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Article ; Online: Meta-Analysis of Dilated Cardiomyopathy Using Cardiac RNA-Seq Transcriptomic Datasets.

Alimadadi, Ahmad / Munroe, Patricia B / Joe, Bina / Cheng, Xi

2020 Volume 11, Issue 1

Abstract: Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Several studies have used RNA-sequencing (RNA-seq) to profile differentially expressed genes (DEGs) associated with DCM. In this study, we aimed to profile gene expression ... ...

Abstract	Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Several studies have used RNA-sequencing (RNA-seq) to profile differentially expressed genes (DEGs) associated with DCM. In this study, we aimed to profile gene expression signatures and identify novel genes associated with DCM through a quantitative meta-analysis of three publicly available RNA-seq studies using human left ventricle tissues from 41 DCM cases and 21 control samples. Our meta-analysis identified 789 DEGs including 581 downregulated and 208 upregulated genes. Several DCM-related genes previously reported, including
MeSH term(s)	Cardiomyopathy, Dilated/genetics ; Cardiomyopathy, Dilated/metabolism ; Databases, Nucleic Acid ; Down-Regulation ; Gene Expression Profiling ; Humans ; RNA-Seq ; Transcriptome ; Up-Regulation
Language	English
Publishing date	2020-01-04
Publishing country	Switzerland
Document type	Journal Article ; Meta-Analysis ; Research Support, Non-U.S. Gov't
ZDB-ID	2527218-4
ISSN	2073-4425 ; 2073-4425
ISSN (online)	2073-4425
ISSN	2073-4425
DOI	10.3390/genes11010060
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Identification of Upstream Transcriptional Regulators of Ischemic Cardiomyopathy Using Cardiac RNA-Seq Meta-Analysis.

Alimadadi, Ahmad / Aryal, Sachin / Manandhar, Ishan / Joe, Bina / Cheng, Xi

International journal of molecular sciences

2020 Volume 21, Issue 10

Abstract: Ischemic cardiomyopathy (ICM), characterized by pre-existing myocardial infarction or severe coronary artery disease, is the major cause of heart failure (HF). Identification of novel transcriptional regulators in ischemic HF can provide important ... ...

Abstract	Ischemic cardiomyopathy (ICM), characterized by pre-existing myocardial infarction or severe coronary artery disease, is the major cause of heart failure (HF). Identification of novel transcriptional regulators in ischemic HF can provide important biomarkers for developing new diagnostic and therapeutic strategies. In this study, we used four RNA-seq datasets from four different studies, including 41 ICM and 42 non-failing control (NF) samples of human left ventricle tissues, to perform the first RNA-seq meta-analysis in the field of clinical ICM, in order to identify important transcriptional regulators and their targeted genes involved in ICM. Our meta-analysis identified 911 differentially expressed genes (DEGs) with 582 downregulated and 329 upregulated. Interestingly, 54 new DEGs were detected only by meta-analysis but not in individual datasets. Upstream regulator analysis through Ingenuity Pathway Analysis (IPA) identified three key transcriptional regulators.
MeSH term(s)	Cardiomyopathies/genetics ; Cardiomyopathies/pathology ; Female ; Gene Expression Profiling ; Gene Expression Regulation/genetics ; Heart Ventricles/metabolism ; Heart Ventricles/pathology ; Humans ; Male ; Myocardial Ischemia/genetics ; Myocardial Ischemia/pathology ; Myocardium/metabolism ; RNA-Seq ; Transcriptome/genetics
Language	English
Publishing date	2020-05-14
Publishing country	Switzerland
Document type	Journal Article ; Meta-Analysis
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms21103472
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Article ; Online: Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease.

Aryal, Sachin / Alimadadi, Ahmad / Manandhar, Ishan / Joe, Bina / Cheng, Xi

Hypertension (Dallas, Tex. : 1979)

2020 Volume 76, Issue 5, Page(s) 1555–1562

Abstract: Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not clearly ...

