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Pradhan UK, Naha S, Das R, Gupta A, Parsad R, Meher PK. RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes. Comput Struct Biotechnol J 2024; 23:1631-1640. [PMID: 38660008 PMCID: PMC11039349 DOI: 10.1016/j.csbj.2024.04.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 04/12/2024] [Accepted: 04/12/2024] [Indexed: 04/26/2024] Open
Abstract
RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.
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Affiliation(s)
- Upendra Kumar Pradhan
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Sanchita Naha
- Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Ritwika Das
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Ajit Gupta
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Rajender Parsad
- ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Prabina Kumar Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
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Catalina-Hernández È, López-Martín M, Masnou-Sánchez D, Martins M, Lorenz-Fonfria VA, Jiménez-Altayó F, Hellmich UA, Inada H, Alcaraz A, Furutani Y, Nonell-Canals A, Vázquez-Ibar JL, Domene C, Gaudet R, Perálvarez-Marín A. Experimental and computational biophysics to identify vasodilator drugs targeted at TRPV2 using agonists based on the probenecid scaffold. Comput Struct Biotechnol J 2024; 23:473-482. [PMID: 38261868 PMCID: PMC10796807 DOI: 10.1016/j.csbj.2023.12.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 12/20/2023] [Accepted: 12/23/2023] [Indexed: 01/25/2024] Open
Abstract
TRP channels are important pharmacological targets in physiopathology. TRPV2 plays distinct roles in cardiac and neuromuscular function, immunity, and metabolism, and is associated with pathologies like muscular dystrophy and cancer. However, TRPV2 pharmacology is unspecific and scarce at best. Using in silico similarity-based chemoinformatics we obtained a set of 270 potential hits for TRPV2 categorized into families based on chemical nature and similarity. Docking the compounds on available rat TRPV2 structures allowed the clustering of drug families in specific ligand binding sites. Starting from a probenecid docking pose in the piperlongumine binding site and using a Gaussian accelerated molecular dynamics approach we have assigned a putative probenecid binding site. In parallel, we measured the EC50 of 7 probenecid derivatives on TRPV2 expressed in Pichia pastoris using a novel medium-throughput Ca2+ influx assay in yeast membranes together with an unbiased and unsupervised data analysis method. We found that 4-(piperidine-1-sulfonyl)-benzoic acid had a better EC50 than probenecid, which is one of the most specific TRPV2 agonists to date. Exploring the TRPV2-dependent anti-hypertensive potential in vivo, we found that 4-(piperidine-1-sulfonyl)-benzoic acid shows a sex-biased vasodilator effect producing larger vascular relaxations in female mice. Overall, this study expands the pharmacological toolbox for TRPV2, a widely expressed membrane protein and orphan drug target.
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Affiliation(s)
- Èric Catalina-Hernández
- Unit of Biophysics, Dept. of Biochemistry and Molecular Biology, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Catalonia, Spain
- Institute of Neurosciences, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Catalonia, Spain
| | - Mario López-Martín
- Unit of Biophysics, Dept. of Biochemistry and Molecular Biology, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Catalonia, Spain
- Institute of Neurosciences, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Catalonia, Spain
| | - David Masnou-Sánchez
- Unit of Biophysics, Dept. of Biochemistry and Molecular Biology, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Catalonia, Spain
- Institute of Neurosciences, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Catalonia, Spain
| | - Marco Martins
- Unit of Biophysics, Dept. of Biochemistry and Molecular Biology, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Catalonia, Spain
| | - Victor A. Lorenz-Fonfria
- Instituto de Ciencia Molecular, Universidad de Valencia, Catedrático José Beltrán-2, 46980 Paterna, Spain
| | - Francesc Jiménez-Altayó
- Institute of Neurosciences, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Catalonia, Spain
- Department of Pharmacology, Toxicology and Therapeutics,Institute of Neurosciences, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Catalonia, Spain
| | - Ute A. Hellmich
- Friedrich Schiller University Jena, Faculty of Chemistry and Earth Sciences, Institute of Organic Chemistry & Macromolecular Chemistry, Humboldtstrasse 10, 07743 Jena, Germany
- Center for Biomolecular Magnetic Resonance (BMRZ), Goethe University, Max-von-Laue Str. 9, 60438 Frankfurt, Germany
| | - Hitoshi Inada
- Department of Biochemistry & Cellular Biology National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Antonio Alcaraz
- Laboratory of Molecular Biophysics, Dept. of Physics, Universitat Jaume I, 12071 Castellón, Spain
| | - Yuji Furutani
- Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Showa-Ku, Nagoya 466-8555, Japan
- Optobiotechnology Research Center, Nagoya Institute of Technology, Showa-Ku, Nagoya 466-8555, Japan
| | | | - Jose Luis Vázquez-Ibar
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Carmen Domene
- Dept. of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY, UK
| | - Rachelle Gaudet
- Dept of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Alex Perálvarez-Marín
- Unit of Biophysics, Dept. of Biochemistry and Molecular Biology, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Catalonia, Spain
- Institute of Neurosciences, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Catalonia, Spain
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Bardini R, Di Carlo S. Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review. Comput Struct Biotechnol J 2024; 23:601-616. [PMID: 38283852 PMCID: PMC10818159 DOI: 10.1016/j.csbj.2023.12.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/22/2023] [Accepted: 12/23/2023] [Indexed: 01/30/2024] Open
Abstract
This literature review rigorously examines the growing scientific interest in computational methods for Tissue Engineering and Regenerative Medicine biofabrication, a leading-edge area in biomedical innovation, emphasizing the need for accurate, multi-stage, and multi-component biofabrication process models. The paper presents a comprehensive bibliometric and contextual analysis, followed by a literature review, to shed light on the vast potential of computational methods in this domain. It reveals that most existing methods focus on single biofabrication process stages and components, and there is a significant gap in approaches that utilize accurate models encompassing both biological and technological aspects. This analysis underscores the indispensable role of these methods in understanding and effectively manipulating complex biological systems and the necessity for developing computational methods that span multiple stages and components. The review concludes that such comprehensive computational methods are essential for developing innovative and efficient Tissue Engineering and Regenerative Medicine biofabrication solutions, driving forward advancements in this dynamic and evolving field.
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Affiliation(s)
- Roberta Bardini
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca Degli Abruzzi, 24, Turin, 10129, Italy
| | - Stefano Di Carlo
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca Degli Abruzzi, 24, Turin, 10129, Italy
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Espina-Ordoñez M, Balderas-Martínez YI, Torres-Machorro AL, Herrera I, Maldonado M, Romero Y, Toscano-Marquez F, Pardo A, Selman M, Cisneros J. Mir-155-5p targets TP53INP1 to promote proliferative phenotype in hypersensitivity pneumonitis lung fibroblasts. Noncoding RNA Res 2024; 9:865-875. [PMID: 38586316 PMCID: PMC10997802 DOI: 10.1016/j.ncrna.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/11/2024] [Accepted: 02/19/2024] [Indexed: 04/09/2024] Open
Abstract
Background Hypersensitivity pneumonitis (HP) is an inflammatory disorder affecting lung parenchyma and often evolves into fibrosis (fHP). The altered regulation of genes involved in the pathogenesis of the disease is not well comprehended, while the role of microRNAs in lung fibroblasts remains unexplored. Methods We used integrated bulk RNA-Seq and enrichment pathway bioinformatic analyses to identify differentially expressed (DE)-miRNAs and genes (DEGs) associated with HP lungs. In vitro, we evaluated the expression and potential role of miR-155-5p in the phenotype of fHP lung fibroblasts. Loss and gain assays were used to demonstrate the impact of miR-155-5p on fibroblast functions. In addition, mir-155-5p and its target TP53INP1 were analyzed after treatment with TGF-β, IL-4, and IL-17A. Results We found around 50 DEGs shared by several databases that differentiate HP from control and IPF lungs, constituting a unique HP lung transcriptional signature. Additionally, we reveal 18 DE-miRNAs that may regulate these DEGs. Among the candidates likely associated with HP pathogenesis was miR-155-5p. Our findings indicate that increased miR-155-5p in fHP fibroblasts coincides with reduced TP53INP1 expression, high proliferative capacity, and a lack of senescence markers compared to IPF fibroblasts. Induced overexpression of miR-155-5p in normal fibroblasts remarkably increases the proliferation rate and decreases TP53INP1 expression. Conversely, miR-155-5p inhibition reduces proliferation and increases senescence markers. TGF-β, IL-4, and IL-17A stimulated miR-155-5p overexpression in HP lung fibroblasts. Conclusion Our findings suggest a distinctive signature of 53 DEGs in HP, including CLDN18, EEF2, CXCL9, PLA2G2D, and ZNF683, as potential targets for future studies. Likewise, 18 miRNAs, including miR-155-5p, could be helpful to establish differences between these two pathologies. The overexpression of miR-155-5p and downregulation of TP53INP1 in fHP lung fibroblasts may be involved in his proliferative and profibrotic phenotype. These findings may help differentiate and characterize their pathogenic features and understand their role in the disease.
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Affiliation(s)
- Marco Espina-Ordoñez
- Laboratorio de Biopatología Pulmonar INER-Ciencias-UNAM, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Ciudad de México, 14080, Mexico
- Posgrado en Ciencias Biológicas, Unidad de Posgrado, Edificio D, Piso 1, Circuito de Posgrados, Ciudad Universidad, Coyoacán, C.P 04510, CDMX, Mexico
| | - Yalbi Itzel Balderas-Martínez
- Laboratorio de Biología Computacional, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Ciudad de México, 14080, Mexico
| | - Ana Lilia Torres-Machorro
- Laboratorio de Biología Celular, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Ciudad de México, 14080, Mexico
| | - Iliana Herrera
- Laboratorio de Biopatología Pulmonar INER-Ciencias-UNAM, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Ciudad de México, 14080, Mexico
| | - Mariel Maldonado
- Laboratorio de Biopatología Pulmonar INER-Ciencias-UNAM, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Ciudad de México, 14080, Mexico
| | - Yair Romero
- Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México, 04510, Mexico
| | - Fernanda Toscano-Marquez
- Laboratorio de Biopatología Pulmonar INER-Ciencias-UNAM, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Ciudad de México, 14080, Mexico
| | - Annie Pardo
- Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México, 04510, Mexico
| | - Moisés Selman
- Laboratorio de Biopatología Pulmonar INER-Ciencias-UNAM, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Ciudad de México, 14080, Mexico
| | - José Cisneros
- Departamento de Investigación en Fibrosis Pulmonar, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Ciudad de México, 14080, Mexico
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Aljabali AAA, Obeid MA, El-Tanani M, Mishra V, Mishra Y, Tambuwala MM. Precision epidemiology at the nexus of mathematics and nanotechnology: Unraveling the dance of viral dynamics. Gene 2024; 905:148174. [PMID: 38242374 DOI: 10.1016/j.gene.2024.148174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/10/2024] [Accepted: 01/16/2024] [Indexed: 01/21/2024]
Abstract
The intersection of mathematical modeling, nanotechnology, and epidemiology marks a paradigm shift in our battle against infectious diseases, aligning with the focus of the journal on the regulation, expression, function, and evolution of genes in diverse biological contexts. This exploration navigates the intricate dance of viral transmission dynamics, highlighting mathematical models as dual tools of insight and precision instruments, a theme relevant to the diverse sections of Gene. In the context of virology, ethical considerations loom large, necessitating robust frameworks to protect individual rights, an aspect essential in infectious disease research. Global collaboration emerges as a critical pillar in our response to emerging infectious diseases, fortified by the predictive prowess of mathematical models enriched by nanotechnology. The synergy of interdisciplinary collaboration, training the next generation to bridge mathematical rigor, biology, and epidemiology, promises accelerated discoveries and robust models that account for real-world complexities, fostering innovation and exploration in the field. In this intricate review, mathematical modeling in viral transmission dynamics and epidemiology serves as a guiding beacon, illuminating the path toward precision interventions, global preparedness, and the collective endeavor to safeguard human health, resonating with the aim of advancing knowledge in gene regulation and expression.
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Affiliation(s)
- Alaa A A Aljabali
- Faculty of Pharmacy, Department of Pharmaceutics & Pharmaceutical Technology, Yarmouk University, Irbid 21163, Jordan.
| | - Mohammad A Obeid
- Faculty of Pharmacy, Department of Pharmaceutics & Pharmaceutical Technology, Yarmouk University, Irbid 21163, Jordan
| | - Mohamed El-Tanani
- College of Pharmacy, Ras Al Khaimah Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates.
| | - Vijay Mishra
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab 144411, India
| | - Yachana Mishra
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab 144411, India
| | - Murtaza M Tambuwala
- Lincoln Medical School, University of Lincoln, Brayford Pool Campus, Lincoln LN6 7TS, United Kingdom.
