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Moeckel C, Mareboina M, Konnaris MA, Chan CS, Mouratidis I, Montgomery A, Chantzi N, Pavlopoulos GA, Georgakopoulos-Soares I. A survey of k-mer methods and applications in bioinformatics. Comput Struct Biotechnol J 2024; 23:2289-2303. [PMID: 38840832 PMCID: PMC11152613 DOI: 10.1016/j.csbj.2024.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 06/07/2024] Open
Abstract
The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.
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Affiliation(s)
- Camille Moeckel
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Manvita Mareboina
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Maxwell A. Konnaris
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Candace S.Y. Chan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ioannis Mouratidis
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Huck Institute of the Life Sciences, Penn State University, University Park, Pennsylvania, USA
| | - Austin Montgomery
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Nikol Chantzi
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | | | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Huck Institute of the Life Sciences, Penn State University, University Park, Pennsylvania, USA
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2
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Wang S, Li H, Zhang K, Wu H, Pang S, Wu W, Ye L, Su J, Zhang Y. scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data. Comput Struct Biotechnol J 2024; 23:589-600. [PMID: 38274993 PMCID: PMC10809081 DOI: 10.1016/j.csbj.2023.12.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/27/2023] [Accepted: 12/27/2023] [Indexed: 01/27/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.
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Affiliation(s)
- Shudong Wang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Hengxiao Li
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Kuijie Zhang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Hao Wu
- College of Information Engineering, Northwest A&F University, 712100, Yangling, China
- School of Software, Shandong University, 250100, Jinan, China
| | - Shanchen Pang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Wenhao Wu
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Lan Ye
- Cancer Center, the Second Hospital of Shandong University, Jinan, 250033, China
| | - Jionglong Su
- School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China
| | - Yulin Zhang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, China
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3
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Xie M, Ye L, Chen K, Xu Q, Yang C, Chen X, Chan EWC, Li F, Chen S. Clinical use of tigecycline may contribute to the widespread dissemination of carbapenem-resistant hypervirulent Klebsiella pneumoniae strains. Emerg Microbes Infect 2024; 13:2306957. [PMID: 38240375 PMCID: PMC10829843 DOI: 10.1080/22221751.2024.2306957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 01/14/2024] [Indexed: 02/01/2024]
Abstract
The emergence of carbapenem-resistant hypervirulent Klebsiella pneumoniae (CR-hvKP) poses grave threats to human health. These strains increased dramatically in clinical settings in China in the past few years but not in other parts of the world. Four isogenic K. pneumoniae strains, including classical K. pneumoniae, carbapenem-resistant K. pneumoniae (CRKP), hypervirulent K. pneumoniae (hvKP) and CR-hvKP, were created and subjected to phenotypic characterization, competition assays, mouse sepsis model and rat colonization tests to investigate the mechanisms underlying the widespread nature of CR-hvKP in China. Acquisition of virulence plasmid led to reduced fitness and abolishment of colonization in the gastrointestinal tract, which may explain why hvKP is not clinically prevalent after its emergence for a long time. However, tigecycline treatment facilitated the colonization of hvKP and CR-hvKP and reduced the population of Lactobacillus spp. in animal gut microbiome. Feeding with Lactobacillus spp. could significantly reduce the colonization of hvKP and CR-hvKP in the animal gastrointestinal tract. Our data implied that the clinical use of tigecycline to treat carbapenem-resistant K. pneumoniae infections facilitated the high spread of CR-hvKP in clinical settings in China and demonstrated that Lactobacillus spp. was a potential candidate for anticolonization strategy against CR-hvKP.
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Affiliation(s)
- Miaomiao Xie
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
- Department of Food Science and Nutrition, Faculty of Science, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Lianwei Ye
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
| | - Kaichao Chen
- Department of Food Science and Nutrition, Faculty of Science, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Qi Xu
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
| | - Chen Yang
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
| | - Xiangnan Chen
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
| | - Edward Wai-Chi Chan
- State Key Lab of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Fuyong Li
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
| | - Sheng Chen
- Department of Food Science and Nutrition, Faculty of Science, The Hong Kong Polytechnic University, Kowloon, Hong Kong
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Korhonen PK, Wang T, Young ND, Byrne JJ, Campos TL, Chang BC, Taki AC, Gasser RB. Analysis of Haemonchus embryos at single cell resolution identifies two eukaryotic elongation factors as intervention target candidates. Comput Struct Biotechnol J 2024; 23:1026-1035. [PMID: 38435301 PMCID: PMC10907403 DOI: 10.1016/j.csbj.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 03/05/2024] Open
Abstract
Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans - a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes' cellular and molecular biology - which is presently lacking for most worms. Here, we created the first single cell atlas for an early developmental stage of Haemonchus contortus - a highly pathogenic, C. elegans-related parasitic nematode. We obtained and curated RNA sequence (snRNA-seq) data from single nuclei from embryonating eggs of H. contortus (150,000 droplets), and selected high-quality transcriptomic data for > 14,000 single nuclei for analysis, and identified 19 distinct clusters of cells. Guided by comparative analyses with C. elegans, we were able to reproducibly assign seven cell clusters to body wall muscle, hypodermis, neuronal, intestinal or seam cells, and identified eight genes that were transcribed in all cell clusters/types, three of which were inferred to be essential in H. contortus. Two of these genes (i.e. Hc-eef-1A and Hc-eef1G), coding for eukaryotic elongation factors (called Hc-eEF1A and Hc-eEF1G), were also demonstrated to be transcribed and expressed in all key developmental stages of H. contortus. Together with these findings, sequence- and structure-based comparative analyses indicated the potential of Hc-eEF1A and/or Hc-eEF1G as intervention targets within the protein biosynthesis machinery of H. contortus. Future work will focus on single cell studies of all key developmental stages and tissues of H. contortus, and on evaluating the suitability of the two elongation factor proteins as drug targets in H. contortus and related nematodes, with a view to finding new nematocidal drug candidates.
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Affiliation(s)
- Pasi K. Korhonen
- Department of Veterinary Biosciences, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Tao Wang
- Department of Veterinary Biosciences, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Neil D. Young
- Department of Veterinary Biosciences, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Joseph J. Byrne
- Department of Veterinary Biosciences, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Tulio L. Campos
- Department of Veterinary Biosciences, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Bill C.H. Chang
- Department of Veterinary Biosciences, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Aya C. Taki
- Department of Veterinary Biosciences, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Robin B. Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, Victoria 3010, Australia
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5
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Steckler R, Magzal F, Kokot M, Walkowiak J, Tamir S. Disrupted gut harmony in attention-deficit/hyperactivity disorder: Dysbiosis and decreased short-chain fatty acids. Brain Behav Immun Health 2024; 40:100829. [PMID: 39184374 PMCID: PMC11342906 DOI: 10.1016/j.bbih.2024.100829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 07/13/2024] [Accepted: 07/20/2024] [Indexed: 08/27/2024] Open
Abstract
Background Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder with complex genetic and environmental underpinnings. Emerging evidence suggests a significant role of gut microbiota in ADHD pathophysiology. This study investigates variations in gut microbiota composition and Short-Chain Fatty Acid (SCFA) profiles between children and adolescents with ADHD and healthy controls. Methods The study included 42 ADHD patients and 31 healthy controls, aged 6-18 years. Fecal samples were analyzed for microbial composition using 16S rRNA gene sequencing and for SCFA profiles through gas chromatography-mass spectrometry (GC-MS). The study assessed both α and β diversity of gut microbiota and quantified various SCFAs to compare between the groups. Results ADHD subjects demonstrated significantly reduced gut microbiota diversity, as indicated by lower α-diversity indices (Shannon index, Observed species, Faith PD index) and a trend towards significance in β-diversity (Weighted UniFrac). Notably, the ADHD group exhibited significantly lower levels of key SCFAs, including acetic, propionic, isobutyric, isovaleric, and valeric acids, highlighting a distinct microbial and metabolic profile in these individuals. Conclusion This study uncovers significant alterations in gut microbiota and SCFA profiles in children with ADHD, compared to healthy controls. The observed changes in SCFAs, known for their associations with other behavioral and neurologic pathologies, and for their role in neural signaling. These findings offer a metabolite fingerprint that could potentially lead to novel diagnostic and treatment approaches for ADHD, emphasizing the importance of gut microbiota in the disorder's pathogenesis and management.
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Affiliation(s)
- Rafi Steckler
- Department of Pediatric Gastroenterology and Metabolic Diseases, Institute of Pediatrics, Poznan University of Medical Sciences, Poland
- Tel Hai Academic College, Israel
- Human Health and Nutrition Sciences Laboratory, MIGAL – Galilee Research Institute, Israel
| | - Faiga Magzal
- Tel Hai Academic College, Israel
- Human Health and Nutrition Sciences Laboratory, MIGAL – Galilee Research Institute, Israel
| | - Marta Kokot
- Department of Pediatric Gastroenterology and Metabolic Diseases, Institute of Pediatrics, Poznan University of Medical Sciences, Poland
| | - Jaroslaw Walkowiak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Institute of Pediatrics, Poznan University of Medical Sciences, Poland
| | - Snait Tamir
- Tel Hai Academic College, Israel
- Human Health and Nutrition Sciences Laboratory, MIGAL – Galilee Research Institute, Israel
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6
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Huang J, Mao L, Lei Q, Guo AY. Bioinformatics tools and resources for cancer and application. Chin Med J (Engl) 2024; 137:2052-2064. [PMID: 39075637 PMCID: PMC11374212 DOI: 10.1097/cm9.0000000000003254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Indexed: 07/31/2024] Open
Abstract
ABSTRACT Tumor bioinformatics plays an important role in cancer research and precision medicine. The primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and genes. In recent years, driven by breakthroughs in high-throughput technologies, large-scale cancer omics data have accumulated rapidly. How to effectively utilize and share these data is particularly important. To address this crucial task, many computational tools and databases have been developed over the past few years. To help researchers quickly learn and understand the functions of these tools, in this review, we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis, regulatory analysis of tumorigenesis, tumor treatment and prognosis, immune infiltration analysis, immune repertoire analysis, cancer driver gene and driver mutation analysis, and cancer single-cell analysis, which may further help researchers find more suitable tools for their research.
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Affiliation(s)
- Jin Huang
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lingzi Mao
- Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Qian Lei
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - An-Yuan Guo
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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7
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Barozzi I, Slaven N, Canale E, Lopes R, Amorim Monteiro Barbosa I, Bleu M, Ivanoiu D, Pacini C, Mensa' E, Chambers A, Bravaccini S, Ravaioli S, Győrffy B, Dieci MV, Pruneri G, Galli GG, Magnani L. A Functional Survey of the Regulatory Landscape of Estrogen Receptor-Positive Breast Cancer Evolution. Cancer Discov 2024; 14:1612-1630. [PMID: 38753319 PMCID: PMC11372371 DOI: 10.1158/2159-8290.cd-23-1157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 03/12/2024] [Accepted: 05/14/2024] [Indexed: 09/05/2024]
Abstract
Only a handful of somatic alterations have been linked to endocrine therapy resistance in hormone-dependent breast cancer, potentially explaining ∼40% of relapses. If other mechanisms underlie the evolution of hormone-dependent breast cancer under adjuvant therapy is currently unknown. In this work, we employ functional genomics to dissect the contribution of cis-regulatory elements (CRE) to cancer evolution by focusing on 12 megabases of noncoding DNA, including clonal enhancers, gene promoters, and boundaries of topologically associating domains. Parallel epigenetic perturbation (CRISPRi) in vitro reveals context-dependent roles for many of these CREs, with a specific impact on dormancy entrance and endocrine therapy resistance. Profiling of CRE somatic alterations in a unique, longitudinal cohort of patients treated with endocrine therapies identifies a limited set of noncoding changes potentially involved in therapy resistance. Overall, our data uncover how endocrine therapies trigger the emergence of transient features which could ultimately be exploited to hinder the adaptive process. Significance: This study shows that cells adapting to endocrine therapies undergo changes in the usage or regulatory regions. Dormant cells are less vulnerable to regulatory perturbation but gain transient dependencies which can be exploited to decrease the formation of dormant persisters.
