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Rana HK, Akhtar MR, Ahmed MB, Liò P, Quinn JM, Huq F, Moni MA. Genetic effects of welding fumes on the progression of neurodegenerative diseases. Neurotoxicology 2019; 71:93-101. [DOI: 10.1016/j.neuro.2018.12.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 12/03/2018] [Accepted: 12/05/2018] [Indexed: 12/14/2022]
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Salvatore JE, Han S, Farris SP, Mignogna KM, Miles MF, Agrawal A. Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder. Addict Biol 2019; 24:275-289. [PMID: 29316088 PMCID: PMC6037617 DOI: 10.1111/adb.12591] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Revised: 11/20/2017] [Accepted: 11/26/2017] [Indexed: 12/16/2022]
Abstract
Alcohol use disorder (AUD) is a heritable complex behavior. Due to the highly polygenic nature of AUD, identifying genetic variants that comprise this heritable variation has proved to be challenging. With the exception of functional variants in alcohol metabolizing genes (e.g. ADH1B and ALDH2), few other candidate loci have been confidently linked to AUD. Genome-wide association studies (GWAS) of AUD and other alcohol-related phenotypes have either produced few hits with genome-wide significance or have failed to replicate on further study. These issues reinforce the complex nature of the genetic underpinnings for AUD and suggest that both GWAS studies with larger samples and additional analysis approaches that better harness the nominally significant loci in existing GWAS are needed. Here, we review approaches of interest in the post-GWAS era, including in silico functional analyses; functional partitioning of single nucleotide polymorphism heritability; aggregation of signal into genes and gene networks; and validation of identified loci, genes and gene networks in postmortem brain tissue and across species. These integrative approaches hold promise to illuminate our understanding of the biological basis of AUD; however, we recognize that the main challenge continues to be the extremely polygenic nature of AUD, which necessitates large samples to identify multiple loci associated with AUD liability.
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Affiliation(s)
- Jessica E. Salvatore
- Department of Psychology; Virginia Commonwealth University; Richmond VA USA
- Virginia Institute for Psychiatric and Behavioral Genetics; Virginia Commonwealth University; Richmond VA USA
| | - Shizhong Han
- Department of Psychiatry; University of Iowa; Iowa City IA USA
- Department of Psychiatry and Behavioral Sciences; Johns Hopkins School of Medicine; Baltimore MD USA
| | - Sean P. Farris
- Waggoner Center for Alcohol and Addiction Research; The University of Texas at Austin; Austin TX USA
| | - Kristin M. Mignogna
- Virginia Institute for Psychiatric and Behavioral Genetics; Virginia Commonwealth University; Richmond VA USA
| | - Michael F. Miles
- Department of Pharmacology and Toxicology; Virginia Commonwealth University; Richmond VA USA
| | - Arpana Agrawal
- Department of Psychiatry; Washington University School of Medicine; Saint Louis MO USA
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53
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Iourov IY, Vorsanova SG, Yurov YB. Pathway-based classification of genetic diseases. Mol Cytogenet 2019; 12:4. [PMID: 30766616 PMCID: PMC6362588 DOI: 10.1186/s13039-019-0418-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 01/22/2019] [Indexed: 02/07/2023] Open
Abstract
Background In medical genetics, diseases are classified according to the nature (hypothetical nature) of the underlying genetic defect. The classification is “gene-centric” and “factor-centric”; a disease may be, thereby, designated as monogenic, oligogenic or polygenic/multifactorial. Chromosomal diseases/syndromes and abnormalities are generally considered apart from these designations due to distinctly different formation mechanisms and simultaneous encompassing from several to several hundreds of co-localized genes. These definitions are ubiquitously used and are perfectly suitable for human genetics issues in historical and academic perspective. However, recent achievements in systems biology have offered a possibility to explore the consequences of a genetic defect from genomic variations to molecular/cellular pathway alterations unique to a disease. Since pathogenetic mechanisms (pathways) are more influential on our understating of disease presentation and progression than genetic defects per se, a need for a disease classification reflecting both genetic causes and molecular/cellular mechanisms appears to exist. Here, we propose an extension to the common disease classification based on the underlying genetic defects, which focuses on disease-specific molecular pathways. Conclusion The basic idea of our classification is to propose pathways as parameters for designating a genetic disease. To proceed, we have followed the tradition of using ancient Greek words and prefixes to create the terms for the pathway-based classification of genetic diseases. We have chosen the word “griphos” (γρῖφος), which simultaneously means “net” and “puzzle”, accurately symbolizing the term “pathway” currently used in molecular biology and medicine. Thus, diseases may be classified as monogryphic (single pathway is altered to result in a phenotype), digryphic (two pathways are altered to result in a phenotype), etc.; additionally, diseases may be designated as oligogryphic (several pathways are altered to result in a phenotype), polygryphic (numerous pathways or cascades of pathways are altered to result in a phenotype) and homeogryphic in cases of comorbid diseases resulted from shared pathway alterations. We suppose that classifying illness this way using both “gene-centric” and “pathway-centric” concepts is able to revolutionize current views on genetic diseases.
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Affiliation(s)
- Ivan Y Iourov
- Mental Health Research Center, 117152 Moscow, Russia.,2Veltischev Research and Clinical Institute for Pediatrics of the Pirogov Russian National Research Medical University, Ministry of Health of Russian Federation, 125412 Moscow, Russia.,Department of Medical Genetics, Russian Medical Academy of Continuous Professional Education, Moscow, 125993 Russia
| | - Svetlana G Vorsanova
- Mental Health Research Center, 117152 Moscow, Russia.,2Veltischev Research and Clinical Institute for Pediatrics of the Pirogov Russian National Research Medical University, Ministry of Health of Russian Federation, 125412 Moscow, Russia
| | - Yuri B Yurov
- Mental Health Research Center, 117152 Moscow, Russia.,2Veltischev Research and Clinical Institute for Pediatrics of the Pirogov Russian National Research Medical University, Ministry of Health of Russian Federation, 125412 Moscow, Russia
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Alaimo S, Micale G, La Ferlita A, Ferro A, Pulvirenti A. Computational Methods to Investigate the Impact of miRNAs on Pathways. Methods Mol Biol 2019; 1970:183-209. [PMID: 30963494 DOI: 10.1007/978-1-4939-9207-2_11] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Pathway analysis is a wide class of methods allowing to determine the alteration of functional processes in complex diseases. However, biological pathways are still partial, and knowledge coming from posttranscriptional regulators has started to be considered in a systematic way only recently. Here we will give a global and updated view of the main pathway and subpathway analysis methodologies, focusing on the improvements obtained through the recent introduction of microRNAs as regulatory elements in these frameworks.
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Affiliation(s)
- Salvatore Alaimo
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Giovanni Micale
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | | | - Alfredo Ferro
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Alfredo Pulvirenti
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy.
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Hossain MA, Asa TA, Rahman MR, Moni MA. Network-based approach to identify key candidate genes and pathways shared by thyroid cancer and chronic kidney disease. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100240] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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56
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Hao J, Kim Y, Kim TK, Kang M. PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data. BMC Bioinformatics 2018; 19:510. [PMID: 30558539 PMCID: PMC6296065 DOI: 10.1186/s12859-018-2500-z] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 11/16/2018] [Indexed: 12/13/2022] Open
Abstract
Background Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data. Results In this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes and pathways to predict clinical outcomes by leveraging deep learning. The sparse solution of PASNet provides the capability of model interpretability that most conventional fully-connected neural networks lack. We applied PASNet for long-term survival prediction in Glioblastoma multiforme (GBM), which is a primary brain cancer that shows poor prognostic performance. The predictive performance of PASNet was evaluated with multiple cross-validation experiments. PASNet showed a higher Area Under the Curve (AUC) and F1-score than previous long-term survival prediction classifiers, and the significance of PASNet’s performance was assessed by Wilcoxon signed-rank test. Furthermore, the biological pathways, found in PASNet, were referred to as significant pathways in GBM in previous biology and medicine research. Conclusions PASNet can describe the different biological systems of clinical outcomes for prognostic prediction as well as predicting prognosis more accurately than the current state-of-the-art methods. PASNet is the first pathway-based deep neural network that represents hierarchical representations of genes and pathways and their nonlinear effects, to the best of our knowledge. Additionally, PASNet would be promising due to its flexible model representation and interpretability, embodying the strengths of deep learning. The open-source code of PASNet is available at https://github.com/DataX-JieHao/PASNet.
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Affiliation(s)
- Jie Hao
- Kennesaw State University, Kennesaw, USA
| | | | - Tae-Kyung Kim
- University of Texas Southwestern Medical Center, Dallas, USA.,Department of Life Sciences, Pohang Institute of Science and Technology (POSTECH), Dallas, USA
| | - Mingon Kang
- Kennesaw State University, Kennesaw, USA. .,Kennesaw State University, Marietta, USA.