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Danaeifar M, Najafi A. Artificial Intelligence and Computational Biology in Gene Therapy: A Review. Biochem Genet 2024:10.1007/s10528-024-10799-1. [PMID: 38635012 DOI: 10.1007/s10528-024-10799-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024]
Abstract
One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.
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Affiliation(s)
- Mohsen Danaeifar
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Science, P.O. Box 19395-5487, Tehran, Iran
| | - Ali Najafi
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Science, P.O. Box 19395-5487, Tehran, Iran.
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Whitfield-Cargile CM, Chung HC, Coleman MC, Cohen ND, Chamoun-Emanuelli AM, Ivanov I, Goldsby JS, Davidson LA, Gaynanova I, Ni Y, Chapkin RS. Integrated analysis of gut metabolome, microbiome, and exfoliome data in an equine model of intestinal injury. Microbiome 2024; 12:74. [PMID: 38622632 PMCID: PMC11017594 DOI: 10.1186/s40168-024-01785-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 02/29/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND The equine gastrointestinal (GI) microbiome has been described in the context of various diseases. The observed changes, however, have not been linked to host function and therefore it remains unclear how specific changes in the microbiome alter cellular and molecular pathways within the GI tract. Further, non-invasive techniques to examine the host gene expression profile of the GI mucosa have been described in horses but not evaluated in response to interventions. Therefore, the objectives of our study were to (1) profile gene expression and metabolomic changes in an equine model of non-steroidal anti-inflammatory drug (NSAID)-induced intestinal inflammation and (2) apply computational data integration methods to examine host-microbiota interactions. METHODS Twenty horses were randomly assigned to 1 of 2 groups (n = 10): control (placebo paste) or NSAID (phenylbutazone 4.4 mg/kg orally once daily for 9 days). Fecal samples were collected on days 0 and 10 and analyzed with respect to microbiota (16S rDNA gene sequencing), metabolomic (untargeted metabolites), and host exfoliated cell transcriptomic (exfoliome) changes. Data were analyzed and integrated using a variety of computational techniques, and underlying regulatory mechanisms were inferred from features that were commonly identified by all computational approaches. RESULTS Phenylbutazone induced alterations in the microbiota, metabolome, and host transcriptome. Data integration identified correlation of specific bacterial genera with expression of several genes and metabolites that were linked to oxidative stress. Concomitant microbiota and metabolite changes resulted in the initiation of endoplasmic reticulum stress and unfolded protein response within the intestinal mucosa. CONCLUSIONS Results of integrative analysis identified an important role for oxidative stress, and subsequent cell signaling responses, in a large animal model of GI inflammation. The computational approaches for combining non-invasive platforms for unbiased assessment of host GI responses (e.g., exfoliomics) with metabolomic and microbiota changes have broad application for the field of gastroenterology. Video Abstract.
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Affiliation(s)
- C M Whitfield-Cargile
- Department of Large Animal Clinical Sciences, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, USA.
| | - H C Chung
- Department of Statistics, College of Arts & Sciences, Texas A&M University, College Station, TX, USA
- Mathematics & Statistics Department, College of Science, University of North Carolina Charlotte, Charlotte, NC, USA
| | - M C Coleman
- Department of Large Animal Clinical Sciences, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - N D Cohen
- Department of Large Animal Clinical Sciences, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - A M Chamoun-Emanuelli
- Department of Large Animal Clinical Sciences, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - I Ivanov
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - J S Goldsby
- Program in Integrative Nutrition & Complex Diseases, College of Agriculture & Life Sciences, Texas A&M University, College Station, TX, USA
| | - L A Davidson
- Program in Integrative Nutrition & Complex Diseases, College of Agriculture & Life Sciences, Texas A&M University, College Station, TX, USA
| | - I Gaynanova
- Department of Statistics, College of Arts & Sciences, Texas A&M University, College Station, TX, USA
| | - Y Ni
- Department of Statistics, College of Arts & Sciences, Texas A&M University, College Station, TX, USA
| | - R S Chapkin
- Program in Integrative Nutrition & Complex Diseases, College of Agriculture & Life Sciences, Texas A&M University, College Station, TX, USA
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Okada D. Application of a mathematical model to clarify the statistical characteristics of a pan-tissue DNA methylation clock. GeroScience 2024; 46:2001-2015. [PMID: 37787856 PMCID: PMC10828133 DOI: 10.1007/s11357-023-00949-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/14/2023] [Indexed: 10/04/2023] Open
Abstract
DNA methylation clocks estimate biological age based on DNA methylation profiles. This study developed a mathematical model to describe DNA methylation aging and the establishment of a pan-tissue DNA methylation clock. The model simulates the aging dynamics of DNA methylation profiles based on passive demethylation as well as the process of cross-sectional bulk data acquisition. As a result, this study identified two conditions under which the pan-tissue DNA methylation clock can successfully predict biological age: one condition is that the target tissues are sufficiently well represented in the training dataset, and the other condition is that the target sample contains cell subsets that are common among different tissues. When either of these conditions is met, the clock performs well. It is also suggested that the epigenetic age of all samples in the target tissue tends to be either over or underestimated when biological age prediction fails. The model can reveal the statistical characteristics of DNA methylation clocks.
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Affiliation(s)
- Daigo Okada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, 53 Syogoin-Kawaramachi, Sakyo-ku, Kyoto, Kyoto, 606-8507, Japan.
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Shen J, Sun H, Zhou S, Wang L, Dong C, Ren K, Du Q, Cao J, Wang Y, Sun J. Development of a screening system of gene sets for estimating the time of early skeletal muscle injury based on second-generation sequencing technology. Int J Legal Med 2024:10.1007/s00414-024-03210-6. [PMID: 38532207 DOI: 10.1007/s00414-024-03210-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 03/13/2024] [Indexed: 03/28/2024]
Abstract
The present study is aimed to address the challenge of wound age estimation in forensic science by identifying reliable genetic markers using low-cost and high-precision second-generation sequencing technology. A total of 54 Sprague-Dawley rats were randomly assigned to a control group or injury groups, with injury groups being further divided into time points (4 h, 8 h, 12 h, 16 h, 20 h, 24 h, 28 h, and 32 h after injury, n = 6) to establish rat skeletal muscle contusion models. Gene expression data were obtained using second-generation sequencing technology, and differential gene expression analysis, weighted gene co-expression network analysis (WGCNA) and time-dependent expression trend analysis were performed. A total of six sets of biomarkers were obtained: differentially expressed genes at adjacent time points (127 genes), co-expressed genes most associated with wound age (213 genes), hub genes exhibiting time-dependent expression (264 genes), and sets of transcription factors (TF) corresponding to the above sets of genes (74, 87, and 99 genes, respectively). Then, random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP), were constructed for wound age estimation from the above gene sets. The results estimated by transcription factors were all superior to the corresponding hub genes, with the transcription factor group of WGCNA performed the best, with average accuracy rates of 96% for three models' internal testing, and 91.7% for the highest external validation. This study demonstrates the advantages of the indicator screening system based on second-generation sequencing technology and transcription factor level for wound age estimation.
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Affiliation(s)
- Junyi Shen
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
- Institute of Forensic Science Public Security Department of Shanxi, Taiyuan, China
| | - Hao Sun
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Shidong Zhou
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Liangliang Wang
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Chaoxiu Dong
- Institute of Forensic Science Public Security Department of Shanxi, Taiyuan, China
| | - Kang Ren
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Qiuxiang Du
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Jie Cao
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Yingyuan Wang
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China.
| | - Junhong Sun
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China.
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Alani M, Altarturih H, Pars S, Al-Mhanawi B, Wolvetang EJ, Shaker MR. A Roadmap for Selecting and Utilizing Optimal Features in scRNA Sequencing Data Analysis for Stem Cell Research: A Comprehensive Review. Int J Stem Cells 2024:ijsc23170. [PMID: 38531607 DOI: 10.15283/ijsc23170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/28/2024] Open
Abstract
Stem cells and the cells they produce are unique because they vary from one cell to another. Traditional methods of studying cells often overlook these differences. However, the development of new technologies for studying individual cells has greatly changed biological research in recent years. Among these innovations, single-cell RNA sequencing (scRNA-seq) stands out. This technique allows scientists to examine the activity of genes in each cell, across thousands or even millions of cells. This makes it possible to understand the diversity of cells, identify new types of cells, and see how cells differ across different tissues, individuals, species, times, and conditions. This paper discusses the importance of scRNA-seq and the computational tools and software that are essential for analyzing the vast amounts of data generated by scRNA-seq studies. Our goal is to provide practical advice for bioinformaticians and biologists who are using scRNA-seq to study stem cells. We offer an overview of the scRNA-seq field, including the tools available, how they can be used, and how to present the results of these studies effectively. Our findings include a detailed overview and classification of tools used in scRNA-seq analysis, based on a review of 2,733 scientific publications. This review is complemented by information from the scRNA-tools database, which lists over 1,400 tools for analyzing scRNA-seq data. This database is an invaluable resource for researchers, offering a wide range of options for analyzing their scRNA-seq data.
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Affiliation(s)
- Maath Alani
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
| | - Hamza Altarturih
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
| | - Selin Pars
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
| | - Bahaa Al-Mhanawi
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
| | - Ernst J Wolvetang
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
| | - Mohammed R Shaker
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
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11
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Monsalve D, Mesa A, Mira LM, Mera C, Orduz S, Branch-Bedoya JW. Antimicrobial peptides designed by computational analysis of proteomes. Antonie Van Leeuwenhoek 2024; 117:55. [PMID: 38488950 DOI: 10.1007/s10482-024-01946-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 02/06/2024] [Indexed: 03/17/2024]
Abstract
Antimicrobial peptides (AMPs) are promising cationic and amphipathic molecules to fight antibiotic resistance. To search for novel AMPs, we applied a computational strategy to identify peptide sequences within the organisms' proteome, including in-house developed software and artificial intelligence tools. After analyzing 150.450 proteins from eight proteomes of bacteria, plants, a protist, and a nematode, nine peptides were selected and modified to increase their antimicrobial potential. The 18 resulting peptides were validated by bioassays with four pathogenic bacterial species, one yeast species, and two cancer cell-lines. Fourteen of the 18 tested peptides were antimicrobial, with minimum inhibitory concentrations (MICs) values under 10 µM against at least three bacterial species; seven were active against Candida albicans with MICs values under 10 µM; six had a therapeutic index above 20; two peptides were active against A549 cells, and eight were active against MCF-7 cells under 30 µM. This study's most active antimicrobial peptides damage the bacterial cell membrane, including grooves, dents, membrane wrinkling, cell destruction, and leakage of cytoplasmic material. The results confirm that the proposed approach, which uses bioinformatic tools and rational modifications, is highly efficient and allows the discovery, with high accuracy, of potent AMPs encrypted in proteins.
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Affiliation(s)
- Dahiana Monsalve
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - Andrea Mesa
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - Laura M Mira
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - Carlos Mera
- Departamento de Sistemas de Información, Instituto Tecnológico Metropolitano, Calle 54A # 30-01, 050013, Medellín, Antioquia, Colombia.
- Departamento de Ingeniería de Sistemas, Facultad de Ingenierías, Universidad de Antioquia, Calle 70 # 52-21, 050010, Medellín, Antioquia, Colombia.
| | - Sergio Orduz
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - John W Branch-Bedoya
- Departamento de Ciencias de la Computación y de la Decisión, Facultad de Minas, Universidad Nacional de Colombia, sede Medellín, Av. 80 # 65-223, 050041, Medellín, Antioquia, Colombia
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12
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de Oliveira AS, Muniz Seif EJ, da Silva Junior PI. In silico prospection of receptors associated with the biological activity of U1-SCTRX-lg1a: an antimicrobial peptide isolated from the venom of Loxosceles gaucho. In Silico Pharmacol 2024; 12:15. [PMID: 38476933 PMCID: PMC10925584 DOI: 10.1007/s40203-024-00190-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 01/11/2024] [Indexed: 03/14/2024] Open
Abstract
The emergence of antibiotic-resistant pathogens generates impairment to human health. U1-SCTRX-lg1a is a peptide isolated from a phospholipase D extracted from the spider venom of Loxosceles gaucho with antimicrobial activity against Gram-negative bacteria (between 1.15 and 4.6 μM). The aim of this study was to suggest potential receptors associated with the antimicrobial activity of U1-SCTRX-lg1a using in silico bioinformatics tools. The search for potential targets of U1-SCRTX-lg1a was performed using the PharmMapper server. Molecular docking between U1-SCRTX-lg1a and the receptor was performed using PatchDock software. The prediction of ligand sites for each receptor was conducted using the PDBSum server. Chimera 1.6 software was used to perform molecular dynamics simulations only for the best dock score receptor. In addition, U1-SCRTX-lg1a and native ligand interactions were compared using AutoDock Vina software. Finally, predicted interactions were compared with the ligand site previously described in the literature. The bioprospecting of U1-SCRTX-lg1a resulted in the identification of three hundred (300) diverse targets (Table S1), forty-nine (49) of which were intracellular proteins originating from Gram-negative microorganisms (Table S2). Docking results indicate Scores (10,702 to 6066), Areas (1498.70 to 728.40) and ACEs (417.90 to - 152.8) values. Among these, NAD + NH3-dependent synthetase (PDB ID: 1wxi) showed a dock score of 9742, area of 1223.6 and ACE of 38.38 in addition to presenting a Normalized Fit score of 8812 on PharmMapper server. Analysis of the interaction of ligands and receptors suggests that the peptide derived from brown spider venom can interact with residues SER48 and THR160. Furthermore, the C terminus (- 7.0 score) has greater affinity for the receptor than the N terminus (- 7.7 score). The molecular dynamics assay shown that free energy value for the protein complex of - 214,890.21 kJ/mol, whereas with rigid docking, this value was - 29.952.8 sugerindo that after the molecular dynamics simulation, the complex exhibits a more favorable energy value compared to the previous state. The in silico bioprospecting of receptors suggests that U1-SCRTX-lg1a may interfere with NAD + production in Escherichia coli, a Gram-negative bacterium, altering the homeostasis of the microorganism and impairing growth. Supplementary Information The online version contains supplementary material available at 10.1007/s40203-024-00190-8.
