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Grassi M, Tarantino B. SEMgsa: topology-based pathway enrichment analysis with structural equation models. BMC Bioinformatics 2022; 23:344. [PMID: 35978279 PMCID: PMC9385099 DOI: 10.1186/s12859-022-04884-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 08/09/2022] [Indexed: 11/25/2022] Open
Abstract
Background Pathway enrichment analysis is extensively used in high-throughput experimental studies to gain insight into the functional roles of pre-defined subsets of genes, proteins and metabolites. Methods that leverages information on the topology of the underlying pathways outperform simpler methods that only consider pathway membership, leading to improved performance. Among all the proposed software tools, there’s the need to combine high statistical power together with a user-friendly framework, making it difficult to choose the best method for a particular experimental environment. Results We propose SEMgsa, a topology-based algorithm developed into the framework of structural equation models. SEMgsa combine the SEM p values regarding node-specific group effect estimates in terms of activation or inhibition, after statistically controlling biological relations among genes within pathways. We used SEMgsa to identify biologically relevant results in a Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) together with a frontotemporal dementia (FTD) DNA methylation dataset (GEO accession: GSE53740) and compared its performance with some existing methods. SEMgsa is highly sensitive to the pathways designed for the specific disease, showing low p values (\documentclass[12pt]{minimal}
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\begin{document}$$< 0.001$$\end{document}<0.001) and ranking in high positions, outperforming existing software tools. Three pathway dysregulation mechanisms were used to generate simulated expression data and evaluate the performance of methods in terms of type I error followed by their statistical power. Simulation results confirm best overall performance of SEMgsa. Conclusions SEMgsa is a novel yet powerful method for identifying enrichment with regard to gene expression data. It takes into account topological information and exploits pathway perturbation statistics to reveal biological information. SEMgsa is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04884-8.
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Affiliation(s)
- Mario Grassi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Barbara Tarantino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
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2
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Igolkina AA, Meshcheryakov G, Gretsova MV, Nuzhdin SV, Samsonova MG. Multi-trait multi-locus SEM model discriminates SNPs of different effects. BMC Genomics 2020; 21:490. [PMID: 32723302 PMCID: PMC7385891 DOI: 10.1186/s12864-020-06833-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 06/16/2020] [Indexed: 11/21/2022] Open
Abstract
Background There is a plethora of methods for genome-wide association studies. However, only a few of them may be classified as multi-trait and multi-locus, i.e. consider the influence of multiple genetic variants to several correlated phenotypes. Results We propose a multi-trait multi-locus model which employs structural equation modeling (SEM) to describe complex associations between SNPs and traits - multi-trait multi-locus SEM (mtmlSEM). The structure of our model makes it possible to discriminate pleiotropic and single-trait SNPs of direct and indirect effect. We also propose an automatic procedure to construct the model using factor analysis and the maximum likelihood method. For estimating a large number of parameters in the model, we performed Bayesian inference and implemented Gibbs sampling. An important feature of the model is that it correctly copes with non-normally distributed variables, such as some traits and variants. Conclusions We applied the model to Vavilov’s collection of 404 chickpea (Cicer arietinum L.) accessions with 20-fold cross-validation. We analyzed 16 phenotypic traits which we organized into five groups and found around 230 SNPs associated with traits, 60 of which were of pleiotropic effect. The model demonstrated high accuracy in predicting trait values.