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Abstract
Background Understanding the effect of human genetic variations on disease can provide insight into phenotype-genotype relationships, and has great potential for improving the effectiveness of personalized medicine. While some genetic markers linked to disease susceptibility have been identified, a large number are still unknown. In this paper, we propose a pathway-based approach to extend disease-variant associations and find new molecular connections between genetic mutations and diseases. Methods We used a compilation of over 80,000 human genetic variants with known disease associations from databases including the Online Mendelian Inheritance in Man (OMIM), Clinical Variance database (ClinVar), Universal Protein Resource (UniProt), and Human Gene Mutation Database (HGMD). Furthermore, we used the Unified Medical Language System (UMLS) to normalize variant phenotype terminologies, mapping 87% of unique genetic variants to phenotypic disorder concepts. Lastly, variants were grouped by UMLS Medical Subject Heading (MeSH) identifiers to determine pathway enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results By linking KEGG pathways through underlying variant associations, we elucidated connections between the human genetic variant-based disease phenome and metabolic pathways, finding novel disease connections not otherwise detected through gene-level analysis. When looking at broader disease categories, our network analysis showed that large complex diseases, such as cancers, are highly linked by their common pathways. In addition, we found Cardiovascular Diseases and Skin and Connective Tissue Diseases to have the highest number of common pathways, among 35 significant main disease category (MeSH) pairings. Conclusions This study constitutes an important contribution to extending disease-variant connections and new molecular links between diseases. Novel disease connections were made by disease-pathway associations not otherwise detected through single-gene analysis. For instance, we found that mutations in different genes associated to Noonan Syndrome and Essential Hypertension share a common pathway. This analysis also provides the foundation to build novel disease-drug networks through their underlying common metabolic pathways, thus enabling new diagnostic and therapeutic interventions. Electronic supplementary material The online version of this article (10.1186/s12920-018-0386-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ann G Cirincione
- Department of Biological Sciences, University of Maryland, Baltimore County (UMBC), Baltimore, MD, 21250, USA
| | - Kaylyn L Clark
- Department of Biological Sciences, University of Maryland, Baltimore County (UMBC), Baltimore, MD, 21250, USA
| | - Maricel G Kann
- Department of Biological Sciences, University of Maryland, Baltimore County (UMBC), Baltimore, MD, 21250, USA.
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Cui P, Ma X, Li H, Lang W, Hao J. Shared Biological Pathways Between Alzheimer's Disease and Ischemic Stroke. Front Neurosci 2018; 12:605. [PMID: 30245614 PMCID: PMC6137293 DOI: 10.3389/fnins.2018.00605] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Accepted: 08/10/2018] [Indexed: 12/21/2022] Open
Abstract
Alzheimer's disease (AD) and ischemic stroke (IS) are an immense socioeconomic burden worldwide. There is a possibility that shared genetic factors lead to their links at epidemiological and pathophysiological levels. Although recent genome-wide association studies (GWAS) have provided profound insights into the genetics of AD and IS, no shared genetic variants have been identified to date. This prompted us to initiate this study, which sought to identify shared pathways linking AD and IS. We took advantage of large-scale GWAS summary data of AD (17,008 AD cases and 37,154 controls) and IS (10,307 cases and 19,326 controls) to conduct pathway analyses using genetic pathways from multiple well-studied databases, including GO, KEGG, PANTHER, Reactome, and Wikipathways. Collectively, we discovered that AD and IS shared 179 GO categories (56 biological processes, 95 cellular components, and 28 molecular functions); and the following pathways: six KEGG pathways; two PANTHER pathways; four Reactome pathways; and one in Wikipathways pathway. The more fine-grained GO terms were mainly summarized into different functional categories: transcriptional and post-transcriptional regulation, synapse, endocytic membrane traffic through the endosomal system, signaling transduction, immune process, multi-organism process, protein catabolic metabolism, and cell adhesion. The shared pathways were roughly classified into three categories: immune system; cancer (NSCLC and glioma); and signal transduction pathways involving the cadherin signaling pathway, Wnt signaling pathway, G-protein signaling and downstream signaling mediated by phosphoinositides (PIPs). The majority of these common pathways linked to both AD and IS were supported by convincing evidence from the literature. In conclusion, our findings contribute to a better understanding of common biological mechanisms underlying AD and IS and serve as a guide to direct future research.
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Affiliation(s)
- Pan Cui
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.,Key Laboratory of Post-neurotrauma Neuro-repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Ministry of Education and Tianjin City, Tianjin, China
| | - Xiaofeng Ma
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.,Key Laboratory of Post-neurotrauma Neuro-repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Ministry of Education and Tianjin City, Tianjin, China
| | - He Li
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.,Key Laboratory of Post-neurotrauma Neuro-repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Ministry of Education and Tianjin City, Tianjin, China
| | - Wenjing Lang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.,Key Laboratory of Post-neurotrauma Neuro-repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Ministry of Education and Tianjin City, Tianjin, China
| | - Junwei Hao
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.,Key Laboratory of Post-neurotrauma Neuro-repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Ministry of Education and Tianjin City, Tianjin, China
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Complex Haplotypes of GSTM1 Gene Deletions Harbor Signatures of a Selective Sweep in East Asian Populations. G3-GENES GENOMES GENETICS 2018; 8:2953-2966. [PMID: 30061374 PMCID: PMC6118300 DOI: 10.1534/g3.118.200462] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The deletion of the metabolizing Glutathione S-transferase Mu 1 (GSTM1) gene has been associated with multiple cancers, metabolic and autoimmune disorders, as well as drug response. It is unusually common, with allele frequency reaching up to 75% in some human populations. Such high allele frequency of a derived allele with apparent impact on an otherwise conserved gene is a rare phenomenon. To investigate the evolutionary history of this locus, we analyzed 310 genomes using population genetics tools. Our analysis revealed a surprising lack of linkage disequilibrium between the deletion and the flanking single nucleotide variants in this locus. Tests that measure extended homozygosity and rapid change in allele frequency revealed signatures of an incomplete sweep in the locus. Using empirical approaches, we identified the Tanuki haplogroup, which carries the GSTM1 deletion and is found in approximately 70% of East Asian chromosomes. This haplogroup has rapidly increased in frequency in East Asian populations, contributing to a high population differentiation among continental human groups. We showed that extended homozygosity and population differentiation for this haplogroup is incompatible with simulated neutral expectations in East Asian populations. In parallel, we found that the Tanuki haplogroup is significantly associated with the expression levels of other GSTM genes. Collectively, our results suggest that standing variation in this locus has likely undergone an incomplete sweep in East Asia with regulatory impact on multiple GSTM genes. Our study provides the necessary framework for further studies to elucidate the evolutionary reasons that maintain disease-susceptibility variants in the GSTM1 locus.
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60
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Gainey SJ, Horn GP, Towers AE, Oelschlager ML, Tir VL, Drnevich J, Fent KW, Kerber S, Smith DL, Freund GG. Exposure to a firefighting overhaul environment without respiratory protection increases immune dysregulation and lung disease risk. PLoS One 2018; 13:e0201830. [PMID: 30130361 PMCID: PMC6103500 DOI: 10.1371/journal.pone.0201830] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 07/23/2018] [Indexed: 01/29/2023] Open
Abstract
Firefighting activities appear to increase the risk of acute and chronic lung disease, including malignancy. While self-contained breathing apparatuses (SCBA) mitigate exposures to inhalable asphyxiates and carcinogens, firefighters frequently remove SCBA during overhaul when the firegrounds appear clear of visible smoke. Using a mouse model of overhaul without airway protection, the impact of fireground environment exposure on lung gene expression was assessed to identify transcripts potentially critical to firefighter-related chronic pulmonary illnesses. Lung tissue was collected 2 hrs post-overhaul and evaluated via whole genome transcriptomics by RNA-seq. Although gas metering showed that the fireground overhaul levels of carbon monoxide (CO), carbon dioxide (CO2), hydrogen cyanine (HCN), hydrogen sulfide (H2S) and oxygen (O2) were within NIOSH ceiling recommendations, 3852 lung genes were differentially expressed when mice exposed to overhaul were compared to mice on the fireground but outside the overhaul environment. Importantly, overhaul exposure was associated with an up/down-regulation of 86 genes with a fold change of 1.5 or greater (p<0.5) including the immunomodulatory-linked genes S100a8 and Tnfsf9 (downregulation) and the cancer-linked genes, Capn11 and Rorc (upregulation). Taken together these findings indicate that, without respiratory protection, exposure to the fireground overhaul environment is associated with transcriptional changes impacting proteins potentially related to inflammation-associated lung disease and cancer.
