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Kim H, Kim DH, Jeong Y, Lee DS, Son J, An S. Chemical gradients on graphene via direct mechanochemical cleavage of atoms from chemically functionalized graphene surfaces. NANOSCALE ADVANCES 2023; 5:2271-2279. [PMID: 37056614 PMCID: PMC10089115 DOI: 10.1039/d3na00066d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Manipulating the surface chemistry of graphene is critical to many applications that are achievable by chemical functionalization. Specifically, tailoring the spatial distribution of functional groups offers more opportunities to explore functionality using continuous changes in surface energy. To this end, careful consideration is required to demonstrate the chemical gradient on graphene surfaces, and it is necessary to develop a technique to pattern the spatial distribution of functional groups. Here, we demonstrate the tailoring of a chemical gradient through direct mechanochemical cleavage of atoms from chemically functionalized graphene surfaces via an atomic force microscope. Additionally, we define the surface characteristics of the fabricated sample by using lateral force microscopy revealing the materials' intrinsic properties at the nanoscale. Furthermore, we perform the cleaning process of the obtained lateral force images by using a machine learning method of truncated singular value decomposition. This work provides a useful technique for many applications utilizing continuous changes in the surface energy of graphene.
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Affiliation(s)
- Hyeonsu Kim
- Department of Physics, Institute of Photonics and Information Technology, Jeonbuk National University Jeonju 54896 South Korea
| | - Dong-Hyun Kim
- Functional Composite Materials Research Center, Korea Institute of Science and Technology Jeonbuk 55324 South Korea
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University Suwon 16419 South Korea
| | - Yunjo Jeong
- Functional Composite Materials Research Center, Korea Institute of Science and Technology Jeonbuk 55324 South Korea
| | - Dong-Su Lee
- Functional Composite Materials Research Center, Korea Institute of Science and Technology Jeonbuk 55324 South Korea
| | - Jangyup Son
- Functional Composite Materials Research Center, Korea Institute of Science and Technology Jeonbuk 55324 South Korea
- Division of Nano and Information Technology, KIST School University of Science and Technology (UST) Jeonbuk 55324 South Korea
| | - Sangmin An
- Department of Physics, Institute of Photonics and Information Technology, Jeonbuk National University Jeonju 54896 South Korea
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Siswantining T, Bustamam A, Sarwinda D, Soemartojo SM, Latief MA, Octaria EA, Siregar ATM, Septa O, Al-Ash HS, Saputra N. Triclustering method for finding biomarkers in human immunodeficiency virus-1 gene expression data. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:6743-6763. [PMID: 35730281 DOI: 10.3934/mbe.2022318] [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: 06/15/2023]
Abstract
HIV-1 is a virus that destroys CD4 + cells in the body's immune system, causing a drastic decline in immune system performance. Analysis of HIV-1 gene expression data is urgently needed. Microarray technology is used to analyze gene expression data by measuring the expression of thousands of genes in various conditions. The gene expression series data, which are formed in three dimensions, are analyzed using triclustering. Triclustering is an analysis technique for 3D data that aims to group data simultaneously into rows and columns across different times/conditions. The result of this technique is called a tricluster. A tricluster is a subspace in the form of a subset of rows, columns, and time/conditions. In this study, we used the δ-Trimax, THD Tricluster, and MOEA methods by applying different measures, namely, transposed virtual error, the New Residue Score, and the Multi Slope Measure. The gene expression data consisted of 22,283 probe gene IDs, 40 observations, and four conditions: normal, acute, chronic, and non-progressor. Tricluster evaluation was carried out based on intertemporal homogeneity. An analysis of the probe ID gene that affects AIDS was carried out through this triclustering process. Based on this analysis, a gene symbol which is biomarkers associated with AIDS due to HIV-1, HLA-C, was found in every condition for normal, acute, chronic, and non-progressive HIV-1 patients.