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Affiliation(s)
- Iros Barozzi
- Center for Cancer Research, Medical University of Vienna, Vienna, Austria
| | - Neil Slaven
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Eleonora Canale
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Rui Lopes
- Disease area Oncology, Novartis Biomedical Research, Basel, Switzerland
| | | | - Melusine Bleu
- Disease area Oncology, Novartis Biomedical Research, Basel, Switzerland
| | - Diana Ivanoiu
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Claudia Pacini
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Emanuela Mensa'
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Alfie Chambers
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Sara Bravaccini
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
- Faculty of Medicine and Surgery, "Kore" University of Enna, Enna, Italy
| | - Sara Ravaioli
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
| | - Balázs Győrffy
- Department of Bioinformatics, Semmelweis University, Budapest, Hungary
- Department of Biophysics, Medical School, University of Pecs, Pecs, Hungary
- Cancer Biomarker Research Group, Institute of Molecular Life Sciences, Research Centre for Natural Sciences, Budapest, Hungary
| | - Maria Vittoria Dieci
- Oncology 2, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy
| | - Giancarlo Pruneri
- Department of Diagnostic Innovation, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | | | - Luca Magnani
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer, Research, London, United Kingdom
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8
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Wang T, Zhuo L, Chen Y, Fu X, Zeng X, Zou Q. ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification. PLoS Comput Biol 2024; 20:e1012400. [PMID: 39213450 DOI: 10.1371/journal.pcbi.1012400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 08/10/2024] [Indexed: 09/04/2024] Open
Abstract
The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.
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Affiliation(s)
- Tao Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China
| | - Yifan Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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9
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Nokin MJ, Mira A, Patrucco E, Ricciuti B, Cousin S, Soubeyran I, San José S, Peirone S, Caizzi L, Vietti Michelina S, Bourdon A, Wang X, Alvarez-Villanueva D, Martínez-Iniesta M, Vidal A, Rodrigues T, García-Macías C, Awad MM, Nadal E, Villanueva A, Italiano A, Cereda M, Santamaría D, Ambrogio C. RAS-ON inhibition overcomes clinical resistance to KRAS G12C-OFF covalent blockade. Nat Commun 2024; 15:7554. [PMID: 39215000 PMCID: PMC11364849 DOI: 10.1038/s41467-024-51828-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 08/20/2024] [Indexed: 09/04/2024] Open
Abstract
Selective KRASG12C inhibitors have been developed to covalently lock the oncogene in the inactive GDP-bound state. Two of these molecules, sotorasib and adagrasib, are approved for the treatment of adult patients with KRASG12C-mutated previously treated advanced non-small cell lung cancer. Drug treatment imposes selective pressures leading to the outgrowth of drug-resistant variants. Mass sequencing from patients' biopsies identified a number of acquired KRAS mutations -both in cis and in trans- in resistant tumors. We demonstrate here that disease progression in vivo can also occur due to adaptive mechanisms and increased KRAS-GTP loading. Using the preclinical tool tri-complex KRASG12C-selective covalent inhibitor, RMC-4998 (also known as RM-029), that targets the active GTP-bound (ON) state of the oncogene, we provide a proof-of-concept that the clinical stage KRASG12C(ON) inhibitor RMC-6291 alone or in combination with KRASG12C(OFF) drugs can be an alternative potential therapeutic strategy to circumvent resistance due to increased KRAS-GTP loading.
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Affiliation(s)
- Marie-Julie Nokin
- INSERM U1312, University of Bordeaux, IECB, Pessac, France
- Laboratory of Biology of Tumor and Development (LBTD), GIGA-Cancer, University of Liège, Liège, Belgium
| | - Alessia Mira
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Torino, Italy
| | - Enrico Patrucco
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Torino, Italy
| | - Biagio Ricciuti
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sophie Cousin
- Department of Medical Oncology, Institut Bergonié, Bordeaux, France
| | | | - Sonia San José
- INSERM U1312, University of Bordeaux, IECB, Pessac, France
- Molecular Mechanisms of Cancer Program, Centro de Investigación del Cáncer, CSIC-Universidad de Salamanca, Salamanca, Spain
| | - Serena Peirone
- Department of Biosciences, Università degli Studi di Milano, Via Celoria 26, Milan, Italy
- Italian Institute for Genomic Medicine, c/o IRCCS, Str. Prov. le 142, km 3.95, Candiolo, Torino, Italy
| | - Livia Caizzi
- Italian Institute for Genomic Medicine, c/o IRCCS, Str. Prov. le 142, km 3.95, Candiolo, Torino, Italy
| | - Sandra Vietti Michelina
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Torino, Italy
| | | | - Xinan Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Daniel Alvarez-Villanueva
- Chemoresistance and Predictive Factors Group, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat, Barcelona, Spain
| | - María Martínez-Iniesta
- Chemoresistance and Predictive Factors Group, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat, Barcelona, Spain
| | - August Vidal
- Chemoresistance and Predictive Factors Group, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Telmo Rodrigues
- Comparative Pathology Unit, Centro de Investigación del Cáncer, CSIC-Universidad de Salamanca, Salamanca, Spain
| | - Carmen García-Macías
- Comparative Pathology Unit, Centro de Investigación del Cáncer, CSIC-Universidad de Salamanca, Salamanca, Spain
| | - Mark M Awad
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ernest Nadal
- Department of Medical Oncology, Catalan Institute of Oncology (ICO); Preclinical and Experimental Research in Thoracic Tumors (PReTT) Group, Oncobell Program, IDIBELL, L'Hospitalet, Barcelona, Spain
| | - Alberto Villanueva
- Chemoresistance and Predictive Factors Group, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat, Barcelona, Spain
- Department of Medical Oncology, Catalan Institute of Oncology (ICO); Preclinical and Experimental Research in Thoracic Tumors (PReTT) Group, Oncobell Program, IDIBELL, L'Hospitalet, Barcelona, Spain
| | - Antoine Italiano
- Department of Medical Oncology, Institut Bergonié, Bordeaux, France.
| | - Matteo Cereda
- Department of Biosciences, Università degli Studi di Milano, Via Celoria 26, Milan, Italy.
- Italian Institute for Genomic Medicine, c/o IRCCS, Str. Prov. le 142, km 3.95, Candiolo, Torino, Italy.
| | - David Santamaría
- INSERM U1312, University of Bordeaux, IECB, Pessac, France.
- Molecular Mechanisms of Cancer Program, Centro de Investigación del Cáncer, CSIC-Universidad de Salamanca, Salamanca, Spain.
| | - Chiara Ambrogio
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Torino, Italy.
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10
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Hillary RF, Gadd DA, Kuncheva Z, Mangelis T, Lin T, Ferber K, McLaughlin H, Runz H, Marioni RE, Foley CN, Sun BB. Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank. Nat Commun 2024; 15:7346. [PMID: 39187491 PMCID: PMC11347662 DOI: 10.1038/s41467-024-51744-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 08/14/2024] [Indexed: 08/28/2024] Open
Abstract
Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include 67 variants that lack conventional additive main effects on protein levels. Over 1100 GEIs are identified between 101 proteins and 153 environmental exposures. GEI analyses uncover possible mechanisms that explain why 13/67 vQTL-only sites lack corresponding main effects. Additional analyses also highlight how age, sex, epistatic interactions and statistical artefacts may underscore associations between genetic variation and variance heterogeneity. This study establishes the most comprehensive database yet of vQTLs and GEIs for the human proteome.
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Affiliation(s)
- Robert F Hillary
- Optima Partners, Edinburgh, EH2 4HQ, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Danni A Gadd
- Optima Partners, Edinburgh, EH2 4HQ, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Zhana Kuncheva
- Optima Partners, Edinburgh, EH2 4HQ, UK
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
- Bayes Centre, The University of Edinburgh, Edinburgh, EH8 9BT, UK
| | - Tasos Mangelis
- Optima Partners, Edinburgh, EH2 4HQ, UK
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
- Bayes Centre, The University of Edinburgh, Edinburgh, EH8 9BT, UK
| | - Tinchi Lin
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Kyle Ferber
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Helen McLaughlin
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Heiko Runz
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Riccardo E Marioni
- Optima Partners, Edinburgh, EH2 4HQ, UK.
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK.
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA.
| | - Christopher N Foley
- Optima Partners, Edinburgh, EH2 4HQ, UK.
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA.
- Bayes Centre, The University of Edinburgh, Edinburgh, EH8 9BT, UK.
| | - Benjamin B Sun
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA.
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
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11
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López-Cortés A, Cabrera-Andrade A, Echeverría-Garcés G, Echeverría-Espinoza P, Pineda-Albán M, Elsitdie N, Bueno-Miño J, Cruz-Segundo CM, Dorado J, Pazos A, Gonzáles-Díaz H, Pérez-Castillo Y, Tejera E, Munteanu CR. Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses. Sci Rep 2024; 14:19359. [PMID: 39169044 PMCID: PMC11339426 DOI: 10.1038/s41598-024-68565-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 07/25/2024] [Indexed: 08/23/2024] Open
Abstract
The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug design. To contribute to this field, we developed an accurate predictive classifier for druggable cancer-driving proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. The optimal classifier was achieved with the support vector machine method, utilizing 200 tri-amino acid composition descriptors. The high performance of the model is evident from an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and an accuracy of 0.929 ± 0.006 (threefold cross-validation). The machine learning prediction model was enhanced with multi-omics approaches, including the target-disease evidence score, the shortest pathways to cancer hallmarks, structure-based ligandability assessment, unfavorable prognostic protein analysis, and the oncogenic variome. Additionally, we performed a drug repurposing analysis to identify drugs with the highest affinity capable of targeting the best predicted proteins. As a result, we identified 79 key druggable cancer-driving proteins with the highest ligandability, and 23 of them demonstrated unfavorable prognostic significance across 16 TCGA PanCancer types: CDKN2A, BCL10, ACVR1, CASP8, JAG1, TSC1, NBN, PREX2, PPP2R1A, DNM2, VAV1, ASXL1, TPR, HRAS, BUB1B, ATG7, MARK3, SETD2, CCNE1, MUTYH, CDKN2C, RB1, and SMARCA4. Moreover, we prioritized 11 clinically relevant drugs targeting these proteins. This strategy effectively predicts and prioritizes biomarkers, therapeutic targets, and drugs for in-depth studies in clinical trials. Scripts are available at https://github.com/muntisa/machine-learning-for-druggable-proteins .
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Affiliation(s)
- Andrés López-Cortés
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador.
| | - Alejandro Cabrera-Andrade
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
- Escuela de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, Quito, Ecuador
| | - Gabriela Echeverría-Garcés
- Centro de Referencia Nacional de Genómica, Secuenciación y Bioinformática, Instituto Nacional de Investigación en Salud Pública "Leopoldo Izquieta Pérez", Quito, Ecuador
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Santiago, Chile
| | | | - Micaela Pineda-Albán
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - Nicole Elsitdie
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - José Bueno-Miño
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - Carlos M Cruz-Segundo
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Tecnológico de Estudios Superiores de Jocotitlán, Jocotitlán, Mexico
| | - Julian Dorado
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain
| | - Alejandro Pazos
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain
- Biomedical Research Institute of A Coruna (INIBIC), University Hospital Complex of A Coruna (CHUAC), A Coruña, Spain
| | - Humberto Gonzáles-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, Biscay, Spain
| | | | - Eduardo Tejera
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain
- Biomedical Research Institute of A Coruna (INIBIC), University Hospital Complex of A Coruna (CHUAC), A Coruña, Spain
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12
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Zhang T, Zhang SW, Xie MY, Li Y. Identifying cooperating cancer driver genes in individual patients through hypergraph random walk. J Biomed Inform 2024; 157:104710. [PMID: 39159864 DOI: 10.1016/j.jbi.2024.104710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 07/30/2024] [Accepted: 08/14/2024] [Indexed: 08/21/2024]
Abstract
OBJECTIVE Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies. METHODS Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients. RESULTS The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments. CONCLUSION We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.
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Affiliation(s)
- Tong Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China; School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Ming-Yu Xie
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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13
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Yang J, Fu H, Xue F, Li M, Wu Y, Yu Z, Luo H, Gong J, Niu X, Zhang W. Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms. Brief Bioinform 2024; 25:bbae418. [PMID: 39210506 PMCID: PMC11361854 DOI: 10.1093/bib/bbae418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 07/08/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024] Open
Abstract
Tumorigenesis arises from the dysfunction of cancer genes, leading to uncontrolled cell proliferation through various mechanisms. Establishing a complete cancer gene catalogue will make precision oncology possible. Although existing methods based on graph neural networks (GNN) are effective in identifying cancer genes, they fall short in effectively integrating data from multiple views and interpreting predictive outcomes. To address these shortcomings, an interpretable representation learning framework IMVRL-GCN is proposed to capture both shared and specific representations from multiview data, offering significant insights into the identification of cancer genes. Experimental results demonstrate that IMVRL-GCN outperforms state-of-the-art cancer gene identification methods and several baselines. Furthermore, IMVRL-GCN is employed to identify a total of 74 high-confidence novel cancer genes, and multiview data analysis highlights the pivotal roles of shared, mutation-specific, and structure-specific representations in discriminating distinctive cancer genes. Exploration of the mechanisms behind their discriminative capabilities suggests that shared representations are strongly associated with gene functions, while mutation-specific and structure-specific representations are linked to mutagenic propensity and functional synergy, respectively. Finally, our in-depth analyses of these candidates suggest potential insights for individualized treatments: afatinib could counteract many mutation-driven risks, and targeting interactions with cancer gene SRC is a reasonable strategy to mitigate interaction-induced risks for NR3C1, RXRA, HNF4A, and SP1.