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Affiliation(s)
- Stephen J. Gainey
- Department of Animal Sciences, University of Illinois, Urbana, Illinois, United States of America
| | - Gavin P. Horn
- Illinois Fire Service Institute, Champaign, Illinois, United States of America
| | - Albert E. Towers
- Division of Nutritional Sciences, University of Illinois, Urbana, Illinois, United States of America
| | - Maci L. Oelschlager
- Department of Pathology, Program in Integrative Immunology and Behavior, University of Illinois College of Medicine, Urbana, Illinois, United States of America
| | - Vincent L. Tir
- Department of Pathology, Program in Integrative Immunology and Behavior, University of Illinois College of Medicine, Urbana, Illinois, United States of America
| | - Jenny Drnevich
- Roy J. Carver Biotechnology Center, University of Illinois, Urbana, Illinois, United States of America
| | - Kenneth W. Fent
- Division of Surveillance, Hazard Evaluations, and Field Studies, National Institute for Occupational Safety and Health, Cincinnati, Ohio, United States of America
| | - Stephen Kerber
- Director, UL Firefighter Safety Research Institute, Columbia, Maryland, United States of America
| | - Denise L. Smith
- Illinois Fire Service Institute, Champaign, Illinois, United States of America
- Department of Health and Human Physiological Sciences, Skidmore College, Saratoga Spring, New York, United States of America
| | - Gregory G. Freund
- Department of Animal Sciences, University of Illinois, Urbana, Illinois, United States of America
- Division of Nutritional Sciences, University of Illinois, Urbana, Illinois, United States of America
- Department of Pathology, Program in Integrative Immunology and Behavior, University of Illinois College of Medicine, Urbana, Illinois, United States of America
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Bidirectional Transcriptome Analysis of Rat Bone Marrow-Derived Mesenchymal Stem Cells and Activated Microglia in an In Vitro Coculture System. Stem Cells Int 2018; 2018:6126413. [PMID: 30151012 PMCID: PMC6087576 DOI: 10.1155/2018/6126413] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 05/15/2018] [Accepted: 05/24/2018] [Indexed: 02/07/2023] Open
Abstract
Microglia contribute to the regulation of neuroinflammation and play an important role in the pathogenesis of brain diseases. Thus, regulation of neuroinflammation triggered by activated microglia in brain diseases has become a promising curative strategy. Bone marrow-derived mesenchymal stem cells (BM-MSCs) have been shown to have therapeutic effects, resulting from the regulation of inflammatory conditions in the brain. In this study, we investigated differential gene expression in rat BM-MSCs (rBM-MSCs) that were cocultured with lipopolysaccharide- (LPS-) stimulated primary rat microglia using microarray analysis and evaluated the functional relationships through Ingenuity Pathway Analysis (IPA). We also evaluated the effects of rBM-MSC on LPS-stimulated microglia using a reverse coculture system and the same conditions of the transcriptomic analysis. In the transcriptome of rBM-MSCs, 67 genes were differentially expressed, which were highly related with migration of cells, compared to control. The prediction of the gene network using IPA and experimental validation showed that LPS-stimulated primary rat microglia increase the migration of rBM-MSCs. Reversely, expression patterns of the transcriptome in LPS-stimulated primary rat microglia were changed when cocultured with rBM-MSCs. Our results showed that 65 genes were changed, which were highly related with inflammatory response, compared to absence of rBM-MSCs. In the same way with the aforementioned, the prediction of the gene network and experimental validation showed that rBM-MSCs decrease the inflammatory response of LPS-stimulated primary rat microglia. Our data indicate that LPS-stimulated microglia increase the migration of rBM-MSCs and that rBM-MSCs reduce the inflammatory activity in LPS-stimulated microglia. The results of this study show complex mechanisms underlying the interaction between rBM-MSCs and activated microglia and may be helpful for the development of stem cell-based strategies for brain diseases.
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Chiu CY, Chen TP, Chen JR, Wang CJ, Yin SY, Lai SH, Wong KS. Overexpression of matrix metalloproteinase-9 in adolescents with primary spontaneous pneumothorax for surgical intervention. J Thorac Cardiovasc Surg 2018; 156:2328-2336.e2. [PMID: 30033106 DOI: 10.1016/j.jtcvs.2018.05.083] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2017] [Revised: 05/06/2018] [Accepted: 05/09/2018] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To determine gene expression profiles associated with bullae formation in patients with primary spontaneous pneumothorax and to identify candidate genes associated with surgical intervention. METHODS Twenty-four adolescents with primary spontaneous pneumothorax were enrolled prospectively. A global gene expression analysis of 9 paired lung biopsies (lesion and normal adjacent sites) was performed to identify differentially expressed genes associated with spontaneous pneumothorax. Pathway and network analysis was performed using the Database for Annotation, Visualization and Integrated Discovery web tool. Candidate genes and encoding proteins were assessed in blood samples and compared between patients with pneumothorax and healthy control patients. RESULTS A total of 1519 differentially expressed transcripts corresponding to known genes were identified comparing the lesion lung with paired adjacent normal lung. The altered genes were mainly associated with focal adhesion and extracellular matrix-receptor interaction pathways. Genes involved in proteolysis and peptidase activity were up-regulated predominantly, especially matrix metalloproteinase-1 and -9 genes. Compared with the recovery stage, blood levels of matrix metalloproteinase-9/tissue inhibitor of metalloproteinase-1 were increased at the acute stage in patients with pneumothorax and, when compared between patients treated operatively with those treated nonoperatively, were also significantly greater. In addition, ratios of their serum levels were significantly greater in patients with pneumothorax compared with healthy control patients. Furthermore, matrix metalloproteinase-9 was predominantly overexpressed in neutrophils, alveolar macrophages, and mesothelial cells of lung biopsies. CONCLUSIONS An imbalance of cell-extracellular matrix interactions appears to be associated with primary spontaneous pneumothorax. Matrix metalloproteinase-9 overexpression may particularly play a role in contributing to pleural porosity for surgical intervention.
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Affiliation(s)
- Chih-Yung Chiu
- Department of Pediatrics, Chang Gung Memorial Hospital at Keelung, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Division of Pediatric Pulmonology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Tzu-Ping Chen
- Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Thoracic & Cardiovascular Surgery, Chang Gung Memorial Hospital at Keelung, Keelung City, Taiwan.
| | - Jim-Ray Chen
- Department of Pathology, Chang Gung Memorial Hospital at Keelung, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chia-Jung Wang
- Department of Pediatrics, Chang Gung Memorial Hospital at Keelung, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Shun-Ying Yin
- Department of Thoracic & Cardiovascular Surgery, Chang Gung Memorial Hospital at Keelung, Keelung City, Taiwan
| | - Shen-Hao Lai
- Division of Pediatric Pulmonology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Kin-Sun Wong
- Division of Pediatric Pulmonology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
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Ahmad K, Baig MH, Mushtaq G, Kamal MA, Greig NH, Choi I. Commonalities in Biological Pathways, Genetics, and Cellular Mechanism between Alzheimer Disease and Other Neurodegenerative Diseases: An In Silico-Updated Overview. Curr Alzheimer Res 2018; 14:1190-1197. [PMID: 28164765 DOI: 10.2174/1567205014666170203141151] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Revised: 12/07/2016] [Accepted: 01/30/2016] [Indexed: 12/29/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common and well-studied neurodegenerative disease (ND). Biological pathways, pathophysiology and genetics of AD show commonalities with other NDs viz. Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), Prion disease and Dentatorubral-pallidoluysian atrophy (DRPLA). Many of the NDs, sharing the common features and molecular mechanisms suggest that pathology may be directly comparable and be implicated in disease prevention and development of highly effective therapies. METHOD In this review, a brief description of pathophysiology, clinical symptoms and available treatment of various NDs have been explored with special emphasis on AD. Commonalities in these fatal NDs provide support for therapeutic advancements and enhance the understanding of disease manifestation. CONCLUSION The studies concentrating on the commonalities in biological pathways, cellular mechanisms and genetics may provide the scope to researchers to identify few novel common target(s) for disease prevention and development of effective common drugs for multi-neurodegenerative diseases.
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Affiliation(s)
- Khurshid Ahmad
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan. Korea
| | - Mohammad Hassan Baig
- Department of Medical Biotechnology, Yeungnam University, 280 Daehak-ro, Gyeongsan, Gyeongbuk 712-749. Korea
| | - Gohar Mushtaq
- Department of Biochemistry, College of Science, King Abdulaziz University, Jeddah 21589. Saudi Arabia
| | - Mohammad Amjad Kamal
- King Fahd Medical Research Centre, King Abdulaziz University, P.O. Box 80216, Jeddah 21589. Saudi Arabia
| | - Nigel H Greig
- Drug Design & Development Section, Translational Gerontology Branch, Intramural Research Program, National, Institute on Aging, National Institutes of Health, Biomedical Research Center, 251 Bayview Boulevard, Baltimore, MD 21224. United States
| | - Inho Choi
- Department of Medical Biotechnology, Yeungnam University, 280 Daehak-ro, Gyeongsan, Gyeongbuk 712-749. Korea
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De Meulder B, Lefaudeux D, Bansal AT, Mazein A, Chaiboonchoe A, Ahmed H, Balaur I, Saqi M, Pellet J, Ballereau S, Lemonnier N, Sun K, Pandis I, Yang X, Batuwitage M, Kretsos K, van Eyll J, Bedding A, Davison T, Dodson P, Larminie C, Postle A, Corfield J, Djukanovic R, Chung KF, Adcock IM, Guo YK, Sterk PJ, Manta A, Rowe A, Baribaud F, Auffray C. A computational framework for complex disease stratification from multiple large-scale datasets. BMC SYSTEMS BIOLOGY 2018; 12:60. [PMID: 29843806 PMCID: PMC5975674 DOI: 10.1186/s12918-018-0556-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 02/21/2018] [Indexed: 01/05/2023]
Abstract
BACKGROUND Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.
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Affiliation(s)
- Bertrand De Meulder
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France.
| | - Diane Lefaudeux
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Aruna T Bansal
- Acclarogen Ltd, St John's Innovation Centre, Cambridge, CB4 OWS, UK
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Amphun Chaiboonchoe
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Hassan Ahmed
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Irina Balaur
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Mansoor Saqi
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Johann Pellet
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Stéphane Ballereau
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Nathanaël Lemonnier
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Kai Sun
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | - Ioannis Pandis
- Data Science Institute, Imperial College, London, SW7 2AZ, UK.,Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | - Xian Yang
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | | | | | | | | | - Timothy Davison
- Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | - Paul Dodson
- AstraZeneca Ltd, Alderley Park, Macclesfield, SK10 4TG, UK
| | | | - Anthony Postle
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
| | - Julie Corfield
- AstraZeneca R & D, 43150, Mölndal, Sweden.,Arateva R & D Ltd, Nottingham, NG1 1GF, UK
| | - Ratko Djukanovic
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
| | - Kian Fan Chung
- National Hearth and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Ian M Adcock
- National Hearth and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Yi-Ke Guo
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | - Peter J Sterk
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, AZ1105, The Netherlands
| | - Alexander Manta
- Research Informatics, Roche Diagnostics GmbH, 82008, Unterhaching, Germany
| | - Anthony Rowe
- Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | | | - Charles Auffray
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France.
