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Article ; Online: CaMKII activity and metabolic imbalance-related neurological diseases: Focus on vascular dysfunction, synaptic plasticity, amyloid beta accumulation, and lipid metabolism.

Yong, Jeongsik / Song, Juhyun

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

2024 Volume 175, Page(s) 116688

Abstract: Metabolic syndrome (MetS) is characterized by insulin resistance, hyperglycemia, excessive fat accumulation and dyslipidemia, and is known to be accompanied by neuropathological symptoms such as memory loss, anxiety, and depression. As the number of MetS ...

Abstract	Metabolic syndrome (MetS) is characterized by insulin resistance, hyperglycemia, excessive fat accumulation and dyslipidemia, and is known to be accompanied by neuropathological symptoms such as memory loss, anxiety, and depression. As the number of MetS patients is rapidly increasing globally, studies on the mechanisms of metabolic imbalance-related neuropathology are emerging as an important issue. Ca2+/calmodulin-dependent kinase II (CaMKII) is the main Ca
Language	English
Publishing date	2024-04-30
Publishing country	France
Document type	Journal Article ; Review
ZDB-ID	392415-4
ISSN	1950-6007 ; 0753-3322 ; 0300-0893
ISSN (online)	1950-6007
ISSN	0753-3322 ; 0300-0893
DOI	10.1016/j.biopha.2024.116688
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Article ; Online: RNA and Protein Interactomes of an RNA-Binding Protein Tagged with FLAG Epitopes Using Combinatory Approaches of Genome Engineering and Stable Transfection.

Cheng, Sze / Park, Meeyeon / Yong, Jeongsik

Methods in molecular biology (Clifton, N.J.)

2023 Volume 2666, Page(s) 247–263

Abstract: To study the function of RNA-binding proteins (RBPs), an overexpression or knockout approach is generally used. However, as many RBPs are essential to cellular functions, the complete knockout of these proteins may be lethal to the cell. Overexpression ... ...

Abstract	To study the function of RNA-binding proteins (RBPs), an overexpression or knockout approach is generally used. However, as many RBPs are essential to cellular functions, the complete knockout of these proteins may be lethal to the cell. Overexpression of RBPs, on the other hand, may create an altered transcriptome and aberrant phenotypes that can mask their physiological function. Additionally, biochemical characterization of RBP often requires highly specific antibodies for efficient immunoprecipitation for downstream mass spectrometry or RNA footprinting profiling. To overcome these hurdles, we have developed a strategy to generate cellular systems either using a CRISPR-Cas9-mediated epitope tag knock-in approach or a two-step workflow to first stably express an exogenous Flag-tagged RBP and subsequently knockout the endogenous RBP using CRISPR-Cas9 gene editing. The generation of these cell lines will be beneficial for downstream RNA footprinting studies and mass spectrometry-mediated interactome studies.
MeSH term(s)	RNA/genetics ; Epitopes/genetics ; RNA-Binding Proteins/metabolism ; Transfection ; Gene Editing/methods ; CRISPR-Cas Systems/genetics
Chemical Substances	RNA (63231-63-0) ; Epitopes ; RNA-Binding Proteins
Language	English
Publishing date	2023-05-11
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-3191-1_18
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Article ; Online: Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion.

Li, Zhuliu / Song, Tianci / Yong, Jeongsik / Kuang, Rui

PLoS computational biology

2021 Volume 17, Issue 4, Page(s) e1008218

Abstract: High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to ... ...

Abstract	High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.
MeSH term(s)	Algorithms ; Animals ; Datasets as Topic ; Gene Expression Profiling ; Kidney/metabolism ; Mice ; Sequence Analysis, RNA/methods ; Transcriptome
Language	English
Publishing date	2021-04-07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1008218
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Article: mTOR-coordinated Post-Transcriptional Gene Regulations: from Fundamental to Pathogenic Insights.

Yeh, Hsin-Sung / Yong, Jeongsik

Journal of lipid and atherosclerosis

2019 Volume 9, Issue 1, Page(s) 8–22

Abstract: Post-transcriptional regulations of mRNA transcripts such as alternative splicing and alternative polyadenylation can affect the expression of genes without changing the transcript levels. Recent studies have demonstrated that these post-transcriptional ... ...