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Affiliation(s)
- Jianye Yang
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Haitao Fu
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- School of Artificial Intelligence, Hubei University, Wuhan 430070, China
| | - Feiyang Xue
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Menglu Li
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuyang Wu
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zhanhui Yu
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Haohui Luo
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jing Gong
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430062, China
| | - Xiaohui Niu
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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14
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Xu S, Hu E, Cai Y, Xie Z, Luo X, Zhan L, Tang W, Wang Q, Liu B, Wang R, Xie W, Wu T, Xie L, Yu G. Using clusterProfiler to characterize multiomics data. Nat Protoc 2024:10.1038/s41596-024-01020-z. [PMID: 39019974 DOI: 10.1038/s41596-024-01020-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 05/13/2024] [Indexed: 07/19/2024]
Abstract
With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ .
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Affiliation(s)
- Shuangbin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Division of Laboratory Medicine, Microbiome Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Erqiang Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yantong Cai
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Zijing Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xiao Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenli Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qianwen Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Bingdong Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Rui Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenqin Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Tianzhi Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Liwei Xie
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
- Division of Laboratory Medicine, Microbiome Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
- Dermatology Hospital, Southern Medical University, Guangzhou, China.
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15
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Li H, Han Z, Sun Y, Wang F, Hu P, Gao Y, Bai X, Peng S, Ren C, Xu X, Liu Z, Chen H, Yang Y, Bo X. CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection. Nat Commun 2024; 15:5997. [PMID: 39013885 PMCID: PMC11252405 DOI: 10.1038/s41467-024-50426-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 07/09/2024] [Indexed: 07/18/2024] Open
Abstract
Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.
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Affiliation(s)
- Hao Li
- Academy of Military Medical Sciences, Beijing, China
| | - Zebei Han
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, China
| | - Yu Sun
- Academy of Military Medical Sciences, Beijing, China
| | - Fu Wang
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, China
| | - Pengzhen Hu
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, China
| | - Yuang Gao
- Department of Hematology, PLA General Hospital, the Fifth Medical Center, Beijing, China
| | - Xuemei Bai
- Academy of Military Medical Sciences, Beijing, China
| | - Shiyu Peng
- Academy of Military Medical Sciences, Beijing, China
| | - Chao Ren
- Academy of Military Medical Sciences, Beijing, China
| | - Xiang Xu
- Academy of Military Medical Sciences, Beijing, China
| | - Zeyu Liu
- Academy of Military Medical Sciences, Beijing, China
| | - Hebing Chen
- Academy of Military Medical Sciences, Beijing, China.
| | - Yang Yang
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, China.
| | - Xiaochen Bo
- Academy of Military Medical Sciences, Beijing, China.
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16
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Xiong D, Han T, Li Y, Hong Y, Li S, Li X, Tao W, Huang YS, Chen W, Li C. TOTEM: a multi-cancer detection and localization approach using circulating tumor DNA methylation markers. BMC Cancer 2024; 24:840. [PMID: 39009999 PMCID: PMC11247868 DOI: 10.1186/s12885-024-12626-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 07/10/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Detection of cancer and identification of tumor origin at an early stage improve the survival and prognosis of patients. Herein, we proposed a plasma cfDNA-based approach called TOTEM to detect and trace the cancer signal origin (CSO) through methylation markers. METHODS We performed enzymatic conversion-based targeted methylation sequencing on plasma cfDNA samples collected from a clinical cohort of 500 healthy controls and 733 cancer patients with seven types of cancer (breast, colorectum, esophagus, stomach, liver, lung, and pancreas) and randomly divided these samples into a training cohort and a testing cohort. An independent validation cohort of 143 healthy controls, 79 liver cancer patients and 100 stomach cancer patients were recruited to validate the generalizability of our approach. RESULTS A total of 57 multi-cancer diagnostic markers and 873 CSO markers were selected for model development. The binary diagnostic model achieved an area under the curve (AUC) of 0.907, 0.908 and 0.868 in the training, testing and independent validation cohorts, respectively. With a training specificity of 98%, the specificities in the testing and independent validation cohorts were 100% and 98.6%, respectively. Overall sensitivity across all cancer stages was 65.5%, 67.3% and 55.9% in the training, testing and independent validation cohorts, respectively. Early-stage (I and II) sensitivity was 50.3% and 45.7% in the training and testing cohorts, respectively. For cancer patients correctly identified by the binary classifier, the top 1 and top 2 CSO accuracies were 77.7% and 86.5% in the testing cohort (n = 148) and 76.0% and 84.0% in the independent validation cohort (n = 100). Notably, performance was maintained with only 21 diagnostic and 214 CSO markers, achieving a training AUC of 0.865, a testing AUC of 0.866, and an integrated top 2 accuracy of 83.1% in the testing cohort. CONCLUSIONS TOTEM demonstrates promising potential for accurate multi-cancer detection and localization by profiling plasma methylation markers. The real-world clinical performance of our approach needs to be investigated in a much larger prospective cohort.
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Affiliation(s)
- Dalin Xiong
- Department of Thoracic Surgery, Yan'an Hospital of Kunming Medical University, Kunming, 650051, China
| | - Tiancheng Han
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Yulong Li
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Yuanyuan Hong
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Suxing Li
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Xi Li
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Wenhui Tao
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Yu S Huang
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Weizhi Chen
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Chunguang Li
- Department of Colorectal and Anal Surgery/Hubei Key Laboratory of Intestinal and Colorectal Diseases, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
- Clinical Center of Intestinal and Colorectal Diseases of Hubei Province, Wuhan, 430071, China.
- Quality Control Center of Colorectal and Anal Surgery of Health Commission of Hubei Province, Wuhan, 430071, China.
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17
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San José-Enériz E, Gimenez-Camino N, Rabal O, Garate L, Miranda E, Gómez-Echarte N, García F, Charalampopoulou S, Sáez E, Vilas-Zornoza A, San Martín-Uriz P, Valcárcel LV, Barrena N, Alignani D, Tamariz-Amador LE, Pérez-Ruiz A, Hilscher S, Schutkowski M, Alfonso-Pierola A, Martinez-Calle N, Larrayoz MJ, Paiva B, Calasanz MJ, Muñoz J, Isasa M, Martin-Subero JI, Pineda-Lucena A, Oyarzabal J, Agirre X, Prósper F. Epigenetic-based differentiation therapy for Acute Myeloid Leukemia. Nat Commun 2024; 15:5570. [PMID: 38956053 PMCID: PMC11219871 DOI: 10.1038/s41467-024-49784-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/18/2024] [Indexed: 07/04/2024] Open
Abstract
Despite the development of novel therapies for acute myeloid leukemia, outcomes remain poor for most patients, and therapeutic improvements are an urgent unmet need. Although treatment regimens promoting differentiation have succeeded in the treatment of acute promyelocytic leukemia, their role in other acute myeloid leukemia subtypes needs to be explored. Here we identify and characterize two lysine deacetylase inhibitors, CM-444 and CM-1758, exhibiting the capacity to promote myeloid differentiation in all acute myeloid leukemia subtypes at low non-cytotoxic doses, unlike other commercial histone deacetylase inhibitors. Analyzing the acetylome after CM-444 and CM-1758 treatment reveals modulation of non-histone proteins involved in the enhancer-promoter chromatin regulatory complex, including bromodomain proteins. This acetylation is essential for enhancing the expression of key transcription factors directly involved in the differentiation therapy induced by CM-444/CM-1758 in acute myeloid leukemia. In summary, these compounds may represent effective differentiation-based therapeutic agents across acute myeloid leukemia subtypes with a potential mechanism for the treatment of acute myeloid leukemia.
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Affiliation(s)
- Edurne San José-Enériz
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain
| | - Naroa Gimenez-Camino
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain
| | - Obdulia Rabal
- Small-Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, Avenida Pío XII 55, 31008, Pamplona, Spain
| | - Leire Garate
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain
| | - Estibaliz Miranda
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain
| | - Nahia Gómez-Echarte
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
| | - Fernando García
- ProteoRed-ISCIII, Unidad de Proteómica, Centro Nacional de Investigaciones Oncológicas (CNIO), Melchor Fernández Almagro 3, 28029, Madrid, Spain
| | - Stella Charalampopoulou
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Casanova 143, 08036, Barcelona, Spain
| | - Elena Sáez
- Small-Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, Avenida Pío XII 55, 31008, Pamplona, Spain
| | - Amaia Vilas-Zornoza
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
| | - Patxi San Martín-Uriz
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
| | - Luis V Valcárcel
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain
- TECNUN, Universidad de Navarra, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Naroa Barrena
- TECNUN, Universidad de Navarra, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Diego Alignani
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain
| | - Luis Esteban Tamariz-Amador
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain
- Departmento de Hematología, Clínica Universidad de Navarra, and CCUN, Universidad de Navarra, Avenida Pío XII 36, 31008, Pamplona, Spain
| | - Ana Pérez-Ruiz
- Biomedical Engineering Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, Avenida Pío XII 55, 31008, Pamplona, Spain
| | - Sebastian Hilscher
- Department of Enzymology, Charles Tanford Protein Center, Institute of Biochemistry and Biotechnology, Martin-Luther-University Halle-Wittenberg, 06120, Halle, Germany
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University Halle-Wittenberg, 06120, Halle, Germany
| | - Mike Schutkowski
- Department of Enzymology, Charles Tanford Protein Center, Institute of Biochemistry and Biotechnology, Martin-Luther-University Halle-Wittenberg, 06120, Halle, Germany
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University Halle-Wittenberg, 06120, Halle, Germany
| | - Ana Alfonso-Pierola
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain
- Departmento de Hematología, Clínica Universidad de Navarra, and CCUN, Universidad de Navarra, Avenida Pío XII 36, 31008, Pamplona, Spain
| | - Nicolás Martinez-Calle
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain
- Departmento de Hematología, Clínica Universidad de Navarra, and CCUN, Universidad de Navarra, Avenida Pío XII 36, 31008, Pamplona, Spain
| | - María José Larrayoz
- CIMA LAB Diagnostics, Universidad de Navarra, Avenida Pío XII 55, 31008, Pamplona, Spain
| | - Bruno Paiva
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain
| | - María José Calasanz
- CIMA LAB Diagnostics, Universidad de Navarra, Avenida Pío XII 55, 31008, Pamplona, Spain
| | - Javier Muñoz
- Biocruces Bizkaia Health Research Institute, Cruces Plaza, 48903, Barakaldo, Spain
- Ikerbasque, Basque Foundation for Science, Plaza Euskadi 5, 48009, Bilbao, Spain
| | - Marta Isasa
- ProteoRed-ISCIII, Unidad de Proteómica, Centro Nacional de Investigaciones Oncológicas (CNIO), Melchor Fernández Almagro 3, 28029, Madrid, Spain
| | - José Ignacio Martin-Subero
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Casanova 143, 08036, Barcelona, Spain
- Departamento de Fundamentos Clínicos, Universitat de Barcelona, Casanova 143, 08036, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig de Lluís Companys 23, 08010, Barcelona, Spain
| | - Antonio Pineda-Lucena
- Small-Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, Avenida Pío XII 55, 31008, Pamplona, Spain
| | - Julen Oyarzabal
- Small-Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, Avenida Pío XII 55, 31008, Pamplona, Spain.
| | - Xabier Agirre
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain.
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain.
| | - Felipe Prósper
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain.
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029, Madrid, Spain.
- Departmento de Hematología, Clínica Universidad de Navarra, and CCUN, Universidad de Navarra, Avenida Pío XII 36, 31008, Pamplona, Spain.
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18
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Deng C, Li HD, Zhang LS, Liu Y, Li Y, Wang J. Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks. Bioinformatics 2024; 40:i511-i520. [PMID: 38940121 PMCID: PMC11211849 DOI: 10.1093/bioinformatics/btae257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited. RESULTS Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes. AVAILABILITY AND IMPLEMENTATION DISHyper is freely available for download at https://github.com/genemine/DISHyper.
