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Matsui Y, Togayachi A, Sakamoto K, Angata K, Kadomatsu K, Nishihara S. Integrated Systems Analysis Deciphers Transcriptome and Glycoproteome Links in Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.25.573290. [PMID: 38234803 PMCID: PMC10793412 DOI: 10.1101/2023.12.25.573290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Glycosylation is increasingly recognized as a potential therapeutic target in Alzheimer's disease. In recent years, evidence of Alzheimer's disease-specific glycoproteins has been established. However, the mechanisms underlying their dysregulation, including tissue- and cell-type specificity, are not fully understood. We aimed to explore the upstream regulators of aberrant glycosylation by integrating multiple data sources using a glycogenomics approach. We identified dysregulation of the glycosyltransferase PLOD3 in oligodendrocytes as an upstream regulator of cerebral vessels and found that it is involved in COL4A5 synthesis, which is strongly correlated with amyloid fiber formation. Furthermore, COL4A5 has been suggested to interact with astrocytes via extracellular matrix receptors as a ligand. This study suggests directions for new therapeutic strategies for Alzheimer's disease targeting glycosyltransferases.
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Affiliation(s)
- Yusuke Matsui
- Institute for Glyco-core Research (iGCORE), Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
- Biomedical and Health Informatics Unit, Department of Integrated Health Science, Nagoya University Graduate School of Medicine, Daiko-minami, Higashi-ku, Nagoya, 461-8673, Japan
| | - Akira Togayachi
- Glycan and Life Systems Integration Center (GaLSIC), Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Kazuma Sakamoto
- Institute for Glyco-core Research (iGCORE), Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
- Department of Biochemistry, Nagoya University Graduate School of Medicine, Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Kiyohiko Angata
- Glycan and Life Systems Integration Center (GaLSIC), Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Kenji Kadomatsu
- Institute for Glyco-core Research (iGCORE), Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
- Department of Biochemistry, Nagoya University Graduate School of Medicine, Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Shoko Nishihara
- Glycan and Life Systems Integration Center (GaLSIC), Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
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Agrawal B, Boulos S, Khatib S, Feuermann Y, Panov J, Kaphzan H. Molecular Insights into Transcranial Direct Current Stimulation Effects: Metabolomics and Transcriptomics Analyses. Cells 2024; 13:205. [PMID: 38334596 PMCID: PMC10854682 DOI: 10.3390/cells13030205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/14/2024] [Accepted: 01/18/2024] [Indexed: 02/10/2024] Open
Abstract
INTRODUCTION Transcranial direct current stimulation (tDCS) is an evolving non-invasive neurostimulation technique. Despite multiple studies, its underlying molecular mechanisms are still unclear. Several previous human studies of the effect of tDCS suggest that it generates metabolic effects. The induction of metabolic effects by tDCS could provide an explanation for how it generates its long-term beneficial clinical outcome. AIM Given these hints of tDCS metabolic effects, we aimed to delineate the metabolic pathways involved in its mode of action. METHODS To accomplish this, we utilized a broad analytical approach of co-analyzing metabolomics and transcriptomic data generated from anodal tDCS in rat models. Since no metabolomic dataset was available, we performed a tDCS experiment of bilateral anodal stimulation of 200 µA for 20 min and for 5 consecutive days, followed by harvesting the brain tissue below the stimulating electrode and generating a metabolomics dataset using LC-MS/MS. The analysis of the transcriptomic dataset was based on a publicly available dataset. RESULTS Our analyses revealed that tDCS alters the metabolic profile of brain tissue, affecting bioenergetic-related pathways, such as glycolysis and mitochondrial functioning. In addition, we found changes in calcium-related signaling. CONCLUSIONS We conclude that tDCS affects metabolism by modulating energy production-related processes. Given our findings concerning calcium-related signaling, we suggest that the immediate effects of tDCS on calcium dynamics drive modifications in distinct metabolic pathways. A thorough understanding of the underlying molecular mechanisms of tDCS has the potential to revolutionize its applicability, enabling the generation of personalized medicine in the field of neurostimulation and thus contributing to its optimization.