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Affiliation(s)
- Titin Siswantining
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
| | - Alhadi Bustamam
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
| | - Devvi Sarwinda
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
| | - Saskya Mary Soemartojo
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
| | - Moh Abdul Latief
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
| | - Elke Annisa Octaria
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
| | | | - Oon Septa
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
| | - Herley Shaori Al-Ash
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
| | - Noval Saputra
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
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Grassner L, Garcia-Ovejero D, Mach O, Lopez-Dolado E, Vargas-Vaquero E, Alcobendas M, Esclarin A, Sanktjohanser L, Wutte C, Becker J, Lener S, Hartmann S, Girod PP, Koegl N, Griessenauer C, Papadopoulos MC, Geisler F, Thomé C, Molina-Holgado E, Vidal J, Curt A, Scivoletto G, Guest J, Maier D, Weidner N, Rupp R, Kramer JLK, Arevalo-Martin A. A NEW SCORE BASED ON THE INTERNATIONAL STANDARDS FOR NEUROLOGICAL CLASSIFICATION OF SPINAL CORD INJURY FOR INTEGRATIVE EVALUATION OF CHANGES IN SENSORIMOTOR FUNCTIONS. J Neurotrauma 2021; 39:613-626. [PMID: 34937399 DOI: 10.1089/neu.2021.0368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
Sensorimotor function of patients with spinal cord injury (SCI) is commonly assessed according to the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI). From the ISNCSCI segmental motor and sensory assessments, upper and lower extremity motor scores (UEMS and LEMS), sum scores of pin prick (PP) and light touch (LT) sensation, the neurological level of injury (NLI) and the classification of lesion severity according to the American Spinal Injury Association Impairment Scale (AIS) grade are derived. Changes of these parameters over time are widely used to evaluate neurological recovery. However, evaluating recovery based on a single ISNCSCI scoring or classification variable may misestimate overall recovery. Here, we propose an Integrated Neurological Change Score (INCS) based on the combination of normalized changes between two-time points of UEMS, LEMS, and total PP and LT scores. To assess the agreement of INCS with clinical judgement of meaningfulness of neurological changes, changes of ISNCSCI variables between two time-points of 88 patients from an independent cohort were rated by 20 clinical experts according to a 5-categories Likert Scale. As for individual ISNCSCI variables, neurological change measured by INCS is associated to severity (AIS grade), age and time since injury, but INCS better reflects clinical judgment about meaningfulness of neurological changes than individual ISNCSCI variables. In addition, INCS is related with changes in functional independence measured by the Spinal Cord Independence Measure (SCIM) in patients with tetraplegia. INCS may be a useful measure of overall neurological change in clinical studies.
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Affiliation(s)
- Lukas Grassner
- Innsbruck Medical University Department of Neurology and Neurosurgery, 417777, Innsbruck, Tirol, Austria.,Paracelsus Medical University Salzburg, 31507, Institute of Molecular Regenerative Medicine, Spinal Cord Injury and Tissue Regeneration Center Salzburg, Salzburg, Salzburg, Austria;
| | - Daniel Garcia-Ovejero
- Hospital Nacional de Parapléjicos, SESCAM, Laboratorio de Neuroinflamación, Finca La Peraleda, Toledo, Spain, 45071;