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Affiliation(s)
- Chao Deng
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Hong-Dong Li
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Li-Shen Zhang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Yiwei Liu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529-0001, United States
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
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19
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Olbromski M, Mrozowska M, Piotrowska A, Kmiecik A, Smolarz B, Romanowicz H, Blasiak P, Maciejczyk A, Wojnar A, Dziegiel P. Prognostic significance of alpha-2-macrglobulin and low-density lipoprotein receptor-related protein-1 in various cancers. Am J Cancer Res 2024; 14:3036-3058. [PMID: 39005669 PMCID: PMC11236788 DOI: 10.62347/vujv9180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 05/21/2024] [Indexed: 07/16/2024] Open
Abstract
Cancer is the leading cause of death worldwide. The World Health Organization (WHO) estimates that 10 million fatalities occurred in 2023. Breast cancer (BC) ranked first among malignancies with 2.26 million cases, lung cancer (LC) second with 2.21 million cases, and colon and rectum cancers (CC, CRC) third with 1.93 million cases. These results highlight the importance of investigating novel cancer prognoses and anti-cancer markers. In this study, we investigated the potential effects of alpha-2 macroglobulin and its receptor, LRP1, on the outcomes of breast, lung, and colorectal malignancies. Immunohistochemical staining was used to analyze the expression patterns of A2M and LRP1 in 545 cases of invasive ductal breast carcinoma (IDC) and 51 cases of mastopathies/fibrocystic breast disease (FBD); 256 cases of non-small cell lung carcinomas (NSCLCs) and 45 cases of non-malignant lung tissue (NMLT); and 108 cases of CRC and 25 cases of non-malignant colorectal tissue (NMCT). A2M and LRP1 expression levels were also investigated in breast (MCF-7, BT-474, SK-BR-3, T47D, MDA-MB-231, and MDA-MB-231/BO2), lung (NCI-H1703, NCI-H522, and A549), and colon (LS 180, Caco-2, HT-29, and LoVo) cancer cell lines. Based on our findings, A2M and LRP1 exhibited various expression patterns in the examined malignancies, which were related to one another. Additionally, the stroma of lung and colorectal cancer has increased levels of A2M/LRP1 areas, which explains the significance of the stroma in the development and maintenance of tumor homeostasis. A2M expression was shown to be downregulated in all types of malignancies under study and was positively linked with an increase in cell line aggressiveness. Although more invasive cells had higher levels of A2M expression, an IHC analysis showed the opposite results. This might be because exogenous alpha-2-macroglobulin is present, which has an inhibitory effect on several cancerous enzymes and receptor-dependent signaling pathways. Additionally, siRNA-induced suppression of the transcripts for A2M and LRPP1 revealed their connection, which provides fresh information on the function of the LRP1 receptor in A2M recurrence in cancer. Further studies on different forms of cancer may corroborate the fact that both A2M and LRP1 have high potential as innovative therapeutic agents.
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Affiliation(s)
- Mateusz Olbromski
- Department of Histology and Embryology, Department of Human Morphology and Embryology, Wroclaw Medical University Chalubinskiego 6A, 50-368 Wroclaw, Poland
| | - Monika Mrozowska
- Department of Histology and Embryology, Department of Human Morphology and Embryology, Wroclaw Medical University Chalubinskiego 6A, 50-368 Wroclaw, Poland
| | - Aleksandra Piotrowska
- Department of Histology and Embryology, Department of Human Morphology and Embryology, Wroclaw Medical University Chalubinskiego 6A, 50-368 Wroclaw, Poland
| | - Alicja Kmiecik
- Department of Histology and Embryology, Department of Human Morphology and Embryology, Wroclaw Medical University Chalubinskiego 6A, 50-368 Wroclaw, Poland
| | - Beata Smolarz
- Department of Pathology, Polish Mother's Memorial Hospital Research Institute Rzgowska 281/289, 93-338 Lodz, Poland
| | - Hanna Romanowicz
- Department of Pathology, Polish Mother's Memorial Hospital Research Institute Rzgowska 281/289, 93-338 Lodz, Poland
| | - Piotr Blasiak
- Department and Clinic of Thoracic Surgery, Wroclaw Medical University Grabiszynska 105, 53-439 Wroclaw, Poland
- Lower Silesian Center of Oncology, Pulmonology and Hematology Hirszfelda 12, 53-413 Wroclaw, Poland
| | - Adam Maciejczyk
- Lower Silesian Center of Oncology, Pulmonology and Hematology Hirszfelda 12, 53-413 Wroclaw, Poland
- Department of Oncology, Wroclaw Medical University Hirszfelda 12, 53-413 Wroclaw, Poland
| | - Andrzej Wojnar
- Department of Pathology, Lower Silesian Oncology Center Hirszfelda 12, 53-413 Wroclaw, Poland
| | - Piotr Dziegiel
- Department of Histology and Embryology, Department of Human Morphology and Embryology, Wroclaw Medical University Chalubinskiego 6A, 50-368 Wroclaw, Poland
- Department of Human Biology, Faculty of Physiotherapy, Wroclaw University of Health and Sport Sciences Paderewskiego 35, 51-612 Wroclaw, Poland
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20
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Wang B, Jin Y, Hu M, Zhao Y, Wang X, Yue J, Ren H. Detecting genetic gain and loss events in terms of protein domain: Method and implementation. Heliyon 2024; 10:e32103. [PMID: 38867972 PMCID: PMC11168390 DOI: 10.1016/j.heliyon.2024.e32103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/08/2024] [Accepted: 05/28/2024] [Indexed: 06/14/2024] Open
Abstract
Continuous gain and loss of genes are the primary driving forces of bacterial evolution and environmental adaptation. Studying bacterial evolution in terms of protein domain, which is the fundamental function and evolutionary unit of proteins, can provide a more comprehensive understanding of bacterial differentiation and phenotypic adaptation processes. Therefore, we proposed a phylogenetic tree-based method for detecting genetic gain and loss events in terms of protein domains. Specifically, the method focuses on a single domain to trace its evolution process or on multiple domains to investigate their co-evolution principles. This novel method was validated using 122 Shigella isolates. We found that the loss of a significant number of domains was likely the main driving force behind the evolution of Shigella, which could reduce energy expenditure and preserve only the most essential functions. Additionally, we observed that simultaneously gained and lost domains were often functionally related, which can facilitate and accelerate phenotypic evolutionary adaptation to the environment. All results obtained using our method agree with those of previous studies, which validates our proposed method.
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Affiliation(s)
- Boqian Wang
- Beijing Institute of Biotechnology, State Key Laboratory of Pathogen and Biosecurity, Beijing, China
| | - Yuan Jin
- Beijing Institute of Biotechnology, State Key Laboratory of Pathogen and Biosecurity, Beijing, China
| | - Mingda Hu
- Beijing Institute of Biotechnology, State Key Laboratory of Pathogen and Biosecurity, Beijing, China
| | - Yunxiang Zhao
- Beijing Institute of Biotechnology, State Key Laboratory of Pathogen and Biosecurity, Beijing, China
| | - Xin Wang
- Beijing Institute of Biotechnology, State Key Laboratory of Pathogen and Biosecurity, Beijing, China
| | - Junjie Yue
- Beijing Institute of Biotechnology, State Key Laboratory of Pathogen and Biosecurity, Beijing, China
| | - Hongguang Ren
- Beijing Institute of Biotechnology, State Key Laboratory of Pathogen and Biosecurity, Beijing, China
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21
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Jung S, Wang S, Lee D. CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders. Comput Biol Med 2024; 176:108568. [PMID: 38744009 DOI: 10.1016/j.compbiomed.2024.108568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 04/13/2024] [Accepted: 05/05/2024] [Indexed: 05/16/2024]
Abstract
Discovery of the cancer type specific-driver genes is important for understanding the molecular mechanisms of each cancer type and for providing proper treatment. Recently, graph deep learning methods became widely used in finding cancer-driver genes. However, previous methods had limited performance in individual cancer types due to a small number of cancer-driver genes used in training and biases toward the cancer-driver genes used in training the models. Here, we introduce a novel pipeline, CancerGATE that predicts the cancer-driver genes using graph attention autoencoder (GATE) to learn in a self-supervised manner and can be applied to each of the cancer types. CancerGATE utilizes biological network topology and multi-omics data from 15 types of cancer of 20,079 samples from the cancer genome atlas (TCGA). Attention coefficients calculated in the model are used to prioritize cancer-driver genes by comparing coefficients of cancer and normal contexts. CancerGATE shows a higher AUPRC with a difference ranging from 1.5 % to 36.5 % compared to the previous graph deep learning models in each cancer type. We also show that CancerGATE is free from the bias toward cancer-driver genes used in training, revealing mechanisms of the cancer-driver genes in specific cancer types. Finally, we propose novel cancer-driver gene candidates that could be therapeutic targets for specific cancer types.
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Affiliation(s)
- Seunghwan Jung
- Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
| | - Seunghyun Wang
- Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
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22
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Rivera-Lopez EO, Nieves-Morales R, Melendez-Martinez G, Paez-Diaz JA, Rodriguez-Carrio SM, Rodriguez-Ramos J, Morales-Valle L, Rios-Velazquez C. Sea cucumber ( Holothuria glaberrima) intestinal microbiome dataset from Puerto Rico, generated by shotgun sequencing. Data Brief 2024; 54:110421. [PMID: 38690316 PMCID: PMC11058721 DOI: 10.1016/j.dib.2024.110421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/03/2024] [Accepted: 04/09/2024] [Indexed: 05/02/2024] Open
Abstract
The sea cucumber (H. glaberrima) is a species found in the shallow waters near coral reefs and seagrass beds in Puerto Rico. To characterize the microbial taxonomic composition and functional profiles present in the sea cucumber, total DNA was obtained from their intestinal system, fosmid libraries constructed, and subsequent sequencing was performed. The diversity profile displayed that the most predominant domain was Bacteria (76.56 %), followed by Viruses (23.24 %) and Archaea (0.04 %). Within the 11 phyla identified, the most abundant was Proteobacteria (73.16 %), followed by Terrabacteria group (3.20 %) and Fibrobacterota, Chlorobiota, Bacteroidota (FCB) superphylum (1.02 %). The most abundant species were Porvidencia rettgeri (21.77 %), Pseudomonas stutzeri (14.78 %), and Alcaligenes faecalis (5.00 %). The functional profile revealed that the most abundant functions are related to transporters, MISC (miscellaneous information systems), organic nitrogen, energy, and carbon utilization. The data collected in this project on the diversity and functional profiles of the intestinal system of the H. glaberrima provided a detailed view of its microbial ecology. These findings may motivate comparative studies aimed at understanding the role of the microbiome in intestinal regeneration.
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Affiliation(s)
- Edwin Omar Rivera-Lopez
- Microbial Biotechnology and Bioprospecting Laboratory, Biology Department, University of Puerto Rico, Mayagüez, P.R. 00681-9000, United States
- Food Science and Technology Program, University of Puerto Rico at Mayagüez, P.R. 00681-9000, United States
| | - Rene Nieves-Morales
- Microbial Biotechnology and Bioprospecting Laboratory, Biology Department, University of Puerto Rico, Mayagüez, P.R. 00681-9000, United States
| | - Gabriela Melendez-Martinez
- Microbial Biotechnology and Bioprospecting Laboratory, Biology Department, University of Puerto Rico, Mayagüez, P.R. 00681-9000, United States
| | - Jessica Alejandra Paez-Diaz
- Microbial Biotechnology and Bioprospecting Laboratory, Biology Department, University of Puerto Rico, Mayagüez, P.R. 00681-9000, United States
| | - Sofia Marie Rodriguez-Carrio
- Microbial Biotechnology and Bioprospecting Laboratory, Biology Department, University of Puerto Rico, Mayagüez, P.R. 00681-9000, United States
| | - Josue Rodriguez-Ramos
- Pacific Northwest National Laboratory, Biological Sciences Division, WA, United States
| | - Luis Morales-Valle
- Microbial Biotechnology and Bioprospecting Laboratory, Biology Department, University of Puerto Rico, Mayagüez, P.R. 00681-9000, United States
| | - Carlos Rios-Velazquez
- Microbial Biotechnology and Bioprospecting Laboratory, Biology Department, University of Puerto Rico, Mayagüez, P.R. 00681-9000, United States
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23
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Boman J, Qvarnström A, Mugal CF. Regulatory and evolutionary impact of DNA methylation in two songbird species and their naturally occurring F 1 hybrids. BMC Biol 2024; 22:124. [PMID: 38807214 PMCID: PMC11134931 DOI: 10.1186/s12915-024-01920-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/15/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND Regulation of transcription by DNA methylation in 5'-CpG-3' context is a widespread mechanism allowing differential expression of genetically identical cells to persist throughout development. Consequently, differences in DNA methylation can reinforce variation in gene expression among cells, tissues, populations, and species. Despite a surge in studies on DNA methylation, we know little about the importance of DNA methylation in population differentiation and speciation. Here we investigate the regulatory and evolutionary impact of DNA methylation in five tissues of two Ficedula flycatcher species and their naturally occurring F1 hybrids. RESULTS We show that the density of CpG in the promoters of genes determines the strength of the association between DNA methylation and gene expression. The impact of DNA methylation on gene expression varies among tissues with the brain showing unique patterns. Differentially expressed genes between parental species are predicted by genetic and methylation differentiation in CpG-rich promoters. However, both these factors fail to predict hybrid misexpression suggesting that promoter mismethylation is not a main determinant of hybrid misexpression in Ficedula flycatchers. Using allele-specific methylation estimates in hybrids, we also determine the genome-wide contribution of cis- and trans effects in DNA methylation differentiation. These distinct mechanisms are roughly balanced in all tissues except the brain, where trans differences predominate. CONCLUSIONS Overall, this study provides insight on the regulatory and evolutionary impact of DNA methylation in songbirds.