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Affiliation(s)
- André Souza de Oliveira
- Applied Toxinology Laboratory, Butantan Institute, São Paulo, SP Brazil
- Post Graduate Program of Biotechnology, University of São Paulo, São Paulo, SP Brazil
| | - Elias Jorge Muniz Seif
- Applied Toxinology Laboratory, Butantan Institute, São Paulo, SP Brazil
- Post Graduate Program of Molecular Biology, Federal University of São Paulo, São Paulo, SP Brazil
| | - Pedro Ismael da Silva Junior
- Applied Toxinology Laboratory, Butantan Institute, São Paulo, SP Brazil
- Post Graduate Program of Biotechnology, University of São Paulo, São Paulo, SP Brazil
- Post Graduate Program of Molecular Biology, Federal University of São Paulo, São Paulo, SP Brazil
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13
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Ziani PR, de Bastiani MA, Scotton E, da Rosa PH, Schons T, Mezzomo G, de Carvalho Q, Kapczinski F, Rosa AR. Drug Repurposing and Personalized Treatment Strategies for Bipolar Disorder Using Transcriptomic. Braz J Psychiatry 2024. [PMID: 38446713 DOI: 10.47626/1516-4446-2023-3441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/10/2024] [Indexed: 03/08/2024]
Abstract
OBJECTIVE The present study combined transcriptomic data and computational techniques based on gene expression signatures to identify novel bioactive compounds or FDA-approved drugs for the management of Bipolar Disorder (BD). METHODS Five transcriptomic datasets, comprising a total of 165 blood samples from BD case-control, were selected from the Gene Expression Omnibus repository (GEO). The number of subjects varied from 6 to 60, with a mean age ranging from 35 to 48, with a gender variation between them. Most of the patients were on pharmacological treatment. Master Regulator Analysis (MRA) and Gene Set Enrichment Analysis (GSEA) were performed to identify statistically significant genes between BD and HC and their association with the mood states of BD. Additionally, existing molecules with the potential to reverse the transcriptomic profiles of disease-altered regulons in BD were identified using the LINCS and cMap databases. RESULTS MRA identified 59 potential MRs candidates modulating the regulatory units enriched with genes altered in BD, while the GSEA identified 134 enriched genes, and a total of 982 regulons had their activation state determined. Both analyses showed genes exclusively associated with mania, depression, or euthymia, and some genes were common between the three mood states. We identified bioactive compounds and licensed drug candidates, including antihypertensives and antineoplastics, as promising candidates for treating BD. Nevertheless, experimental validation is essential to authenticate these findings in subsequent studies. CONCLUSION Although preliminary, our data provides some insights regarding the biological patterns of BD into distinct mood states and potential therapeutic targets. The combined transcriptomic and bioinformatics strategy offers a route to advance drug discovery and personalized medicine by tapping into gene expression information.
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Affiliation(s)
- Paola Rampelotto Ziani
- Laboratory of Molecular Psychiatry, Hospital Clinic of Porto Alegre, Porto Alegre, Brazil. Postgraduate Program in Biological Sciences: Pharmacology and Therapeutics - Institute of Basic Health Sciences, University Federal of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Marco Antônio de Bastiani
- Postgraduate Program in Biological Sciences: Pharmacology and Therapeutics - Institute of Basic Health Sciences, University Federal of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Ellen Scotton
- Laboratory of Molecular Psychiatry, Hospital Clinic of Porto Alegre, Porto Alegre, Brazil. Postgraduate Program in Biological Sciences: Pharmacology and Therapeutics - Institute of Basic Health Sciences, University Federal of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Pedro Henrique da Rosa
- Laboratory of Molecular Psychiatry, Hospital Clinic of Porto Alegre, Porto Alegre, Brazil. Postgraduate Program in Biological Sciences: Pharmacology and Therapeutics - Institute of Basic Health Sciences, University Federal of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Tainá Schons
- Laboratory of Molecular Psychiatry, Hospital Clinic of Porto Alegre, Porto Alegre, Brazil
| | - Giovana Mezzomo
- Laboratory of Molecular Psychiatry, Hospital Clinic of Porto Alegre, Porto Alegre, Brazil. Postgraduate Program in Biological Sciences: Pharmacology and Therapeutics - Institute of Basic Health Sciences, University Federal of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Quênia de Carvalho
- Laboratory of Molecular Psychiatry, Hospital Clinic of Porto Alegre, Porto Alegre, Brazil. Postgraduate Program in Biological Sciences: Pharmacology and Therapeutics - Institute of Basic Health Sciences, University Federal of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Flávio Kapczinski
- Laboratory of Molecular Psychiatry, Hospital Clinic of Porto Alegre, Porto Alegre, Brazil. Department of Psychiatry, University Federal of Rio Grande do Sul, Porto Alegre, RS, Brazil. National Institute of Translational Science and Technology in Medicine, Porto Alegre, RS, Brazil. Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Adriane R Rosa
- Laboratory of Molecular Psychiatry, Hospital Clinic of Porto Alegre, Porto Alegre, Brazil. Postgraduate Program in Biological Sciences: Pharmacology and Therapeutics - Institute of Basic Health Sciences, University Federal of Rio Grande do Sul, Porto Alegre, RS, Brazil. Department of Pharmacology, Institute of Basic Science Health, University Federal of Rio Grande do Sul, Porto Alegre, RS, Brazil
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14
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Xu Q, Jiang S, Kang R, Wang Y, Zhang B, Tian J. Deciphering the molecular pathways underlying dopaminergic neuronal damage in Parkinson's disease associated with SARS-CoV-2 infection. Comput Biol Med 2024; 171:108200. [PMID: 38428099 DOI: 10.1016/j.compbiomed.2024.108200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 01/24/2024] [Accepted: 02/18/2024] [Indexed: 03/03/2024]
Abstract
BACKGROUND The COVID-19 pandemic caused by SARS-CoV-2 has led to significant global morbidity and mortality, with potential neurological consequences, such as Parkinson's disease (PD). However, the underlying mechanisms remain elusive. METHODS To address this critical question, we conducted an in-depth transcriptome analysis of dopaminergic (DA) neurons in both COVID-19 and PD patients. We identified common pathways and differentially expressed genes (DEGs), performed enrichment analysis, constructed protein‒protein interaction networks and gene regulatory networks, and employed machine learning methods to develop disease diagnosis and progression prediction models. To further substantiate our findings, we performed validation of hub genes using a single-cell sequencing dataset encompassing DA neurons from PD patients, as well as transcriptome sequencing of DA neurons from a mouse model of MPTP(1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)-induced PD. Furthermore, a drug-protein interaction network was also created. RESULTS We gained detailed insights into biological functions and signaling pathways, including ion transport and synaptic signaling pathways. CD38 was identified as a potential key biomarker. Disease diagnosis and progression prediction models were specifically tailored for PD. Molecular docking simulations and molecular dynamics simulations were employed to predict potential therapeutic drugs, revealing that genistein holds significant promise for exerting dual therapeutic effects on both PD and COVID-19. CONCLUSIONS Our study provides innovative strategies for advancing PD-related research and treatment in the context of the ongoing COVID-19 pandemic by elucidating the common pathogenesis between COVID-19 and PD in DA neurons.
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Affiliation(s)
- Qiuhan Xu
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China
| | - Sisi Jiang
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China
| | - Ruiqing Kang
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China
| | - Yiling Wang
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China.
| | - Jun Tian
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China.
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15
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Munna MMR, Islam MA, Shanta SS, Monty MA. Structural, functional, molecular docking analysis of a hypothetical protein from Talaromyces marneffei and its molecular dynamic simulation: an in-silico approach. J Biomol Struct Dyn 2024:1-20. [PMID: 38345137 DOI: 10.1080/07391102.2024.2314264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/29/2024] [Indexed: 03/01/2024]
Abstract
Telaromyces marneffei (formerly Penicillium marneffei) is an endemic pathogenic fungus in Southern China and Southeast Asia. It can cause disease in patients with travel-related exposure to this organism and high morbidity and mortality in acquired immune deficiency syndrome (AIDS). In this study, we analyzed the structure and function of a hypothetical protein from T. marneffei using several bioinformatics tools and servers to unveil novel pharmacological targets and design a peptide vaccine against specific epitopes. A total of seven functional epitopes were screened on the protein, and 'STGVDMWSV' was the most antigenic, non-allergenic and non-toxic. Molecular docking showed stronger affinity between the CTL epitope 'STGVDMWSV' and the MHC I allele HLA-A*02:01, a higher docking score -234.98 kcal/mol, revealed stable interactions during a 100 ns molecular dynamic simulation. Overall, the results of this study revealed that this hypothetical protein is crucial for comprehending biochemical, physiological pathways and identifying novel therapeutic targets for human health. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Md Masudur Rahman Munna
- Department of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Md Ariful Islam
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, PR China
| | - Saima Sajnin Shanta
- Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Masuma Akter Monty
- Institute of Biomedical Engineering and Technology, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, PR China
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16
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Cheng JH, Okada D. Data-driven detection of age-related arbitrary monotonic changes in single-cell gene expression distributions. PeerJ 2024; 12:e16851. [PMID: 38344300 PMCID: PMC10859082 DOI: 10.7717/peerj.16851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Identification of genes whose expression increases or decreases with age is central to understanding the mechanisms behind aging. Recent scRNA-seq studies have shown that changes in single-cell expression profiles with aging are complex and diverse. In this study, we introduce a novel workflow to detect changes in the distribution of arbitrary monotonic age-related changes in single-cell expression profiles. Since single-cell gene expression profiles can be analyzed as probability distributions, our approach uses information theory to quantify the differences between distributions and employs distance matrices for association analysis. We tested this technique on simulated data and confirmed that potential parameter changes could be detected in a set of probability distributions. Application of the technique to a public scRNA-seq dataset demonstrated its potential utility as a straightforward screening method for identifying aging-related cellular features.
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Affiliation(s)
- Jian Hao Cheng
- Center for Genomics Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan
| | - Daigo Okada
- Center for Genomics Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan
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Gallo E. Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances. Mol Biotechnol 2024:10.1007/s12033-024-01064-2. [PMID: 38308755 DOI: 10.1007/s12033-024-01064-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/02/2024] [Indexed: 02/05/2024]
Abstract
Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.
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Affiliation(s)
- Eugenio Gallo
- Avance Biologicals, Department of Medicinal Chemistry, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada.
- RevivAb, Department of Protein Engineering, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil.
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18
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Pal S, Bhattacharya M, Lee SS, Chakraborty C. Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics. Mol Biotechnol 2024; 66:163-178. [PMID: 37244882 PMCID: PMC10224669 DOI: 10.1007/s12033-023-00765-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/04/2023] [Indexed: 05/29/2023]
Abstract
Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology.
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Affiliation(s)
- Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha, 756020, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do, 24252, Republic of Korea
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, 700126, India.
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Okada D. Plasma proteins as potential biomarkers of aging of single tissue and cell type. Biogerontology 2024; 25:177-181. [PMID: 37707684 DOI: 10.1007/s10522-023-10065-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/21/2023] [Indexed: 09/15/2023]
Abstract
Plasma proteins serve as biomarkers of aging and various age-related diseases. While a number of plasma proteins have been identified that increase or decrease with age, the interpretation of each protein is challenging. This is due to the nature of plasma, which is a mixture of factors secreted by many different tissues and cells. Therefore, the catalog of age-related proteins secreted by a single cell type in a single tissue would be useful for understanding tissue-specific aging patterns. In this study, the author addressed this challenge by integrative data mining of the Human Protein Atlas and the recently published result of large-scale aging proteomics research. Finally, we identified the 17 age-related proteins produced by a single tissue and a single cell type: MBL2 and HP in the liver (hepatocytes), SFTPC in the lung (type II alveolar cells), PRL and POMC in the pituitary (anterior cells), GCG, CUZD1 and CPA2 in the pancreas (pancreatic cells), MYBPC1 in skeletal muscle (myocytes), PTH in the parathyroid gland (glandular cells), LPO and AMY1A in the salivary gland (glandular cells), INSL3 in the male testis (Leydig cells), KLK3 and KLK4 in the male prostate (glandular cells), MPO and ACP5 in immune cells. This list of proteins would be potentially useful for understanding age-related changes in the plasma proteome and inter-tissue networks.
