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Choudhury P, Dasgupta S, Bhattacharyya P, Roychowdhury S, Chaudhury K. Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach. Mol Omics 2024; 20:366-389. [PMID: 38853716 DOI: 10.1039/d3mo00266g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.
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Affiliation(s)
- Priyanka Choudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
| | - Sanjukta Dasgupta
- Department of Biotechnology, Brainware University, Barasat, West Bengal, India
| | | | | | - Koel Chaudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
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Liu Y, Lyons CJ, Ayu C, O'Brien T. Recent advances in endothelial colony-forming cells: from the transcriptomic perspective. J Transl Med 2024; 22:313. [PMID: 38532420 DOI: 10.1186/s12967-024-05108-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/18/2024] [Indexed: 03/28/2024] Open
Abstract
Endothelial colony-forming cells (ECFCs) are progenitors of endothelial cells with significant proliferative and angiogenic ability. ECFCs are a promising treatment option for various diseases, such as ischemic heart disease and peripheral artery disease. However, some barriers hinder the clinical application of ECFC therapeutics. One of the current obstacles is that ECFCs are dysfunctional due to the underlying disease states. ECFCs exhibit dysfunctional phenotypes in pathologic states, which include but are not limited to the following: premature neonates and pregnancy-related diseases, diabetes mellitus, cancers, haematological system diseases, hypoxia, pulmonary arterial hypertension, coronary artery diseases, and other vascular diseases. Besides, ECFCs are heterogeneous among donors, tissue sources, and within cell subpopulations. Therefore, it is important to elucidate the underlying mechanisms of ECFC dysfunction and characterize their heterogeneity to enable clinical application. In this review, we summarize the current and potential application of transcriptomic analysis in the field of ECFC biology. Transcriptomic analysis is a powerful tool for exploring the key molecules and pathways involved in health and disease and can be used to characterize ECFC heterogeneity.
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Affiliation(s)
- Yaqiong Liu
- Regenerative Medicine Institute (REMEDI), Biomedical Sciences Building, University of Galway, Galway, Ireland
| | - Caomhán J Lyons
- Regenerative Medicine Institute (REMEDI), Biomedical Sciences Building, University of Galway, Galway, Ireland
| | - Christine Ayu
- Regenerative Medicine Institute (REMEDI), Biomedical Sciences Building, University of Galway, Galway, Ireland
| | - Timothy O'Brien
- Regenerative Medicine Institute (REMEDI), Biomedical Sciences Building, University of Galway, Galway, Ireland.
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Hasib RA, Ali MC, Rahman MH, Ahmed S, Sultana S, Summa SZ, Shimu MSS, Afrin Z, Jamal MAHM. Integrated gene expression profiling and functional enrichment analyses to discover biomarkers and pathways associated with Guillain-Barré syndrome and autism spectrum disorder to identify new therapeutic targets. J Biomol Struct Dyn 2023:1-23. [PMID: 37776011 DOI: 10.1080/07391102.2023.2262586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 09/17/2023] [Indexed: 10/01/2023]
Abstract
Guillain-Barré syndrome (GBS) is one of the most prominent and acute immune-mediated peripheral neuropathy, while autism spectrum disorders (ASD) are a group of heterogeneous neurodevelopmental disorders. The complete mechanism regarding the neuropathophysiology of these disorders is still ambiguous. Even after recent breakthroughs in molecular biology, the link between GBS and ASD remains a mystery. Therefore, we have implemented well-established bioinformatic techniques to identify potential biomarkers and drug candidates for GBS and ASD. 17 common differentially expressed genes (DEGs) were identified for these two disorders, which later guided the rest of the research. Common genes identified the protein-protein interaction (PPI) network and pathways associated with both disorders. Based on the PPI network, the constructed