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Affiliation(s)
- Jesper Boman
- Department of Ecology and Genetics (IEG), Division of Evolutionary Biology, Uppsala University, Norbyvägen 18D, Uppsala, SE-752 36, Sweden.
| | - Anna Qvarnström
- Department of Ecology and Genetics (IEG), Division of Animal Ecology, Uppsala University, Norbyvägen 18D, Uppsala, SE-752 36, Sweden
| | - Carina F Mugal
- Department of Ecology and Genetics (IEG), Division of Evolutionary Biology, Uppsala University, Norbyvägen 18D, Uppsala, SE-752 36, Sweden.
- CNRS, Laboratory of Biometry and Evolutionary Biology (LBBE), UMR 5558, University of Lyon 1, Villeurbanne, France.
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24
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Hu D, Zhang Z, Liu X, Wu Y, An Y, Wang W, Yang M, Pan Y, Qiao K, Du C, Zhao Y, Li Y, Bao J, Qin T, Pan Y, Xia Z, Zhao X, Sun K. Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology. J Transl Med 2024; 22:512. [PMID: 38807223 PMCID: PMC11134716 DOI: 10.1186/s12967-024-05326-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 05/19/2024] [Indexed: 05/30/2024] Open
Abstract
In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.
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Affiliation(s)
- Dingxue Hu
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Ziteng Zhang
- Hepato-Biliary Surgery Division, The Second Affiliated Hospital, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, 518100, China
| | - Xiaoyi Liu
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China
| | - Youchun Wu
- Hepato-Biliary Surgery Division, The Second Affiliated Hospital, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, 518100, China
| | - Yunyun An
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China
| | - Wanqiu Wang
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Mengqi Yang
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China
| | - Yuqi Pan
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China
- Department of Biology, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Kun Qiao
- Thoracic Surgical Department, Shenzhen Third People's Hospital, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518100, China
| | - Changzheng Du
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, China
- Beijing Tsinghua Changgung Hospital, Tsinghua University School of Medicine, Beijing, 102218, China
| | - Yu Zhao
- Molecular Cancer Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, 518107, China
| | - Yan Li
- Department of Biology, Southern University of Science and Technology, Shenzhen, 518055, China
- Integrative Microecology Clinical Center, Shenzhen Key Laboratory of Gastrointestinal Microbiota and Disease, Shenzhen Clinical Research Center for Digestive Disease, Shenzhen Technology Research Center of Gut Microbiota Transplantation, Shenzhen Hospital, Southern Medical University, Shenzhen, 510086, China
| | - Jianqiang Bao
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Tao Qin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat- Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Yue Pan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat- Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Zhaohua Xia
- Thoracic Surgical Department, Shenzhen Third People's Hospital, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518100, China.
| | - Xin Zhao
- Hepato-Biliary Surgery Division, The Second Affiliated Hospital, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, 518100, China.
| | - Kun Sun
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China.
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25
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Chen L, Li C, Li B, Zhou X, Bai Y, Zou X, Zhou Z, He Q, Chen B, Wang M, Xue Y, Jiang Z, Feng J, Zhou T, Liu Z, Xu P. Evolutionary divergence of subgenomes in common carp provides insights into speciation and allopolyploid success. FUNDAMENTAL RESEARCH 2024; 4:589-602. [PMID: 38933191 PMCID: PMC11197550 DOI: 10.1016/j.fmre.2023.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 06/28/2024] Open
Abstract
Hybridization and polyploidization have made great contributions to speciation, heterosis, and agricultural production within plants, but there is still limited understanding and utilization in animals. Subgenome structure and expression reorganization and cooperation post hybridization and polyploidization are essential for speciation and allopolyploid success. However, the mechanisms have not yet been comprehensively assessed in animals. Here, we produced a high-fidelity reference genome sequence for common carp, a typical allotetraploid fish species cultured worldwide. This genome enabled in-depth analysis of the evolution of subgenome architecture and expression responses. Most genes were expressed with subgenome biases, with a trend of transition from the expression of subgenome A during the early stages to that of subgenome B during the late stages of embryonic development. While subgenome A evolved more rapidly, subgenome B contributed to a greater level of expression during development and under stressful conditions. Stable dominant patterns for homoeologous gene pairs both during development and under thermal stress suggest a potential fixed heterosis in the allotetraploid genome. Preferentially expressing either copy of a homoeologous gene at higher levels to confer development and response to stress indicates the dominant effect of heterosis. The plasticity of subgenomes and their shifting of dominant expression during early development, and in response to stressful conditions, provide novel insights into the molecular basis of the successful speciation, evolution, and heterosis of the allotetraploid common carp.
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Affiliation(s)
- Lin Chen
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Chengyu Li
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Bijun Li
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Xiaofan Zhou
- Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou 510642, China
| | - Yulin Bai
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Xiaoqing Zou
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Zhixiong Zhou
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Qian He
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Baohua Chen
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Mei Wang
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Yaguo Xue
- College of Fisheries, Henan Normal University, Xinxiang 453007, China
| | - Zhou Jiang
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Jianxin Feng
- Henan Academy of Fishery Science, Zhengzhou 450044, China
| | - Tao Zhou
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Zhanjiang Liu
- Department of Biology, College of Arts and Sciences, Syracuse University, Syracuse 13244, USA
| | - Peng Xu
- State Key Laboratory of Mariculture Breeding, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
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26
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Niu R, Guo Y, Shang X. GLIMS: A two-stage gradual-learning method for cancer genes prediction using multi-omics data and co-splicing network. iScience 2024; 27:109387. [PMID: 38510118 PMCID: PMC10951990 DOI: 10.1016/j.isci.2024.109387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/30/2023] [Accepted: 02/27/2024] [Indexed: 03/22/2024] Open
Abstract
Identifying cancer genes is vital for cancer diagnosis and treatment. However, because of the complexity of cancer occurrence and limited cancer genes knowledge, it is hard to identify cancer genes accurately using only a few omics data, and the overall performance of existing methods is being called for further improvement. Here, we introduce a two-stage gradual-learning strategy GLIMS to predict cancer genes using integrative features from multi-omics data. Firstly, it uses a semi-supervised hierarchical graph neural network to predict the initial candidate cancer genes by integrating multi-omics data and protein-protein interaction (PPI) network. Then, it uses an unsupervised approach to further optimize the initial prediction by integrating the co-splicing network in post-transcriptional regulation, which plays an important role in cancer development. Systematic experiments on multi-omics cancer data demonstrated that GLIMS outperforms the state-of-the-art methods for the identification of cancer genes and it could be a useful tool to help advance cancer analysis.
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Affiliation(s)
- Rui Niu
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Yang Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
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27
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Vimal SR, Singh JS, Kumar A, Prasad SM. The plant endomicrobiome: Structure and strategies to produce stress resilient future crop. CURRENT RESEARCH IN MICROBIAL SCIENCES 2024; 6:100236. [PMID: 38756233 PMCID: PMC11097330 DOI: 10.1016/j.crmicr.2024.100236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024] Open
Abstract
Plants have a microbiome, a diverse community of microorganisms, including bacteria, fungi, and viruses, living inside and on their tissues. Versatile endophytic microorganisms inhabited in every plant part without causing disease and develop endophytic microbiome or endo-microbiome. Plant endo-microbiome are drawn by the nutrient rich micro-environment, and in turn some microbes mutualistically endorse and protect plant from adverse environmental stresses. Plant endo-microbiome interact within well-designed host equilibrium containing xylem, phloem, nutrients, phytohormones, metabolites and shift according to environmental and nutritional change. Plant endo-microbiome regulate and respond to environmental variations, pathogens, herbivores by producing stress regulators, organic acids, secondary metabolites, stress hormones as well as unknown substances and signalling molecules. Endomicrobiome efficiently synthesizes multiple bioactive compounds, stress phytohormones with high competence. The technological innovation as next generation genomics biology and high-throughput multiomics techniques stepping stones on the illumination of critical endo-microbiome communities and functional characterization that aid in improving plant physiology, biochemistry and immunity interplay for best crop productivity. This review article contains deeper insight in endomicrobiome related research work in last years, recruitment, niche development, nutrient dynamics, stress removal mechanisms, bioactive services in plant health development, community architecture and communication, and immunity interplay in producing stress resilient future crop.
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Affiliation(s)
- Shobhit Raj Vimal
- Ranjan Plant Physiology & Biochemistry Laboratory, Department of Botany, University of Allahabad, Prayagraj 211002, Uttar Pradesh, India
| | - Jay Shankar Singh
- Department of Environmental Microbiology, School for Earth and Environmental Sciences, Babasaheb Bhimrao Ambedkar University, Lucknow 226025, Uttar Pradesh, India
| | - Ashwani Kumar
- Metagenomics and Secretomics Research Laboratory, Department of Botany, University of Allahabad (A Central University), Prayagraj 211002, Uttar Pradesh, India
| | - Sheo Mohan Prasad
- Ranjan Plant Physiology & Biochemistry Laboratory, Department of Botany, University of Allahabad, Prayagraj 211002, Uttar Pradesh, India
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28
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Ko SR, Lee S, Koo H, Seo H, Yu J, Kim YM, Kwon SY, Shin AY. High-quality chromosome-level genome assembly of Nicotiana benthamiana. Sci Data 2024; 11:386. [PMID: 38627408 PMCID: PMC11021556 DOI: 10.1038/s41597-024-03232-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/05/2024] [Indexed: 04/19/2024] Open
Abstract
Nicotiana benthamiana is a fundamental model organism in plant research. Recent advancements in genomic sequencing have revealed significant intraspecific genetic variations. This study addresses the pressing need for a precise genome sequence specific to its geographic origin by presenting a comprehensive genome assembly of the N. benthamiana LAB strain from the Republic of Korea (NbKLAB). We compare this assembly with the widely used NbLAB360 strain, shedding light on essential genomic differences between them. The outcome is a high-quality, chromosome-level genome assembly comprising 19 chromosomes, spanning 2,762 Mb, with an N50 of 142.6 Mb. Comparative analyses revealed notable variations, including 46,215 protein-coding genes, with an impressive 99.5% BUSCO completeness score. Furthermore, the NbKLAB assembly substantially improved the QV from 33% for NbLAB360 to 49%. This refined chromosomal genome assembly for N. benthamiana, in conjunction with comparative insights, provides a valuable resource for genomics research and molecular biology. This accomplishment forms a strong foundation for in-depth exploration into the intricacies of plant genetics and genomics, improved precision, and a comparative framework.
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Affiliation(s)
- Seo-Rin Ko
- Plant Systems Engineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea
- Department of Bioinformatics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea
| | - Sanghee Lee
- Plant Systems Engineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea
- Biosystems and Bioengineering Program, KRIBB School of Biotechnology, Korea University of Science and Technology (UST), Daejeon, 34113, Korea
| | - Hyunjin Koo
- Plant Systems Engineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea
| | | | | | - Yong-Min Kim
- Plant Systems Engineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea.
- Department of Bioinformatics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea.