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Affiliation(s)
- Daigo Okada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, 53 Syogoin-Kawaramachi, Sakyo-ku, Kyoto, 606-8507, Japan.
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20
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Rahnavard A, Chatterjee R, Wen H, Gaylord C, Mugusi S, Klatt KC, Smith ER. Molecular epidemiology of pregnancy using omics data: advances, success stories, and challenges. J Transl Med 2024; 22:106. [PMID: 38279125 PMCID: PMC10821542 DOI: 10.1186/s12967-024-04876-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 12/26/2023] [Indexed: 01/28/2024] Open
Abstract
Multi-omics approaches have been successfully applied to investigate pregnancy and health outcomes at a molecular and genetic level in several studies. As omics technologies advance, research areas are open to study further. Here we discuss overall trends and examples of successfully using omics technologies and techniques (e.g., genomics, proteomics, metabolomics, and metagenomics) to investigate the molecular epidemiology of pregnancy. In addition, we outline omics applications and study characteristics of pregnancy for understanding fundamental biology, causal health, and physiological relationships, risk and prediction modeling, diagnostics, and correlations.
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Affiliation(s)
- Ali Rahnavard
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
| | - Ranojoy Chatterjee
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Hui Wen
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Clark Gaylord
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Sabina Mugusi
- Department of Clinical Pharmacology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Kevin C Klatt
- Nutritional Sciences & Toxicology, University of California, Berkeley, CA, 94720, USA
| | - Emily R Smith
- Department of Global Health, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
- Department of Exercise and Nutrition Sciences, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
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Otero-Carrasco B, Ugarte Carro E, Prieto-Santamaría L, Diaz Uzquiano M, Caraça-Valente Hernández JP, Rodríguez-González A. Identifying patterns to uncover the importance of biological pathways on known drug repurposing scenarios. BMC Genomics 2024; 25:43. [PMID: 38191292 PMCID: PMC10775474 DOI: 10.1186/s12864-023-09913-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/15/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Drug repurposing plays a significant role in providing effective treatments for certain diseases faster and more cost-effectively. Successful repurposing cases are mostly supported by a classical paradigm that stems from de novo drug development. This paradigm is based on the "one-drug-one-target-one-disease" idea. It consists of designing drugs specifically for a single disease and its drug's gene target. In this article, we investigated the use of biological pathways as potential elements to achieve effective drug repurposing. METHODS Considering a total of 4214 successful cases of drug repurposing, we identified cases in which biological pathways serve as the underlying basis for successful repurposing, referred to as DREBIOP. Once the repurposing cases based on pathways were identified, we studied their inherent patterns by considering the different biological elements associated with this dataset, as well as the pathways involved in these cases. Furthermore, we obtained gene-disease association values to demonstrate the diminished significance of the drug's gene target in these repurposing cases. To achieve this, we compared the values obtained for the DREBIOP set with the overall association values found in DISNET, as well as with the drug's target gene (DREGE) based repurposing cases using the Mann-Whitney U Test. RESULTS A collection of drug repurposing cases, known as DREBIOP, was identified as a result. DREBIOP cases exhibit distinct characteristics compared with DREGE cases. Notably, DREBIOP cases are associated with a higher number of biological pathways, with Vitamin D Metabolism and ACE inhibitors being the most prominent pathways. Additionally, it was observed that the association values of GDAs in DREBIOP cases were significantly lower than those in DREGE cases (p-value < 0.05). CONCLUSIONS Biological pathways assume a pivotal role in drug repurposing cases. This investigation successfully revealed patterns that distinguish drug repurposing instances associated with biological pathways. These identified patterns can be applied to any known repurposing case, enabling the detection of pathway-based repurposing scenarios or the classical paradigm.
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Affiliation(s)
- Belén Otero-Carrasco
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain
| | - Esther Ugarte Carro
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
| | - Lucía Prieto-Santamaría
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain
| | - Marina Diaz Uzquiano
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
| | | | - Alejandro Rodríguez-González
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain.
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain.
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22
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Abstract
Phylogenetic comparative methods comprise the general endeavor of using an estimated phylogenetic tree (or set of trees) to make secondary inferences: about trait evolution, diversification dynamics, biogeography, community ecology, and a wide range of other phenomena or processes. Over the past ten years or so, the phytools R package has grown to become an important research tool for phylogenetic comparative analysis. phytools is a diverse contributed R library now consisting of hundreds of different functions covering a variety of methods and purposes in phylogenetic biology. As of the time of writing, phytools included functionality for fitting models of trait evolution, for reconstructing ancestral states, for studying diversification on trees, and for visualizing phylogenies, comparative data, and fitted models, as well numerous other tasks related to phylogenetic biology. Here, I describe some significant features of and recent updates to phytools, while also illustrating several popular workflows of the phytools computational software.
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Affiliation(s)
- Liam J. Revell
- Department of Biology, University of Massachusetts Boston, Boston, MA, USA
- Facultad de Ciencias, Universidad Católica de la Santísima Concepción, Concepción, Chile
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23
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Kerkvliet JJ, Bossers A, Kers JG, Meneses R, Willems R, Schürch AC. Metagenomic assembly is the main bottleneck in the identification of mobile genetic elements. PeerJ 2024; 12:e16695. [PMID: 38188174 PMCID: PMC10771768 DOI: 10.7717/peerj.16695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 11/28/2023] [Indexed: 01/09/2024] Open
Abstract
Antimicrobial resistance genes (ARG) are commonly found on acquired mobile genetic elements (MGEs) such as plasmids or transposons. Understanding the spread of resistance genes associated with mobile elements (mARGs) across different hosts and environments requires linking ARGs to the existing mobile reservoir within bacterial communities. However, reconstructing mARGs in metagenomic data from diverse ecosystems poses computational challenges, including genome fragment reconstruction (assembly), high-throughput annotation of MGEs, and identification of their association with ARGs. Recently, several bioinformatics tools have been developed to identify assembled fragments of plasmids, phages, and insertion sequence (IS) elements in metagenomic data. These methods can help in understanding the dissemination of mARGs. To streamline the process of identifying mARGs in multiple samples, we combined these tools in an automated high-throughput open-source pipeline, MetaMobilePicker, that identifies ARGs associated with plasmids, IS elements and phages, starting from short metagenomic sequencing reads. This pipeline was used to identify these three elements on a simplified simulated metagenome dataset, comprising whole genome sequences from seven clinically relevant bacterial species containing 55 ARGs, nine plasmids and five phages. The results demonstrated moderate precision for the identification of plasmids (0.57) and phages (0.71), and moderate sensitivity of identification of IS elements (0.58) and ARGs (0.70). In this study, we aim to assess the main causes of this moderate performance of the MGE prediction tools in a comprehensive manner. We conducted a systematic benchmark, considering metagenomic read coverage, contig length cutoffs and investigating the performance of the classification algorithms. Our analysis revealed that the metagenomic assembly process is the primary bottleneck when linking ARGs to identified MGEs in short-read metagenomics sequencing experiments rather than ARGs and MGEs identification by the different tools.
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Affiliation(s)
- Jesse J. Kerkvliet
- Department of Medical Microbiology, UMC Utrecht, Utrecht, The Netherlands
| | - Alex Bossers
- Utrecht University, Institute for Risk Assessment Sciences, Utrecht, The Netherlands
- Wageningen University, Wageningen Bioveterinary Research, Lelystad, The Netherlands
| | - Jannigje G. Kers
- Utrecht University, Institute for Risk Assessment Sciences, Utrecht, The Netherlands
| | - Rodrigo Meneses
- Department of Medical Microbiology, UMC Utrecht, Utrecht, The Netherlands
| | - Rob Willems
- Department of Medical Microbiology, UMC Utrecht, Utrecht, The Netherlands
| | - Anita C. Schürch
- Department of Medical Microbiology, UMC Utrecht, Utrecht, The Netherlands
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24
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Cao W, Ji Z, Zhu S, Wang M, Sun R. Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer's disease. BMC Med Genomics 2024; 17:6. [PMID: 38167011 PMCID: PMC10763315 DOI: 10.1186/s12920-023-01775-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 12/12/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disease that can cause dementia. We aim to screen out the hub genes involved in AD based on microarray datasets. METHODS Gene expression profiles GSE5281 and GSE28146 were retrieved from Gene Expression Omnibus database to acquire differentially expressed genes (DEGs). Gene Ontology and pathway enrichment were conducted using DAVID online tool. The STRING database and Cytoscape tools were employed to analyze protein-protein interactions and identify hub genes. The predictive value of hub genes was assessed by principal component analysis and receiver operating characteristic curves. AD mice model was constructed, and histology was then observed by hematoxylin-eosin staining. Gene expression levels were finally determined by real-time quantitative PCR. RESULTS We obtained 197 overlapping DEGs from GSE5281 and GSE28146 datasets. After constructing protein-protein interaction network, three highly interconnected clusters were identified and 6 hub genes (RBL1, BUB1, HDAC7, KAT5, SIRT2, and ITGB1) were selected. The hub genes could be used as basis to predict AD. Histological abnormalities of brain were observed, suggesting successful AD model was constructed. Compared with the control group, the mRNA expression levels of RBL1, BUB1, HDAC7, KAT5 and SIRT2 were significantly increased, while the mRNA expression level of ITGB1 was significantly decreased in AD groups. CONCLUSION RBL1, BUB1, HDAC7, KAT5, SIRT2 and ITGB1 are promising gene signatures for diagnosis and therapy of AD.
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Affiliation(s)
- Wenyuan Cao
- Department of Neurology Second Ward, Zibo Municipal Hospital, No. 139, Huangong Road, Linzi District, Zibo City, 255400, Shandong Province, China
| | - Zhangge Ji
- Department of Neurology Second Ward, Zibo Municipal Hospital, No. 139, Huangong Road, Linzi District, Zibo City, 255400, Shandong Province, China
| | - Shoulian Zhu
- Department of Neurology Second Ward, Zibo Municipal Hospital, No. 139, Huangong Road, Linzi District, Zibo City, 255400, Shandong Province, China
| | - Mei Wang
- Department of Rehabilitation, Zibo Municipal Hospital, No. 139, Huangong Road, Linzi District, Zibo City, 255400, Shandong Province, China
| | - Runming Sun
- Department of Neurology Second Ward, Zibo Municipal Hospital, No. 139, Huangong Road, Linzi District, Zibo City, 255400, Shandong Province, China.
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25
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Abstract
The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.
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Affiliation(s)
- Adam Serghini
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Stephanie Portelli
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
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26
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Li Z, Yang W, Wu P, Shan Y, Zhang X, Chen F, Yang J, Yang JR. Reconstructing cell lineage trees with genomic barcoding: approaches and applications. J Genet Genomics 2024; 51:35-47. [PMID: 37269980 DOI: 10.1016/j.jgg.2023.05.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/19/2023] [Accepted: 05/20/2023] [Indexed: 06/05/2023]
Abstract
In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.
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Affiliation(s)
- Zizhang Li
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Wenjing Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Peng Wu
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yuyan Shan
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xiaoyu Zhang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Feng Chen
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Junnan Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jian-Rong Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
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27
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Townsend-Nicholson A. Teaching Medical Students to Use Supercomputers: A Personal Reflection. Methods Mol Biol 2024; 2716:413-420. [PMID: 37702952 DOI: 10.1007/978-1-0716-3449-3_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
At the "Kick Off" meeting for CompBioMed (compbiomed.eu), which was first funded in October 2016, I had no idea that one single sentence ("I wish I could teach this to medical students") would lead to a dedicated program of work to engage the clinicians and biomedical researchers of the future with supercomputing. This program of work which, within the CompBiomed Centre of Excellence, we have been calling "the CompBioMed Education and Training Programme," is a holistic endeavor that has been developed by and continues to be delivered with the expertise and support from experimental researchers, computer scientists, clinicians, HPC centers, and industrial partners within or associated with CompBioMed. The original description of the initial educational approach to training has previously been published (Townsend-Nicholson Interface Focus 10:20200003, 2020). In this chapter, I describe the refinements to the program and its delivery, emphasizing the highs and lows of delivering this program over the past 6 years. I conclude with suggestions for feasible measures that I believe will help overcome the barriers and challenges we have encountered in bringing a community of users with little familiarity of computing beyond the desktop to the petascale and beyond.
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Affiliation(s)
- Andrea Townsend-Nicholson
- Research Department of Structural & Molecular Biology, Division of Biosciences, University College London, London, UK.