hub gene and module analysis network determined two common DEGs, namely CXCL9 and CXCL10, which are vital in predicting the top drug candidates. Furthermore, coregulatory networks of TF-gene and TF-miRNA were built to detect the regulatory biomolecules. Among drug candidates, imatinib had the highest docking and MM-GBSA score with the well-known chemokine receptor CXCR3 and remained stable during the 100 ns molecular dynamics simulation validated by the principal component analysis and the dynamic cross-correlation map. This study predicted the gene-based disease network for GBS and ASD and suggested prospective drug candidates. However, more in-depth research is required for clinical validation.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rizone Al Hasib
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
- Laboratory of Medical and Environmental Biotechnology Islamic University, Kushtia, Bangladesh
| | - Md Chayan Ali
- Department of Biochemistry, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Md Habibur Rahman
- Department of Computer Science and Engineering, Islamic University, Kushtia, Bangladesh
- Center for Advanced Bioinformatics and Artificial Intelligent Research, Islamic University, Kushtia, Bangladesh
| | - Sabbir Ahmed
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
| | - Shaharin Sultana
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
- Laboratory of Medical and Environmental Biotechnology Islamic University, Kushtia, Bangladesh
| | - Sadia Zannat Summa
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
- Laboratory of Medical and Environmental Biotechnology Islamic University, Kushtia, Bangladesh
| | | | - Zinia Afrin
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
| | - Mohammad Abu Hena Mostofa Jamal
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
- Laboratory of Medical and Environmental Biotechnology Islamic University, Kushtia, Bangladesh
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Jahanyar B, Tabatabaee H, Rowhanimanesh A. MS-ACGAN: A modified auxiliary classifier generative adversarial network for schizophrenia's samples augmentation based on microarray gene expression data. Comput Biol Med 2023; 162:107024. [PMID: 37263150 DOI: 10.1016/j.compbiomed.2023.107024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 05/01/2023] [Accepted: 05/09/2023] [Indexed: 06/03/2023]
Abstract
Artificial intelligence-based models and robust computational methods have expedited the data-to-knowledge trajectory in precision medicine. Although machine learning models have been widely applied in medical data analysis, some barriers are yet to be challenging, such as available biosample shortage, prohibitive costs, rare diseases, and ethical considerations. Transcriptomics, an omics approach that studies gene activities and provides gene expression data such as microarray and RNA-Sequences faces the difficulties of biospecimen collection, particularly for mental disorders, as some psychiatric patients avoid medical care. Microarray data suffers from the low number of available samples, making it challenging to apply machine learning models. However, adversarial generative network (GAN), the hottest paradigm in deep learning, has created unprecedented momentum in data augmentation and efficiently expands datasets. This paper proposes a novel model termed MS-ACGAN, where the generator feeds on a bordered Gaussian distribution. In machine learning, calibration is of utmost importance, which gives insight into model uncertainty and is considered a crucial step toward improving the robustness and reliability of models. Therefore, we apply calibration techniques to classifiers and focus on estimating their probabilities as accurately as possible. Additionally, we present our trustworthy outputs by harnessing confidence intervals that confine the point estimate limitations and report a range of expected values for performance metrics. Both concepts statistically describe the implemented model's reliability in this study. Furthermore, we employ two quantitative measures, GAN-train and GAN-test, to demonstrate that the artificial data generated by our robust approach remarkably resembles the original data characteristics.