| | - Orpheus Mach
- Trauma Center Murnau, Center for Spinal Cord Injuries, Prof.-Kuentscher-Str. 8, Murnau, Germany, 82418;
| | - Elisa Lopez-Dolado
- Hospital Nacional de Paraplejicos, Physical Medicine and Rehabilitation, Toledo, Spain;
| | | | - Monica Alcobendas
- Hospital Nacional de Paraplejicos, Physical Medicine and Rehabilitation, Toledo, Spain;
| | - Ana Esclarin
- HOSPITAL NACIONAL DE PARAPLEJICOS, Physical Medicine and Rehabilitation, Finca de la Peraleda S/N, Toledo, Toledo, Spain, 45007.,Fund;
| | | | - Christof Wutte
- Trauma Center Murnau, Center for Spinal Cord Injuries, Murnau, Germany;
| | - Johannes Becker
- Trauma Center Murnau, Center for Spinal Cord Injuries, Murnau, Germany;
| | - Sara Lener
- Innsbruck Medical University Department of Neurology and Neurosurgery, 417777, Innsbruck, Tirol, Austria;
| | - Sebastian Hartmann
- Innsbruck Medical University Department of Neurology and Neurosurgery, 417777, Innsbruck, Tirol, Austria;
| | - Pierre-Pascal Girod
- Innsbruck Medical University Department of Neurology and Neurosurgery, 417777, Innsbruck, Tirol, Austria;
| | - Nikolaus Koegl
- Innsbruck Medical University Department of Neurology and Neurosurgery, 417777, Innsbruck, Tirol, Austria;
| | - Christoph Griessenauer
- Geisinger Health System, 2780, Neurosurgery, Danville, Pennsylvania, United States.,Harvard Medical School, 1811, Neurological Surgery, Boston, Massachusetts, United States;
| | - Marios C Papadopoulos
- St George's University of London, Academic Neurosurgery Unit, St George's, University of London, 1.122 Jenner Wing, Cranmer Terrace, London, United Kingdom of Great Britain and Northern Ireland, SW17 0RE;
| | - Fred Geisler
- University of Saskatchewan College of Medicine, 12371, Saskatoon, Saskatchewan, Canada;
| | - Claudius Thomé
- Medical University Innsbruck, Dept. of Neurosurgery, Anichstr. 35, Innsbruck, Austria, 6020;
| | - Eduardo Molina-Holgado
- Hospital Nacional de Parapléjicos, SESCAM, Laboratorio de Neuroinflamación, Finca La Peraleda s/n, Toledo, Spain, 45071;
| | - Joan Vidal
- Institut Guttmann, 83068, Badalona, Catalunya, Spain;
| | - Armin Curt
- University Hospital Balgrist, Spinal Cord Injury Center, Forchstrasse, Zurich, Switzerland, 8008;
| | - Giorgio Scivoletto
- IRCCS Fondazioen S. Lucia, Spinal Cord Unit, via Ardeatina 306, Rome, Italy, 00179;
| | - James Guest
- University of Miami, Neurological Surgery, 1095 NW 14th Terrace, Miami, Florida, United States, 33136;
| | - Doris Maier
- Trauma Center Murnau, Center for Spinal Cord Injuries, Murnau, Germany;
| | - Norbert Weidner
- University Hospital Heidelberg, Spinal Cord Injury Center, Schlierbacher Landstr, Heidelberg, Germany, 69118;
| | - Rüdiger Rupp
- University Hospital Heidelberg, Spinal Cord Injury Center, Schlierbacher Landstr. 200a, Heidelberg, BW, Germany, 69118;
| | - John L K Kramer
- University of British Columbia International Collaboration on Repair Discoveries, 507272, Vancouver, British Columbia, Canada;
| | - Angel Arevalo-Martin
- Hospital Nacional de Paraplejicos, Laboratory of Neuroinflammation, Finca la Peraleda, s/n, Toledo, Spain, 45071;
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McGivney DF, Pierre E, Ma D, Jiang Y, Saybasili H, Gulani V, Griswold MA. SVD compression for magnetic resonance fingerprinting in the time domain. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:2311-22. [PMID: 25029380 PMCID: PMC4753055 DOI: 10.1109/tmi.2014.2337321] [Citation(s) in RCA: 176] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.