Abstract	Post-transcriptional regulations of mRNA transcripts such as alternative splicing and alternative polyadenylation can affect the expression of genes without changing the transcript levels. Recent studies have demonstrated that these post-transcriptional events can have significant physiological impacts on various biological systems and play important roles in the pathogenesis of a number of diseases, including cancers. Nevertheless, how cellular signaling pathways control these post-transcriptional processes in cells are not very well explored in the field yet. The mammalian target of rapamycin complex 1 (mTORC1) pathway plays a key role in sensing cellular nutrient and energy status and regulating the proliferation and growth of cells by controlling various anabolic and catabolic processes. Dysregulation of mTORC1 pathway can tip the metabolic balance of cells and is associated with a number of pathological conditions, including various types of cancers, diabetes, and cardiovascular diseases. Numerous reports have shown that mTORC1 controls its downstream pathways through translational and/or transcriptional regulation of the expression of key downstream effectors. And, recent studies have also shown that mTORC1 can control downstream pathways via post-transcriptional regulations. In this review, we will discuss the roles of post-transcriptional processes in gene expression regulations and how mTORC1-mediated post-transcriptional regulations contribute to cellular physiological changes. We highlight post-transcriptional regulation as an additional layer of gene expression control by mTORC1 to steer cellular biology. These emphasize the importance of studying post-transcriptional events in transcriptome datasets for gaining a fuller understanding of gene expression regulations in the biological systems of interest.
Language	English
Publishing date	2019-10-18
Publishing country	Korea (South)
Document type	Journal Article ; Review
ZDB-ID	3016001-7
ISSN	2288-2561 ; 2287-2892
ISSN (online)	2288-2561
ISSN	2287-2892
DOI	10.12997/jla.2020.9.1.8
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Article ; Online: Immune Modulation of Adipocyte Mitochondrial Metabolism.

Hertzel, Ann V / Yong, Jeongsik / Chen, Xiaoli / Bernlohr, David A

Endocrinology

2022 Volume 163, Issue 8

Abstract: Immune cells infiltrate adipose tissue as a function of age, sex, and diet, leading to a variety of regulatory processes linked to metabolic disease and dysfunction. Cytokines and chemokines produced by resident macrophages, B cells, T cells and ... ...

Abstract	Immune cells infiltrate adipose tissue as a function of age, sex, and diet, leading to a variety of regulatory processes linked to metabolic disease and dysfunction. Cytokines and chemokines produced by resident macrophages, B cells, T cells and eosinophils play major role(s) in fat cell mitochondrial functions modulating pyruvate oxidation, electron transport and oxidative stress, branched chain amino acid metabolism, fatty acid oxidation, and apoptosis. Indeed, cytokine-dependent downregulation of numerous genes affecting mitochondrial metabolism is strongly linked to the development of the metabolic syndrome, whereas the potentiation of mitochondrial metabolism represents a counterregulatory process improving metabolic outcomes. In contrast, inflammatory cytokines activate mitochondrially linked cell death pathways such as apoptosis, pyroptosis, necroptosis, and ferroptosis. As such, the adipocyte mitochondrion represents a major intersection point for immunometabolic regulation of central metabolism.
MeSH term(s)	Adipocytes/metabolism ; Adipose Tissue/metabolism ; Cytokines/metabolism ; Mitochondria/metabolism ; Oxidative Stress
Chemical Substances	Cytokines
Language	English
Publishing date	2022-06-21
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	427856-2
ISSN	1945-7170 ; 0013-7227
ISSN (online)	1945-7170
ISSN	0013-7227
DOI	10.1210/endocr/bqac094
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Article ; Online: Deletion of exons 2 and 3 from Actb and cell immortalization lead to widespread, β-actin independent alterations in gene expression associated with cell cycle control.

Sundby, Lauren J / Southern, William M / Sun, Jiao / Patrinostro, Xiaobai / Zhang, Wei / Yong, Jeongsik / Ervasti, James M

European journal of cell biology

2024 Volume 103, Issue 2, Page(s) 151397

Abstract: The cytoplasmic actin proteins, β- and γ-actin, are 99% identical but thought to perform non-redundant functions. The nucleotide coding regions of cytoplasmic actin genes, Actb and Actg1, are 89% identical. Knockout (KO) of Actb by Cre-mediated deletion ... ...