- Digital Biotech Innovation Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea.
| | - Suk-Yoon Kwon
- Plant Systems Engineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea.
- Biosystems and Bioengineering Program, KRIBB School of Biotechnology, Korea University of Science and Technology (UST), Daejeon, 34113, Korea.
| | - Ah-Young Shin
- Plant Systems Engineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea.
- Department of Bioinformatics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea.
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29
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Hadad E, Rokach L, Veksler-Lublinsky I. Empowering prediction of miRNA-mRNA interactions in species with limited training data through transfer learning. Heliyon 2024; 10:e28000. [PMID: 38560149 PMCID: PMC10981012 DOI: 10.1016/j.heliyon.2024.e28000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
MicroRNAs (miRNAs) play a crucial role in mRNA regulation. Identifying functionally important mRNA targets of a specific miRNA is essential for uncovering its biological function and assisting miRNA-based drug development. Datasets of high-throughput direct bona fide miRNA-target interactions (MTIs) exist only for a few model organisms, prompting the need for computational prediction. However, the scarcity of data poses a challenge in training accurate machine learning models for MTI prediction. In this study, we explored the potential of transfer learning technique (with ANN and XGB) to address the limited data challenge by leveraging the similarities in interaction rules between species. Furthermore, we introduced a novel approach called TransferSHAP for estimating the feature importance of transfer learning in tabular dataset tasks. We demonstrated that transfer learning improves MTI prediction accuracy for species with limited datasets and identified the specific interaction features the models employed to transfer information across different species.
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Affiliation(s)
- Eyal Hadad
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, David Ben-Gurion Blvd. 1, Beer-Sheva 8410501, Israel
| | - Lior Rokach
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, David Ben-Gurion Blvd. 1, Beer-Sheva 8410501, Israel
| | - Isana Veksler-Lublinsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, David Ben-Gurion Blvd. 1, Beer-Sheva 8410501, Israel
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30
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Anatskaya OV, Vinogradov AE. Polyploidy Promotes Hypertranscription, Apoptosis Resistance, and Ciliogenesis in Cancer Cells and Mesenchymal Stem Cells of Various Origins: Comparative Transcriptome In Silico Study. Int J Mol Sci 2024; 25:4185. [PMID: 38673782 PMCID: PMC11050069 DOI: 10.3390/ijms25084185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/06/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Mesenchymal stem cells (MSC) attract an increasing amount of attention due to their unique therapeutic properties. Yet, MSC can undergo undesirable genetic and epigenetic changes during their propagation in vitro. In this study, we investigated whether polyploidy can compromise MSC oncological safety and therapeutic properties. For this purpose, we compared the impact of polyploidy on the transcriptome of cancer cells and MSC of various origins (bone marrow, placenta, and heart). First, we identified genes that are consistently ploidy-induced or ploidy-repressed through all comparisons. Then, we selected the master regulators using the protein interaction enrichment analysis (PIEA). The obtained ploidy-related gene signatures were verified using the data gained from polyploid and diploid populations of early cardiomyocytes (CARD) originating from iPSC. The multistep bioinformatic analysis applied to the cancer cells, MSC, and CARD indicated that polyploidy plays a pivotal role in driving the cell into hypertranscription. It was evident from the upregulation of gene modules implicated in housekeeping functions, stemness, unicellularity, DNA repair, and chromatin opening by means of histone acetylation operating via DNA damage associated with the NUA4/TIP60 complex. These features were complemented by the activation of the pathways implicated in centrosome maintenance and ciliogenesis and by the impairment of the pathways related to apoptosis, the circadian clock, and immunity. Overall, our findings suggest that, although polyploidy does not induce oncologic transformation of MSC, it might compromise their therapeutic properties because of global epigenetic changes and alterations in fundamental biological processes. The obtained results can contribute to the development and implementation of approaches enhancing the therapeutic properties of MSC by removing polyploid cells from the cell population.
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Affiliation(s)
- Olga V. Anatskaya
- Institute of Cytology Russian Academy of Sciences, 194064 St. Petersburg, Russia;
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31
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Joubert PM, Krasileva KV. Distinct genomic contexts predict gene presence-absence variation in different pathotypes of Magnaporthe oryzae. Genetics 2024; 226:iyae012. [PMID: 38290434 PMCID: PMC10990425 DOI: 10.1093/genetics/iyae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 11/28/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024] Open
Abstract
Fungi use the accessory gene content of their pangenomes to adapt to their environments. While gene presence-absence variation contributes to shaping accessory gene reservoirs, the genomic contexts that shape these events remain unclear. Since pangenome studies are typically species-wide and do not analyze different populations separately, it is yet to be uncovered whether presence-absence variation patterns and mechanisms are consistent across populations. Fungal plant pathogens are useful models for studying presence-absence variation because they rely on it to adapt to their hosts, and members of a species often infect distinct hosts. We analyzed gene presence-absence variation in the blast fungus, Magnaporthe oryzae (syn. Pyricularia oryzae), and found that presence-absence variation genes involved in host-pathogen and microbe-microbe interactions may drive the adaptation of the fungus to its environment. We then analyzed genomic and epigenomic features of presence-absence variation and observed that proximity to transposable elements, gene GC content, gene length, expression level in the host, and histone H3K27me3 marks were different between presence-absence variation genes and conserved genes. We used these features to construct a model that was able to predict whether a gene is likely to experience presence-absence variation with high precision (86.06%) and recall (92.88%) in M. oryzae. Finally, we found that presence-absence variation genes in the rice and wheat pathotypes of M. oryzae differed in their number and their genomic context. Our results suggest that genomic and epigenomic features of gene presence-absence variation can be used to better understand and predict fungal pangenome evolution. We also show that substantial intra-species variation can exist in these features.
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Affiliation(s)
- Pierre M Joubert
- Department of Plant and Microbial Biology, University of California-Berkeley, Berkeley, CA 94720, USA
- Center for Computational Biology, University of California-Berkeley, Berkeley, CA 94720, USA
| | - Ksenia V Krasileva
- Department of Plant and Microbial Biology, University of California-Berkeley, Berkeley, CA 94720, USA
- Center for Computational Biology, University of California-Berkeley, Berkeley, CA 94720, USA
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32
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Sirak K, Jansen Van Rensburg J, Brielle E, Chen B, Lazaridis I, Ringbauer H, Mah M, Mallick S, Micco A, Rohland N, Callan K, Curtis E, Kearns A, Lawson AM, Workman JN, Zalzala F, Ahmed Al-Orqbi AS, Ahmed Salem EM, Salem Hasan AM, Britton DC, Reich D. Medieval DNA from Soqotra points to Eurasian origins of an isolated population at the crossroads of Africa and Arabia. Nat Ecol Evol 2024; 8:817-829. [PMID: 38332026 PMCID: PMC11009077 DOI: 10.1038/s41559-024-02322-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 12/11/2023] [Indexed: 02/10/2024]
Abstract
Soqotra, an island situated at the mouth of the Gulf of Aden in the northwest Indian Ocean between Africa and Arabia, is home to ~60,000 people subsisting through fishing and semi-nomadic pastoralism who speak a Modern South Arabian language. Most of what is known about Soqotri history derives from writings of foreign travellers who provided little detail about local people, and the geographic origins and genetic affinities of early Soqotri people has not yet been investigated directly. Here we report genome-wide data from 39 individuals who lived between ~650 and 1750 CE at six locations across the island and document strong genetic connections between Soqotra and the similarly isolated Hadramawt region of coastal South Arabia that likely reflects a source for the peopling of Soqotra. Medieval Soqotri can be modelled as deriving ~86% of their ancestry from a population such as that found in the Hadramawt today, with the remaining ~14% best proxied by an Iranian-related source with up to 2% ancestry from the Indian sub-continent, possibly reflecting genetic exchanges that occurred along with archaeologically documented trade from these regions. In contrast to all other genotyped populations of the Arabian Peninsula, genome-level analysis of the medieval Soqotri is consistent with no sub-Saharan African admixture dating to the Holocene. The deep ancestry of people from medieval Soqotra and the Hadramawt is also unique in deriving less from early Holocene Levantine farmers and more from groups such as Late Pleistocene hunter-gatherers from the Levant (Natufians) than other mainland Arabians. This attests to migrations by early farmers having less impact in southernmost Arabia and Soqotra and provides compelling evidence that there has not been complete population replacement between the Pleistocene and Holocene throughout the Arabian Peninsula. Medieval Soqotra harboured a small population that showed qualitatively different marriage practices from modern Soqotri, with first-cousin unions occurring significantly less frequently than today.
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Affiliation(s)
- Kendra Sirak
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | | | - Esther Brielle
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Bowen Chen
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Iosif Lazaridis
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Harald Ringbauer
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Matthew Mah
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Swapan Mallick
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Adam Micco
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Nadin Rohland
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Kimberly Callan
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Elizabeth Curtis
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Aisling Kearns
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Ann Marie Lawson
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - J Noah Workman
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Fatma Zalzala
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | | | | | | | | | - David Reich
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
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33
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Ullah MA, Moin AT, Nipa JF, Islam NN, Johora FT, Chowdhury RH, Islam S. Exploring risk factors and molecular targets in leukemia patients with COVID-19: a bioinformatics analysis of differential gene expression. J Leukoc Biol 2024; 115:723-737. [PMID: 38323674 DOI: 10.1093/jleuko/qiae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 11/13/2023] [Accepted: 12/14/2023] [Indexed: 02/08/2024] Open
Abstract
The molecular mechanism of COVID-19's pathogenic effects in leukemia patients is still poorly known. Our study investigated the possible disease mechanism of COVID-19 and its associated risk factors in patients with leukemia utilizing differential gene expression analysis. We also employed network-based approaches to identify molecular targets that could potentially diagnose and treat COVID-19-infected leukemia patients. Our study demonstrated a shared set of 60 genes that are expressed differentially among patients with leukemia and COVID-19. Most of these genes are expressed in blood and bone marrow tissues and are predominantly implicated in the pathogenesis of different hematologic malignancies, increasingly imperiling COVID-19 morbidity and mortality among the affected patients. Additionally, we also found that COVID-19 may influence the expression of several cancer-associated genes in leukemia patients, such as CCR7, LEF1, and 13 candidate cancer-driver genes. Furthermore, our findings reveal that COVID-19 may predispose leukemia patients to altered blood homeostasis, increase the risk of COVID-19-related liver injury, and deteriorate leukemia-associated injury and patient prognosis. Our findings imply that molecular signatures, like transcription factors, proteins such as TOP21, and 25 different microRNAs, may be potential targets for diagnosing and treating COVID-19-infected leukemia patients. Nevertheless, additional experimental studies will contribute to further validating the study's findings.
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Affiliation(s)
- Md Asad Ullah
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
| | - Abu Tayab Moin
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Hathazari, Chattogram-4331, Bangladesh
| | - Jannatul Ferdous Nipa
- Department of Genetic Engineering and Biotechnology, East West University, Aftabnagar, Dhaka-1212, Bangladesh
| | - Nafisa Nawal Islam
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
| | - Fatema Tuz Johora
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
| | - Rahee Hasan Chowdhury
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Hathazari, Chattogram-4331, Bangladesh
| | - Saiful Islam
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Chattogram Laboratories, Chittagong Cantonment, Chattogram-4220, Bangladesh
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Wei PJ, Zhu AD, Cao R, Zheng C. Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields. BIOLOGY 2024; 13:184. [PMID: 38534453 DOI: 10.3390/biology13030184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/03/2024] [Accepted: 03/10/2024] [Indexed: 03/28/2024]
Abstract
Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample-gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.
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Affiliation(s)
- Pi-Jing Wei
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei 230601, China
| | - An-Dong Zhu
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei 230601, China
| | - Ruifen Cao
- School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Hefei 230601, China
| | - Chunhou Zheng
- School of Artificial Intelligence, Anhui University, 111 Jiulong Road, Hefei 230601, China
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Lynn N, Tuller T. Detecting and understanding meaningful cancerous mutations based on computational models of mRNA splicing. NPJ Syst Biol Appl 2024; 10:25. [PMID: 38453965 PMCID: PMC10920900 DOI: 10.1038/s41540-024-00351-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 02/22/2024] [Indexed: 03/09/2024] Open
Abstract
Cancer research has long relied on non-silent mutations. Yet, it has become overwhelmingly clear that silent mutations can affect gene expression and cancer cell fitness. One fundamental mechanism that apparently silent mutations can severely disrupt is alternative splicing. Here we introduce Oncosplice, a tool that scores mutations based on models of proteomes generated using aberrant splicing predictions. Oncosplice leverages a highly accurate neural network that predicts splice sites within arbitrary mRNA sequences, a greedy transcript constructor that considers alternate arrangements of splicing blueprints, and an algorithm that grades the functional divergence between proteins based on evolutionary conservation. By applying this tool to 12M somatic mutations we identify 8K deleterious variants that are significantly depleted within the healthy population; we demonstrate the tool's ability to identify clinically validated pathogenic variants with a positive predictive value of 94%; we show strong enrichment of predicted deleterious mutations across pan-cancer drivers. We also achieve improved patient survival estimation using a proposed set of novel cancer-involved genes. Ultimately, this pipeline enables accelerated insight-gathering of sequence-specific consequences for a class of understudied mutations and provides an efficient way of filtering through massive variant datasets - functionalities with immediate experimental and clinical applications.