Abstract	Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not clearly established, the reported associations between alterations in gut microbiota and CVD are prominent. Therefore, we hypothesized that machine learning (ML) could be used for gut microbiome-based diagnostic screening of CVD. To test our hypothesis, fecal 16S ribosomal RNA sequencing data of 478 CVD and 473 non-CVD human subjects collected through the American Gut Project were analyzed using 5 supervised ML algorithms including random forest, support vector machine, decision tree, elastic net, and neural networks. Thirty-nine differential bacterial taxa were identified between the CVD and non-CVD groups. ML modeling using these taxonomic features achieved a testing area under the receiver operating characteristic curve (0.0, perfect antidiscrimination; 0.5, random guessing; 1.0, perfect discrimination) of ≈0.58 (random forest and neural networks). Next, the ML models were trained with the top 500 high-variance features of operational taxonomic units, instead of bacterial taxa, and an improved testing area under the receiver operating characteristic curves of ≈0.65 (random forest) was achieved. Further, by limiting the selection to only the top 25 highly contributing operational taxonomic unit features, the area under the receiver operating characteristic curves was further significantly enhanced to ≈0.70. Overall, our study is the first to identify dysbiosis of gut microbiota in CVD patients as a group and apply this knowledge to develop a gut microbiome-based ML approach for diagnostic screening of CVD.
MeSH term(s)	Cardiovascular Diseases/diagnosis ; Cardiovascular Diseases/microbiology ; Feces/microbiology ; Gastrointestinal Microbiome/physiology ; Humans ; Machine Learning ; Mass Screening/methods ; Metagenome
Language	English
Publishing date	2020-09-10
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	423736-5
ISSN	1524-4563 ; 0194-911X ; 0362-4323
ISSN (online)	1524-4563
ISSN	0194-911X ; 0362-4323
DOI	10.1161/HYPERTENSIONAHA.120.15885
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Article ; Online: Nonclassical monocytes potentiate anti-tumoral CD8

Padgett, Lindsey E / Marcovecchio, Paola M / Olingy, Claire E / Araujo, Daniel J / Steel, Kathleen / Dinh, Huy Q / Alimadadi, Ahmad / Zhu, Yanfang Peipei / Meyer, Melissa A / Kiosses, William B / Thomas, Graham D / Hedrick, Catherine C

Frontiers in immunology

2023 Volume 14, Page(s) 1101497

Abstract: ... ...

Abstract	CD8
MeSH term(s)	Mice ; Animals ; Monocytes ; CD8-Positive T-Lymphocytes ; Endothelial Cells ; Lung ; Neoplasms/metabolism ; Tumor Microenvironment
Language	English
Publishing date	2023-06-22
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2023.1101497
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Article ; Online: Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases.

Manandhar, Ishan / Alimadadi, Ahmad / Aryal, Sachin / Munroe, Patricia B / Joe, Bina / Cheng, Xi

American journal of physiology. Gastrointestinal and liver physiology

2021 Volume 320, Issue 3, Page(s) G328–G337

Abstract: Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in patients with ... ...

Abstract	Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in patients with IBD, we hypothesized that supervised machine learning (ML) could be used to analyze gut microbiome data for predictive diagnostics of IBD. To test our hypothesis, fecal 16S metagenomic data of 729 subjects with IBD and 700 subjects without IBD from the American Gut Project were analyzed using five different ML algorithms. Fifty differential bacterial taxa were identified [linear discriminant analysis effect size (LEfSe): linear discriminant analysis (LDA) score > 3] between the IBD and non-IBD groups, and ML classifications trained with these taxonomic features using random forest (RF) achieved a testing area under the receiver operating characteristic curves (AUC) of ∼0.80. Next, we tested if operational taxonomic units (OTUs), instead of bacterial taxa, could be used as ML features for diagnostic classification of IBD. Top 500 high-variance OTUs were used for ML training, and an improved testing AUC of ∼0.82 (RF) was achieved. Lastly, we tested if supervised ML could be used for differentiating Crohn's disease (CD) and ulcerative colitis (UC). Using 331 CD and 141 UC samples, 117 differential bacterial taxa (LEfSe: LDA score > 3) were identified, and the RF model trained with differential taxonomic features or high-variance OTU features achieved a testing AUC > 0.90. In summary, our study demonstrates the promising potential of artificial intelligence via supervised ML modeling for predictive diagnostics of IBD using gut microbiome data.
MeSH term(s)	Diagnosis, Computer-Assisted/methods ; Gastrointestinal Microbiome ; Humans ; Inflammatory Bowel Diseases/diagnosis ; Inflammatory Bowel Diseases/microbiology ; Supervised Machine Learning
Language	English
Publishing date	2021-01-13
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	603840-2
ISSN	1522-1547 ; 0193-1857
ISSN (online)	1522-1547
ISSN	0193-1857
DOI	10.1152/ajpgi.00360.2020
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Single Cell High Dimensional Analysis of Human Peripheral Blood Mononuclear Cells Reveals Unique Intermediate Monocyte Subsets Associated with Sex Differences in Coronary Artery Disease.