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Affiliation(s)
- Debra F. McGivney
- Department of Radiology, Case Western Reserve University, Cleveland, OH, 44106 USA,
| | - Eric Pierre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106 USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106 USA
| | - Yun Jiang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106 USA
| | | | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, OH, 44106 USA
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, OH, 44106 USA
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Coordinated metabolic transitions during Drosophila embryogenesis and the onset of aerobic glycolysis. G3-GENES GENOMES GENETICS 2014; 4:839-50. [PMID: 24622332 PMCID: PMC4025483 DOI: 10.1534/g3.114.010652] [Citation(s) in RCA: 92] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Rapidly proliferating cells such as cancer cells and embryonic stem cells rely on a specialized metabolic program known as aerobic glycolysis, which supports biomass production from carbohydrates. The fruit fly Drosophila melanogaster also utilizes aerobic glycolysis to support the rapid growth that occurs during larval development. Here we use singular value decomposition analysis of modENCODE RNA-seq data combined with GC-MS-based metabolomic analysis to analyze the changes in gene expression and metabolism that occur during Drosophila embryogenesis, spanning the onset of aerobic glycolysis. Unexpectedly, we find that the most common pattern of co-expressed genes in embryos includes the global switch to glycolytic gene expression that occurs midway through embryogenesis. In contrast to the canonical aerobic glycolytic pathway, however, which is accompanied by reduced mitochondrial oxidative metabolism, the expression of genes involved in the tricarboxylic cycle (TCA cycle) and the electron transport chain are also upregulated at this time. Mitochondrial activity, however, appears to be attenuated, as embryos exhibit a block in the TCA cycle that results in elevated levels of citrate, isocitrate, and α-ketoglutarate. We also find that genes involved in lipid breakdown and β-oxidation are upregulated prior to the transcriptional initiation of glycolysis, but are downregulated before the onset of larval development, revealing coordinated use of lipids and carbohydrates during development. These observations demonstrate the efficient use of nutrient stores to support embryonic development, define sequential metabolic transitions during this stage, and demonstrate striking similarities between the metabolic state of late-stage fly embryos and tumor cells.
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Samuels BA, Leonardo ED, Dranovsky A, Williams A, Wong E, Nesbitt AMI, McCurdy RD, Hen R, Alter M. Global state measures of the dentate gyrus gene expression system predict antidepressant-sensitive behaviors. PLoS One 2014; 9:e85136. [PMID: 24465494 PMCID: PMC3894967 DOI: 10.1371/journal.pone.0085136] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Accepted: 11/23/2013] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine are the most common form of medication treatment for major depression. However, approximately 50% of depressed patients fail to achieve an effective treatment response. Understanding how gene expression systems respond to treatments may be critical for understanding antidepressant resistance. METHODS We take a novel approach to this problem by demonstrating that the gene expression system of the dentate gyrus responds to fluoxetine (FLX), a commonly used antidepressant medication, in a stereotyped-manner involving changes in the expression levels of thousands of genes. The aggregate behavior of this large-scale systemic response was quantified with principal components analysis (PCA) yielding a single quantitative measure of the global gene expression system state. RESULTS Quantitative measures of system state were highly correlated with variability in levels of antidepressant-sensitive behaviors in a mouse model of depression treated with fluoxetine. Analysis of dorsal and ventral dentate samples in the same mice indicated that system state co-varied across these regions despite their reported functional differences. Aggregate measures of gene expression system state were very robust and remained unchanged when different microarray data processing algorithms were used and even when completely different sets of gene expression levels were used for their calculation. CONCLUSIONS System state measures provide a robust method to quantify and relate global gene expression system state variability to behavior and treatment. State variability also suggests that the diversity of reported changes in gene expression levels in response to treatments such as fluoxetine may represent different perspectives on unified but noisy global gene expression system state level responses. Studying regulation of gene expression systems at the state level may be useful in guiding new approaches to augmentation of traditional antidepressant treatments.