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3
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Pepe D, Do JH. Analyzing Apomorphine-Mediated Effects in a Cell Model for Parkinson's Disease with Partial Least Squares Structure Equation Modeling. J Comput Biol 2019; 27:1273-1282. [PMID: 31855451 DOI: 10.1089/cmb.2019.0386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Genome-wide gene expression data for cell model of Parkinson's disease (PD) have considerably improved our understanding of the underlying molecular mechanisms involved in cell death during PD neurodegeneration. Apomorphine (APOM), a nonselective dopamine agonist, has been used to treat patients with advanced PD showing no response to levodopa or other dopamine agonists. Although APOM plays a role as a free radical scavenger with neuroprotective effect, it has been reported that long-term use of APOM in PD treatment brings about side effects such as nausea and orthostatic hypotension. For safe use of APOM in PD treatment, it is crucial to understand the underlying molecular mechanisms of APOM in PD. In this study, two groups of microarray data including PD cell model and APOM added PD cell model were used to survey mediation effects of APOM in PD cell model. Mediation analysis between disease genes obtained from PD cell model and drug genes obtained from APOM added PD cell model was carried out with shortest path model on KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways and partial least squares structure equation modeling. Our results suggest that drug genes responding to APOM might contribute to negative regulation of disease genes by direct or indirect ways.
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Affiliation(s)
- Daniele Pepe
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Jin Hwan Do
- Department of Biomolecular and Chemical Engineering, DongYang University, Yeongju, Korea
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4
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Yue Z, Neylon MT, Nguyen T, Ratliff T, Chen JY. "Super Gene Set" Causal Relationship Discovery from Functional Genomics Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1991-1998. [PMID: 30040650 PMCID: PMC6380687 DOI: 10.1109/tcbb.2018.2858755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this article, we present a computational framework to identify "causal relationships" among super gene sets. For "causal relationships," we refer to both stimulatory and inhibitory regulatory relationships, regardless of through direct or indirect mechanisms. For super gene sets, we refer to "pathways, annotated lists, and gene signatures," or PAGs. To identify causal relationships among PAGs, we extend the previous work on identifying PAG-to-PAG regulatory relationships by further requiring them to be significantly enriched with gene-to-gene co-expression pairs across the two PAGs involved. This is achieved by developing a quantitative metric based on PAG-to-PAG Co-expressions (PPC), which we use to infer the likelihood that PAG-to-PAG relationships under examination are causal-either stimulatory or inhibitory. Since true causal relationships are unknown, we approximate the overall performance of inferring causal relationships with the performance of recalling known r-type PAG-to-PAG relationships from causal PAG-to-PAG inference, using a functional genomics benchmark dataset from the GEO database. We report the area-under-curve (AUC) performance for both precision and recall being 0.81. By applying our framework to a myeloid-derived suppressor cells (MDSC) dataset, we further demonstrate that this framework is effective in helping build multi-scale biomolecular systems models with new insights on regulatory and causal links for downstream biological interpretations.
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Affiliation(s)
- Zongliang Yue
- Informatics Institute, the University of Alabama at Birmingham, Birmingham, AL 35233, US.
| | - Michael T. Neylon
- School of Informatics and Computing, Indiana University, Indianapolis, IN 46202, US.
| | - Thanh Nguyen
- Informatics Institute, the University of Alabama at Birmingham, Birmingham, AL 35233, US.
| | - Timothy Ratliff
- Purdue University Center for Cancer Research, West Lafayette, IN 47906, US.
| | - Jake Y. Chen
- Informatics Institute, the University of Alabama at Birmingham, Birmingham, AL 35233, US.