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28
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Rezaei S, Jafari Najaf Abadi MH, Bazyari MJ, Jalili A, Kazemi Oskuee R, Aghaee-Bakhtiari SH. Dysregulated microRNAs in prostate cancer: In silico prediction and in vitro validation. Iran J Basic Med Sci 2024; 27:611-620. [PMID: 38629091 PMCID: PMC11017842 DOI: 10.22038/ijbms.2024.75164.16299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/04/2023] [Indexed: 04/19/2024]
Abstract
Objectives MicroRNAs, which are micro-coordinators of gene expression, have been recently investigated as a potential treatment for cancer. The study used computational techniques to identify microRNAs that could target a set of genes simultaneously. Due to their multi-target-directed nature, microRNAs have the potential to impact multiple key pathways and their pathogenic cross-talk. Materials and Methods We identified microRNAs that target a prostate cancer-associated gene set using integrated bioinformatics analyses and experimental validation. The candidate gene set included genes targeted by clinically approved prostate cancer medications. We used STRING, GO, and KEGG web tools to confirm gene-gene interactions and their clinical significance. Then, we employed integrated predicted and validated bioinformatics approaches to retrieve hsa-miR-124-3p, 16-5p, and 27a-3p as the top three relevant microRNAs. KEGG and DIANA-miRPath showed the related pathways for the candidate genes and microRNAs. Results The Real-time PCR results showed that miR-16-5p simultaneously down-regulated all genes significantly except for PIK3CA/CB in LNCaP; miR-27a-3p simultaneously down-regulated all genes significantly, excluding MET in LNCaP and PIK3CA in PC-3; and miR-124-3p could not down-regulate significantly PIK3CB, MET, and FGFR4 in LNCaP and FGFR4 in PC-3. Finally, we used a cell cycle assay to show significant G0/G1 arrest by transfecting miR-124-3p in LNCaP and miR-16-5p in both cell lines. Conclusion Our findings suggest that this novel approach may have therapeutic benefits and these predicted microRNAs could effectively target the candidate genes.
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Affiliation(s)
- Samaneh Rezaei
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Mohammad Javad Bazyari
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amin Jalili
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Reza Kazemi Oskuee
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyed Hamid Aghaee-Bakhtiari
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Bioinformatics Research Center, Mashhad University of Medical Science, Mashhad, Iran
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29
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Wani AK, Chopra C, Dhanjal DS, Akhtar N, Singh H, Bhau P, Singh A, Sharma V, Pinheiro RSB, Américo-Pinheiro JHP, Singh R. Metagenomics in the fight against zoonotic viral infections: A focus on SARS-CoV-2 analogues. J Virol Methods 2024; 323:114837. [PMID: 37914040 DOI: 10.1016/j.jviromet.2023.114837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/03/2023]
Abstract
Zoonotic viral infections continue to pose significant threats to global public health, as highlighted by the COVID-19 pandemic caused by the SARS-CoV-2 virus. The emergence of SARS-CoV-2 served as a stark reminder of the potential for zoonotic transmission of viruses from animals to humans. Understanding the origins and dynamics of zoonotic viruses is critical for early detection, prevention, and effective management of future outbreaks. Metagenomics has emerged as a powerful tool for investigating the virome of diverse ecosystems, shedding light on the diversity of viral populations, their hosts, and potential zoonotic spillover events. We provide an in-depth examination of metagenomic approaches, including, NGS metagenomics, shotgun metagenomics, viral metagenomics, and single-virus metagenomics, highlighting their strengths and limitations in identifying and characterizing zoonotic viral pathogens. This review underscores the pivotal role of metagenomics in enhancing our ability to detect, monitor, and mitigate zoonotic viral infections, using SARS-CoV-2 analogues as a case study. We emphasize the need for continued interdisciplinary collaboration among virologists, ecologists, and bioinformaticians to harness the full potential of metagenomic approaches in safeguarding public health against emerging zoonotic threats.
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Affiliation(s)
- Atif Khurshid Wani
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Chirag Chopra
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Daljeet Singh Dhanjal
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Nahid Akhtar
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Himanshu Singh
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Poorvi Bhau
- School of Biotechnology, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | - Anjuvan Singh
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Varun Sharma
- NMC Genetics India Pvt. Ltd, Gurugram, Harayana, India
| | - Rafael Silvio Bonilha Pinheiro
- School of Veterinary Medicine and Animal Science, Department of Animal Production, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | - Juliana Heloisa Pinê Américo-Pinheiro
- Department of Forest Science, Soils and Environment, School of Agronomic Sciences, São Paulo State University (UNESP), Ave. Universitária, 3780, Botucatu, SP 18610-034, Brazil; Graduate Program in Environmental Sciences, Brazil University, Street Carolina Fonseca, 584, São Paulo, SP 08230-030, Brazil
| | - Reena Singh
- School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India.
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30
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Masson HO, Samoudi M, Robinson CM, Kuo CC, Weiss L, Shams Ud Doha K, Campos A, Tejwani V, Dahodwala H, Menard P, Voldborg BG, Robasky B, Sharfstein ST, Lewis NE. Inferring secretory and metabolic pathway activity from omic data with secCellFie. Metab Eng 2024; 81:273-285. [PMID: 38145748 DOI: 10.1016/j.ymben.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 11/29/2023] [Accepted: 12/14/2023] [Indexed: 12/27/2023]
Abstract
Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.
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Affiliation(s)
- Helen O Masson
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | | | | | - Chih-Chung Kuo
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | - Linus Weiss
- Department of Biochemistry, Eberhard Karls University of Tübingen, Germany
| | - Km Shams Ud Doha
- Proteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Alex Campos
- Proteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Vijay Tejwani
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Hussain Dahodwala
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Patrice Menard
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Bjorn G Voldborg
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark; National Biologics Facility, Technical University of Denmark, Lyngby, Denmark
| | | | - Susan T Sharfstein
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Nathan E Lewis
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA; Department of Pediatrics, UC San Diego, La Jolla, CA, USA.
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31
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He S, Ye X, Dou L, Sakurai T. FIAMol-AB: A feature fusion and attention-based deep learning method for enhanced antibiotic discovery. Comput Biol Med 2024; 168:107762. [PMID: 38056212 DOI: 10.1016/j.compbiomed.2023.107762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 10/31/2023] [Accepted: 11/21/2023] [Indexed: 12/08/2023]
Abstract
Antibiotic resistance continues to be a growing concern for global health, accentuating the need for novel antibiotic discoveries. Traditional methodologies in this field have relied heavily on extensive experimental screening, which is often time-consuming and costly. Contrastly, computer-assisted drug screening offers rapid, cost-effective solutions. In this work, we propose FIAMol-AB, a deep learning model that combines graph neural networks, text convolutional networks and molecular fingerprint techniques. This method also combines an attention mechanism to fuse multiple forms of information within the model. The experiments show that FIAMol-AB may offer potential advantages in antibiotic discovery tasks over some existing methods. We conducted some analysis based on our model's results, which help highlight the potential significance of certain features in the model's predictive performance. Compared to different models, ours demonstrate promising results, indicating potential robustness and versatility. This suggests that by integrating multi-view information and attention mechanisms, FIAMol-AB might better learn complex molecular structures, potentially improving the precision and efficiency of antibiotic discovery. We hope our FIAMol-AB can be used as a useful method in the ongoing fight against antibiotic resistance.
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Affiliation(s)
- Shida He
- Department of Computer Science, University of Tsukuba, Tsukuba, Ibaraki, 305-8577, Japan
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba, Ibaraki, 305-8577, Japan.
| | - Lijun Dou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland, OH, 44106, USA
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba, Ibaraki, 305-8577, Japan
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Martini L, Amprimo G, Di Carlo S, Olmo G, Ferraris C, Savino A, Bardini R. Neuronal Spike Shapes (NSS): A straightforward approach to investigate heterogeneity in neuronal excitability states. Comput Biol Med 2024; 168:107783. [PMID: 38056213 DOI: 10.1016/j.compbiomed.2023.107783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/23/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023]
Abstract
The mammalian brain exhibits a remarkable diversity of neurons, contributing to its intricate architecture and functional complexity. The analysis of multimodal single-cell datasets enables the investigation of cell types and states heterogeneity. In this study, we introduce the Neuronal Spike Shapes (NSS), a straightforward approach for the exploration of excitability states of neurons based on their Action Potential (AP) waveforms. The NSS method describes the AP waveform based on a triangular representation complemented by a set of derived electrophysiological (EP) features. To support this hypothesis, we validate the proposed approach on two datasets of murine cortical neurons, focusing it on GABAergic neurons. The validation process involves a combination of NSS-based clustering analysis, features exploration, Differential Expression (DE), and Gene Ontology (GO) enrichment analysis. Results show that the NSS-based analysis captures neuronal excitability states that possess biological relevance independently of cell subtype. In particular, Neuronal Spike Shapes (NSS) captures, among others, a well-characterized fast-spiking excitability state, supported by both electrophysiological and transcriptomic validation. Gene Ontology Enrichment Analysis reveals voltage-gated potassium (K+) channels as specific markers of the identified NSS partitions. This finding strongly corroborates the biological relevance of NSS partitions as excitability states, as the expression of voltage-gated K+ channels regulates the hyperpolarization phase of the AP, being directly implicated in the regulation of neuronal excitability.
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Affiliation(s)
- Lorenzo Martini
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy.
| | - Gianluca Amprimo
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy; Institute of Electronics, Information Engineering and Telecommunications, National Research Council, Corso Duca degli Abruzzi, 24, Turin, 10029, Italy.
| | - Stefano Di Carlo
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy. https://www.smilies.polito.it
| | - Gabriella Olmo
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy. https://www.sysbio.polito.it/analytics-technologies-health/
| | - Claudia Ferraris
- Institute of Electronics, Information Engineering and Telecommunications, National Research Council, Corso Duca degli Abruzzi, 24, Turin, 10029, Italy. https://www.ieiit.cnr.it/people/Ferraris-Claudia
| | - Alessandro Savino
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy. https://www.smilies.polito.it
| | - Roberta Bardini
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy.
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Kumar P, Paul RK, Roy HS, Yeasin M, Ajit, Paul AK. Big Data Analysis in Computational Biology and Bioinformatics. Methods Mol Biol 2024; 2719:181-197. [PMID: 37803119 DOI: 10.1007/978-1-0716-3461-5_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Advancements in high-throughput technologies, genomics, transcriptomics, and metabolomics play an important role in obtaining biological information about living organisms. The field of computational biology and bioinformatics has experienced significant growth with the advent of high-throughput sequencing technologies and other high-throughput techniques. The resulting large amounts of data present both opportunities and challenges for data analysis. Big data analysis has become essential for extracting meaningful insights from the massive amount of data. In this chapter, we provide an overview of the current status of big data analysis in computational biology and bioinformatics. We discuss the various aspects of big data analysis, including data acquisition, storage, processing, and analysis. We also highlight some of the challenges and opportunities of big data analysis in this area of research. Despite the challenges, big data analysis presents significant opportunities like development of efficient and fast computing algorithms for advancing our understanding of biological processes, identifying novel biomarkers for breeding research and developments, predicting disease, and identifying potential drug targets for drug development programs.
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Affiliation(s)
- Prakash Kumar
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Ranjit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Himadri Shekhar Roy
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Md Yeasin
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Ajit
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Amrit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
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Chouhan U, Sahu RK, Bhatt S, Kurmi S, Choudhari JK. Emerging Trends in Big Data Analysis in Computational Biology and Bioinformatics in Health Informatics: A Case Study on Epilepsy and Seizures. Methods Mol Biol 2024; 2719:99-119. [PMID: 37803114 DOI: 10.1007/978-1-0716-3461-5_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Advanced technology innovations allow cost-effective, high-throughput profiling of biological systems. It enabled genome sequencing in days using advanced technologies (e.g., next-generation sequencing, microarrays, and mass spectrometry). Since technology has been developed, massive biological data (e.g., genomics, proteomics) has been produced cheaply, allowing the "big data" era to create new opportunities to solve medical and biological complications in many disciplines-preventive medicine, biology, Personalized Medicine, gene sequencing, healthcare, and industry. Computational biology and bioinformatics are interdisciplinary fields that develop and apply computational methods (e.g., analytical methods, mathematical modeling, and simulation) to analyze large collections of biological data, such as genetic sequences, cell populations, or protein samples, to make new predictions or discover new biology. Biological data storage, mining, and analysis have challenges because data is much more heterogeneous. In this study, the big data resources of genomics, proteomics, and metabolomics have been explored to solve biological problems using big data analysis approaches. The goal is to build a network of relationship-based gene-disease associations to prioritize phenotypes common to epilepsy and seizure disease. Through network analysis, The 10 seed genes, 22 associated genes, 132 microRNAs, and 38 transcription factors have been identified that have a direct effect on all forms of epilepsy and seizures. The majority of seed genes, according to the results of a functional analysis of seed genes, are involved in the acetylcholine-gated channel complex (10%) and the heterotrimeric G-protein complex (10%) pathways related to cellular components, followed by a role in the regulation of action potential (20%) and positive regulation of vascular endothelial growth factor production (20%) in Epilepsy and Seizures pathways related to biological processes. This study might provide insight into the workings of the disease and shows the importance of continued research into epilepsy and other conditions that can trigger seizure activity.