Chatterjee, Nandini / Komaravolu, Ravi K / Durant, Christopher P / Wu, Runpei / McSkimming, Chantel / Drago, Fabrizio / Kumar, Sunil / Valentin-Guillama, Gabriel / Miller, Yury I / McNamara, Coleen A / Ley, Klaus / Taylor, Angela / Alimadadi, Ahmad / Hedrick, Catherine C

International journal of molecular sciences

2024 Volume 25, Issue 5

Abstract: Monocytes are associated with human cardiovascular disease progression. Monocytes are segregated into three major subsets: classical (cMo), intermediate (iMo), and nonclassical (nMo). Recent studies have identified heterogeneity within each of these main ...

Abstract	Monocytes are associated with human cardiovascular disease progression. Monocytes are segregated into three major subsets: classical (cMo), intermediate (iMo), and nonclassical (nMo). Recent studies have identified heterogeneity within each of these main monocyte classes, yet the extent to which these subsets contribute to heart disease progression is not known. Peripheral blood mononuclear cells (PBMC) were obtained from 61 human subjects within the Coronary Assessment of Virginia (CAVA) Cohort. Coronary atherosclerosis severity was quantified using the Gensini Score (GS). We employed high-dimensional single-cell transcriptome and protein methods to define how human monocytes differ in subjects with low to severe coronary artery disease. We analyzed 487 immune-related genes and 49 surface proteins at the single-cell level using Antibody-Seq (Ab-Seq). We identified six subsets of myeloid cells (cMo, iMo, nMo, plasmacytoid DC, classical DC, and DC3) at the single-cell level based on surface proteins, and we associated these subsets with coronary artery disease (CAD) incidence based on Gensini score (GS) in each subject. Only frequencies of iMo were associated with high CAD (GS > 32),
MeSH term(s)	Humans ; Female ; Male ; Coronary Artery Disease/metabolism ; Monocytes/metabolism ; Leukocytes, Mononuclear ; Sex Characteristics ; HLA-DR Antigens/metabolism
Chemical Substances	HLA-DR Antigens
Language	English
Publishing date	2024-03-01
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms25052894
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Article ; Online: Single cell transcriptomics reveals recent CD8T cell receptor signaling in patients with coronary artery disease.

Iqneibi, Shahad / Saigusa, Ryosuke / Khan, Amir / Oliaeimotlagh, Mohammad / Armstrong Suthahar, Sujit Silas / Kumar, Sunil / Alimadadi, Ahmad / Durant, Christopher P / Ghosheh, Yanal / McNamara, Coleen A / Hedrick, Catherine C / Ley, Klaus

Frontiers in immunology

2023 Volume 14, Page(s) 1239148

Abstract: Coronary artery disease (CAD) is a major cause of death worldwide. The role of CD8+ T cells in CAD is unknown. Recent studies suggest a breakdown of tolerance in atherosclerosis, resulting in active T cell receptor (TCR) engagement with self-antigens. We ...

Abstract	Coronary artery disease (CAD) is a major cause of death worldwide. The role of CD8+ T cells in CAD is unknown. Recent studies suggest a breakdown of tolerance in atherosclerosis, resulting in active T cell receptor (TCR) engagement with self-antigens. We hypothesized that TCR engagement would leave characteristic gene expression signatures. In a single cell RNA-sequencing analysis of CD8+ T cells from 30 patients with CAD and 30 controls we found significant enrichment of TCR signaling pathways in CAD+ subjects, suggesting recent TCR engagement. We also found significant enrichment of cytotoxic and exhaustion pathways in CAD cases compared to controls. Highly significant upregulation of TCR signaling in CAD indicates that CD8 T cells reactive to atherosclerosis antigens are prominent in the blood of CAD cases compared to controls.
MeSH term(s)	Humans ; Coronary Artery Disease ; Transcriptome ; CD8-Positive T-Lymphocytes ; Receptors, Antigen, T-Cell ; Atherosclerosis/metabolism
Chemical Substances	Receptors, Antigen, T-Cell
Language	English
Publishing date	2023-09-27
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2023.1239148
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Meta-Analysis of Dilated Cardiomyopathy Using Cardiac RNA-Seq Transcriptomic Datasets