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5
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Igolkina AA, Samsonova MG. SEM: Structural Equation Modeling in Molecular Biology. Biophysics (Nagoya-shi) 2018. [DOI: 10.1134/s0006350918020100] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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6
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Igolkina AA, Armoskus C, Newman JRB, Evgrafov OV, McIntyre LM, Nuzhdin SV, Samsonova MG. Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling. Front Mol Neurosci 2018; 11:192. [PMID: 29942251 PMCID: PMC6004421 DOI: 10.3389/fnmol.2018.00192] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/15/2018] [Indexed: 01/02/2023] Open
Abstract
Schizophrenia (SCZ) is a psychiatric disorder of unknown etiology. There is evidence suggesting that aberrations in neurodevelopment are a significant attribute of schizophrenia pathogenesis and progression. To identify biologically relevant molecular abnormalities affecting neurodevelopment in SCZ we used cultured neural progenitor cells derived from olfactory neuroepithelium (CNON cells). Here, we tested the hypothesis that variance in gene expression differs between individuals from SCZ and control groups. In CNON cells, variance in gene expression was significantly higher in SCZ samples in comparison with control samples. Variance in gene expression was enriched in five molecular pathways: serine biosynthesis, PI3K-Akt, MAPK, neurotrophin and focal adhesion. More than 14% of variance in disease status was explained within the logistic regression model (C-value = 0.70) by predictors accounting for gene expression in 69 genes from these five pathways. Structural equation modeling (SEM) was applied to explore how the structure of these five pathways was altered between SCZ patients and controls. Four out of five pathways showed differences in the estimated relationships among genes: between KRAS and NF1, and KRAS and SOS1 in the MAPK pathway; between PSPH and SHMT2 in serine biosynthesis; between AKT3 and TSC2 in the PI3K-Akt signaling pathway; and between CRK and RAPGEF1 in the focal adhesion pathway. Our analysis provides evidence that variance in gene expression is an important characteristic of SCZ, and SEM is a promising method for uncovering altered relationships between specific genes thus suggesting affected gene regulation associated with the disease. We identified altered gene-gene interactions in pathways enriched for genes with increased variance in expression in SCZ. These pathways and loci were previously implicated in SCZ, providing further support for the hypothesis that gene expression variance plays important role in the etiology of SCZ.
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Affiliation(s)
- Anna A Igolkina
- Institute of Applied Mathematics and Mechanics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
| | - Chris Armoskus
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Jeremy R B Newman
- Department of Molecular Genetics & Microbiology, Genetics Institute, University of Florida, Gainesville, FL, United States
| | - Oleg V Evgrafov
- Department of Cell Biology, SUNY Downstate Medical Center, Brooklyn, NY, United States
| | - Lauren M McIntyre
- Department of Molecular Genetics & Microbiology, Genetics Institute, University of Florida, Gainesville, FL, United States
| | - Sergey V Nuzhdin
- Institute of Applied Mathematics and Mechanics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia.,Molecular and Computation Biology, University of Southern California, Los Angeles, CA, United States
| | - Maria G Samsonova
- Institute of Applied Mathematics and Mechanics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
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7
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Hidalgo MR, Cubuk C, Amadoz A, Salavert F, Carbonell-Caballero J, Dopazo J. High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes. Oncotarget 2018; 8:5160-5178. [PMID: 28042959 PMCID: PMC5354899 DOI: 10.18632/oncotarget.14107] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 11/21/2016] [Indexed: 12/21/2022] Open
Abstract
Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.
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Affiliation(s)
- Marta R Hidalgo
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Cankut Cubuk
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Alicia Amadoz
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain.,Functional Genomics Node (INB-ELIXIR-es), Valencia, 46012, Spain
| | - Francisco Salavert
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain.,Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Valencia, 46012, Spain
| | - José Carbonell-Caballero
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Joaquin Dopazo
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain.,Functional Genomics Node (INB-ELIXIR-es), Valencia, 46012, Spain.,Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Valencia, 46012, Spain
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8
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Palluzzi F, Ferrari R, Graziano F, Novelli V, Rossi G, Galimberti D, Rainero I, Benussi L, Nacmias B, Bruni AC, Cusi D, Salvi E, Borroni B, Grassi M. A novel network analysis approach reveals DNA damage, oxidative stress and calcium/cAMP homeostasis-associated biomarkers in frontotemporal dementia. PLoS One 2017; 12:e0185797. [PMID: 29020091 PMCID: PMC5636111 DOI: 10.1371/journal.pone.0185797] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Accepted: 09/19/2017] [Indexed: 01/04/2023] Open
Abstract
