesiology, University of Tennessee Medical Center, Knoxville, Tennessee
- Department of Psychology, University of Tennessee, Knoxville, Tennessee
- Oak Ridge National Laboratory, Oak Ridge, Tennessee
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