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Affiliation(s)
- Benjamin A. Samuels
- Departments of Psychiatry and Neuroscience, Columbia University, New York, New York, United States of America
| | - E. David Leonardo
- Departments of Psychiatry and Neuroscience, Columbia University, New York, New York, United States of America
| | - Alex Dranovsky
- Departments of Psychiatry and Neuroscience, Columbia University, New York, New York, United States of America
| | - Amanda Williams
- AstraZeneca Pharmaceuticals, CNS Discovery, Wilmington, Delaware, United States of America
| | - Erik Wong
- AstraZeneca Pharmaceuticals, CNS Discovery, Wilmington, Delaware, United States of America
| | - Addie May I. Nesbitt
- Center for Neurobiology and Behavior, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Richard D. McCurdy
- Center for Neurobiology and Behavior, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Rene Hen
- Departments of Psychiatry and Neuroscience, Columbia University, New York, New York, United States of America
- * E-mail: (MA); (RH)
| | - Mark Alter
- Center for Neurobiology and Behavior, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail: (MA); (RH)
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7
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SVD identifies transcript length distribution functions from DNA microarray data and reveals evolutionary forces globally affecting GBM metabolism. PLoS One 2013; 8:e78913. [PMID: 24282503 PMCID: PMC3839928 DOI: 10.1371/journal.pone.0078913] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Accepted: 09/25/2013] [Indexed: 01/10/2023] Open
Abstract
To search for evolutionary forces that might act upon transcript length, we use the singular value decomposition (SVD) to identify the length distribution functions of sets and subsets of human and yeast transcripts from profiles of mRNA abundance levels across gel electrophoresis migration distances that were previously measured by DNA microarrays. We show that the SVD identifies the transcript length distribution functions as “asymmetric generalized coherent states” from the DNA microarray data and with no a-priori assumptions. Comparing subsets of human and yeast transcripts of the same gene ontology annotations, we find that in both disparate eukaryotes, transcripts involved in protein synthesis or mitochondrial metabolism are significantly shorter than typical, and in particular, significantly shorter than those involved in glucose metabolism. Comparing the subsets of human transcripts that are overexpressed in glioblastoma multiforme (GBM) or normal brain tissue samples from The Cancer Genome Atlas, we find that GBM maintains normal brain overexpression of significantly short transcripts, enriched in transcripts that are involved in protein synthesis or mitochondrial metabolism, but suppresses normal overexpression of significantly longer transcripts, enriched in transcripts that are involved in glucose metabolism and brain activity. These global relations among transcript length, cellular metabolism and tumor development suggest a previously unrecognized physical mode for tumor and normal cells to differentially regulate metabolism in a transcript length-dependent manner. The identified distribution functions support a previous hypothesis from mathematical modeling of evolutionary forces that act upon transcript length in the manner of the restoring force of the harmonic oscillator.
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8
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Jenssen R. Mean vector component analysis for visualization and clustering of nonnegative data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2013; 24:1553-1564. [PMID: 24808593 DOI: 10.1109/tnnls.2013.2262774] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Mean vector component analysis (MVCA) is introduced as a new method for visualization and clustering of nonnegative data. The method is based on dimensionality reduction by preserving the squared length, and implicitly also the direction, of the mean vector of the original data. The optimal mean vector preserving basis is obtained from the spectral decomposition of the inner-product matrix, and it is shown to capture clustering structure. MVCA corresponds to certain uncentered principal component analysis (PCA) axes. Unlike traditional PCA, these axes are in general not corresponding to the top eigenvalues. MVCA is shown to produce different visualizations and sometimes considerably improved clustering results for nonnegative data, compared with PCA.
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9
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Biologic phenotyping of the human small airway epithelial response to cigarette smoking. PLoS One 2011; 6:e22798. [PMID: 21829517 PMCID: PMC3145669 DOI: 10.1371/journal.pone.0022798] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2010] [Accepted: 07/07/2011] [Indexed: 01/23/2023] Open
Abstract
Background The first changes associated with smoking are in the small airway epithelium (SAE). Given that smoking alters SAE gene expression, but only a fraction of smokers develop chronic obstructive pulmonary disease (COPD), we hypothesized that assessment of SAE genome-wide gene expression would permit biologic phenotyping of the smoking response, and that a subset of healthy smokers would have a “COPD-like” SAE transcriptome. Methodology/Principal Findings SAE (10th–12th generation) was obtained via bronchoscopy of healthy nonsmokers, healthy smokers and COPD smokers and microarray analysis was used to identify differentially expressed genes. Individual responsiveness to smoking was quantified with an index representing the % of smoking-responsive genes abnormally expressed (ISAE), with healthy smokers grouped into “high” and “low” responders based on the proportion of smoking-responsive genes up- or down-regulated in each smoker. Smokers demonstrated significant variability in SAE transcriptome with ISAE ranging from 2.9 to 51.5%. While the SAE transcriptome of “low” responder healthy smokers differed from both “high” responders and smokers with COPD, the transcriptome of the “high” responder healthy smokers was indistinguishable from COPD smokers. Conclusion/Significance The SAE transcriptome can be used to classify clinically healthy smokers into subgroups with lesser and greater responses to cigarette smoking, even though these subgroups are indistinguishable by clinical criteria. This identifies a group of smokers with a “COPD-like” SAE transcriptome.