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Affiliation(s)
- Usha Chouhan
- Department of Mathematics, Bioinformatics & Computer Applications, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India
| | - Rakesh Kumar Sahu
- Department of Biotechnology, Government V.Y.T. Post Graduate Autonomous College, Durg, Chhattisgarh, India
| | - Shaifali Bhatt
- Department of Mathematics, Bioinformatics & Computer Applications, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India
| | - Sonu Kurmi
- Department of Mathematics, Bioinformatics & Computer Applications, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India
| | - Jyoti Kant Choudhari
- Department of Mathematics, Bioinformatics & Computer Applications, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India
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Lee J, Shin DY, Jang Y, Han JP, Cho EM, Seo YR. Cadmium-induced Carcinogenesis in Respiratory Organs and the Prostate: Insights from Three Perspectives on Toxicogenomic Approach. J Cancer Prev 2023; 28:150-159. [PMID: 38205367 PMCID: PMC10774485 DOI: 10.15430/jcp.2023.28.4.150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
Cadmium (Cd) exposure primarily occurs through inhalation, either by smoking or occupational exposure to contaminated air. Upon inhalation, Cd ultimately reaches the prostate through the bloodstream. In this review, we investigate the carcinogenic potential of Cd in both respiratory organs and the prostate. Specifically, this review examines cellular metabolism, comprehensive toxicity, and carcinogenic mechanisms by exploring gene ontology, biological networks, and adverse outcome pathways. In the respiratory organs, Cd induces lung cancer by altering the expression of IL1B and FGF2, causing DNA damage, reducing cell junction integrity, and promoting apoptosis. In the prostate, Cd induces prostate cancer by modifying the expression of EDN1 and HMOX1, leading to abnormal protein activities and maturation, suppressing tumor suppressors, and inducing apoptosis. Collectively, this review provides a comprehensive understanding of the carcinogenic mechanisms of Cd in two different organs by adopting toxicogenomic approaches. These insights can serve as a foundation for further research on cadmium-induced cancer, contributing to the establishment of future cancer prevention strategies.
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Affiliation(s)
- Jun Lee
- Department of Life Science, Institute of Environmental Medicine for Green Chemistry, Dongguk University Biomedi Campus, Goyang, Korea
| | - Dong Yeop Shin
- Department of Life Science, Institute of Environmental Medicine for Green Chemistry, Dongguk University Biomedi Campus, Goyang, Korea
| | - Yujin Jang
- Department of Life Science, Institute of Environmental Medicine for Green Chemistry, Dongguk University Biomedi Campus, Goyang, Korea
| | - Jun Pyo Han
- Department of Life Science, Institute of Environmental Medicine for Green Chemistry, Dongguk University Biomedi Campus, Goyang, Korea
| | - Eun-Min Cho
- Department of Nano, Chemical & Biological Engineering, College of Natural Science and Engineering, Seokyeong University, Seoul, Korea
| | - Young Rok Seo
- Department of Life Science, Institute of Environmental Medicine for Green Chemistry, Dongguk University Biomedi Campus, Goyang, Korea
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Xia Y, Ling AL, Zhang W, Lee A, Su MC, Gruener RF, Jena S, Huang Y, Pareek S, Shan Y, Huang RS. A Web Application for Predicting Drug Combination Efficacy Using Monotherapy Data and IDACombo. J Cancer Sci Clin Ther 2023; 7:253-258. [PMID: 38344217 PMCID: PMC10852200 DOI: 10.26502/jcsct.5079218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
We recently reported a computational method (IDACombo) designed to predict the efficacy of cancer drug combinations using monotherapy response data and the assumptions of independent drug action. Given the strong agreement between IDACombo predictions and measured drug combination efficacy in vitro and in clinical trials, we believe IDACombo can be of immediate use to researchers who are working to develop novel drug combinations. While we previously released our method as an R package, we have now created an R Shiny application to allow researchers without programming experience to easily utilize this method. The app provides a graphical interface which enables users to easily generate efficacy predictions with IDACombo using provided data from several high-throughput cell line screens or using custom, user-provided data.
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Affiliation(s)
- Yunong Xia
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Alexander L Ling
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
- Harvey Cushing Neuro-oncology Laboratories, Department of Neurosurgery, Hale Building for Transformative Medicine, 4th and 8th floor, Brigham and Women's Hospital; 60 Fenwood Road, Boston, MA 02116
| | - Weijie Zhang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mei-Chi Su
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Robert F Gruener
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Siddhika Pareek
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yuting Shan
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - R Stephanie Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
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Ling AL, Zhang W, Lee A, Xia Y, Su MC, Gruener RF, Jena S, Huang Y, Pareek S, Shan Y, Stephanie Huang R. Simplicity: Web-Based Visualization and Analysis of High-Throughput Cancer Cell Line Screens. J Cancer Sci Clin Ther 2023; 7:249-252. [PMID: 38435702 PMCID: PMC10906814 DOI: 10.26502/jcsct.5079217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatics skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high- throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.
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Affiliation(s)
- Alexander L Ling
- Harvey Cushing Neuro-oncology Laboratories, Department of Neurosurgery, Hale Building for Transformative Medicine, 4th and 8th floor, Brigham and Women's Hospital; 60 Fenwood Road, Boston, MA 02116
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Weijie Zhang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yunong Xia
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mei-Chi Su
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Robert F Gruener
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Siddhika Pareek
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yuting Shan
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - R Stephanie Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
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38
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Corrêa DEDC, Bargi-Souza P, Oliveira IM, Razera A, Oliveira CA, Romano MA, Romano RM. Quantitative proteomic profile analysis of thyroid dysfunction effects on seminal vesicles and repercussions on male fertility. Mol Cell Endocrinol 2023; 578:112048. [PMID: 37633588 DOI: 10.1016/j.mce.2023.112048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/17/2023] [Accepted: 08/23/2023] [Indexed: 08/28/2023]
Abstract
Hypothyroidism and thyrotoxicosis are associated with male reproductive disorders, but little is known about the influence of the thyroid hormone milieu on seminal vesicle (SV) function and metabolism. In this sense, we investigated the effects of hypothyroidism and thyrotoxicosis induced in adulthood Wistar male rats on SV function and identified new thyroid hormone targets on male reproduction regulation using novel proteomic approaches. Hypothyroidism reduces SV size and seminal fluid volume, which are directly associated with low testosterone and estradiol levels, while thyrotoxicosis increases Esr2 and Dio1 expression in the SV. We found 116 differentially expressed proteins. Hypothyroidism reduces the expression of molecular protein markers related to sperm viability, capacitation and fertilization, protection against oxidative stress and energetic metabolism in SV, while it increases the expression of proteins related to tissue damage. In conclusion, thyroid dysfunction in the adult phase impairs several morphological, molecular and functional characteristics of SV.
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Affiliation(s)
| | - Paula Bargi-Souza
- Department of Physiology and Biophysics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | | | - Amanda Razera
- Department of Medicine, State University of Central-West (UNICENTRO), Guarapuava, Parana, Brazil
| | - Claudio Alvarenga Oliveira
- Department of Animal Reproduction, Faculty of Veterinary Medicine, University of Sao Paulo, Sao Paulo, Brazil
| | - Marco Aurelio Romano
- Department of Medicine, State University of Central-West (UNICENTRO), Guarapuava, Parana, Brazil
| | - Renata Marino Romano
- Department of Medicine, State University of Central-West (UNICENTRO), Guarapuava, Parana, Brazil.
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Pal S, Bhattacharya M, Dash S, Lee SS, Chakraborty C. A next-generation dynamic programming language Julia: Its features and applications in biological science. J Adv Res 2023:S2090-1232(23)00352-1. [PMID: 37992995 DOI: 10.1016/j.jare.2023.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 11/07/2023] [Accepted: 11/14/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND The advent of Julia as a sophisticated and dynamic programming language in 2012 represented a significant milestone in computational programming, mathematical analysis, and statistical modeling. Having reached its stable release in version 1.9.0 on May 7, 2023, Julia has developed into a powerful and versatile instrument. Despite its potential and widespread adoption across various scientific and technical domains, there exists a noticeable knowledge gap in comprehending its utilization within biological sciences. THE AIM OF REVIEW This comprehensive review aims to address this particular knowledge gap and offer a thorough examination of Julia's fundamental characteristics and its applications in biology. KEY SCIENTIFIC CONCEPTS OF THE REVIEW The review focuses on a research gap in the biological science. The review aims to equip researchers with knowledge and tools to utilize Julia's capabilities in biological science effectively and to demonstrate the gap. It paves the way for innovative solutions and discoveries in this rapidly evolving field. It encompasses an analysis of Julia's characteristics, packages, and performance compared to the other programming languages in this field. The initial part of this review discusses the key features of Julia, such as its dynamic and interactive nature, fast processing speed, ease of expression manipulation, user-friendly syntax, code readability, strong support for multiple dispatch, and advanced type system. It also explores Julia's capabilities in data analysis, visualization, machine learning, and algorithms, making it suitable for scientific applications. The next section emphasizes the importance of using Julia in biological research, highlighting its seamless integration with biological studies for data analysis, and computational biology. It also compares Julia with other programming languages commonly used in biological research through benchmarking and performance analysis. Additionally, it provides insights into future directions and potential challenges in Julia's applications in biology.
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Affiliation(s)
- Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore 756020, Odisha, India
| | - Snehasish Dash
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do 24252, Republic of Korea.
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India.
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Dhasmana S, Dhasmana A, Rios S, Enriquez-Perez IA, Khan S, Afaq F, Haque S, Manne U, Yallapu MM, Chauhan SC. An integrated computational biology approach defines the crucial role of TRIP13 in pancreatic cancer. Comput Struct Biotechnol J 2023; 21:5765-5775. [PMID: 38074464 PMCID: PMC10709078 DOI: 10.1016/j.csbj.2023.11.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/13/2023] [Accepted: 11/14/2023] [Indexed: 02/12/2024] Open
Abstract
Pancreatic cancer (PanCa) is one of the most aggressive forms of cancer and its incidence rate is continuously increasing every year. It is expected that by 2030, PanCa will become the 2nd leading cause of cancer-related deaths in the United States due to the lack of early diagnosis and extremely poor survival. Despite great advancements in biomedical research, there are very limited early diagnostic modalities available for the early detection of PanCa. Thus, understanding of disease biology and identification of newer diagnostic and therapeutic modalities are high priority. Herein, we have utilized high dimensional omics data along with some wet laboratory experiments to decipher the expression level of hormone receptor interactor 13 (TRIP13) in various pathological staging including functional enrichment analysis. The functional enrichment analyses specifically suggest that TRIP13 and its related oncogenic network genes are involved in very important patho-physiological pathways. These analyses are supported by qPCR, immunoblotting and IHC analysis. Based on our study we proposed TRIP13 as a novel molecular target for PanCa diagnosis and therapeutic interventions. Overall, we have demonstrated a crucial role of TRIP13 in pathogenic events and progression of PanCa through applied integrated computational biology approaches.
