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Hsieh PH, Lopes-Ramos CM, Zucknick M, Sandve GK, Glass K, Kuijjer ML. Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data. Bioinformatics 2023; 39:btad610. [PMID: 37802917 PMCID: PMC10598588 DOI: 10.1093/bioinformatics/btad610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 08/05/2023] [Accepted: 10/05/2023] [Indexed: 10/08/2023] Open
Abstract
MOTIVATION Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes. RESULTS We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data. AVAILABILITY AND IMPLEMENTATION The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.
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Affiliation(s)
- Ping-Han Hsieh
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo 0318, Norway
- Department of Informatics, University of Oslo, Oslo 0316, Norway
| | - Camila Miranda Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Manuela Zucknick
- Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo 0317, Norway
| | | | - Kimberly Glass
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Marieke Lydia Kuijjer
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo 0318, Norway
- Department of Pathology, Leiden University Medical Center, Leiden 2300RC, The Netherlands
- Leiden Center of Computational Oncology, Leiden University Medical Center,Leiden 2300RC, The Netherlands
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2
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Belova T, Biondi N, Hsieh PH, Lutsik P, Chudasama P, Kuijjer M. Heterogeneity in the gene regulatory landscape of leiomyosarcoma. NAR Cancer 2023; 5:zcad037. [PMID: 37492373 PMCID: PMC10365024 DOI: 10.1093/narcan/zcad037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 07/06/2023] [Accepted: 07/18/2023] [Indexed: 07/27/2023] Open
Abstract
Characterizing inter-tumor heterogeneity is crucial for selecting suitable cancer therapy, as the presence of diverse molecular subgroups of patients can be associated with disease outcome or response to treatment. While cancer subtypes are often characterized by differences in gene expression, the mechanisms driving these differences are generally unknown. We set out to model the regulatory mechanisms driving sarcoma heterogeneity based on patient-specific, genome-wide gene regulatory networks. We developed a new computational framework, PORCUPINE, which combines knowledge on biological pathways with permutation-based network analysis to identify pathways that exhibit significant regulatory heterogeneity across a patient population. We applied PORCUPINE to patient-specific leiomyosarcoma networks modeled on data from The Cancer Genome Atlas and validated our results in an independent dataset from the German Cancer Research Center. PORCUPINE identified 37 heterogeneously regulated pathways, including pathways representing potential targets for treatment of subgroups of leiomyosarcoma patients, such as FGFR and CTLA4 inhibitory signaling. We validated the detected regulatory heterogeneity through analysis of networks and chromatin states in leiomyosarcoma cell lines. We showed that the heterogeneity identified with PORCUPINE is not associated with methylation profiles or clinical features, thereby suggesting an independent mechanism of patient heterogeneity driven by the complex landscape of gene regulatory interactions.
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Affiliation(s)
- Tatiana Belova
- Computational Biology and Systems Medicine Group, Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - Nicola Biondi
- Precision Sarcoma Research Group, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases, Heidelberg, Germany
| | - Ping-Han Hsieh
- Computational Biology and Systems Medicine Group, Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Oncology, Catholic University (KU) Leuven, Leuven, Belgium
| | - Priya Chudasama
- Precision Sarcoma Research Group, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases, Heidelberg, Germany
| | - Marieke L Kuijjer
- Computational Biology and Systems Medicine Group, Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, the Netherlands
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3
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Krauze AV, Sierk M, Nguyen T, Chen Q, Yan C, Hu Y, Jiang W, Tasci E, Zgela TC, Sproull M, Mackey M, Shankavaram U, Meerzaman D, Camphausen K. Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel. Front Oncol 2023; 13:1127645. [PMID: 37637066 PMCID: PMC10448824 DOI: 10.3389/fonc.2023.1127645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 06/20/2023] [Indexed: 08/29/2023] Open
Abstract
Background Glioblastomas (GBM) are rapidly progressive, nearly uniformly fatal brain tumors. Proteomic analysis represents an opportunity for noninvasive GBM classification and biological understanding of treatment response. Purpose We analyzed differential proteomic expression pre vs. post completion of concurrent chemoirradiation (CRT) in patient serum samples to explore proteomic alterations and classify GBM by integrating clinical and proteomic parameters. Materials and methods 82 patients with GBM were clinically annotated and serum samples obtained pre- and post-CRT. Serum samples were then screened using the aptamer-based SOMAScan® proteomic assay. Significant traits from uni- and multivariate Cox models for overall survival (OS) were designated independent prognostic factors and principal component analysis (PCA) was carried out. Differential expression of protein signals was calculated using paired t-tests, with KOBAS used to identify associated KEGG pathways. GSEA pre-ranked analysis was employed on the overall list of differentially expressed proteins (DEPs) against the MSigDB Hallmark, GO Biological Process, and Reactome databases with weighted gene correlation network analysis (WGCNA) and Enrichr used to validate pathway hits internally. Results 3 clinical clusters of patients with differential survival were identified. 389 significantly DEPs pre vs. post-treatment were identified, including 284 upregulated and 105 downregulated, representing several pathways relevant to cancer metabolism and progression. The lowest survival group (median OS 13.2 months) was associated with DEPs affiliated with proliferative pathways and exhibiting distinct oppositional response including with respect to radiation therapy related pathways, as compared to better-performing groups (intermediate, median OS 22.4 months; highest, median OS 28.7 months). Opposite signaling patterns across multiple analyses in several pathways (notably fatty acid metabolism, NOTCH, TNFα via NF-κB, Myc target V1 signaling, UV response, unfolded protein response, peroxisome, and interferon response) were distinct between clinical survival groups and supported by WGCNA. 23 proteins were statistically signficant for OS with 5 (NETO2, CST7, SEMA6D, CBLN4, NPS) supported by KM. Conclusion Distinct proteomic alterations with hallmarks of cancer, including progression, resistance, stemness, and invasion, were identified in serum samples obtained from GBM patients pre vs. post CRT and corresponded with clinical survival. The proteome can potentially be employed for glioma classification and biological interrogation of cancer pathways.