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Al–Taie Z, Thanintorn N, Ersoy I, Kholod O, Taylor K, Hammer R, Shin D. REDESIGN: RDF-based Differential Signaling Framework for Precision Medicine Analytics. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2018; 2017:35-44. [PMID: 29888036 PMCID: PMC5961787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Pathway-based analysis holds promise to be instrumental in precision and personalized medicine analytics. However, the majority of pathway-based analysis methods utilize "fixed" or "rigid" data sets that limit their ability to account for complex biological inter-dependencies. Here, we present REDESIGN: RDF-based Differential Signaling Pathway informatics framework. The distinctive feature of the REDESIGN is that it is designed to run on "flexible" ontology-enabled data sets of curated signal transduction pathway maps to uncover high explanatory differential pathway mechanisms on gene-to-gene level. The experiments on two morphoproteomic cases demonstrated REDESIGN's capability to generate actionable hypotheses in precision/personalized medicine analytics.
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Affiliation(s)
- Zainab Al–Taie
- MU Informatics Institute, University of Missouri, Columbia, MO,Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Nattapon Thanintorn
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Ilker Ersoy
- MU Informatics Institute, University of Missouri, Columbia, MO,Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Olha Kholod
- MU Informatics Institute, University of Missouri, Columbia, MO,Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Kristen Taylor
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Richard Hammer
- MU Informatics Institute, University of Missouri, Columbia, MO,Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Dmitriy Shin
- MU Informatics Institute, University of Missouri, Columbia, MO,Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
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Nishiwaki H, Ito M, Negishi S, Sobue S, Ichihara M, Ohno K. Molecular hydrogen upregulates heat shock response and collagen biosynthesis, and downregulates cell cycles: meta-analyses of gene expression profiles. Free Radic Res 2018; 52:434-445. [PMID: 29424253 DOI: 10.1080/10715762.2018.1439166] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Molecular hydrogen exerts its effect on multiple pathologies, including oxidative stress, inflammation, and apoptosis. However, its molecular mechanisms have not been fully elucidated. In order to explore the effects of molecular hydrogen, we meta-analysed gene expression profiles modulated by molecular hydrogen. We performed microarray analysis of the mouse liver with or without drinking hydrogen water. We also integrated two previously reported microarray datasets of the rat liver into meta-analyses. We used two categories of meta-analysis methods: the cross-platform method and the conventional meta-analysis method (Fisher's method). For each method, hydrogen-modulated pathways were analysed by (i) the hypergeometric test (HGT) in the class of over-representation analysis (ORA), (ii) the gene set enrichment analysis (GSEA) in the class of functional class scoring (FCS), and (iii) the signalling pathway impact analysis (SPIA), pathway regulation score (PRS), and others in the class of pathway topology-based approach (PTA). Pathways in the collagen biosynthesis and the heat-shock response were up-regulated according to (a) HGT with the cross-platform method, (b) GSEA with the cross-platform method, and (c) PRS with the cross-platform method. Pathways in cell cycles were down-regulated according to (a) HGT with the cross-platform method, (b) GSEA with the cross-platform method, and (d) GSEA with the conventional meta-analysis method. Because the heat-shock response leads to up-regulation of collagen biosynthesis and a transient arrest of cell cycles, induction of the heat-shock response is likely to be a primary event induced by molecular hydrogen in the liver of wild-type rodents.
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Affiliation(s)
- Hiroshi Nishiwaki
- a Division of Neurogenetics , Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine , Nagoya , Japan
| | - Mikako Ito
- a Division of Neurogenetics , Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine , Nagoya , Japan
| | - Shuto Negishi
- a Division of Neurogenetics , Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine , Nagoya , Japan
| | - Sayaka Sobue
- b Department of Biomedical Sciences , College of Life and Health Sciences, Chubu University , Kasugai , Japan
| | - Masatoshi Ichihara
- b Department of Biomedical Sciences , College of Life and Health Sciences, Chubu University , Kasugai , Japan
| | - Kinji Ohno
- a Division of Neurogenetics , Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine , Nagoya , Japan
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Lee HS, Park T. Nuclear receptor and VEGF pathways for gene-blood lead interactions, on bone mineral density, in Korean smokers. PLoS One 2018; 13:e0193323. [PMID: 29518117 PMCID: PMC5843219 DOI: 10.1371/journal.pone.0193323] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 02/08/2018] [Indexed: 11/19/2022] Open
Abstract
Osteoporosis has a complex etiology and is considered a multifactorial polygenic disease, in which genetic determinants are modulated by hormonal, lifestyle, environmental, and nutritional factors. Therefore, investigating these multiple factors, and the interactions between them, might lead to a better understanding of osteoporosis pathogenesis, and possible therapeutic interventions. The objective of this study was to identify the relationship between three blood metals (Pb, Cd, and Al), in smoking and nonsmoking patients' sera, and prevalence of osteoporosis. In particular, we focused on gene-environment interactions of metal exposure, including a dataset obtained through genome-wide association study (GWAS). Subsequently, we conducted a pathway-based analysis, using a GWAS dataset, to elucidate how metal exposure influences susceptibility to osteoporosis. In this study, we evaluated blood metal exposures for estimating the prevalence of osteoporosis in 443 participants (aged 53.24 ± 8.29), from the Republic of Korea. Those analyses revealed a negative association between lead blood levels and bone mineral density in current smokers (p trend <0.01). By further using GWAS-based pathway analysis, we found nuclear receptor (FDR<0.05) and VEGF pathways (FDR<0.05) to be significantly upregulated by blood lead burden, with regard to the prevalence of osteoporosis, in current smokers. These findings suggest that the intracellular pathways of angiogenesis and nuclear hormonal signaling can modulate interactions between lead exposure and genetic variation, with regard to susceptibility to diminished bone mineral density. Our findings may provide new leads for understanding the mechanisms underlying the development of osteoporosis, including possible interventions.
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Affiliation(s)
- Ho-Sun Lee
- Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Gwanak 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea
- Daegu Institution, National Forensic Service, Hogukro, Waegwon-eup, Chilgok-gun, Gyeomgsamgbuk-do, Republic of Korea
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Gwanak 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea
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Nguyen HH, Tilton SC, Kemp CJ, Song M. Nonmonotonic Pathway Gene Expression Analysis Reveals Oncogenic Role of p27/Kip1 at Intermediate Dose. Cancer Inform 2017; 16:1176935117740132. [PMID: 29162974 PMCID: PMC5692148 DOI: 10.1177/1176935117740132] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2017] [Accepted: 09/16/2017] [Indexed: 11/15/2022] Open
Abstract
The mechanistic basis by which the level of p27Kip1 expression influences tumor aggressiveness and patient mortality remains unclear. To elucidate the competing tumor-suppressing and oncogenic effects of p27Kip1 on gene expression in tumors, we analyzed the transcriptomes of squamous cell papilloma derived from Cdkn1b nullizygous, heterozygous, and wild-type mice. We developed a novel functional pathway analysis method capable of testing directional and nonmonotonic dose response. This analysis can reveal potential causal relationships that might have been missed by other nondirectional pathway analysis methods. Applying this method to capture dose-response curves in papilloma gene expression data, we show that several known cancer pathways are dominated by low-high-low gene expression responses to increasing p27 gene doses. The oncogene cyclin D1, whose expression is elevated at an intermediate p27 dose, is the most responsive gene shared by these cancer pathways. Therefore, intermediate levels of p27 may promote cellular processes favoring tumorigenesis-strikingly consistent with the dominance of heterozygous mutations in CDKN1B seen in human cancers. Our findings shed new light on regulatory mechanisms for both pro- and anti-tumorigenic roles of p27Kip1. Functional pathway dose-response analysis provides a unique opportunity to uncover nonmonotonic patterns in biological systems.
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Affiliation(s)
- Hien H Nguyen
- Department of Computer Science, New Mexico State University, Las Cruces, NM, USA
| | - Susan C Tilton
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, USA
| | - Christopher J Kemp
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Mingzhou Song
- Department of Computer Science, New Mexico State University, Las Cruces, NM, USA
- Mingzhou Song, Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA.
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69
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Buas MF, He Q, Johnson LG, Onstad L, Levine DM, Thrift AP, Gharahkhani P, Palles C, Lagergren J, Fitzgerald RC, Ye W, Caldas C, Bird NC, Shaheen NJ, Bernstein L, Gammon MD, Wu AH, Hardie LJ, Pharoah PD, Liu G, Iyer P, Corley DA, Risch HA, Chow WH, Prenen H, Chegwidden L, Love S, Attwood S, Moayyedi P, MacDonald D, Harrison R, Watson P, Barr H, deCaestecker J, Tomlinson I, Jankowski J, Whiteman DC, MacGregor S, Vaughan TL, Madeleine MM. Germline variation in inflammation-related pathways and risk of Barrett's oesophagus and oesophageal adenocarcinoma. Gut 2017; 66:1739-1747. [PMID: 27486097 PMCID: PMC5296402 DOI: 10.1136/gutjnl-2016-311622] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 06/22/2016] [Accepted: 07/02/2016] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Oesophageal adenocarcinoma (OA) incidence has risen sharply in Western countries over recent decades. Local and systemic inflammation is considered an important contributor to OA pathogenesis. Established risk factors for OA and its precursor, Barrett's oesophagus (BE), include symptomatic reflux, obesity and smoking. The role of inherited genetic susceptibility remains an area of active investigation. Here, we explore whether germline variation related to inflammatory processes influences susceptibility to BE/OA. DESIGN We used data from a genomewide association study of 2515 OA cases, 3295 BE cases and 3207 controls. Our analysis included 7863 single-nucleotide polymorphisms (SNPs) in 449 genes assigned to five pathways: cyclooxygenase (COX), cytokine signalling, oxidative stress, human leucocyte antigen and nuclear factor-κB. A principal components-based analytic framework was employed to evaluate pathway-level and gene-level associations with disease risk. RESULTS We identified a significant signal for the COX pathway in relation to BE risk (p=0.0059, false discovery rate q=0.03), and in gene-level analyses found an association with microsomal glutathione-S-transferase 1 (MGST1); (p=0.0005, q=0.005). Assessment of 36 MGST1 SNPs identified 14 variants associated with elevated BE risk (q<0.05). Four of these were subsequently confirmed (p<5.5×10-5) in a meta-analysis encompassing an independent set of 1851 BE cases and 3496 controls, and are known strong expression quantitative trait loci for MGST1. Three such variants were associated with similar elevations in OA risk. CONCLUSIONS This study provides the most comprehensive evaluation of inflammation-related germline variation in relation to risk of BE/OA and suggests that variants in MGST1 influence disease susceptibility.