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Affiliation(s)
- Swati Dhasmana
- Department of Immunology and Microbiology, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
- South Texas Center of Excellence in Cancer Research, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
| | - Anupam Dhasmana
- Department of Immunology and Microbiology, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
- South Texas Center of Excellence in Cancer Research, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
| | - Stella Rios
- Department of Immunology and Microbiology, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
- South Texas Center of Excellence in Cancer Research, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
| | - Iris A. Enriquez-Perez
- Department of Immunology and Microbiology, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
- South Texas Center of Excellence in Cancer Research, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
| | - Sheema Khan
- Department of Immunology and Microbiology, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
- South Texas Center of Excellence in Cancer Research, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
| | - Farrukh Afaq
- Department of Pathology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Shafiul Haque
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia
- Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon
- Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
| | - Upender Manne
- Department of Pathology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
- O’Neal Comprehensive Cancer Center, UAB, Birmingham, AL, USA
| | - Murali M. Yallapu
- Department of Immunology and Microbiology, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
- South Texas Center of Excellence in Cancer Research, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
| | - Subhash C. Chauhan
- Department of Immunology and Microbiology, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
- South Texas Center of Excellence in Cancer Research, School of Medicine, University of Texas Rio Grande Valley, McAllen, USA
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Ni H, Morotti S, Zhang X, Dobrev D, Grandi E. Integrative human atrial modelling unravels interactive protein kinase A and Ca2+/calmodulin-dependent protein kinase II signalling as key determinants of atrial arrhythmogenesis. Cardiovasc Res 2023; 119:2294-2311. [PMID: 37523735 DOI: 10.1093/cvr/cvad118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/18/2023] [Accepted: 06/05/2023] [Indexed: 08/02/2023] Open
Abstract
AIMS Atrial fibrillation (AF), the most prevalent clinical arrhythmia, is associated with atrial remodelling manifesting as acute and chronic alterations in expression, function, and regulation of atrial electrophysiological and Ca2+-handling processes. These AF-induced modifications crosstalk and propagate across spatial scales creating a complex pathophysiological network, which renders AF resistant to existing pharmacotherapies that predominantly target transmembrane ion channels. Developing innovative therapeutic strategies requires a systems approach to disentangle quantitatively the pro-arrhythmic contributions of individual AF-induced alterations. METHODS AND RESULTS Here, we built a novel computational framework for simulating electrophysiology and Ca2+-handling in human atrial cardiomyocytes and tissues, and their regulation by key upstream signalling pathways [i.e. protein kinase A (PKA), and Ca2+/calmodulin-dependent protein kinase II (CaMKII)] involved in AF-pathogenesis. Populations of atrial cardiomyocyte models were constructed to determine the influence of subcellular ionic processes, signalling components, and regulatory networks on atrial arrhythmogenesis. Our results reveal a novel synergistic crosstalk between PKA and CaMKII that promotes atrial cardiomyocyte electrical instability and arrhythmogenic triggered activity. Simulations of heterogeneous tissue demonstrate that this cellular triggered activity is further amplified by CaMKII- and PKA-dependent alterations of tissue properties, further exacerbating atrial arrhythmogenesis. CONCLUSIONS Our analysis reveals potential mechanisms by which the stress-associated adaptive changes turn into maladaptive pro-arrhythmic triggers at the cellular and tissue levels and identifies potential anti-AF targets. Collectively, our integrative approach is powerful and instrumental to assemble and reconcile existing knowledge into a systems network for identifying novel anti-AF targets and innovative approaches moving beyond the traditional ion channel-based strategy.
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Affiliation(s)
- Haibo Ni
- Department of Pharmacology, University of California Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Stefano Morotti
- Department of Pharmacology, University of California Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Xianwei Zhang
- Department of Pharmacology, University of California Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Dobromir Dobrev
- Institute of Pharmacology, Faculty of Medicine, University Duisburg-Essen, Essen, Germany
- Department of Medicine and Research Center, Montreal Heart Institute and Université de Montréal, Montréal, Canada
- Department of Molecular Physiology and Biophysics, Baylor College of Medicine, Houston, TX, USA
| | - Eleonora Grandi
- Department of Pharmacology, University of California Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
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Stillman NR, Mayor R. Generative models of morphogenesis in developmental biology. Semin Cell Dev Biol 2023; 147:83-90. [PMID: 36754751 PMCID: PMC10615838 DOI: 10.1016/j.semcdb.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023]
Abstract
Understanding the mechanism by which cells coordinate their differentiation and migration is critical to our understanding of many fundamental processes such as wound healing, disease progression, and developmental biology. Mathematical models have been an essential tool for testing and developing our understanding, such as models of cells as soft spherical particles, reaction-diffusion systems that couple cell movement to environmental factors, and multi-scale multi-physics simulations that combine bottom-up rule-based models with continuum laws. However, mathematical models can often be loosely related to data or have so many parameters that model behaviour is weakly constrained. Recent methods in machine learning introduce new means by which models can be derived and deployed. In this review, we discuss examples of mathematical models of aspects of developmental biology, such as cell migration, and how these models can be combined with these recent machine learning methods.
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Affiliation(s)
- Namid R Stillman
- Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK.
| | - Roberto Mayor
- Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK; Center for Integrative Biology, Faculty of Sciences, Universidad Mayor; Santiago, Chile Santiago, Chile..
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Laguillo-Gómez A, Calvo E, Martín-Cófreces N, Lozano-Prieto M, Sánchez-Madrid F, Vázquez J. ReCom: A semi-supervised approach to ultra-tolerant database search for improved identification of modified peptides. J Proteomics 2023; 287:104968. [PMID: 37463622 DOI: 10.1016/j.jprot.2023.104968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/27/2023] [Accepted: 06/27/2023] [Indexed: 07/20/2023]
Abstract
Open-search methods allow unbiased, high-throughput identification of post-translational modifications in proteins at an unprecedented scale. The performance of current open-search algorithms is diminished by experimental errors in the determination of the precursor peptide mass. In this work we propose a semi-supervised open search approach, called ReCom, that minimizes this effect by taking advantage of a priori known information from a reference database, such as Unimod or a database provided by the user. We present a proof-of-concept study using Comet-ReCom, an improved version of Comet-PTM. Comet-ReCom increased identification performance of Comet-PTM by 68%. This increased performance of Comet-ReCom to score the MS/MS spectrum comes in parallel with a significantly better assignation of the monoisotopic peak of the precursor peptide in the MS spectrum, even in cases of peptide coelution. Our data demonstrate that open searches using ultra-tolerant mass windows can benefit from using a semi-supervised approach that takes advantage from previous knowledge on the nature of protein modifications. SIGNIFICANCE: The present study introduces a novel approach to ultra-tolerant database search, which employs prior knowledge of post-translational modifications (PTMs) to improve identification of modified peptides. This method addresses the limitations related to experimental errors and precursor mass assignation of previous open-search methods. Thus, it enables the study of the biological significance of a wider variety of PTMs, including unknown or unexpected modifications that may have gone unnoticed using non-supervised search methods.
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Affiliation(s)
- Andrea Laguillo-Gómez
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid 28029, Spain.
| | - Enrique Calvo
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid 28029, Spain; CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid 28029, Spain.
| | - Noa Martín-Cófreces
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid 28029, Spain; CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid 28029, Spain.
| | - Marta Lozano-Prieto
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid 28029, Spain
| | - Francisco Sánchez-Madrid
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid 28029, Spain; CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid 28029, Spain.
| | - Jesús Vázquez
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid 28029, Spain; CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid 28029, Spain.
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Darabi M, Lhomme M, Ponnaiah M, Pučić-Baković M, Guillas I, Frisdal E, Bittar R, Croyal M, Matheron-Duriez L, Poupel L, Bonnefont-Rousselot D, Frere C, Varret M, Krempf M, Cariou B, Lauc G, Guerin M, Carrie A, Bruckert E, Giral P, Le Goff W, Kontush A. Integrated omics approach for the identification of HDL structure-function relationships in PCSK9-related familial hypercholesterolemia. J Clin Lipidol 2023; 17:643-658. [PMID: 37550151 DOI: 10.1016/j.jacl.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 07/07/2023] [Accepted: 07/13/2023] [Indexed: 08/09/2023]
Abstract
BACKGROUND The role of proprotein convertase subtilisin/kexin type 9 (PCSK9) in dyslipidemia may go beyond its immediate effects on low-density lipoprotein receptor (LDL-R) activity. OBJECTIVE This study aimed to assess PCSK9-derived alterations of high-density lipoprotein (HDL) physiology, which bear a potential to contribute to cardiovascular risk profile. METHODS HDL was isolated from 33 patients with familial autosomal dominant hypercholesterolemia (FH), including those carrying PCSK9 gain-of-function (GOF) genetic variants (FH-PCSK9, n = 11), together with two groups of dyslipidemic patients employed as controls and carrying genetic variants in the LDL-R not treated (ntFH-LDLR, n = 11) and treated (tFH-LDLR, n = 11) with statins, and 11 normolipidemic controls. Biological evaluations paralleled by proteomic, lipidomic and glycomic analyses were applied to characterize functional and compositional properties of HDL. RESULTS Multiple deficiencies in the HDL function were identified in the FH-PCSK9 group relative to dyslipidemic FH-LDLR patients and normolipidemic controls, which involved reduced antioxidative, antiapoptotic, anti-thrombotic and anti-inflammatory activities. By contrast, cellular cholesterol efflux capacity of HDL was unchanged. In addition, multiple alterations of the proteomic, lipidomic and glycomic composition of HDL were found in the FH-PCSK9 group. Remarkably, HDLs from FH-PCSK9 patients were systematically enriched in several lysophospholipids as well as in A2G2S2 (GP13) glycan and apolipoprotein A-IV. Based on network analysis of functional and compositional data, a novel mosaic structure-function model of HDL biology involving FH was developed. CONCLUSION Several metrics of anti-atherogenic HDL functionality are altered in FH-PCSK9 patients paralleled by distinct compositional alterations. These data provide a first-ever overview of the impact of GOF PCSK9 genetic variants on structure-function relationships in HDL.
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Affiliation(s)
- Maryam Darabi
- Sorbonne Université, INSERM (Drs Darabi, Guillas, Frisdal, Poupel, Carrie,Bittar, Guerin, Le Goff, and Kontush), Institute of Cardiometabolism and Nutrition (ICAN), UMR_S1166, F-75013 Paris, France; LPS-BioSciences (Current affiliation of Dr Darabi), Université de Paris-Saclay, Orsay, France
| | - Marie Lhomme
- ICAN Analytics (Dr Lhomme), Lipidomics Core, Foundation for Innovation in Cardiometabolism and Nutrition (IHU-ICAN, ANR-10-IAHU-05), Paris, France
| | - Maharajah Ponnaiah
- ICAN I/O (Dr Ponnaiah), Foundation for Innovation in Cardiometabolism and Nutrition (IHU-ICAN, ANR-10-IAHU-05), Paris, France
| | - Maja Pučić-Baković
- Genos Glycoscience Research Laboratory (Drs Pučić-Baković and Lauc), Borongajska cesta 83H, HR-10 000 Zagreb, Croatia
| | - Isabelle Guillas
- Sorbonne Université, INSERM (Drs Darabi, Guillas, Frisdal, Poupel, Carrie,Bittar, Guerin, Le Goff, and Kontush), Institute of Cardiometabolism and Nutrition (ICAN), UMR_S1166, F-75013 Paris, France
| | - Eric Frisdal
- Sorbonne Université, INSERM (Drs Darabi, Guillas, Frisdal, Poupel, Carrie,Bittar, Guerin, Le Goff, and Kontush), Institute of Cardiometabolism and Nutrition (ICAN), UMR_S1166, F-75013 Paris, France
| | - Randa Bittar
- Sorbonne Université, INSERM (Drs Darabi, Guillas, Frisdal, Poupel, Carrie,Bittar, Guerin, Le Goff, and Kontush), Institute of Cardiometabolism and Nutrition (ICAN), UMR_S1166, F-75013 Paris, France; Department of Metabolic Biochemistry (Drs Bittar and Bonnefont-Rousselot), Pitié-Salpêtrière-Charles Foix Hospital, AP-HP, Paris, France
| | - Mikaël Croyal
- Université de Nantes (Drs Cariou et Croyal), CHU Nantes, CNRS, INSERM, l'Institut du Thorax, F-44000 Nantes, France; Université de Nantes (Dr Croyal), CHU Nantes, Inserm, CNRS, SFR Santé, Inserm UMS 016, CNRS UMS 3556, F-44000 Nantes, France; CRNH-Ouest Mass Spectrometry Core Facility (Drs Croyal and Krempf), F-44000 Nantes, France
| | - Lucrèce Matheron-Duriez
- Platform MS3U (Dr Matheron), Institut de Biologie Paris Seine FR 3631, Sorbonne Université, Paris, France
| | - Lucie Poupel
- Sorbonne Université, INSERM (Drs Darabi, Guillas, Frisdal, Poupel, Carrie,Bittar, Guerin, Le Goff, and Kontush), Institute of Cardiometabolism and Nutrition (ICAN), UMR_S1166, F-75013 Paris, France
| | - Dominique Bonnefont-Rousselot
- Department of Metabolic Biochemistry (Drs Bittar and Bonnefont-Rousselot), Pitié-Salpêtrière-Charles Foix Hospital, AP-HP, Paris, France; Université de Paris (Dr Bonnefont-Rousselot), CNRS, INSERM, UTCBS, F-75006 Paris, France
| | - Corinne Frere
- Department of Haematology (Dr Frere), Pitié-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris, Sorbonne Université, Paris, France
| | - Mathilde Varret
- Paris University and Sorbonne Paris Nord University (Dr Varret), National Institute for Health and Medical Research (INSERM, LVTS), F-75018 Paris, France
| | - Michel Krempf
- CRNH-Ouest Mass Spectrometry Core Facility (Drs Croyal and Krempf), F-44000 Nantes, France; Clinique Bretéché (Dr Krempf), Groupe Elsan, Nantes, France
| | - Bertrand Cariou
- Université de Nantes (Drs Cariou et Croyal), CHU Nantes, CNRS, INSERM, l'Institut du Thorax, F-44000 Nantes, France
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory (Drs Pučić-Baković and Lauc), Borongajska cesta 83H, HR-10 000 Zagreb, Croatia
| | - Maryse Guerin
- Sorbonne Université, INSERM (Drs Darabi, Guillas, Frisdal, Poupel, Carrie,Bittar, Guerin, Le Goff, and Kontush), Institute of Cardiometabolism and Nutrition (ICAN), UMR_S1166, F-75013 Paris, France
| | - Alain Carrie
- Sorbonne Université, INSERM (Drs Darabi, Guillas, Frisdal, Poupel, Carrie,Bittar, Guerin, Le Goff, and Kontush), Institute of Cardiometabolism and Nutrition (ICAN), UMR_S1166, F-75013 Paris, France
| | - Eric Bruckert
- Endocrinologie Métabolisme et Prévention Cardiovasculaire (Drs Bruckert and Giral), Institut E3M et IHU Cardiométabolique (ICAN), Hôpital Pitié Salpêtrière, Paris, France
| | - Philippe Giral
- Endocrinologie Métabolisme et Prévention Cardiovasculaire (Drs Bruckert and Giral), Institut E3M et IHU Cardiométabolique (ICAN), Hôpital Pitié Salpêtrière, Paris, France
| | - Wilfried Le Goff
- Sorbonne Université, INSERM (Drs Darabi, Guillas, Frisdal, Poupel, Carrie,Bittar, Guerin, Le Goff, and Kontush), Institute of Cardiometabolism and Nutrition (ICAN), UMR_S1166, F-75013 Paris, France
| | - Anatol Kontush
- Sorbonne Université, INSERM (Drs Darabi, Guillas, Frisdal, Poupel, Carrie,Bittar, Guerin, Le Goff, and Kontush), Institute of Cardiometabolism and Nutrition (ICAN), UMR_S1166, F-75013 Paris, France.