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Affiliation(s)
- Bhanumita Agrawal
- Sagol Department of Neurobiology, University of Haifa, Haifa 3103301, Israel
| | - Soad Boulos
- Sagol Department of Neurobiology, University of Haifa, Haifa 3103301, Israel
| | - Soliman Khatib
- Department of Biotechnology, Tel-Hai College, Upper Galilee 1220800, Israel
| | - Yonatan Feuermann
- Sagol Department of Neurobiology, University of Haifa, Haifa 3103301, Israel
| | - Julia Panov
- Sagol Department of Neurobiology, University of Haifa, Haifa 3103301, Israel
- Tauber Bioinformatics Research Center, University of Haifa, Haifa 3103301, Israel
| | - Hanoch Kaphzan
- Sagol Department of Neurobiology, University of Haifa, Haifa 3103301, Israel
- Tauber Bioinformatics Research Center, University of Haifa, Haifa 3103301, Israel
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Chattopadhyay A, Shih CY, Hsu YC, Juang JMJ, Chuang EY, Lu TP. CLIN_SKAT: an R package to conduct association analysis using functionally relevant variants. BMC Bioinformatics 2022; 23:441. [PMID: 36274122 PMCID: PMC9590128 DOI: 10.1186/s12859-022-04987-2] [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: 05/19/2022] [Accepted: 10/16/2022] [Indexed: 12/03/2022] Open
Abstract
Background Availability of next generation sequencing data, allows low-frequency and rare variants to be studied through strategies other than the commonly used genome-wide association studies (GWAS). Rare variants are important keys towards explaining the heritability for complex diseases that remains to be explained by common variants due to their low effect sizes. However, analysis strategies struggle to keep up with the huge amount of data at disposal therefore creating a bottleneck. This study describes CLIN_SKAT, an R package, that provides users with an easily implemented analysis pipeline with the goal of (i) extracting clinically relevant variants (both rare and common), followed by (ii) gene-based association analysis by grouping the selected variants.
Results CLIN_SKAT offers four simple functions that can be used to obtain clinically relevant variants, map them to genes or gene sets, calculate weights from global healthy populations and conduct weighted case–control analysis. CLIN_SKAT introduces improvements by adding certain pre-analysis steps and customizable features to make the SKAT results clinically more meaningful. Moreover, it offers several plot functions that can be availed towards obtaining visualizations for interpretation of the analyses results. CLIN_SKAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It can be freely downloaded from https://github.com/ShihChingYu/CLIN_SKAT, installed through devtools::install_github("ShihChingYu/CLIN_SKAT", force=T) and executed by loading the package into R using library(CLIN_SKAT). All outputs (tabular and graphical) can be downloaded in simple, publishable formats.
Conclusions Statistical association analysis is often underpowered due to low sample sizes and high numbers of variants to be tested, limiting detection of causal ones. Therefore, retaining a subset of variants that are biologically meaningful seems to be a more effective strategy for identifying explainable associations while reducing the degrees of freedom. CLIN_SKAT offers users a one-stop R package that identifies disease risk variants with improved power via a series of tailor-made procedures that allows dimension reduction, by retaining functionally relevant variants, and incorporating ethnicity based priors. Furthermore, it also eliminates the requirement for high computational resources and bioinformatics expertise. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04987-2.
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Odom GJ, Colaprico A, Silva TC, Chen XS, Wang L. PathwayMultiomics: An R Package for Efficient Integrative Analysis of Multi-Omics Datasets With Matched or Un-matched Samples. Front Genet 2022; 12:783713. [PMID: 35003218 PMCID: PMC8729182 DOI: 10.3389/fgene.2021.783713] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 12/07/2021] [Indexed: 01/27/2023] Open
Abstract
Recent advances in technology have made multi-omics datasets increasingly available to researchers. To leverage the wealth of information in multi-omics data, a number of integrative analysis strategies have been proposed recently. However, effectively extracting biological insights from these large, complex datasets remains challenging. In particular, matched samples with multiple types of omics data measured on each sample are often required for multi-omics analysis tools, which can significantly reduce the sample size. Another challenge is that analysis techniques such as dimension reductions, which extract association signals in high dimensional datasets by estimating a few variables that explain most of the variations in the samples, are typically applied to whole-genome data, which can be computationally demanding. Here we present pathwayMultiomics, a pathway-based approach for integrative analysis of multi-omics data with categorical, continuous, or survival outcome variables. The input of pathwayMultiomics is pathway p-values for individual omics data types, which are then integrated using a novel statistic, the MiniMax statistic, to prioritize pathways dysregulated in multiple types of omics datasets. Importantly, pathwayMultiomics is computationally efficient and does not require matched samples in multi-omics data. We performed a comprehensive simulation study to show that pathwayMultiomics significantly outperformed currently available multi-omics tools with improved power and well-controlled false-positive rates. In addition, we also analyzed real multi-omics datasets to show that pathwayMultiomics was able to recover known biology by nominating biologically meaningful pathways in complex diseases such as Alzheimer's disease.