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Affiliation(s)
- Bahareh Jahanyar
- Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Hamid Tabatabaee
- Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
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Xie X, Liao X, Xu Z, Liang W, Su Y, Lin L, Xie J, Lin W. Transcriptome analysis of the muscle of fast- and slow-growing phoenix barb (Spinibarbus denticulatus denticulatus). JOURNAL OF FISH BIOLOGY 2023; 102:504-515. [PMID: 36437626 DOI: 10.1111/jfb.15280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 11/21/2022] [Indexed: 06/16/2023]
Abstract
Growth rate is a commercial trait in aquaculture that is influenced by multiple factors, among which genetic composition plays a fundamental role in the growth rate of species. The phoenix barb (Spinibarbus denticulatus denticulatus) is a widely distributed freshwater fish species in South China. Although S. d. denticulatus is reared in South China, the molecular mechanisms underlying the growth rate of the species remain unclear. Here, the authors performed transcriptome analysis of muscle tissues from fast-growing (FG) and slow-growing (SG) S. d. denticulatus at 90, 150, and 300 days after hatch (DAH) to elucidate its growth mechanism. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that differentially expressed genes (DEGs) between the two groups were enriched in pathways related to muscle growth, glycolysis, and energy and lipid metabolism. Nonetheless, a higher number of DEGs were identified in the FG vs. SG groups at 90 and 300 DAH compared with 150 DAH. DEGs identified at 90 DAH were mainly enriched in the GH/IGF axis, PI3K-Akt signalling pathway, AMPK signalling pathway and lipid metabolism highly expressed in FG individuals. DEGs identified at 300 DAH were mainly enriched in PI3K-Akt signalling pathway, glycolysis/gluconeogenesis, gene translation and lipid metabolism. In addition, some genes were expressed during the early growth stage in FG individuals but expressed during the late stage in SG individuals, indicating considerable variations in the expression profiles of growth-related genes at different developmental stages. Overall, these findings contribute to the understanding of the growth mechanism of S. d. denticulatus, which would be useful for the propagation of fast-growing breeds.
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Affiliation(s)
- Xi Xie
- Guangdong Provincial Key Laboratory of Lingnan Specialty Food Science and Technology, College of Light Industry and Food, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Xianping Liao
- Fishery Research Institute of Zhaoqing, Zhaoqing, China
| | - Zhengsheng Xu
- Fishery Research Institute of Zhaoqing, Zhaoqing, China
| | - Wenlang Liang
- Fishery Research Institute of Zhaoqing, Zhaoqing, China
| | - Yilin Su
- Fishery Research Institute of Zhaoqing, Zhaoqing, China
| | - Li Lin
- Guangdong Provincial Water Environment and Aquatic Products Security Engineering Technology Research Center, Guangzhou Key Laboratory of Aquatic Animal Diseases and Waterfowl Breeding, College of Animal Science Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Jungang Xie
- Fishery Research Institute of Zhaoqing, Zhaoqing, China
| | - Weiqiang Lin
- Fishery Research Institute of Zhaoqing, Zhaoqing, China
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Wang A, Liu H, Yang J, Chen G. Ensemble feature selection for stable biomarker identification and cancer classification from microarray expression data. Comput Biol Med 2022; 142:105208. [PMID: 35016102 DOI: 10.1016/j.compbiomed.2021.105208] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/19/2021] [Accepted: 12/31/2021] [Indexed: 01/31/2023]
Abstract
Microarray technology facilitates the simultaneous measurement of expression of tens of thousands of genes and enables us to study cancers and tumors at the molecular level. Because microarray data are typically characterized by small sample size and high dimensionality, accurate and stable feature selection is thus of fundamental importance to the diagnostic accuracy and deep understanding of disease mechanism. Hence, we in this study present an ensemble feature selection framework to improve the discrimination and stability of finally selected features. Specifically, we utilize sampling techniques to obtain multiple sampled datasets, from each of which we use a base feature selector to select a subset of features. Afterwards, we develop two aggregation strategies to combine multiple feature subsets into one set. Finally, comparative experiments are conducted on four publicly available microarray datasets covering both binary and multi-class cases in terms of classification accuracy and three stability metrics. Results show that the proposed method obtains better stability scores and achieves comparable to and even better classification performance than its competitors.
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Affiliation(s)
- Aiguo Wang
- School of Electronic Information Engineering, Foshan University, Foshan, China.
| | - Huancheng Liu
- School of Electronic Information Engineering, Foshan University, Foshan, China.
| | - Jing Yang
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China.
| | - Guilin Chen
- School of Computer and Information Engineering, Chuzhou University, Chuzhou, China.
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