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NIKULIN VLADIMIR, HUANG TIANHSIANG, MCLACHLAN GEOFFREYJ. CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2011. [DOI: 10.1142/s1469026811002969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.
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Affiliation(s)
| | - TIAN-HSIANG HUANG
- Institute of Information Management, National Cheng Kung University, Taiwan
| | - GEOFFREY J. MCLACHLAN
- Department of Mathematics and Institute for Molecular Bioscience, University of Queensland, Australia
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11
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Chen X, Chen C, Jin L. Principal Component Analyses in Anthropological Genetics. ACTA ACUST UNITED AC 2011. [DOI: 10.4236/aa.2011.12002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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12
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Markovsky I, Niranjan M. Approximate low-rank factorization with structured factors. Comput Stat Data Anal 2010. [DOI: 10.1016/j.csda.2009.06.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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13
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Nordling T, Jacobsen E. Interampatteness – a generic property of biochemical networks. IET Syst Biol 2009; 3:388-403. [DOI: 10.1049/iet-syb.2009.0008] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Yener B, Acar E, Aguis P, Bennett K, Vandenberg SL, Plopper GE. Multiway modeling and analysis in stem cell systems biology. BMC SYSTEMS BIOLOGY 2008; 2:63. [PMID: 18625054 PMCID: PMC2527292 DOI: 10.1186/1752-0509-2-63] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2008] [Accepted: 07/14/2008] [Indexed: 12/22/2022]
Abstract
Background Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells. Results We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate. Conclusion Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.
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Affiliation(s)
- Bülent Yener
- Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA.
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15
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A tensor higher-order singular value decomposition for integrative analysis of DNA microarray data from different studies. Proc Natl Acad Sci U S A 2007; 104:18371-6. [PMID: 18003902 DOI: 10.1073/pnas.0709146104] [Citation(s) in RCA: 105] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
We describe the use of a higher-order singular value decomposition (HOSVD) in transforming a data tensor of genes x "x-settings," that is, different settings of the experimental variable x x "y-settings," which tabulates DNA microarray data from different studies, to a "core tensor" of "eigenarrays" x "x-eigengenes" x "y-eigengenes." Reformulating this multilinear HOSVD such that it decomposes the data tensor into a linear superposition of all outer products of an eigenarray, an x- and a y-eigengene, that is, rank-1 "subtensors," we define the significance of each subtensor in terms of the fraction of the overall information in the data tensor that it captures. We illustrate this HOSVD with an integration of genome-scale mRNA expression data from three yeast cell cycle time courses, two of which are under exposure to either hydrogen peroxide or menadione. We find that significant subtensors represent independent biological programs or experimental phenomena. The picture that emerges suggests that the conserved genes YKU70, MRE11, AIF1, and ZWF1, and the processes of retrotransposition, apoptosis, and the oxidative pentose phosphate pathway that these genes are involved in, may play significant, yet previously unrecognized, roles in the differential effects of hydrogen peroxide and menadione on cell cycle progression. A genome-scale correlation between DNA replication initiation and RNA transcription, which is equivalent to a recently discovered correlation and might be due to a previously unknown mechanism of regulation, is independently uncovered.