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Affiliation(s)
- Matthew F. Buas
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Qianchuan He
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Lisa G. Johnson
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Lynn Onstad
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - David M. Levine
- Department of Biostatistics, University of Washington, School of Public Health, Seattle, Washington, USA
| | - Aaron P. Thrift
- Department of Medicine and Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Puya Gharahkhani
- Queensland Institute of Medical Research Berghofer Medical Research Institute Brisbane, Queensland, Australia
| | - Claire Palles
- Wellcome Trust Centre for Human Genetics and NIHR Comprehensive Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Jesper Lagergren
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Division of Cancer Studies, King’s College London, United Kingdom
| | - Rebecca C. Fitzgerald
- Medical Research Council (MRC) MRC Cancer Unit, Hutchison-MRC Research Centre, University of Cambridge, Cambridge, UK
| | - Weimin Ye
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Carlos Caldas
- Cancer Research UK, Cambridge Institute, Cambridge, UK
| | - Nigel C. Bird
- Department of Oncology, Medical School, University of Sheffield, Sheffield, UK
| | - Nicholas J. Shaheen
- Division of Gastroenterology and Hepatology, University of North Carolina School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Leslie Bernstein
- Department of Population Sciences, Beckman Research Institute and City of Hope Comprehensive Cancer Center, Duarte, California, USA
| | - Marilie D. Gammon
- Department of Epidemiology, University of North Carolina, Chapel Hill,North Carolina, USA
| | - Anna H. Wu
- Department of Preventive Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, USA
| | | | - Paul D. Pharoah
- Department of Oncology, University of Cambridge, Cambridge, UK; Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Geoffrey Liu
- Pharmacogenomic Epidemiology, Ontario Cancer Institute, Toronto, Ontario, Canada M5G 2M9
| | - Prassad Iyer
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A. Corley
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
- San Francisco Medical Center, Kaiser Permanente Northern California, San Francisco, California, USA
| | - Harvey A. Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Wong-Ho Chow
- Department of Epidemiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Hans Prenen
- Department of Digestive Oncology, University Hospital Gasthuisberg, Leuven, Belgium
| | - Laura Chegwidden
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - Sharon Love
- Centre for Statistics in Medicine and Oxford Clinical Trials Research Unit, Oxford, UK
| | - Stephen Attwood
- Department of General Surgery, North Tyneside General Hospital, North Shields, UK
| | - Paul Moayyedi
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - David MacDonald
- Department of Oral Biological and Medical Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Rebecca Harrison
- Department of Pathology, Leicester Royal Infirmary, Leicester, UK
| | - Peter Watson
- Department of Medicine, Institute of Clinical Science, Royal Victoria Hospital, Belfast, UK
| | - Hugh Barr
- Department of Upper GI Surgery, Gloucestershire Royal Hospital, Gloucester, UK
| | - John deCaestecker
- Department of Gastroenterology, Leicester General Hospital, Leicester, UK
| | - Ian Tomlinson
- Wellcome Trust Centre for Human Genetics and NIHR Comprehensive Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Janusz Jankowski
- University Hospitals Coventry and Warwickshire and University of Warwick, Coventry, UK
| | - David C. Whiteman
- Cancer Control, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Stuart MacGregor
- Queensland Institute of Medical Research Berghofer Medical Research Institute Brisbane, Queensland, Australia
| | - Thomas L. Vaughan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, School of Public Health, Seattle, Washington, USA
| | - Margaret M. Madeleine
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, School of Public Health, Seattle, Washington, USA
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Armenise C, Lefebvre G, Carayol J, Bonnel S, Bolton J, Di Cara A, Gheldof N, Descombes P, Langin D, Saris WH, Astrup A, Hager J, Viguerie N, Valsesia A. Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects. Am J Clin Nutr 2017; 106:736-746. [PMID: 28793995 DOI: 10.3945/ajcn.117.156216] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 06/21/2017] [Indexed: 11/14/2022] Open
Abstract
Background: A low-calorie diet (LCD) reduces fat mass excess, improves insulin sensitivity, and alters adipose tissue (AT) gene expression, yet the relation with clinical outcomes remains unclear.Objective: We evaluated AT transcriptome alterations during an LCD and the association with weight and glycemic outcomes both at LCD termination and 6 mo after the LCD.Design: Using RNA sequencing (RNAseq), we analyzed transcriptome changes in AT from 191 obese, nondiabetic patients within a multicenter, controlled dietary intervention. Expression changes were associated with outcomes after an 8-wk LCD (800-1000 kcal/d) and 6 mo after the LCD. Results were validated by using quantitative reverse transcriptase-polymerase chain reaction in 350 subjects from the same cohort. Statistical models were constructed to classify weight maintainers or glycemic improvers.Results: With RNAseq analyses, we identified 1173 genes that were differentially expressed after the LCD, of which 350 and 33 were associated with changes in body mass index (BMI; in kg/m2) and Matsuda index values, respectively, whereas 29 genes were associated with both endpoints. Pathway analyses highlighted enrichment in lipid and glucose metabolism. Classification models were constructed to identify weight maintainers. A model based on clinical baseline variables could not achieve any classification (validation AUC: 0.50; 95% CI: 0.36, 0.64). However, clinical changes during the LCD yielded better performance of the model (AUC: 0.73; 95% CI: 0.60, 0.87]). Adding baseline expression to this model improved the performance significantly (AUC: 0.87; 95% CI: 0.77, 0.96; Delong's P = 0.012). Similar analyses were performed to classify subjects with good glycemic improvements. Baseline- and LCD-based clinical models yielded similar performance (best AUC: 0.73; 95% CI: 0.60, 0.86). The addition of expression changes during the LCD improved the performance substantially (AUC: 0.80; 95% CI: 0.69, 0.92; P = 0.058).Conclusions: This study investigated AT transcriptome alterations after an LCD in a large cohort of obese, nondiabetic patients. Gene expression combined with clinical variables enabled us to distinguish weight and glycemic responders from nonresponders. These potential biomarkers may help clinicians understand intersubject variability and better predict the success of dietary interventions. This trial was registered at clinicaltrials.gov as NCT00390637.
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Affiliation(s)
| | | | - Jérôme Carayol
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Sophie Bonnel
- Institute of Metabolic and Cardiovascular Diseases, French National Institute of Health and Medical Research, Paul Sabatier University, UMR1048, Obesity Research Laboratory, University of Toulouse, Toulouse, France
| | - Jennifer Bolton
- Institute of Metabolic and Cardiovascular Diseases, French National Institute of Health and Medical Research, Paul Sabatier University, UMR1048, Obesity Research Laboratory, University of Toulouse, Toulouse, France
| | | | - Nele Gheldof
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | | | - Dominique Langin
- Institute of Metabolic and Cardiovascular Diseases, French National Institute of Health and Medical Research, Paul Sabatier University, UMR1048, Obesity Research Laboratory, University of Toulouse, Toulouse, France.,Department of Clinical Biochemistry, Toulouse University Hospitals, Toulouse, France
| | - Wim Hm Saris
- Department of Human Biology, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre, Maastricht, Netherlands; and
| | - Arne Astrup
- University of Copenhagen, Department of Nutrition, Exercise and Sports, Faculty of Science, Copenhagen, Denmark
| | - Jörg Hager
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Nathalie Viguerie
- Institute of Metabolic and Cardiovascular Diseases, French National Institute of Health and Medical Research, Paul Sabatier University, UMR1048, Obesity Research Laboratory, University of Toulouse, Toulouse, France
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Lee S, Park Y, Kim S. MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data. Methods 2017; 124:13-24. [PMID: 28579402 DOI: 10.1016/j.ymeth.2017.05.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Accepted: 05/30/2017] [Indexed: 11/18/2022] Open
Abstract
Pathway based analysis of high throughput transcriptome data is a widely used approach to investigate biological mechanisms. Since a pathway consists of multiple functions, the recent approach is to determine condition specific sub-pathways or subpaths. However, there are several challenges. First, few existing methods utilize explicit gene expression information from RNA-seq. More importantly, subpath activity is usually an average of statistical scores, e.g., correlations, of edges in a candidate subpath, which fails to reflect gene expression quantity information. In addition, none of existing methods can handle multiple phenotypes. To address these technical problems, we designed and implemented an algorithm, MIDAS, that determines condition specific subpaths, each of which has different activities across multiple phenotypes. MIDAS utilizes gene expression quantity information fully and the network centrality information to determine condition specific subpaths. To test performance of our tool, we used TCGA breast cancer RNA-seq gene expression profiles with five molecular subtypes. 36 differentially activate subpaths were determined. The utility of our method, MIDAS, was demonstrated in four ways. All 36 subpaths are well supported by the literature information. Subsequently, we showed that these subpaths had a good discriminant power for five cancer subtype classification and also had a prognostic power in terms of survival analysis. Finally, in a performance comparison of MIDAS to a recent subpath prediction method, PATHOME, our method identified more subpaths and much more genes that are well supported by the literature information. AVAILABILITY http://biohealth.snu.ac.kr/software/MIDAS/.