Abstract	The cytoplasmic actin proteins, β- and γ-actin, are 99% identical but thought to perform non-redundant functions. The nucleotide coding regions of cytoplasmic actin genes, Actb and Actg1, are 89% identical. Knockout (KO) of Actb by Cre-mediated deletion of first coding exons 2 and 3 in mice is embryonic lethal and fibroblasts derived from KO embryos (MEFs) fail to proliferate. In contrast, Actg1 KO MEFs display with a much milder defect in cell proliferation and Actg1 KO mice are viable, but present with increased perinatal lethality. Recent studies have identified important protein-independent functions for both Actb and Actg1 and demonstrate that deletions within the Actb nucleotide sequence, and not loss of the β-actin protein, cause the most severe phenotypes in KO mice and cells. Here, we use a multi-omics approach to better understand what drives the phenotypes of Actb KO MEFs. RNA-sequencing and mass spectrometry reveal largescale changes to the transcriptome, proteome, and phosphoproteome in cells lacking Actb but not those only lacking β-actin protein. Pathway analysis of genes and proteins differentially expressed upon Actb KO suggest widespread dysregulation of genes involved in the cell cycle that may explain the severe defect in proliferation.
Language	English
Publishing date	2024-02-15
Publishing country	Germany
Document type	Journal Article
ZDB-ID	391967-5
ISSN	1618-1298 ; 0070-2463 ; 0171-9335
ISSN (online)	1618-1298
ISSN	0070-2463 ; 0171-9335
DOI	10.1016/j.ejcb.2024.151397
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Zs.A 667: Show issues

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Article ; Online: Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.

Tanvir Ahmed, Khandakar / Cheng, Sze / Li, Qian / Yong, Jeongsik / Zhang, Wei

Briefings in bioinformatics

2022 Volume 24, Issue 1

Abstract: Type 1 diabetes (T1D) outcome prediction plays a vital role in identifying novel risk factors, ensuring early patient care and designing cohort studies. TEDDY is a longitudinal cohort study that collects a vast amount of multi-omics and clinical data ... ...

Abstract	Type 1 diabetes (T1D) outcome prediction plays a vital role in identifying novel risk factors, ensuring early patient care and designing cohort studies. TEDDY is a longitudinal cohort study that collects a vast amount of multi-omics and clinical data from its participants to explore the progression and markers of T1D. However, missing data in the omics profiles make the outcome prediction a difficult task. TEDDY collected time series gene expression for less than 6% of enrolled participants. Additionally, for the participants whose gene expressions are collected, 79% time steps are missing. This study introduces an advanced bioinformatics framework for gene expression imputation and islet autoimmunity (IA) prediction. The imputation model generates synthetic data for participants with partially or entirely missing gene expression. The prediction model integrates the synthetic gene expression with other risk factors to achieve better predictive performance. Comprehensive experiments on TEDDY datasets show that: (1) Our pipeline can effectively integrate synthetic gene expression with family history, HLA genotype and SNPs to better predict IA status at 2 years (sensitivity 0.622, AUC 0.715) compared with the individual datasets and state-of-the-art results in the literature (AUC 0.682). (2) The synthetic gene expression contains predictive signals as strong as the true gene expression, reducing reliance on expensive and long-term longitudinal data collection. (3) Time series gene expression is crucial to the proposed improvement and shows significantly better predictive ability than cross-sectional gene expression. (4) Our pipeline is robust to limited data availability. Availability: Code is available at https://github.com/compbiolabucf/TEDDY.
MeSH term(s)	Humans ; Diabetes Mellitus, Type 1/genetics ; Autoimmunity/genetics ; Islets of Langerhans ; Longitudinal Studies ; Time Factors ; Cross-Sectional Studies ; Genetic Predisposition to Disease ; Gene Expression
Language	English
Publishing date	2022-12-13
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbac537
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Article ; Online: Alternative Polyadenylation of mRNAs: 3'-Untranslated Region Matters in Gene Expression.