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Affiliation(s)
- Andra Valentina Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Michael Sierk
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - Trinh Nguyen
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - Qingrong Chen
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - Chunhua Yan
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - Ying Hu
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - William Jiang
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Erdal Tasci
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Theresa Cooley Zgela
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Mary Sproull
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Megan Mackey
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Uma Shankavaram
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Daoud Meerzaman
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - Kevin Camphausen
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
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4
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Ben Guebila M, Wang T, Lopes-Ramos CM, Fanfani V, Weighill D, Burkholz R, Schlauch D, Paulson JN, Altenbuchinger M, Shutta KH, Sonawane AR, Lim J, Calderer G, van IJzendoorn DGP, Morgan D, Marin A, Chen CY, Song Q, Saha E, DeMeo DL, Padi M, Platig J, Kuijjer ML, Glass K, Quackenbush J. The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks. Genome Biol 2023; 24:45. [PMID: 36894939 PMCID: PMC9999668 DOI: 10.1186/s13059-023-02877-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 02/15/2023] [Indexed: 03/11/2023] Open
Abstract
Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.
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Affiliation(s)
- Marouen Ben Guebila
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tian Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Biology Department, Boston College, Chestnut Hill, MA, USA
| | - Camila M Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Viola Fanfani
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Des Weighill
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rebekka Burkholz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
| | - Daniel Schlauch
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Genospace, LLC, Boston, MA, USA
| | - Joseph N Paulson
- Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Michael Altenbuchinger
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Katherine H Shutta
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Abhijeet R Sonawane
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - James Lim
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
- Present Address: Monoceros Biosystems, LLC, San Diego, CA, USA
| | - Genis Calderer
- Center for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo, Oslo, Norway
| | - David G P van IJzendoorn
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Present Address: Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Daniel Morgan
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: School of Biomedical Sciences, Hong Kong University, Pokfulam, Hong Kong
| | | | - Cho-Yi Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Present Address: Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Qi Song
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Enakshi Saha
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Megha Padi
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - John Platig
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Marieke L Kuijjer
- Center for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Center for Computational Oncology, Leiden University, Leiden, The Netherlands
| | - Kimberly Glass
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Dana-Farber Cancer Institute, Boston, MA, USA.
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5
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Galindez G, Sadegh S, Baumbach J, Kacprowski T, List M. Network-based approaches for modeling disease regulation and progression. Comput Struct Biotechnol J 2022; 21:780-795. [PMID: 36698974 PMCID: PMC9841310 DOI: 10.1016/j.csbj.2022.12.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
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Affiliation(s)
- Gihanna Galindez
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.,Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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6
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Bou Zerdan M, Atoui A, Hijazi A, Basbous L, Abou Zeidane R, Alame SM, Assi HI. Latest updates on cellular and molecular biomarkers of gliomas. Front Oncol 2022; 12:1030366. [PMID: 36425564 PMCID: PMC9678906 DOI: 10.3389/fonc.2022.1030366] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 10/05/2022] [Indexed: 03/05/2024] Open
Abstract
Gliomas are the most common central nervous system malignancies, compromising almost 80% of all brain tumors and is associated with significant mortality. The classification of gliomas has shifted from basic histological perspective to one that is based on molecular biomarkers. Treatment of this type of tumors consists currently of surgery, chemotherapy and radiation therapy. During the past years, there was a limited development of effective glioma diagnostics and therapeutics due to multiple factors including the presence of blood-brain barrier and the heterogeneity of this type of tumors. Currently, it is necessary to highlight the advantage of molecular diagnosis of gliomas to develop patient targeted therapies based on multiple oncogenic pathway. In this review, we will evaluate the development of cellular and molecular biomarkers for the diagnosis of gliomas and the impact of these diagnostic tools for better tailored and targeted therapies.