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Affiliation(s)
- Sangseon Lee
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Youngjune Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea; Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea.
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Makarev E, Schubert AD, Kanherkar RR, London N, Teka M, Ozerov I, Lezhnina K, Bedi A, Ravi R, Mehra R, Hoque MO, Sloma I, Gaykalova DA, Csoka AB, Sidransky D, Zhavoronkov A, Izumchenko E. In silico analysis of pathways activation landscape in oral squamous cell carcinoma and oral leukoplakia. Cell Death Discov 2017; 3:17022. [PMID: 28580171 PMCID: PMC5439156 DOI: 10.1038/cddiscovery.2017.22] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 02/23/2017] [Accepted: 03/13/2017] [Indexed: 12/16/2022] Open
Abstract
A subset of patients with oral squamous cell carcinoma (OSCC), the most common subtype of head and neck squamous cell carcinoma (HNSCC), harbor dysplastic lesions (often visually identified as leukoplakia) prior to cancer diagnosis. Although evidence suggest that leukoplakia represents an initial step in the progression to cancer, signaling networks driving this progression are poorly understood. Here, we applied in silico Pathway Activation Network Decomposition Analysis (iPANDA), a new bioinformatics software suite for qualitative analysis of intracellular signaling pathway activation using transcriptomic data, to assess a network of molecular signaling in OSCC and pre-neoplastic oral lesions. In tumor samples, our analysis detected major conserved mitogenic and survival signaling pathways strongly associated with HNSCC, suggesting that some of the pathways identified by our algorithm, but not yet validated as HNSCC related, may be attractive targets for future research. While pathways activation landscape in the majority of leukoplakias was different from that seen in OSCC, a subset of pre-neoplastic lesions has demonstrated some degree of similarity to the signaling profile seen in tumors, including dysregulation of the cancer-driving pathways related to survival and apoptosis. These results suggest that dysregulation of these signaling networks may be the driving force behind the early stages of OSCC tumorigenesis. While future studies with larger leukoplakia data sets are warranted to further estimate the values of this approach for capturing signaling features that characterize relevant lesions that actually progress to cancers, our platform proposes a promising new approach for detecting cancer-promoting pathways and tailoring the right therapy to prevent tumorigenesis.
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Affiliation(s)
- Eugene Makarev
- Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, MD 21218, USA
| | - Adrian D Schubert
- Department of Otolaryngology-Head and Neck Cancer Research, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | | | - Nyall London
- Department of Otolaryngology-Head and Neck Cancer Research, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Mahder Teka
- Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, MD 21218, USA
| | - Ivan Ozerov
- Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, MD 21218, USA
| | - Ksenia Lezhnina
- Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, MD 21218, USA
| | - Atul Bedi
- Department of Otolaryngology-Head and Neck Cancer Research, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Rajani Ravi
- Department of Otolaryngology-Head and Neck Cancer Research, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Rannee Mehra
- Department of Oncology, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Mohammad O Hoque
- Department of Otolaryngology-Head and Neck Cancer Research, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Ido Sloma
- R&D, Champions Oncology, Baltimore, MD, USA
| | - Daria A Gaykalova
- Department of Otolaryngology-Head and Neck Cancer Research, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Antonei B Csoka
- Department of Anatomy, Howard University, Washington, DC, USA
| | - David Sidransky
- Department of Otolaryngology-Head and Neck Cancer Research, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Alex Zhavoronkov
- Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, MD 21218, USA.,D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology, and Immunology, Samory Mashela 1, Moscow 117997, Russia.,The Biogerontology Research Foundation, 2354 Chynoweth House, Trevissome Park, Truro TR4 8UN, UK
| | - Evgeny Izumchenko
- Department of Otolaryngology-Head and Neck Cancer Research, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
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73
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Hanoudi S, Donato M, Draghici S. Identifying biologically relevant putative mechanisms in a given phenotype comparison. PLoS One 2017; 12:e0176950. [PMID: 28486531 PMCID: PMC5423614 DOI: 10.1371/journal.pone.0176950] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Accepted: 04/19/2017] [Indexed: 11/19/2022] Open
Abstract
A major challenge in life science research is understanding the mechanism involved in a given phenotype. The ability to identify the correct mechanisms is needed in order to understand fundamental and very important phenomena such as mechanisms of disease, immune systems responses to various challenges, and mechanisms of drug action. The current data analysis methods focus on the identification of the differentially expressed (DE) genes using their fold change and/or p-values. Major shortcomings of this approach are that: i) it does not consider the interactions between genes; ii) its results are sensitive to the selection of the threshold(s) used, and iii) the set of genes produced by this approach is not always conducive to formulating mechanistic hypotheses. Here we present a method that can construct networks of genes that can be considered putative mechanisms. The putative mechanisms constructed by this approach are not limited to the set of DE genes, but also considers all known and relevant gene-gene interactions. We analyzed three real datasets for which both the causes of the phenotype, as well as the true mechanisms were known. We show that the method identified the correct mechanisms when applied on microarray datasets from mouse. We compared the results of our method with the results of the classical approach, showing that our method produces more meaningful biological insights.
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Affiliation(s)
- Samer Hanoudi
- Department of Computer Science, Wayne State University, Detroit, MI, United States of America
| | - Michele Donato
- Department of Computer Science, Wayne State University, Detroit, MI, United States of America
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, United States of America
- Department of Obstetrics and Gynecology, Detroit, MI, United States of America
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74
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Detecting Perturbed Subpathways towards Mouse Lung Regeneration Following H1N1 Influenza Infection. COMPUTATION 2017. [DOI: 10.3390/computation5020020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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75
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Minicã CC, Mbarek H, Pool R, Dolan CV, Boomsma DI, Vink JM. Pathways to smoking behaviours: biological insights from the Tobacco and Genetics Consortium meta-analysis. Mol Psychiatry 2017; 22:82-88. [PMID: 27021816 PMCID: PMC5777181 DOI: 10.1038/mp.2016.20] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Revised: 02/04/2016] [Accepted: 02/05/2016] [Indexed: 01/19/2023]
Abstract
By running gene and pathway analyses for several smoking behaviours in the Tobacco and Genetics Consortium (TAG) sample of 74 053 individuals, 21 genes and several chains of biological pathways were implicated. Analyses were carried out using the HYbrid Set-based Test (HYST) as implemented in the Knowledge-based mining system for Genome-wide Genetic studies software. Fifteen genes are novel and were not detected with the single nucleotide polymorphism-based approach in the original TAG analysis. For quantity smoked, 14 genes passed the false discovery rate of 0.05 (corrected for multiple testing), with the top association signal located at the IREB2 gene (P=1.57E-37). Three genomic loci were significantly associated with ever smoked. The top signal is located at the noncoding antisense RNA transcript BDNF-AS (P=6.25E-07) on 11p14. The SLC25A21 gene (P=2.09E-08) yielded the top association signal in the analysis of smoking cessation. The 19q13 noncoding RNA locus exceeded the genome-wide significance in the analysis of age at initiation (P=1.33E-06). Pathways belonging to the Neuronal system pathways, harbouring the nicotinic acetylcholine receptor genes expressing the α (CHRNA 1-9), β (CHRNB 1-4), γ, δ and ɛ subunits, yielded the smallest P-values in the pathway analysis of the quantity smoked (lowest P=4.90E-42). Additionally, pathways belonging to 'a subway map of cancer pathways' regulating the cell cycle, mitotic DNA replication, axon growth and synaptic plasticity were found significantly enriched for genetic variants in ever smokers relative to never smokers (lowest P=1.61E-07). In addition, these pathways were also significantly associated with the quantity smoked (lowest P=4.28E-17). Our results shed light on one of the world's leading causes of preventable death and open a path to potential therapeutic targets. These results are informative in decoding the biological bases of other disease traits, such as depression and cancers, with which smoking shares genetic vulnerabilities.
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Affiliation(s)
- Camelia C. Minicã
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Hamdi Mbarek
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Conor V. Dolan
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
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76
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Dubovenko A, Nikolsky Y, Rakhmatulin E, Nikolskaya T. Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform. Methods Mol Biol 2017; 1613:101-124. [PMID: 28849560 DOI: 10.1007/978-1-4939-7027-8_6] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).
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Affiliation(s)
- Alexey Dubovenko
- Clarivate Analytics, 1500 Spring Garden Street, Fourth Floor, Philadelphia, PA, 19130, USA.
| | - Yuri Nikolsky
- Prosapia Genetics, Solana Beach, CA, 92075, USA.,School of Systems Biology, George Mason University, Fairfax, VA, USA
| | - Eugene Rakhmatulin
- Clarivate Analytics, 1500 Spring Garden Street, Fourth Floor, Philadelphia, PA, 19130, USA
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PerSubs: A Graph-Based Algorithm for the Identification of Perturbed Subpathways Caused by Complex Diseases. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 988:215-224. [DOI: 10.1007/978-3-319-56246-9_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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78
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Lee J, Jo K, Lee S, Kang J, Kim S. Prioritizing biological pathways by recognizing context in time-series gene expression data. BMC Bioinformatics 2016; 17:477. [PMID: 28155707 PMCID: PMC5259824 DOI: 10.1186/s12859-016-1335-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information. Results In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets. Conclusions Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1335-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jusang Lee
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Kyuri Jo
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sunwon Lee
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea. .,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea. .,Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea.