Alimadadi, Ahmad / Munroe, Patricia B / Joe, Bina / Cheng, Xi

Genes. 2020 Jan. 04, v. 11, no. 1

2020

Abstract	Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Several studies have used RNA-sequencing (RNA-seq) to profile differentially expressed genes (DEGs) associated with DCM. In this study, we aimed to profile gene expression signatures and identify novel genes associated with DCM through a quantitative meta-analysis of three publicly available RNA-seq studies using human left ventricle tissues from 41 DCM cases and 21 control samples. Our meta-analysis identified 789 DEGs including 581 downregulated and 208 upregulated genes. Several DCM-related genes previously reported, including MYH6, CKM, NKX2–5 and ATP2A2, were among the top 50 DEGs. Our meta-analysis also identified 39 new DEGs that were not detected using those individual RNA-seq datasets. Some of those genes, including PTH1R, ADAM15 and S100A4, confirmed previous reports of associations with cardiovascular functions. Using DEGs from this meta-analysis, the Ingenuity Pathway Analysis (IPA) identified five activated toxicity pathways, including failure of heart as the most significant pathway. Among the upstream regulators, SMARCA4 was downregulated and prioritized by IPA as the top affected upstream regulator for several DCM-related genes. To our knowledge, this study is the first to perform a transcriptomic meta-analysis for clinical DCM using RNA-seq datasets. Overall, our meta-analysis successfully identified a core set of genes associated with DCM.
Keywords	cardiomyopathy ; gene expression ; gene expression regulation ; genes ; heart ; heart failure ; humans ; meta-analysis ; sequence analysis ; tissues ; toxicity ; transcriptomics
Language	English
Dates of publication	2020-0104
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2527218-4
ISSN	2073-4425
ISSN	2073-4425
DOI	10.3390/genes11010060
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Artificial intelligence and machine learning to fight COVID-19.

Alimadadi, Ahmad / Aryal, Sachin / Manandhar, Ishan / Munroe, Patricia B / Joe, Bina / Cheng, Xi

Physiological genomics

2020 Volume 52, Issue 4, Page(s) 200–202

MeSH term(s)	Artificial Intelligence ; Betacoronavirus ; COVID-19 ; COVID-19 Vaccines ; Coronavirus Infections/drug therapy ; Coronavirus Infections/prevention & control ; Coronavirus Infections/therapy ; Disease Susceptibility/diagnosis ; Drug Development/standards ; Drug Development/trends ; Drug Repositioning/standards ; Humans ; Machine Learning ; Pandemics/prevention & control ; Pneumonia, Viral/prevention & control ; Pneumonia, Viral/therapy ; SARS-CoV-2 ; Viral Vaccines/analysis ; COVID-19 Drug Treatment
Chemical Substances	COVID-19 Vaccines ; Viral Vaccines
Keywords	covid19
Language	English
Publishing date	2020-03-27
Publishing country	United States
Document type	Editorial ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2038823-8
ISSN	1531-2267 ; 1094-8341
ISSN (online)	1531-2267
ISSN	1094-8341
DOI	10.1152/physiolgenomics.00029.2020
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Article ; Online: Identification of Upstream Transcriptional Regulators of Ischemic Cardiomyopathy Using Cardiac RNA-Seq Meta-Analysis

Ahmad Alimadadi / Sachin Aryal / Ishan Manandhar / Bina Joe / Xi Cheng

International Journal of Molecular Sciences, Vol 21, Iss 3472, p

2020 Volume 3472

Abstract	Ischemic cardiomyopathy (ICM), characterized by pre-existing myocardial infarction or severe coronary artery disease, is the major cause of heart failure (HF). Identification of novel transcriptional regulators in ischemic HF can provide important biomarkers for developing new diagnostic and therapeutic strategies. In this study, we used four RNA-seq datasets from four different studies, including 41 ICM and 42 non-failing control (NF) samples of human left ventricle tissues, to perform the first RNA-seq meta-analysis in the field of clinical ICM, in order to identify important transcriptional regulators and their targeted genes involved in ICM. Our meta-analysis identified 911 differentially expressed genes (DEGs) with 582 downregulated and 329 upregulated. Interestingly, 54 new DEGs were detected only by meta-analysis but not in individual datasets. Upstream regulator analysis through Ingenuity Pathway Analysis (IPA) identified three key transcriptional regulators. TBX5 was identified as the only inhibited regulator ( z -score = −2.89). F2R and SFRP4 were identified as the activated regulators ( z -scores = 2.56 and 2.00, respectively). Multiple downstream genes regulated by TBX5 , F2R , and SFRP4 were involved in ICM-related diseases such as HF and arrhythmia. Overall, our study is the first to perform an RNA-seq meta-analysis for clinical ICM and provides robust candidate genes, including three key transcriptional regulators, for future diagnostic and therapeutic applications in ischemic heart failure.
Keywords	ischemic cardiomyopathy ; heart failure ; transcriptional regulators ; meta-analysis ; RNA-seq ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Subject code	610
Language	English
Publishing date	2020-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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