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Affiliation(s)
- Gabriel J Odom
- Department of Biostatistics, Stempel College of Public Health, Florida International University, Miami, FL, United States.,Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Antonio Colaprico
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Tiago C Silva
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - X Steven Chen
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States.,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Lily Wang
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States.,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States.,Dr. John T Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, United States.,John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, United States
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Pettini F, Visibelli A, Cicaloni V, Iovinelli D, Spiga O. Multi-Omics Model Applied to Cancer Genetics. Int J Mol Sci 2021; 22:ijms22115751. [PMID: 34072237 PMCID: PMC8199287 DOI: 10.3390/ijms22115751] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/18/2021] [Accepted: 05/26/2021] [Indexed: 12/29/2022] Open
Abstract
In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and transcriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.
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Affiliation(s)
- Francesco Pettini
- Department of Medical Biotechnology, University of Siena, Via M. Bracci 2, 53100 Siena, Italy
- Correspondence: ; Tel.: +39-3755461426
| | - Anna Visibelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via A. Moro 2, 53100 Siena, Italy; (A.V.); (D.I.); (O.S.)
| | - Vittoria Cicaloni
- Toscana Life Sciences Foundation, Via Fiorentina 1, 53100 Siena, Italy;
| | - Daniele Iovinelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via A. Moro 2, 53100 Siena, Italy; (A.V.); (D.I.); (O.S.)
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via A. Moro 2, 53100 Siena, Italy; (A.V.); (D.I.); (O.S.)
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Danna V, Mitchell H, Anderson L, Godinez I, Gosline SJC, Teeguarden J, McDermott JE. leapR: An R Package for Multiomic Pathway Analysis. J Proteome Res 2021; 20:2116-2121. [PMID: 33703901 DOI: 10.1021/acs.jproteome.0c00963] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A generalized goal of many high-throughput data studies is to identify functional mechanisms that underlie observed biological phenomena, whether they be disease outcomes or metabolic output. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, transcriptomic, proteomic, metabolomic) are faced with a challenge of summarizing the data to generate testable hypotheses. However, this requires a time-consuming process to evaluate numerous statistical methods across numerous data sources. Here, we introduce the leapR package, a framework to rapidly assess biological pathway activity using diverse statistical tests and data sources, allowing facile integration of multisource data. The leapR package with a user manual and example workflow is available for download from GitHub (https://github.com/biodataganache/leapR).
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Affiliation(s)
- Vincent Danna
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Hugh Mitchell
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Lindsey Anderson
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Iobani Godinez
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Sara J C Gosline
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Justin Teeguarden
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Jason E McDermott
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.,Department of Molecular Microbiology and Immunology, Oregon Health & Sciences University, Portland, Oregon 97201, United States
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Wen B, Zhang B. Computational Proteomics: Focus on Deep Learning. Proteomics 2020; 20:e2000258. [PMID: 33210458 DOI: 10.1002/pmic.202000258] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 10/14/2020] [Indexed: 11/09/2022]
Affiliation(s)
- Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
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COCOA: coordinate covariation analysis of epigenetic heterogeneity. Genome Biol 2020; 21:240. [PMID: 32894181 PMCID: PMC7487606 DOI: 10.1186/s13059-020-02139-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 08/07/2020] [Indexed: 12/20/2022] Open
Abstract
A key challenge in epigenetics is to determine the biological significance of epigenetic variation among individuals. We present Coordinate Covariation Analysis (COCOA), a computational framework that uses covariation of epigenetic signals across individuals and a database of region sets to annotate epigenetic heterogeneity. COCOA is the first such tool for DNA methylation data and can also analyze any epigenetic signal with genomic coordinates. We demonstrate COCOA’s utility by analyzing DNA methylation, ATAC-seq, and multi-omic data in supervised and unsupervised analyses, showing that COCOA provides new understanding of inter-sample epigenetic variation. COCOA is available on Bioconductor (http://bioconductor.org/packages/COCOA).
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