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Wang S, Zhang Y, Zhang Y, Wu W, Ye L, Li Y, Su J, Pang S. scASGC: An adaptive simplified graph convolution model for clustering single-cell RNA-seq data. Comput Biol Med 2023; 163:107152. [PMID: 37364529 DOI: 10.1016/j.compbiomed.2023.107152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/24/2023] [Accepted: 06/07/2023] [Indexed: 06/28/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is now a successful technique for identifying cellular heterogeneity, revealing novel cell subpopulations, and forecasting developmental trajectories. A crucial component of the processing of scRNA-seq data is the precise identification of cell subpopulations. Although many unsupervised clustering methods have been developed to cluster cell subpopulations, the performance of these methods is vulnerable to dropouts and high dimensionality. In addition, most existing methods are time-consuming and fail to adequately account for potential associations between cells. In the manuscript, we present an unsupervised clustering method based on an adaptive simplified graph convolution model called scASGC. The proposed method builds plausible cell graphs, aggregates neighbor information using a simplified graph convolution model, and adaptively determines the most optimal number of convolution layers for various graphs. Experiments on 12 public datasets show that scASGC outperforms both classical and state-of-the-art clustering methods. In addition, in a study of mouse intestinal muscle containing 15,983 cells, we identified distinct marker genes based on the clustering results of scASGC. The source code of scASGC is available at https://github.com/ZzzOctopus/scASGC.
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Affiliation(s)
- Shudong Wang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao, 266580, China.
| | - Yu Zhang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao, 266580, China.
| | - Yulin Zhang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, China.
| | - Wenhao Wu
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao, 266580, China.
| | - Lan Ye
- Cancer Center, the Second Hospital of Shandong University, Jinan, 250033, China.
| | - YunYin Li
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao, 266580, China.
| | - Jionglong Su
- School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
| | - Shanchen Pang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao, 266580, China.
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Hoffstedt M, Stein MO, Baumann K, Wätzig H. Experimentally Observed Conformational Changes in Antibodies Due to Binding and Paratope-epitope Asymmetries. J Pharm Sci 2023; 112:2404-2411. [PMID: 37295605 DOI: 10.1016/j.xphs.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023]
Abstract
Understanding binding related changes in antibody conformations is important for epitope prediction and antibody refinement. The increase of available data in the PDB allowed a more detailed investigation of the conformational landscape for free and bound antibodies. A dataset containing a total of 835 unique PDB entries of antibodies that were crystallized in complex with their antigen and in a free state was constructed. It was examined for binding related conformation changes. We present further evidence supporting the theory of a pre-existing-equilibrium in experimental data. Multiple sequence alignments did not show binding induced tendencies in the solvent accessibility of residues in any specific position. Evaluating the changes in solvent accessibility per residue revealed a certain binding induced increase for several amino acids. Antibody-antigen interaction statistics were established and quantify a significant directional asymmetry between many interacting antibody and antigen residue pairs, especially a richness in tyrosine in the antibody epitope compared to its paratope. This asymmetry could potentially facilitate an increase in the success rate of computationally guided antibody refinement.
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Affiliation(s)
- Marc Hoffstedt
- Institute of Medicinal and Pharmaceutical Chemistry, TU Braunschweig, Braunschweig, Deutschland
| | - Matthias Oliver Stein
- Institute of Medicinal and Pharmaceutical Chemistry, TU Braunschweig, Braunschweig, Deutschland
| | - Knut Baumann
- Institute of Medicinal and Pharmaceutical Chemistry, TU Braunschweig, Braunschweig, Deutschland
| | - Hermann Wätzig
- Institute of Medicinal and Pharmaceutical Chemistry, TU Braunschweig, Braunschweig, Deutschland
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Cuevas MVR, Hardy MP, Larouche JD, Apavaloaei A, Kina E, Vincent K, Gendron P, Laverdure JP, Durette C, Thibault P, Lemieux S, Perreault C, Ehx G. BamQuery: a proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens. Genome Biol 2023; 24:188. [PMID: 37582761 PMCID: PMC10426134 DOI: 10.1186/s13059-023-03029-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 07/31/2023] [Indexed: 08/17/2023] Open
Abstract
MHC-I-associated peptides deriving from non-coding genomic regions and mutations can generate tumor-specific antigens, including neoantigens. Quantifying tumor-specific antigens' RNA expression in malignant and benign tissues is critical for discriminating actionable targets. We present BamQuery, a tool attributing an exhaustive RNA expression to MHC-I-associated peptides of any origin from bulk and single-cell RNA-sequencing data. We show that many cryptic and mutated tumor-specific antigens can derive from multiple discrete genomic regions, abundantly expressed in normal tissues. BamQuery can also be used to predict MHC-I-associated peptides immunogenicity and identify actionable tumor-specific antigens de novo.
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Affiliation(s)
- Maria Virginia Ruiz Cuevas
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Marie-Pierre Hardy
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Jean-David Larouche
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Anca Apavaloaei
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Eralda Kina
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Krystel Vincent
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Patrick Gendron
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Jean-Philippe Laverdure
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Chantal Durette
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Pierre Thibault
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Chemistry, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Sébastien Lemieux
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Grégory Ehx
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada.
- Laboratory of Hematology, GIGA-I3, University of Liege, CHU of Liege, Liege, Belgium.
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Ghafoor NA, Kırboğa KK, Baysal Ö, Süzek BE, Silme RS. Data mining and molecular dynamics analysis to detect HIV-1 reverse transcriptase RNase H activity inhibitor. Mol Divers 2023:10.1007/s11030-023-10707-6. [PMID: 37561229 DOI: 10.1007/s11030-023-10707-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/26/2023] [Indexed: 08/11/2023]
Abstract
HIV-1 is a deadly virus that affects millions of people worldwide. In this study, we aimed to inhibit viral replication by targeting one of the HIV-1 proteins and identifying a new drug candidate. We used data mining and molecular dynamics methods on HIV-1 genomes. Based on MAUVE analysis, we selected the RNase H activity of the reverse transcriptase (R.T) enzyme as a potential target due to its low mutation rate and high conservation level. We screened about 94,000 small molecule inhibitors by virtual screening. We validated the hit compounds' stability and binding free energy through molecular dynamics simulations and MM/PBSA. Phomoarcherin B, known for its anticancer properties, emerged as the best candidate and showed potential as an HIV-1 reverse transcriptase RNase H activity inhibitor. This study presents a new target and drug candidate for HIV-1 treatment. However, in vitro and in vivo tests are required. Also, the effect of RNase H activity on viral replication and the interaction of Phomoarcherin B with other HIV-1 proteins should be investigated.
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Affiliation(s)
- Naeem Abdul Ghafoor
- Department of Molecular Biology and Genetics, Faculty of Science, Muğla Sıtkı Koçman University, Kötekli, 48121, Muğla, Turkey
| | - Kevser Kübra Kırboğa
- Bioengineering Department, Bilecik Seyh Edebali University, 11230, Bilecik, Turkey
- Informatics Institute, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey
| | - Ömür Baysal
- Molecular Microbiology Unit, Department of Molecular Biology and Genetics, Faculty of Science, Muğla Sıtkı Koçman University, Kötekli, 48121, Muğla, Turkey.
| | - Barış Ethem Süzek
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Kötekli, 48000, Muğla, Turkey
| | - Ragıp Soner Silme
- Center for Research and Practice in Biotechnology and Genetic Engineering, Istanbul University, Vezneciler, Fatih, 34119, Istanbul, Turkey
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Horlacher M, Wagner N, Moyon L, Kuret K, Goedert N, Salvatore M, Ule J, Gagneur J, Winther O, Marsico A. Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning. Genome Biol 2023; 24:180. [PMID: 37542318 PMCID: PMC10403857 DOI: 10.1186/s13059-023-03015-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 07/17/2023] [Indexed: 08/06/2023] Open
Abstract
We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers. RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal. Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences that correspond to known and novel binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.
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Affiliation(s)
- Marc Horlacher
- Computational Health Center, Helmholtz Center Munich, Munich, Germany.
- Department of Biology, University of Copenhagen, Copenhagen, Denmark.
- Department of Informatics, Technical University of Munich, Garching, Germany.
- Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany.
| | - Nils Wagner
- Department of Informatics, Technical University of Munich, Garching, Germany
- Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany
| | - Lambert Moyon
- Computational Health Center, Helmholtz Center Munich, Munich, Germany
| | - Klara Kuret
- National Institute of Chemistry, Ljubljana, Slovenia
- The Francis Crick Institute, London, UK
- Jozef Stefan International Postgraduate School, Jamova cesta 39, 1000, Ljubljana, Slovenia
| | - Nicolas Goedert
- Computational Health Center, Helmholtz Center Munich, Munich, Germany
| | - Marco Salvatore
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jernej Ule
- National Institute of Chemistry, Ljubljana, Slovenia
- The Francis Crick Institute, London, UK
| | - Julien Gagneur
- Computational Health Center, Helmholtz Center Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Garching, Germany
- Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany
| | - Ole Winther
- Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Annalisa Marsico
- Computational Health Center, Helmholtz Center Munich, Munich, Germany.
- Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany.
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50
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Patrício A, Costa RS, Henriques R. On the challenges of predicting treatment response in Hodgkin's Lymphoma using transcriptomic data. BMC Med Genomics 2023; 16:170. [PMID: 37474945 PMCID: PMC10360230 DOI: 10.1186/s12920-023-01508-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 04/03/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Despite the advancements in multiagent chemotherapy in the past years, up to 10% of Hodgkin's Lymphoma (HL) cases are refractory to treatment and, after remission, patients experience an elevated risk of death from all causes. These complications are dependent on the treatment and therefore an increase in the prognostic accuracy of HL can help improve these outcomes and control treatment-related toxicity. Due to the low incidence of this cancer, there is a lack of works comprehensively assessing the predictability of treatment response, especially by resorting to machine learning (ML) advances and high-throughput technologies. METHODS We present a methodology for predicting treatment response after two courses of Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD) chemotherapy, through the analysis of gene expression profiles using state-of-the-art ML algorithms. We work with expression levels of tumor samples of Classical Hodgkin's Lymphoma patients, obtained through the NanoString's nCounter platform. The presented approach combines dimensionality reduction procedures and hyperparameter optimization of various elected classifiers to retrieve reference predictability levels of refractory response to ABVD treatment using the regulatory profile of diagnostic tumor samples. In addition, we propose a data transformation procedure to map the original data space into a more discriminative one using biclustering, where features correspond to discriminative putative regulatory modules. RESULTS Through an ensemble of feature selection procedures, we identify a set of 14 genes highly representative of the result of an fuorodeoxyglucose Positron Emission Tomography (FDG-PET) after two courses of ABVD chemotherapy. The proposed methodology further presents an increased performance against reference levels, with the proposed space transformation yielding improvements in the majority of the tested predictive models (e.g. Decision Trees show an improvement of 20pp in both precision and recall). CONCLUSIONS Taken together, the results reveal improvements for predicting treatment response in HL disease by resorting to sophisticated statistical and ML principles. This work further consolidates the current hypothesis on the structural difficulty of this prognostic task, showing that there is still a considerable gap to be bridged for these technologies to reach the necessary maturity for clinical practice.
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Affiliation(s)
- André Patrício
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Rafael S. Costa
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Rui Henriques
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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