Frontotemporal Dementia (FTD) is the form of neurodegenerative dementia with the highest prevalence after Alzheimer’s disease, equally distributed in men and women. It includes several variants, generally characterized by behavioural instability and language impairments. Although few mendelian genes (MAPT, GRN, and C9orf72) have been associated to the FTD phenotype, in most cases there is only evidence of multiple risk loci with relatively small effect size. To date, there are no comprehensive studies describing FTD at molecular level, highlighting possible genetic interactions and signalling pathways at the origin FTD-associated neurodegeneration. In this study, we designed a broad FTD genetic interaction map of the Italian population, through a novel network-based approach modelled on the concepts of disease-relevance and interaction perturbation, combining Steiner tree search and Structural Equation Model (SEM) analysis. Our results show a strong connection between Calcium/cAMP metabolism, oxidative stress-induced Serine/Threonine kinases activation, and postsynaptic membrane potentiation, suggesting a possible combination of neuronal damage and loss of neuroprotection, leading to cell death. In our model, Calcium/cAMP homeostasis and energetic metabolism impairments are primary causes of loss of neuroprotection and neural cell damage, respectively. Secondly, the altered postsynaptic membrane potentiation, due to the activation of stress-induced Serine/Threonine kinases, leads to neurodegeneration. Our study investigates the molecular underpinnings of these processes, evidencing key genes and gene interactions that may account for a significant fraction of unexplained FTD aetiology. We emphasized the key molecular actors in these processes, proposing them as novel FTD biomarkers that could be crucial for further epidemiological and molecular studies.
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Affiliation(s)
- Fernando Palluzzi
- Department of Brain and Behavioural Sciences, Medical and Genomic Statistics Unit, University of Pavia, Pavia, Italy
- * E-mail:
| | - Raffaele Ferrari
- Department of Molecular Neuroscience, Institute of Neurology, University College London (UCL), London, United Kingdom
| | - Francesca Graziano
- Department of Brain and Behavioural Sciences, Medical and Genomic Statistics Unit, University of Pavia, Pavia, Italy
| | - Valeria Novelli
- Department of Genetics, Fondazione Policlinico A. Gemelli, Roma, Italy
| | - Giacomina Rossi
- Division of Neurology V and Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy
| | - Daniela Galimberti
- Department of Neurological Sciences, Dino Ferrari Institute, University of Milan, Milano, Italy
| | - Innocenzo Rainero
- Department of Neuroscience, Neurology I, University of Torino and Città della Salute e della Scienza di Torino, Torino, Italy
| | - Luisa Benussi
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Firenze, Italy
| | - Amalia C. Bruni
- Neurogenetic Regional Centre ASPCZ Lamezia Terme, Lamezia Terme (CZ), Italy
| | - Daniele Cusi
- Department of Health Sciences, University of Milan at San Paolo Hospital, Milano, Italy
- Institute of Biomedical Technologies, Italian National Research Council, Milano, Italy
| | - Erika Salvi
- Institute of Biomedical Technologies, Italian National Research Council, Milano, Italy
| | - Barbara Borroni
- Department of Medical Sciences, Neurology Clinic, University of Brescia, Brescia, Italy
| | - Mario Grassi
- Department of Brain and Behavioural Sciences, Medical and Genomic Statistics Unit, University of Pavia, Pavia, Italy
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9
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Vitale G, Dicitore A, Pepe D, Gentilini D, Grassi ES, Borghi MO, Gelmini G, Cantone MC, Gaudenzi G, Misso G, Di Blasio AM, Hofland LJ, Caraglia M, Persani L. Synergistic activity of everolimus and 5-aza-2'-deoxycytidine in medullary thyroid carcinoma cell lines. Mol Oncol 2017; 11:1007-1022. [PMID: 28453190 PMCID: PMC5537710 DOI: 10.1002/1878-0261.12070] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Revised: 04/14/2017] [Accepted: 04/14/2017] [Indexed: 12/19/2022] Open
Abstract
Medullary thyroid cancer (MTC) is a tumor highly resistant to chemo‐ and radiotherapy. Drug resistance can be induced by epigenetic changes such as aberrant DNA methylation. To overcome drug resistance, we explored a promising approach based on the use of 5‐aza‐2′‐deoxycytidine (AZA), a demethylating agent, in combination with the mTOR inhibitor everolimus in MTC cells (MZ‐CRC‐1 and TT). This combined treatment showed a strong synergistic antiproliferative activity through the induction of apoptosis. The effect of everolimus and/or AZA on genome‐wide expression profiling was evaluated by Illumina BeadChip in MZ‐CRC‐1 cells. An innovative bioinformatic pipeline identified four potential molecular pathways implicated in the synergy between AZA and everolimus: PI3K‐Akt signaling, the neurotrophin pathway, ECM/receptor interaction, and focal adhesion. Among these, the neurotrophin signaling pathway was most directly involved in apoptosis, through the overexpression of NGFR and Bax genes. The increased expression of genes involved in the NGFR‐MAPK10‐TP53‐Bax/Bcl2 pathway during incubation with AZA plus everolimus was validated by western blotting in MZ‐CRC‐1 cells. Interestingly, addition of a neutralizing anti‐NGFR antibody inhibited the synergistic cytotoxic activity between AZA and everolimus. These results open a new therapeutic scenario for MTC and potentially other neuroendocrine tumors, where therapy with mTOR inhibitors is currently approved.