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Affiliation(s)
- Nicolas Lynn
- Department of Biomedical Engineering, the Engineering Faculty, Tel Aviv University, Tel-Aviv, 69978, Israel
| | - Tamir Tuller
- Department of Biomedical Engineering, the Engineering Faculty, Tel Aviv University, Tel-Aviv, 69978, Israel.
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36
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Asfaw E, Lin AY, Huffman A, Li S, George M, Darancou C, Kalter M, Wehbi N, Bartels D, Fleck E, Tran N, Faghihnia D, Berke K, Sutariya R, Reyal F, Tammam Y, Zhao B, Ong E, Xiang Z, He V, Song J, Seleznev A, Guo J, Pan Y, Zheng J, He Y. CanVaxKB: a web-based cancer vaccine knowledgebase. NAR Cancer 2024; 6:zcad060. [PMID: 38204924 PMCID: PMC10776203 DOI: 10.1093/narcan/zcad060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/01/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
Cancer vaccines have been increasingly studied and developed to prevent or treat various types of cancers. To systematically survey and analyze different reported cancer vaccines, we developed CanVaxKB (https://violinet.org/canvaxkb), the first web-based cancer vaccine knowledgebase that compiles over 670 therapeutic or preventive cancer vaccines that have been experimentally verified to be effective at various stages. Vaccine construction and host response data are also included. These cancer vaccines are developed against various cancer types such as melanoma, hematological cancer, and prostate cancer. CanVaxKB has stored 263 genes or proteins that serve as cancer vaccine antigen genes, which we have collectively termed 'canvaxgens'. Top three mostly used canvaxgens are PMEL, MLANA and CTAG1B, often targeting multiple cancer types. A total of 193 canvaxgens are also reported in cancer-related ONGene, Network of Cancer Genes and/or Sanger Cancer Gene Consensus databases. Enriched functional annotations and clusters of canvaxgens were identified and analyzed. User-friendly web interfaces are searchable for querying and comparing cancer vaccines. CanVaxKB cancer vaccines are also semantically represented by the community-based Vaccine Ontology to support data exchange. Overall, CanVaxKB is a timely and vital cancer vaccine source that facilitates efficient collection and analysis, further helping researchers and physicians to better understand cancer mechanisms.
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Affiliation(s)
- Eliyas Asfaw
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
- School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Asiyah Yu Lin
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Anthony Huffman
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Siqi Li
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Madison George
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chloe Darancou
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Madison Kalter
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Nader Wehbi
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Davis Bartels
- College of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
| | - Elyse Fleck
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Nancy Tran
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Daniel Faghihnia
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kimberly Berke
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ronak Sutariya
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Farah Reyal
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Youssef Tammam
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Bin Zhao
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Zuoshuang Xiang
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Virginia He
- The College of Brown University, Brown University, Providence, RI 02912, USA
| | - Justin Song
- College of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
| | - Andrey I Seleznev
- Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Jinjing Guo
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- School of Information Management, Nanjing University, Nanjing, Jiangsu 210023, China
| | - Yuanyi Pan
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- School of Medicine, Guizhou University, Guiyang, Guizhou 550025, China
| | - Jie Zheng
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Yongqun He
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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Li Z, Liu G, Yang X, Shu M, Jin W, Tong Y, Liu X, Wang Y, Yuan J, Yang Y. An atlas of cell-type-specific interactome networks across 44 human tumor types. Genome Med 2024; 16:30. [PMID: 38347596 PMCID: PMC10860273 DOI: 10.1186/s13073-024-01303-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/06/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Biological processes are controlled by groups of genes acting in concert. Investigating gene-gene interactions within different cell types can help researchers understand the regulatory mechanisms behind human complex diseases, such as tumors. METHODS We collected extensive single-cell RNA-seq data from tumors, involving 563 patients with 44 different tumor types. Through our analysis, we identified various cell types in tumors and created an atlas of different immune cell subsets across different tumor types. Using the SCINET method, we reconstructed interactome networks specific to different cell types. Diverse functional data was then integrated to gain biological insights into the networks, including somatic mutation patterns and gene functional annotation. Additionally, genes with prognostic relevance within the networks were also identified. We also examined cell-cell communications to investigate how gene interactions modulate cell-cell interactions. RESULTS We developed a data portal called CellNetdb for researchers to study cell-type-specific interactome networks. Our findings indicate that these networks can be used to identify genes with topological specificity in different cell types. We also found that prognostic genes can deconvolved into cell types through analyzing network connectivity. Additionally, we identified commonalities and differences in cell-type-specific networks across different tumor types. Our results suggest that these networks can be used to prioritize risk genes. CONCLUSIONS This study presented CellNetdb, a comprehensive repository featuring an atlas of cell-type-specific interactome networks across 44 human tumor types. The findings underscore the utility of these networks in delineating the intricacies of tumor microenvironments and advancing the understanding of molecular mechanisms underpinning human tumors.
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Affiliation(s)
- Zekun Li
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Gerui Liu
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Xiaoxiao Yang
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Meng Shu
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Wen Jin
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Yang Tong
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Xiaochuan Liu
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Yuting Wang
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China
| | - Jiapei Yuan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China.
- Tianjin Institutes of Health Science, Tianjin, 301600, China.
| | - Yang Yang
- Department of Bioinformatics, School of Basic Medical Sciences, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Center for Reproductive Medicine, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Medical University, Tianjin, 300070, China.
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
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Guo Z, Duan D, Tang W, Zhu J, Bush WS, Zhang L, Zhu X, Jin F, Feng H. magpie: A power evaluation method for differential RNA methylation analysis in N6-methyladenosine sequencing. PLoS Comput Biol 2024; 20:e1011875. [PMID: 38346081 PMCID: PMC10890765 DOI: 10.1371/journal.pcbi.1011875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/23/2024] [Accepted: 01/30/2024] [Indexed: 02/25/2024] Open
Abstract
Recently, novel biotechnologies to quantify RNA modifications became an increasingly popular choice for researchers who study epitranscriptome. When studying RNA methylations such as N6-methyladenosine (m6A), researchers need to make several decisions in its experimental design, especially the sample size and a proper statistical power. Due to the complexity and high-throughput nature of m6A sequencing measurements, methods for power calculation and study design are still currently unavailable. In this work, we propose a statistical power assessment tool, magpie, for power calculation and experimental design for epitranscriptome studies using m6A sequencing data. Our simulation-based power assessment tool will borrow information from real pilot data, and inspect various influential factors including sample size, sequencing depth, effect size, and basal expression ranges. We integrate two modules in magpie: (i) a flexible and realistic simulator module to synthesize m6A sequencing data based on real data; and (ii) a power assessment module to examine a set of comprehensive evaluation metrics.
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Affiliation(s)
- Zhenxing Guo
- School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Shenzhen, Guangdong, China
| | - Daoyu Duan
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Julia Zhu
- Hathaway Brown School, Shaker Heights, Ohio, United States of America
| | - William S. Bush
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Liangliang Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Fulai Jin
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
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Huang Y, Chen F, Sun H, Zhong C. Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation. BMC Bioinformatics 2024; 25:34. [PMID: 38254011 PMCID: PMC10804660 DOI: 10.1186/s12859-024-05662-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 01/18/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. RESULTS Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. CONCLUSIONS LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .
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Affiliation(s)
- Yiran Huang
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China
- Key Laboratory of Parallel, Distributed and Intelligent Computing in Guangxi Universities and Colleges, Guangxi University, Nanning, 530004, China
- Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, 530004, China
| | - Fuhao Chen
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China
| | - Hongtao Sun
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China
| | - Cheng Zhong
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China.
- Key Laboratory of Parallel, Distributed and Intelligent Computing in Guangxi Universities and Colleges, Guangxi University, Nanning, 530004, China.
- Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, 530004, China.
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40
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Nourbakhsh M, Degn K, Saksager A, Tiberti M, Papaleo E. Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks. Brief Bioinform 2024; 25:bbad519. [PMID: 38261338 PMCID: PMC10805075 DOI: 10.1093/bib/bbad519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.
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Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
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Hui TX, Kasim S, Aziz IA, Fudzee MFM, Haron NS, Sutikno T, Hassan R, Mahdin H, Sen SC. Robustness evaluations of pathway activity inference methods on gene expression data. BMC Bioinformatics 2024; 25:23. [PMID: 38216898 PMCID: PMC10785356 DOI: 10.1186/s12859-024-05632-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 01/02/2024] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches. RESULTS Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets. CONCLUSION However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.
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Affiliation(s)
- Tay Xin Hui
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia
| | - Shahreen Kasim
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia.
| | - Izzatdin Abdul Aziz
- Computer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Malaysia
| | - Mohd Farhan Md Fudzee
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia
| | - Nazleeni Samiha Haron
- Computer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Malaysia
| | - Tole Sutikno
- Department of Electrical Engineering, Universitas Ahmad Dahlan (UAD), 55166, Yogyakarta, Indonesia
| | - Rohayanti Hassan
- Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia
| | - Hairulnizam Mahdin
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia
| | - Seah Choon Sen
- Faculty of Computing, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia
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Jeuken GS, Käll L. Pathway analysis through mutual information. Bioinformatics 2024; 40:btad776. [PMID: 38195928 PMCID: PMC10783954 DOI: 10.1093/bioinformatics/btad776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 12/09/2023] [Accepted: 01/08/2024] [Indexed: 01/11/2024] Open
Abstract
MOTIVATION In pathway analysis, we aim to establish a connection between the activity of a particular biological pathway and a difference in phenotype. There are many available methods to perform pathway analysis, many of them rely on an upstream differential expression analysis, and many model the relations between the abundances of the analytes in a pathway as linear relationships. RESULTS Here, we propose a new method for pathway analysis, MIPath, that relies on information theoretical principles and, therefore, does not model the association between pathway activity and phenotype, resulting in relatively few assumptions. For this, we construct a graph of the data points for each pathway using a nearest-neighbor approach and score the association between the structure of this graph and the phenotype of these same samples using Mutual Information while adjusting for the effects of random chance in each score. The initial nearest neighbor approach evades individual gene-level comparisons, hence making the method scalable and less vulnerable to missing values. These properties make our method particularly useful for single-cell data. We benchmarked our method on several single-cell datasets, comparing it to established and new methods, and found that it produces robust, reproducible, and meaningful scores. AVAILABILITY AND IMPLEMENTATION Source code is available at https://github.com/statisticalbiotechnology/mipath, or through Python Package Index as "mipathway."
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Affiliation(s)
- Gustavo S Jeuken
- Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm 171 65, Sweden
- Computer Science Department, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Lukas Käll
- Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm 171 65, Sweden
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Balraj AS, Muthamilselvan S, Raja R, Palaniappan A. PRADclass: Hybrid Gleason Grade-Informed Computational Strategy Identifies Consensus Biomarker Features Predictive of Aggressive Prostate Adenocarcinoma. Technol Cancer Res Treat 2024; 23:15330338231222389. [PMID: 38226611 PMCID: PMC10793196 DOI: 10.1177/15330338231222389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/18/2023] [Accepted: 12/06/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Prostate adenocarcinoma (PRAD) is a common cancer diagnosis among men globally, yet large gaps in our knowledge persist with respect to the molecular bases of its progression and aggression. It is mostly indolent and slow-growing, but aggressive prostate cancers need to be recognized early for optimising treatment, with a view to reducing mortality. METHODS Based on TCGA transcriptomic data pertaining to PRAD and the associated clinical metadata, we determined the sample Gleason grade, and used it to execute: (i) Gleason-grade wise linear modeling, followed by five contrasts against controls and ten contrasts between grades; and (ii) Gleason-grade wise network modeling via weighted gene correlation network analysis (WGCNA). Candidate biomarkers were obtained from the above analysis and the consensus found. The consensus biomarkers were used as the feature space to train ML models for classifying a sample as benign, indolent or aggressive. RESULTS The statistical modeling yielded 77 Gleason grade-salient genes while the WGCNA algorithm yielded 1003 trait-specific key genes in grade-wise significant modules. Consensus analysis of the two approaches identified two genes in Grade-1 (SLC43A1 and PHGR1), 26 genes in Grade-4 (including LOC100128675, PPP1R3C, NECAB1, UBXN10, SERPINA5, CLU, RASL12, DGKG, FHL1, NCAM1, and CEND1), and seven genes in Grade-5 (CBX2, DPYS, FAM72B, SHCBP1, TMEM132A, TPX2, UBE2C). A RandomForest model trained and optimized on these 35 biomarkers for the ternary classification problem yielded a balanced accuracy ∼ 86% on external validation. CONCLUSIONS The consensus of multiple parallel computational strategies has unmasked candidate Gleason grade-specific biomarkers. PRADclass, a validated AI model featurizing these biomarkers achieved good performance, and could be trialed to predict the differentiation of prostate cancers. PRADclass is available for academic use at: https://apalania.shinyapps.io/pradclass (online) and https://github.com/apalania/pradclass (command-line interface).