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Affiliation(s)
- Maroun Bou Zerdan
- Department of Internal Medicine, State University of New York (SUNY) Upstate Medical University, Syracuse, NY, United States
| | - Ali Atoui
- Hematology-Oncology Division, Internal Medicine Department, American University of Beirut Medical Center, Beirut, Lebanon
| | - Ali Hijazi
- Hematology-Oncology Division, Internal Medicine Department, American University of Beirut Medical Center, Beirut, Lebanon
| | - Lynn Basbous
- Hematology-Oncology Division, Internal Medicine Department, American University of Beirut Medical Center, Beirut, Lebanon
| | - Reine Abou Zeidane
- Hematology-Oncology Division, Internal Medicine Department, American University of Beirut Medical Center, Beirut, Lebanon
| | - Saada M Alame
- Department of Pediatrics, Faculty of Medicine, Lebanese University, Beirut, Lebanon
| | - Hazem I Assi
- Hematology-Oncology Division, Internal Medicine Department, American University of Beirut Medical Center, Beirut, Lebanon
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7
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de Toledo VHC, Feltrin AS, Barbosa AR, Tahira AC, Brentani H. Sex differences in gene regulatory networks during mid-gestational brain development. Front Hum Neurosci 2022; 16:955607. [PMID: 36061507 PMCID: PMC9428411 DOI: 10.3389/fnhum.2022.955607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Neurodevelopmental disorders differ considerably between males and females, and fetal brain development is one of the most critical periods to determine risk for these disorders. Transcriptomic studies comparing male and female fetal brain have demonstrated that the highest difference in gene expression occurs in sex chromosomes, but several autossomal genes also demonstrate a slight difference that has not been yet explored. In order to investigate biological pathways underlying fetal brain sex differences, we applied medicine network principles using integrative methods such as co-expression networks (CEMiTool) and regulatory networks (netZoo). The pattern of gene expression from genes in the same pathway tend to reflect biologically relevant phenomena. In this study, network analysis of fetal brain expression reveals regulatory differences between males and females. Integrating two different bioinformatics tools, our results suggest that biological processes such as cell cycle, cell differentiation, energy metabolism and extracellular matrix organization are consistently sex-biased. MSET analysis demonstrates that these differences are relevant to neurodevelopmental disorders, including autism.
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Affiliation(s)
- Victor Hugo Calegari de Toledo
- Departamento e Instituto de Psiquiatria, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo, Brazil
- Laboratório de Psicopatologia e Terapêutica Psiquiátrica (LIM23), Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | | | | | - Ana Carolina Tahira
- Laboratório de Expressão Gênica, Departamento de Parasitologia, Instituto Butantan, São Paulo, Brazil
| | - Helena Brentani
- Departamento e Instituto de Psiquiatria, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo, Brazil
- Laboratório de Psicopatologia e Terapêutica Psiquiátrica (LIM23), Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo, Brazil
- *Correspondence: Helena Brentani
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8
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An online notebook resource for reproducible inference, analysis and publication of gene regulatory networks. Nat Methods 2022; 19:511-513. [PMID: 35459940 PMCID: PMC9239854 DOI: 10.1038/s41592-022-01479-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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9
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Guebila MB, Morgan DC, Glass K, Kuijjer ML, DeMeo DL, Quackenbush J. gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit. NAR Genom Bioinform 2022; 4:lqac002. [PMID: 35156023 PMCID: PMC8826808 DOI: 10.1093/nargab/lqac002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 12/28/2021] [Accepted: 02/02/2022] [Indexed: 11/14/2022] Open
Abstract
Gene regulatory network inference allows for the modeling of genome-scale regulatory processes that are altered during development, in disease, and in response to perturbations. Our group has developed a collection of tools to model various regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, as well as gene regulation in individual samples (LIONESS). These methods work by postulating a network structure and then optimizing that structure to be consistent with multiple lines of biological evidence through repeated operations on data matrices. Although our methods are widely used, the corresponding computational complexity, and the associated costs and run times, do limit some applications. To improve the cost/time performance of these algorithms, we developed gpuZoo which implements GPU-accelerated calculations, dramatically improving the performance of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than multi-core CPU implementation of the same methods. gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.
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Affiliation(s)
- Marouen Ben Guebila
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Daniel C Morgan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kimberly Glass
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Marieke L Kuijjer
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo, Norway
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
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