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79
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Zhao X, Luan YZ, Zuo X, Chen YD, Qin J, Jin L, Tan Y, Lin M, Zhang N, Liang Y, Rao SQ. Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data. GENOMICS PROTEOMICS & BIOINFORMATICS 2016; 14:349-356. [PMID: 27965104 PMCID: PMC5200919 DOI: 10.1016/j.gpb.2016.04.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2016] [Revised: 03/30/2016] [Accepted: 04/10/2016] [Indexed: 02/06/2023]
Abstract
Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium (WTCCC) SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction (PPI) knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer’s disease, non-alcoholic fatty liver disease, and Huntington’s disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer’s disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer’s disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.
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Affiliation(s)
- Xiang Zhao
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Yi-Zhao Luan
- School of Life Sciences, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiaoyu Zuo
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Ye-Da Chen
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Jiheng Qin
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Lv Jin
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Yiqing Tan
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Meihua Lin
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Naizun Zhang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yan Liang
- Maoming People's Hospital, Maoming 525000, China
| | - Shao-Qi Rao
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China; Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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80
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Koumakis L, Kanterakis A, Kartsaki E, Chatzimina M, Zervakis M, Tsiknakis M, Vassou D, Kafetzopoulos D, Marias K, Moustakis V, Potamias G. MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways. PLoS Comput Biol 2016; 12:e1005187. [PMID: 27832067 PMCID: PMC5104320 DOI: 10.1371/journal.pcbi.1005187] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 10/10/2016] [Indexed: 01/04/2023] Open
Abstract
Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.
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Affiliation(s)
- Lefteris Koumakis
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Alexandros Kanterakis
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Evgenia Kartsaki
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Maria Chatzimina
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Michalis Zervakis
- School of Electrical and Computer Engineering, Technical University of Crete, Greece
| | - Manolis Tsiknakis
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
- Department of Informatics Engineering, Technological Educational Institute of Crete, Greece
| | - Despoina Vassou
- Institute of Molecular Biology & Biotechnology, FORTH, Heraklion, Crete, Greece
| | | | - Kostas Marias
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Vassilis Moustakis
- School of Production Engineering & Management, Technical University of Crete, Greece
| | - George Potamias
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
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81
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Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases. PLoS One 2016; 11:e0162910. [PMID: 27622767 PMCID: PMC5021324 DOI: 10.1371/journal.pone.0162910] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 08/30/2016] [Indexed: 01/08/2023] Open
Abstract
Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn’s disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn’s disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.
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82
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Erdem C, Nagle AM, Casa AJ, Litzenburger BC, Wang YF, Taylor DL, Lee AV, Lezon TR. Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways. Mol Cell Proteomics 2016; 15:3045-57. [PMID: 27364358 PMCID: PMC5013316 DOI: 10.1074/mcp.m115.057729] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Revised: 06/23/2016] [Indexed: 01/22/2023] Open
Abstract
Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of mitogen-activated protein kinase (MAPK) phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro.
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Affiliation(s)
- Cemal Erdem
- From the ‡Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; §University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Alison M Nagle
- ¶Department of Pharmacology & Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; ‖Women's Cancer Research Center, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania
| | - Angelo J Casa
- **Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas
| | - Beate C Litzenburger
- **Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas
| | - Yu-Fen Wang
- **Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas
| | - D Lansing Taylor
- From the ‡Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; §University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Adrian V Lee
- ¶Department of Pharmacology & Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; ‖Women's Cancer Research Center, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania; ‡‡Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Timothy R Lezon
- From the ‡Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; §University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania;
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83
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Fasano M, Monti C, Alberio T. A systems biology-led insight into the role of the proteome in neurodegenerative diseases. Expert Rev Proteomics 2016; 13:845-55. [PMID: 27477319 DOI: 10.1080/14789450.2016.1219254] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Multifactorial disorders are the result of nonlinear interactions of several factors; therefore, a reductionist approach does not appear to be appropriate. Proteomics is a global approach that can be efficiently used to investigate pathogenetic mechanisms of neurodegenerative diseases. AREAS COVERED Here, we report a general introduction about the systems biology approach and mechanistic insights recently obtained by over-representation analysis of proteomics data of cellular and animal models of Alzheimer's disease, Parkinson's disease and other neurodegenerative disorders, as well as of affected human tissues. Expert commentary: As an inductive method, proteomics is based on unbiased observations that further require validation of generated hypotheses. Pathway databases and over-representation analysis tools allow researchers to assign an expectation value to pathogenetic mechanisms linked to neurodegenerative diseases. The systems biology approach based on omics data may be the key to unravel the complex mechanisms underlying neurodegeneration.
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Affiliation(s)
- Mauro Fasano
- a Department of Science and High Technology and Center of Neuroscience , University of Insubria , Busto Arsizio , Italy
| | - Chiara Monti
- a Department of Science and High Technology and Center of Neuroscience , University of Insubria , Busto Arsizio , Italy
| | - Tiziana Alberio
- a Department of Science and High Technology and Center of Neuroscience , University of Insubria , Busto Arsizio , Italy
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84
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Rost B, Radivojac P, Bromberg Y. Protein function in precision medicine: deep understanding with machine learning. FEBS Lett 2016; 590:2327-41. [PMID: 27423136 PMCID: PMC5937700 DOI: 10.1002/1873-3468.12307] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Revised: 07/12/2016] [Accepted: 07/12/2016] [Indexed: 12/21/2022]
Abstract
Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.
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Affiliation(s)
- Burkhard Rost
- Department of Informatics and Bioinformatics, Institute for Advanced Studies, Technical University of Munich, Garching, Germany
| | - Predrag Radivojac
- School of Informatics and Computing, Indiana University, Bloomington, IN, USA
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA
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85
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Makarev E, Izumchenko E, Aihara F, Wysocki PT, Zhu Q, Buzdin A, Sidransky D, Zhavoronkov A, Atala A. Common pathway signature in lung and liver fibrosis. Cell Cycle 2016; 15:1667-73. [PMID: 27267766 PMCID: PMC4957589 DOI: 10.1080/15384101.2016.1152435] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Fibrosis, a progressive accumulation of extracellular matrix components, encompasses a wide spectrum of distinct organs, and accounts for an increasing burden of morbidity and mortality worldwide. Despite the tremendous clinical impact, the mechanisms governing the fibrotic process are not yet understood, and to date, no clinically reliable therapies for fibrosis have been discovered. Here we applied Regeneration Intelligence, a new bioinformatics software suite for qualitative analysis of intracellular signaling pathway activation using transcriptomic data, to assess a network of molecular signaling in lung and liver fibrosis. In both tissues, our analysis detected major conserved signaling pathways strongly associated with fibrosis, suggesting that some of the pathways identified by our algorithm but not yet wet-lab validated as fibrogenesis related, may be attractive targets for future research. While the majority of significantly disrupted pathways were specific to histologically distinct organs, several pathways have been concurrently activated or downregulated among the hepatic and pulmonary fibrosis samples, providing new evidence of evolutionary conserved pathways that may be relevant as possible therapeutic targets. While future confirmatory studies are warranted to validate these observations, our platform proposes a promising new approach for detecting fibrosis-promoting pathways and tailoring the right therapy to prevent fibrogenesis.
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Affiliation(s)
- Eugene Makarev
- a Atlas Regeneration, Inc. , Winston-Salem , NC , USA.,b Insilico Medicine, Inc., ETC, Johns Hopkins University , Baltimore , MD , USA
| | - Evgeny Izumchenko
- c Department of Otolaryngology-Head & Neck Surgery , Johns Hopkins University School of Medicine , Baltimore , MD , USA
| | - Fumiaki Aihara
- d Advanced Academic Programs, Johns Hopkins University , Baltimore , MD , USA
| | - Piotr T Wysocki
- c Department of Otolaryngology-Head & Neck Surgery , Johns Hopkins University School of Medicine , Baltimore , MD , USA
| | - Qingsong Zhu
- b Insilico Medicine, Inc., ETC, Johns Hopkins University , Baltimore , MD , USA
| | - Anton Buzdin
- e The Biogerontology Research Foundation , London , UK
| | - David Sidransky
- c Department of Otolaryngology-Head & Neck Surgery , Johns Hopkins University School of Medicine , Baltimore , MD , USA
| | - Alex Zhavoronkov
- b Insilico Medicine, Inc., ETC, Johns Hopkins University , Baltimore , MD , USA.,f Wake Forest Institute for Regenerative Medicine, Wake Forest School of Medicine , Winston-Salem , NC , USA
| | - Anthony Atala
- a Atlas Regeneration, Inc. , Winston-Salem , NC , USA.,g Pathway Pharmaceuticals, Ltd , Hong Kong , Hong Kong
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86
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Zheng WY, Zheng WX, Hua L. Detecting shared pathways linked to rheumatoid arthritis with other autoimmune diseases in a in silico analysis. Mol Biol 2016. [DOI: 10.1134/s0026893316030146] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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87
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Genome-wide association study of antidepressant response: involvement of the inorganic cation transmembrane transporter activity pathway. BMC Psychiatry 2016; 16:106. [PMID: 27091189 PMCID: PMC4836090 DOI: 10.1186/s12888-016-0813-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Accepted: 04/11/2016] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) represent the current frontier in pharmacogenomics. Thousands of subjects of Caucasian ancestry have been included in previous GWAS investigating antidepressant response. GWAS focused on this phenotype are lacking in Asian populations. METHODS A sample of 109 major depressive disorder (MDD) patients of Korean origin in antidepressant treatment was collected. Phenotypes were response and remission according to the Hamilton Rating Scale for Depression (HRSD). Genome-wide genotyping was performed using the Illumina Human Omni2.5-8 platform. The same phenotypes were used in the STAR*D level 1 (n = 1677) for independent replication. In order to corroborate findings and increase the comparability between the two datasets, three levels of analysis (SNPs, genes and pathways) were carried out. Bonferroni correction, permutations, and replication across samples were used to reduce the risk of false positives. RESULTS Among the genes replicated across the two samples (permutated p < 0.05 in both of them), CTNNA3 appeared promising. The inorganic cation transmembrane transporter activity pathway (GO:0022890) was associated with antidepressant response in both samples (p = 2.9e-5 and p = 0.001 in the Korean and STAR*D samples, respectively) and this pathway included CACNA1A, CACNA1C, and CACNB2 genes. CONCLUSIONS The present study supported the involvement of genes coding for subunits of L-type voltage-gated calcium channel in antidepressant efficacy across different ethnicities but replication of findings is required before any definitive statement.