Yeh, Hsin-Sung / Yong, Jeongsik

Molecules and cells

2016 Volume 39, Issue 4, Page(s) 281–285

Abstract: Almost all of eukaryotic mRNAs are subjected to polyadenylation during mRNA processing. Recent discoveries showed that many of these mRNAs contain more than one polyadenylation sites in their 3' untranslated regions (UTR) and that alternative ... ...

Abstract	Almost all of eukaryotic mRNAs are subjected to polyadenylation during mRNA processing. Recent discoveries showed that many of these mRNAs contain more than one polyadenylation sites in their 3' untranslated regions (UTR) and that alternative polyadenylation (APA) is prevalent among these genes. Many biological processes such as differentiation, proliferation, and tumorigenesis have been correlated to global APA events in the 3' UTR of mRNAs, suggesting that these APA events are tightly regulated and may play important physiological roles. In this review, recent discoveries in the physiological roles of APA events, as well as the known and proposed mechanisms are summarized. Perspective for future directions is also discussed.
MeSH term(s)	3' Untranslated Regions ; Animals ; Gene Expression ; Gene Expression Regulation ; Humans ; Polyadenylation ; RNA, Messenger/genetics ; RNA, Messenger/metabolism
Chemical Substances	3' Untranslated Regions ; RNA, Messenger
Language	English
Publishing date	2016-02-25
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1148964-9
ISSN	0219-1032 ; 1016-8478
ISSN (online)	0219-1032
ISSN	1016-8478
DOI	10.14348/molcells.2016.0035
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Zs.A 4713: Show issues

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Article ; Online: Multi-omics data integration by generative adversarial network.

Ahmed, Khandakar Tanvir / Sun, Jiao / Cheng, Sze / Yong, Jeongsik / Zhang, Wei

Bioinformatics (Oxford, England)

2021 Volume 38, Issue 1, Page(s) 179–186

Abstract: Motivation: Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is ... ...

Abstract	Motivation: Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals. Results: Large-scale experiments on The Cancer Genome Atlas breast cancer, lung cancer and ovarian cancer datasets validate that (i) the model can effectively integrate two omics data (e.g. mRNA and microRNA expression data) and their interaction network (e.g. microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (ii) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals. Availability and implementation: Source code is available at: https://github.com/CompbioLabUCF/omicsGAN. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Humans ; Multiomics ; Software ; Genome ; Lung Neoplasms ; MicroRNAs/genetics
Chemical Substances	MicroRNAs
Language	English
Publishing date	2021-08-19
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btab608
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Zs.A 2374: Show issues

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Article ; Online: APA-Scan: detection and visualization of 3'-UTR alternative polyadenylation with RNA-seq and 3'-end-seq data.

Fahmi, Naima Ahmed / Ahmed, Khandakar Tanvir / Chang, Jae-Woong / Nassereddeen, Heba / Fan, Deliang / Yong, Jeongsik / Zhang, Wei

BMC bioinformatics

2022 Volume 23, Issue Suppl 3, Page(s) 396

Abstract: Background: The eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3'-untranslated region (3'-UTR) of mRNA produces transcripts with shorter or longer 3'- ... ...

Abstract	Background: The eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3'-untranslated region (3'-UTR) of mRNA produces transcripts with shorter or longer 3'-UTR. Often, 3'-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3'-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3'-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3'-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3'-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations. Methods: APA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3'-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3'-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3'-UTR annotation and read coverage on the 3'-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive user's manual are freely available at https://github.com/compbiolabucf/APA-Scan . Result: APA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3'-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3'-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3'-UTR APA events and improve genome annotation. Conclusion: APA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3'-UTR APA events. The pipeline integrates both RNA-seq and 3'-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots.
MeSH term(s)	3' Untranslated Regions/genetics ; Animals ; Fibroblasts/metabolism ; Mice ; MicroRNAs/metabolism ; Polyadenylation ; Protein Isoforms/genetics ; RNA Precursors/metabolism ; RNA, Messenger/genetics ; RNA, Messenger/metabolism ; RNA-Seq
Chemical Substances	3' Untranslated Regions ; MicroRNAs ; Protein Isoforms ; RNA Precursors ; RNA, Messenger
Language	English
Publishing date	2022-09-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-022-04939-w
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