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Affiliation(s)
- Giovanni Vitale
- Department of Clinical Sciences and Community Health (DISCCO), University of Milan, Italy.,Laboratory of Endocrine and Metabolic Research, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Alessandra Dicitore
- Laboratory of Endocrine and Metabolic Research, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | | | - Davide Gentilini
- Molecular Biology Laboratory, Istituto Auxologico Italiano, Milan, Italy
| | - Elisa S Grassi
- Department of Clinical Sciences and Community Health (DISCCO), University of Milan, Italy
| | - Maria O Borghi
- Department of Clinical Sciences and Community Health (DISCCO), University of Milan, Italy.,Experimental Laboratory of Immuno-rheumatologic Researches, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Giulia Gelmini
- Laboratory of Endocrine and Metabolic Research, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Maria C Cantone
- Department of Clinical Sciences and Community Health (DISCCO), University of Milan, Italy
| | - Germano Gaudenzi
- Department of Clinical Sciences and Community Health (DISCCO), University of Milan, Italy
| | - Gabriella Misso
- Department of Biochemistry, Biophysics and General Pathology, Second University of Naples, Italy
| | - Anna M Di Blasio
- Molecular Biology Laboratory, Istituto Auxologico Italiano, Milan, Italy
| | - Leo J Hofland
- Section Endocrinology, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Michele Caraglia
- Department of Biochemistry, Biophysics and General Pathology, Second University of Naples, Italy
| | - Luca Persani
- Department of Clinical Sciences and Community Health (DISCCO), University of Milan, Italy.,Laboratory of Endocrine and Metabolic Research, Istituto Auxologico Italiano IRCCS, Milan, Italy
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10
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Fear JM, Arbeitman MN, Salomon MP, Dalton JE, Tower J, Nuzhdin SV, McIntyre LM. The Wright stuff: reimagining path analysis reveals novel components of the sex determination hierarchy in Drosophila melanogaster. BMC SYSTEMS BIOLOGY 2015; 9:53. [PMID: 26335107 PMCID: PMC4558766 DOI: 10.1186/s12918-015-0200-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Accepted: 08/20/2015] [Indexed: 11/10/2022]
Abstract
BACKGROUND The Drosophila sex determination hierarchy is a classic example of a transcriptional regulatory hierarchy, with sex-specific isoforms regulating morphology and behavior. We use a structural equation modeling approach, leveraging natural genetic variation from two studies on Drosophila female head tissues--DSPR collection (596 F1-hybrids from crosses between DSPR sub-populations) and CEGS population (75 F1-hybrids from crosses between DGRP/Winters lines to a reference strain w1118)--to expand understanding of the sex hierarchy gene regulatory network (GRN). This approach is completely generalizable to any natural population, including humans. RESULTS We expanded the sex hierarchy GRN adding novel links among genes, including a link from fruitless (fru) to Sex-lethal (Sxl) identified in both populations. This link is further supported by the presence of fru binding sites in the Sxl locus. 754 candidate genes were added to the pathway, including the splicing factors male-specific lethal 2 and Rm62 as downstream targets of Sxl which are well-supported links in males. Independent studies of doublesex and transformer mutants support many additions, including evidence for a link between the sex hierarchy and metabolism, via Insulin-like receptor. CONCLUSIONS The genes added in the CEGS population were enriched for genes with sex-biased splicing and components of the spliceosome. A common goal of molecular biologists is to expand understanding about regulatory interactions among genes. Using natural alleles we can not only identify novel relationships, but using supervised approaches can order genes into a regulatory hierarchy. Combining these results with independent large effect mutation studies, allows clear candidates for detailed molecular follow-up to emerge.