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88
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Gonçalves AC, Alves R, Baldeiras I, Cortesão E, Carda JP, Branco CC, Oliveiros B, Loureiro L, Pereira A, Nascimento Costa JM, Sarmento-Ribeiro AB, Mota-Vieira L. Genetic variants involved in oxidative stress, base excision repair, DNA methylation, and folate metabolism pathways influence myeloid neoplasias susceptibility and prognosis. Mol Carcinog 2016; 56:130-148. [PMID: 26950655 DOI: 10.1002/mc.22478] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2015] [Revised: 01/22/2016] [Accepted: 02/17/2016] [Indexed: 12/27/2022]
Abstract
Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) share common features: elevated oxidative stress, DNA repair deficiency, and aberrant DNA methylation. We performed a hospital-based case-control study to evaluate the association in variants of genes involved in oxidative stress, folate metabolism, DNA repair, and DNA methylation with susceptibility and prognosis of these malignancies. To that end, 16 SNPs (one per gene: CAT, CYBA, DNMT1, DNMT3A, DNMT3B, GPX1, KEAP1, MPO, MTRR, NEIL1, NFE2F2, OGG1, SLC19A1, SOD1, SOD2, and XRCC1) were genotyped in 191 patients (101 MDS and 90 AML) and 261 controls. We also measured oxidative stress (reactive oxygen species/total antioxidant status ratio), DNA damage (8-hydroxy-2'-deoxyguanosine), and DNA methylation (5-methylcytosine) in 50 subjects (40 MDS and 10 controls). Results showed that five genes (GPX1, NEIL1, NFE2L2, OGG1, and SOD2) were associated with MDS, two (DNMT3B and SLC19A1) with AML, and two (CYBA and DNMT1) with both diseases. We observed a correlation of CYBA TT, GPX1 TT, and SOD2 CC genotypes with increased oxidative stress levels, as well as NEIL1 TT and OGG1 GG genotypes with higher DNA damage. The 5-methylcytosine levels were negatively associated with DNMT1 CC, DNMT3A CC, and MTRR AA genotypes, and positively with DNMT3B CC genotype. Furthermore, DNMT3A, MTRR, NEIL1, and OGG1 variants modulated AML transformation in MDS patients. Additionally, DNMT3A, OGG1, GPX1, and KEAP1 variants influenced survival of MDS and AML patients. Altogether, data suggest that genetic variability influence predisposition and prognosis of MDS and AML patients, as well AML transformation rate in MDS patients. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Ana Cristina Gonçalves
- Laboratory of Oncobiology and Hematology (LOH) and University Clinic of Hematology, Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal.,Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), FMUC, Coimbra, Portugal.,Center for Neuroscience and Cell Biology and Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Coimbra, Portugal
| | - Raquel Alves
- Laboratory of Oncobiology and Hematology (LOH) and University Clinic of Hematology, Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal.,Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), FMUC, Coimbra, Portugal.,Center for Neuroscience and Cell Biology and Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Coimbra, Portugal
| | - Inês Baldeiras
- Center for Neuroscience and Cell Biology and Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Coimbra, Portugal.,Department of Neurology, Laboratory of Neurochemistry, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal
| | - Emília Cortesão
- Laboratory of Oncobiology and Hematology (LOH) and University Clinic of Hematology, Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal.,Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), FMUC, Coimbra, Portugal.,Clinical Hematology Department, Centro Hospitalar e Universitário de Coimbra, EPE (CHUC, EPE), Coimbra, Portugal
| | - José Pedro Carda
- Laboratory of Oncobiology and Hematology (LOH) and University Clinic of Hematology, Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal.,Clinical Hematology Department, Centro Hospitalar e Universitário de Coimbra, EPE (CHUC, EPE), Coimbra, Portugal
| | - Claudia C Branco
- Molecular Genetics and Pathology Unit, Hospital of Divino Espírito Santo of Ponta Delgada, EPE, Ponta Delgada, São Miguel Island, Azores, Portugal.,Azores Genetics Research Group, Instituto Gulbenkian de Ciência, Oeiras, Portugal.,Faculty of Sciences, BioISI-Biosystems and Integrative Sciences Institute, University of Lisboa, Lisbon, Portugal
| | - Bárbara Oliveiros
- Laboratory for Biostatistics and Medical Informatics, FMUC, Coimbra, Portugal
| | - Luísa Loureiro
- Department of Medicine, Hospital Distrital da Figueira da Foz, EPE (HDFF, EPE), Figueira da Foz, Portugal
| | - Amélia Pereira
- Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), FMUC, Coimbra, Portugal.,Department of Medicine, Hospital Distrital da Figueira da Foz, EPE (HDFF, EPE), Figueira da Foz, Portugal
| | - José Manuel Nascimento Costa
- Department of Oncology, Centro Hospitalar e Universitário de Coimbra, EPE (CHUC, EPE), Coimbra, Portugal.,Faculty of Medicine, University Clinic of Oncology, University of Coimbra-FMUC, Coimbra, Portugal
| | - Ana Bela Sarmento-Ribeiro
- Laboratory of Oncobiology and Hematology (LOH) and University Clinic of Hematology, Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal.,Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), FMUC, Coimbra, Portugal.,Center for Neuroscience and Cell Biology and Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Coimbra, Portugal.,Clinical Hematology Department, Centro Hospitalar e Universitário de Coimbra, EPE (CHUC, EPE), Coimbra, Portugal
| | - Luisa Mota-Vieira
- Molecular Genetics and Pathology Unit, Hospital of Divino Espírito Santo of Ponta Delgada, EPE, Ponta Delgada, São Miguel Island, Azores, Portugal.,Azores Genetics Research Group, Instituto Gulbenkian de Ciência, Oeiras, Portugal.,Faculty of Sciences, BioISI-Biosystems and Integrative Sciences Institute, University of Lisboa, Lisbon, Portugal
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89
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Spies D, Ciaudo C. Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis. Comput Struct Biotechnol J 2015; 13:469-77. [PMID: 26430493 PMCID: PMC4564389 DOI: 10.1016/j.csbj.2015.08.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 08/05/2015] [Accepted: 08/07/2015] [Indexed: 12/17/2022] Open
Abstract
Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.
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Affiliation(s)
- Daniel Spies
- Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, Zurich, Otto-Stern Weg 7, 8093 Zurich, Switzerland
- Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich, Institute of Molecular Life Sciences, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Constance Ciaudo
- Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, Zurich, Otto-Stern Weg 7, 8093 Zurich, Switzerland
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90
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Fabbri C, Serretti A. Pharmacogenetics of major depressive disorder: top genes and pathways toward clinical applications. Curr Psychiatry Rep 2015; 17:50. [PMID: 25980509 DOI: 10.1007/s11920-015-0594-9] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The pharmacogenetics of antidepressants has been not only a challenging but also frustrating research field since its birth in the 1990s. Indeed, great expectations followed the first evidence of familiar aggregation of antidepressant response. Despite the progress from candidate gene studies to genome-wide association studies (GWAS), results fell out the expectations and they were often inconsistent. Anyway, the cumulative evidence supports the involvement of some genes and molecular pathways in antidepressant efficacy. The best single genes are SLC6A4, HTR2A, BDNF, GNB3, FKBP5, ABCB1, and cytochrome P450 genes (CYP2D6 and CYP2C19). Molecular pathways involved in inflammation and neuroplasticity show the greatest support. The first studies evaluating benefits of genotype-guided antidepressant treatments provided encouraging results and confirmed the relevance of SLC6A4, HTR2A, ABCB1, and cytochrome P450 genes. Further progress in genotyping and data analysis would allow to move forward and complete the understanding of antidepressant pharmacogenetics and its translation into clinical applications.
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Affiliation(s)
- Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Viale Carlo Pepoli 5, 40123, Bologna, Italy,
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91
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Abstract
Neurodegenerative diseases (NDs) collectively afflict more than 40 million people worldwide. The majority of these diseases lack therapies to slow or stop progression due in large part to the challenge of disentangling the simultaneous presentation of broad, multifaceted pathophysiologic changes. Present technologies and computational capabilities suggest an optimistic future for deconvolving these changes to identify novel mechanisms driving ND onset and progression. In particular, integration of highly multi-dimensional omic analytical techniques (e.g., microarray, mass spectrometry) with computational systems biology approaches provides a systematic methodology to elucidate new mechanisms driving NDs. In this review, we begin by summarizing the complex pathophysiology of NDs associated with protein aggregation, emphasizing the shared complex dysregulation found in all of these diseases, and discuss available experimental ND models. Next, we provide an overview of technological and computational techniques used in systems biology that are applicable to studying NDs. We conclude by reviewing prior studies that have applied these approaches to NDs and comment on the necessity of combining analysis from both human tissues and model systems to identify driving mechanisms. We envision that the integration of computational approaches with multiple omic analyses of human tissues, and mouse and in vitro models, will enable the discovery of new therapeutic strategies for these devastating diseases.
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Affiliation(s)
- Levi B Wood
- Cancer Research Institute, Beth Israel Deaconess Medical Center and Department of Medicine, Harvard Medical School, Boston, MA 02215, USA.
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