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Tamayo P, Scanfeld D, Ebert BL, Gillette MA, Roberts CWM, Mesirov JP. Metagene projection for cross-platform, cross-species characterization of global transcriptional states. Proc Natl Acad Sci U S A 2007; 104:5959-64. [PMID: 17389406 PMCID: PMC1838404 DOI: 10.1073/pnas.0701068104] [Citation(s) in RCA: 113] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The high dimensionality of global transcription profiles, the expression level of 20,000 genes in a much small number of samples, presents challenges that affect the sensitivity and general applicability of analysis results. In principle, it would be better to describe the data in terms of a small number of metagenes, positive linear combinations of genes, which could reduce noise while still capturing the invariant biological features of the data. Here, we describe how to accomplish such a reduction in dimension by a metagene projection methodology, which can greatly reduce the number of features used to characterize microarray data. We show, in applications to the analysis of leukemia and lung cancer data sets, how this approach can help assess and interpret similarities and differences between independent data sets, enable cross-platform and cross-species analysis, improve clustering and class prediction, and provide a computational means to detect and remove sample contamination.
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Affiliation(s)
- Pablo Tamayo
- *Eli and Edythe L. Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02141
| | - Daniel Scanfeld
- *Eli and Edythe L. Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02141
| | - Benjamin L. Ebert
- *Eli and Edythe L. Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02141
| | - Michael A. Gillette
- *Eli and Edythe L. Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02141
- Pulmonary and Critical Care Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114; and
| | | | - Jill P. Mesirov
- *Eli and Edythe L. Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02141
- To whom correspondence should be addressed. E-mail:
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Abstract
DNA microarrays make it possible, for the first time, to record the complete genomic signals that guide the progression of cellular processes. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment, and drug development. This chapter reviews the first data-driven models that were created from these genome-scale data, through adaptations and generalizations of mathematical frameworks from matrix algebra that have proven successful in describing the physical world, in such diverse areas as mechanics and perception: the singular value decomposition model, the generalized singular value decomposition model comparative model, and the pseudoinverse projection integrative model. These models provide mathematical descriptions of the genetic networks that generate and sense the measured data, where the mathematical variables and operations represent biological reality. The variables, patterns uncovered in the data, correlate with activities of cellular elements such as regulators or transcription factors that drive the measured signals and cellular states where these elements are active. The operations, such as data reconstruction, rotation, and classification in subspaces of selected patterns, simulate experimental observation of only the cellular programs that these patterns represent. These models are illustrated in the analyses of RNA expression data from yeast and human during their cell cycle programs and DNA-binding data from yeast cell cycle transcription factors and replication initiation proteins. Two alternative pictures of RNA expression oscillations during the cell cycle that emerge from these analyses, which parallel well-known designs of physical oscillators, convey the capacity of the models to elucidate the design principles of cellular systems, as well as guide the design of synthetic ones. In these analyses, the power of the models to predict previously unknown biological principles is demonstrated with a prediction of a novel mechanism of regulation that correlates DNA replication initiation with cell cycle-regulated RNA transcription in yeast. These models may become the foundation of a future in which biological systems are modeled as physical systems are today.
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Affiliation(s)
- Orly Alter
- Department of Biomedical Engineering, Institute for Cellular and Molecular Biology and Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
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Yang WH, Dai DQ, Yan H. Biclustering of Microarray Data Based on Singular Value Decomposition. EMERGING TECHNOLOGIES IN KNOWLEDGE DISCOVERY AND DATA MINING 2007. [DOI: 10.1007/978-3-540-77018-3_21] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Alter O. Discovery of principles of nature from mathematical modeling of DNA microarray data. Proc Natl Acad Sci U S A 2006; 103:16063-4. [PMID: 17060616 PMCID: PMC1637536 DOI: 10.1073/pnas.0607650103] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Orly Alter
- Department of Biomedical Engineering, Institute for Cellular and Molecular Biology and Institute for Computational Engineering and Sciences, University of Texas, Austin, TX 78712, USA.
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