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Affiliation(s)
- Justin M Fear
- Department of Molecular Genetics and Microbiology, University of Florida, CGRC Room 116, PO Box 100266, FL 32610-0266, Gainesville, FL, USA.
| | | | - Matthew P Salomon
- Molecular and Computational Biology, University of California, Los Angeles, CA, USA.
| | - Justin E Dalton
- Biomedical Science, Florida State University, Tallahassee, FL, USA.
| | - John Tower
- Molecular and Computational Biology, University of California, Los Angeles, CA, USA.
| | - Sergey V Nuzhdin
- Molecular and Computational Biology, University of California, Los Angeles, CA, USA.
| | - Lauren M McIntyre
- Department of Molecular Genetics and Microbiology, University of Florida, CGRC Room 116, PO Box 100266, FL 32610-0266, Gainesville, FL, USA.
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11
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Estimation of dysregulated pathway regions in MPP+ treated human neuroblastoma SH-EP cells with structural equation model. BIOCHIP JOURNAL 2015. [DOI: 10.1007/s13206-015-9206-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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12
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Do JH. Neurotoxin-induced pathway perturbation in human neuroblastoma SH-EP cells. Mol Cells 2014; 37:672-84. [PMID: 25234470 PMCID: PMC4179136 DOI: 10.14348/molcells.2014.0173] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Revised: 08/09/2014] [Accepted: 08/11/2014] [Indexed: 01/20/2023] Open
Abstract
The exact causes of cell death in Parkinson's disease (PD) remain unknown despite extensive studies on PD.The identification of signaling and metabolic pathways involved in PD might provide insight into the molecular mechanisms underlying PD. The neurotoxin 1-methyl-4-phenylpyridinium (MPP(+)) induces cellular changes characteristic of PD, and MPP(+)-based models have been extensively used for PD studies. In this study, pathways that were significantly perturbed in MPP(+)-treated human neuroblastoma SH-EP cells were identified from genome-wide gene expression data for five time points (1.5, 3, 9, 12, and 24 h) after treatment. The mitogen-activated protein kinase (MAPK) signaling pathway and endoplasmic reticulum (ER) protein processing pathway showed significant perturbation at all time points. Perturbation of each of these pathways resulted in the common outcome of upregulation of DNA-damage-inducible transcript 3 (DDIT3). Genes involved in ER protein processing pathway included ubiquitin ligase complex genes and ER-associated degradation (ERAD)-related genes. Additionally, overexpression of DDIT3 might induce oxidative stress via glutathione depletion as a result of overexpression of CHAC1. This study suggests that upregulation of DDIT3 caused by perturbation of the MAPK signaling pathway and ER protein processing pathway might play a key role in MPP(+)-induced neuronal cell death. Moreover, the toxicity signal of MPP(+) resulting from mitochondrial dysfunction through inhibition of complex I of the electron transport chain might feed back to the mitochondria via ER stress. This positive feedback could contribute to amplification of the death signal induced by MPP(+).
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Affiliation(s)
- Jin Hwan Do
- Department of Biomolecular and Chemical Engineering, DongYang University, Yeongju 750-711, Korea
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