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Affiliation(s)
- Alex Stanley Balraj
- Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India
| | - Sangeetha Muthamilselvan
- Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India
| | - Rachanaa Raja
- Department of Pharmaceutical Technology, UCE, Anna University (BIT campus), Trichy, India
| | - Ashok Palaniappan
- Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India
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Sapoval N, Tanevski M, Treangen TJ. KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:506-520. [PMID: 38160303 PMCID: PMC10764071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
The microbes present in the human gastrointestinal tract are regularly linked to human health and disease outcomes. Thanks to technological and methodological advances in recent years, metagenomic sequencing data, and computational methods designed to analyze metagenomic data, have contributed to improved understanding of the link between the human gut microbiome and disease. However, while numerous methods have been recently developed to extract quantitative and qualitative results from host-associated microbiome data, improved computational tools are still needed to track microbiome dynamics with short-read sequencing data. Previously we have proposed KOMB as a de novo tool for identifying copy number variations in metagenomes for characterizing microbial genome dynamics in response to perturbations. In this work, we present KombOver (KO), which includes four key contributions with respect to our previous work: (i) it scales to large microbiome study cohorts, (ii) it includes both k-core and K-truss based analysis, (iii) we provide the foundation of a theoretical understanding of the relation between various graph-based metagenome representations, and (iv) we provide an improved user experience with easier-to-run code and more descriptive outputs/results. To highlight the aforementioned benefits, we applied KO to nearly 1000 human microbiome samples, requiring less than 10 minutes and 10 GB RAM per sample to process these data. Furthermore, we highlight how graph-based approaches such as k-core and K-truss can be informative for pinpointing microbial community dynamics within a myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) cohort. KO is open source and available for download/use at: https://github.com/treangenlab/komb.
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Affiliation(s)
- Nicolae Sapoval
- Department of Computer Science, Rice University, Houston, TX 77005, USA,
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Huang Y, Gao X, He QY, Liu W. A Interacting Model: How TRIM21 Orchestrates with Proteins in Intracellular Immunity. SMALL METHODS 2024; 8:e2301142. [PMID: 37922533 DOI: 10.1002/smtd.202301142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/12/2023] [Indexed: 11/07/2023]
Abstract
Tripartite motif-containing protein 21 (TRIM21), identified as both a cytosolic E3 ubiquitin ligase and FcR (Fragment crystallizable receptor), primarily interacts with proteins via its PRY/SPRY domains and promotes their proteasomal degradation to regulate intracellular immunity. But how TRIM21 involves in intracellular immunity still lacks systematical understanding. Herein, it is probed into the TRIM21-related literature and raises an interacting model about how TRIM21 orchestrates proteins in cytosol. In this novel model, TRIM21 generally interacts with miscellaneous protein in intracellular immunity in two ways: For one, TRIM21 solely plays as an E3, ubiquitylating a glut of proteins that contain specific interferon-regulatory factor, nuclear transcription factor kappaB, virus sensors and others, and involving inflammatory responses. For another, TRIM21 serves as both E3 and specific FcR that detects antibody-complexes and facilitates antibody destroying target proteins. Correspondingly delineated as Fc-independent signaling and Fc-dependent signaling in this review, how TRIM21's interactions contribute to intracellular immunity, expecting to provide a systematical understanding of this important protein and invest enlightenment for further research on the pathogenesis of related diseases and its prospective application is elaborated.
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Affiliation(s)
- Yisha Huang
- MOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China
| | - Xuejuan Gao
- MOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China
| | - Qing-Yu He
- MOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China
| | - Wanting Liu
- MOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China
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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. IRANIAN JOURNAL OF BASIC MEDICAL SCIENCES 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] [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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Pei J, Zhang J, Cong Q. Computational analysis of protein-protein interactions of cancer drivers in renal cell carcinoma. FEBS Open Bio 2024; 14:112-126. [PMID: 37964489 PMCID: PMC10761929 DOI: 10.1002/2211-5463.13732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/30/2023] [Accepted: 11/06/2023] [Indexed: 11/16/2023] Open
Abstract
Renal cell carcinoma (RCC) is the most common type of kidney cancer with rising cases in recent years. Extensive research has identified various cancer driver proteins associated with different subtypes of RCC. Most RCC drivers are encoded by tumor suppressor genes and exhibit enrichment in functional categories such as protein degradation, chromatin remodeling, and transcription. To further our understanding of RCC, we utilized powerful deep-learning methods based on AlphaFold to predict protein-protein interactions (PPIs) involving RCC drivers. We predicted high-confidence complexes formed by various RCC drivers, including TCEB1, KMT2C/D and KDM6A of the COMPASS-related complexes, TSC1 of the MTOR pathway, and TRRAP. These predictions provide valuable structural insights into the interaction interfaces, some of which are promising targets for cancer drug design, such as the NRF2-MAFK interface. Cancer somatic missense mutations from large datasets of genome sequencing of RCCs were mapped to the interfaces of predicted and experimental structures of PPIs involving RCC drivers, and their effects on the binding affinity were evaluated. We observed more than 100 cancer somatic mutations affecting the binding affinity of complexes formed by key RCC drivers such as VHL and TCEB1. These findings emphasize the importance of these mutations in RCC pathogenesis and potentially offer new avenues for targeted therapies.
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Affiliation(s)
- Jimin Pei
- Eugene McDermott Center for Human Growth and DevelopmentUniversity of Texas Southwestern Medical CenterDallasTXUSA
- Department of BiophysicsUniversity of Texas Southwestern Medical CenterDallasTXUSA
- Harold C. Simmons Comprehensive Cancer CenterUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Jing Zhang
- Eugene McDermott Center for Human Growth and DevelopmentUniversity of Texas Southwestern Medical CenterDallasTXUSA
- Department of BiophysicsUniversity of Texas Southwestern Medical CenterDallasTXUSA
- Harold C. Simmons Comprehensive Cancer CenterUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Qian Cong
- Eugene McDermott Center for Human Growth and DevelopmentUniversity of Texas Southwestern Medical CenterDallasTXUSA
- Department of BiophysicsUniversity of Texas Southwestern Medical CenterDallasTXUSA
- Harold C. Simmons Comprehensive Cancer CenterUniversity of Texas Southwestern Medical CenterDallasTXUSA
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Wang S, Wang C, Lv F, Chu P, Jin H. Genome-wide identification of the OMT gene family in Cucumis melo L. and expression analysis under abiotic and biotic stress. PeerJ 2023; 11:e16483. [PMID: 38107581 PMCID: PMC10725674 DOI: 10.7717/peerj.16483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/27/2023] [Indexed: 12/19/2023] Open
Abstract
Background O-methyltransferase (OMT)-mediated O-methylation is a frequent modification that occurs during natural product biosynthesis, and it increases the diversity and stability of secondary metabolites. However, detailed genome-wide identification and expression analyses of OMT gene family members have not been performed in melons. In this study, we aimed to perform the genome-wide identification of OMT gene family members in melon to identify and clarify their actions during stress. Methods Genome-wide identification of OMT gene family members was performed using data from the melon genome database. The Cucumis melo OMT genes (CmOMTs) were then compared with the genes from two representative monocotyledons and three representative dicotyledons. The basic information, cis-regulatory elements in the promoter, predicted 3-D-structures, and GO enrichment results of the 21 CmOMTs were analyzed. Results In our study, 21 CmOMTs (named CmOMT1-21) were obtained by analyzing the melon genome. These genes were located on six chromosomes and divided into three groups composed of nine, six, and six CmOMTs based on phylogenetic analysis. Gene structure and motif descriptions were similar within the same classes. Each CmOMT gene contains at least one cis-acting element associated with hormone transport regulation. Analysis of cis-acting elements illustrated the potential role of CmOMTs in developmental regulation and adaptations to various abiotic and biotic stresses. The RNA-seq and quantitative real-time PCR (qRT-PCR) results indicated that NaCl stress significantly induced CmOMT6/9/14/18 and chilling and high temperature and humidity (HTH) stresses significantly upregulated CmOMT14/18. Furthermore, the expression pattern of CmOMT18 may be associated with Fusarium oxysporum f. sp. melonis race 1.2 (FOM1.2) and powdery mildew resistance. Our study tentatively explored the biological functions of CmOMT genes in various stress regulation pathways and provided a conceptual basis for further detailed studies of the molecular mechanisms.
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Affiliation(s)
| | - Chuang Wang
- Liaocheng Vocational & Technical College, Liaocheng, China
| | - Futang Lv
- Liaocheng University, Liaocheng, China
| | | | - Han Jin
- Liaocheng University, Liaocheng, China
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Wang Y, Zhou B, Ru J, Meng X, Wang Y, Liu W. Advances in computational methods for identifying cancer driver genes. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21643-21669. [PMID: 38124614 DOI: 10.3934/mbe.2023958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.
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Affiliation(s)
- Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Bohao Zhou
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Jidong Ru
- School of Textile Garment and Design, Changshu Institute of Technology, Changshu 215500, China
| | - Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
| | - Yundong Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
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50
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Stöger R, Choi M, Begum K, Leeman G, Emes RD, Melamed P, Bentley GR. Childhood environment influences epigenetic age and methylation concordance of a CpG clock locus in British-Bangladeshi migrants. Epigenetics 2023; 18:2153511. [PMID: 36495138 PMCID: PMC9980690 DOI: 10.1080/15592294.2022.2153511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Migration from one location to another often comes with a change in environmental conditions. Here, we analysed features of DNA methylation in young, adult British-Bangladeshi women who experienced different environments during their childhoods: a) migrants, who grew up in Bangladesh with exposure to comparatively higher pathogen loads and poorer health care, and b) second-generation British-Bangladeshis, born to Bangladeshi parents, who grew up in the UK. We used buccal DNA to estimate DNA methylation-based age (DNAm age) from 14 migrants and 11 second-generation migrants, aged 18-35 years. 'AgeAccel,' a measure of DNAm age, independent of chronological age, showed that the group of women who spent their childhood in Bangladesh had higher AgeAccel (P = 0.028), compared to their UK peers. Since epigenetic clocks have been proposed to be associated with maintenance processes of epigenetic systems, we evaluated the preference for concordant DNA methylation at the luteinizing hormone/choriogonadotropin receptor (LHCGR/LHR) locus, which harbours one of the CpGs contributing to Horvath's epigenetic clock. Measurements on both strands of individual, double-stranded DNA molecules indicate higher stability of DNA methylation states at this LHCGR/LHR locus in samples of women who grew up in Bangladesh. Together, our two independent analytical approaches imply that childhood environments may induce subtle changes that are detectable long after exposure occurred, which might reflect altered activity of the epigenetic maintenance system or a difference in the proportion of cell types in buccal tissue. This exploratory work supports our earlier findings that adverse childhood environments lead to phenotypic life history trade-offs.
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Affiliation(s)
- Reinhard Stöger
- School of Biosciences, University of Nottingham, Nottingham, UK
| | - Minseung Choi
- School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Gregory Leeman
- School of Biosciences, University of Nottingham, Nottingham, UK
| | - Richard D Emes
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, UK.,Advanced Data Analysis Centre, University of Nottingham, Nottingham, UK
| | - Philippa Melamed
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel
| | - Gillian R Bentley
- Department of Anthropology, Durham University, Durham, UK.,Wolfson Research Institute for Health and Wellbeing, Durham University, Durham, UK
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