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Intra-tumor heterogeneity, turnover rate and karyotype space shape susceptibility to missegregation-induced extinction. PLoS Comput Biol 2023; 19:e1010815. [PMID: 36689467 PMCID: PMC9917311 DOI: 10.1371/journal.pcbi.1010815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/10/2023] [Accepted: 12/12/2022] [Indexed: 01/24/2023] Open
Abstract
The phenotypic efficacy of somatic copy number alterations (SCNAs) stems from their incidence per base pair of the genome, which is orders of magnitudes greater than that of point mutations. One mitotic event stands out in its potential to significantly change a cell's SCNA burden-a chromosome missegregation. A stochastic model of chromosome mis-segregations has been previously developed to describe the evolution of SCNAs of a single chromosome type. Building upon this work, we derive a general deterministic framework for modeling missegregations of multiple chromosome types. The framework offers flexibility to model intra-tumor heterogeneity in the SCNAs of all chromosomes, as well as in missegregation- and turnover rates. The model can be used to test how selection acts upon coexisting karyotypes over hundreds of generations. We use the model to calculate missegregation-induced population extinction (MIE) curves, that separate viable from non-viable populations as a function of their turnover- and missegregation rates. Turnover- and missegregation rates estimated from scRNA-seq data are then compared to theoretical predictions. We find convergence of theoretical and empirical results in both the location of MIE curves and the necessary conditions for MIE. When a dependency of missegregation rate on karyotype is introduced, karyotypes associated with low missegregation rates act as a stabilizing refuge, rendering MIE impossible unless turnover rates are exceedingly high. Intra-tumor heterogeneity, including heterogeneity in missegregation rates, increases as tumors progress, rendering MIE unlikely.
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52
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Pinto MJ, Cottin L, Dingli F, Laigle V, Ribeiro LF, Triller A, Henderson F, Loew D, Fabre V, Bessis A. Microglial TNFα orchestrates protein phosphorylation in the cortex during the sleep period and controls homeostatic sleep. EMBO J 2023; 42:e111485. [PMID: 36385434 PMCID: PMC9811617 DOI: 10.15252/embj.2022111485] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 10/04/2022] [Accepted: 10/11/2022] [Indexed: 11/18/2022] Open
Abstract
Sleep intensity is adjusted by the length of previous awake time, and under tight homeostatic control by protein phosphorylation. Here, we establish microglia as a new cellular component of the sleep homeostasis circuit. Using quantitative phosphoproteomics of the mouse frontal cortex, we demonstrate that microglia-specific deletion of TNFα perturbs thousands of phosphorylation sites during the sleep period. Substrates of microglial TNFα comprise sleep-related kinases such as MAPKs and MARKs, and numerous synaptic proteins, including a subset whose phosphorylation status encodes sleep need and determines sleep duration. As a result, microglial TNFα loss attenuates the build-up of sleep need, as measured by electroencephalogram slow-wave activity and prevents immediate compensation for loss of sleep. Our data suggest that microglia control sleep homeostasis by releasing TNFα which acts on neuronal circuitry through dynamic control of phosphorylation.
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Affiliation(s)
- Maria J Pinto
- Institut de Biologie de l'École normale supérieure (IBENS), École normale supérieure, CNRS, INSERMUniversité PSLParisFrance
| | - Léa Cottin
- Institut de Biologie de l'École normale supérieure (IBENS), École normale supérieure, CNRS, INSERMUniversité PSLParisFrance
| | - Florent Dingli
- Centre de Recherche, Laboratoire de Spectrométrie de Masse ProtéomiqueInstitut Curie, PSL Research UniversityParisFrance
| | - Victor Laigle
- Centre de Recherche, Laboratoire de Spectrométrie de Masse ProtéomiqueInstitut Curie, PSL Research UniversityParisFrance
| | - Luís F Ribeiro
- Center for Neuroscience and Cell Biology (CNC), Institute for Interdisciplinary Research (IIIUC)University of CoimbraCoimbraPortugal
| | - Antoine Triller
- Institut de Biologie de l'École normale supérieure (IBENS), École normale supérieure, CNRS, INSERMUniversité PSLParisFrance
| | - Fiona Henderson
- Sorbonne Université, CNRS UMR 8246, INSERM U1130, Neuroscience Paris Seine – Institut de Biologie Paris Seine (NPS – IBPS)ParisFrance
| | - Damarys Loew
- Centre de Recherche, Laboratoire de Spectrométrie de Masse ProtéomiqueInstitut Curie, PSL Research UniversityParisFrance
| | - Véronique Fabre
- Sorbonne Université, CNRS UMR 8246, INSERM U1130, Neuroscience Paris Seine – Institut de Biologie Paris Seine (NPS – IBPS)ParisFrance
| | - Alain Bessis
- Institut de Biologie de l'École normale supérieure (IBENS), École normale supérieure, CNRS, INSERMUniversité PSLParisFrance
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53
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Rani S, Chandna P. Multiomics Analysis-Based Biomarkers in Diagnosis of Polycystic Ovary Syndrome. Reprod Sci 2023; 30:1-27. [PMID: 35084716 PMCID: PMC10010205 DOI: 10.1007/s43032-022-00863-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 01/20/2022] [Indexed: 01/06/2023]
Abstract
Polycystic ovarian syndrome is an utmost communal endocrine, psychological, reproductive, and metabolic disorder that occurs in women of reproductive age with extensive range of clinical manifestations. This may even lead to long-term multiple morbidities including obesity, diabetes mellitus, insulin resistance, cardiovascular disease, infertility, cerebrovascular diseases, and ovarian and endometrial cancer. Women affliction from PCOS in midst assemblage of manifestations allied with menstrual dysfunction and androgen exorbitance, which considerably affects eminence of life. PCOS is recognized as a multifactorial disorder and systemic syndrome in first-degree family members; therefore, the etiology of PCOS syndrome has not been copiously interpreted. The disorder of PCOS comprehends numerous allied health conditions and has influenced various metabolic processes. Due to multifaceted pathophysiology engaging several pathways and proteins, single genetic diagnostic tests cannot be supportive to determine in straight way. Clarification of cellular and biochemical pathways and various genetic players underlying PCOS could upsurge our consideration of pathophysiology of this syndrome. It is requisite to know pathophysiological relationship between biomarker and their reflection towards PCOS disease. Biomarkers deliver vibrantly and potent ways to apprehend the spectrum of PCOS with applications in screening, diagnosis, characterization, and monitoring. This paper relies on the endeavor to point out many candidates as potential biomarkers based on omics technologies, thus highlighting correlation between PCOS disease with innovative technologies. Therefore, the objective of existing review is to encapsulate more findings towards cutting-edge advances in prospective use of biomarkers for PCOS disease. Discussed biomarkers may be fruitful in guiding therapies, addressing disease risk, and predicting clinical outcomes in future.
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Affiliation(s)
- Shikha Rani
- Department of Biophysics, University of Delhi, South Campus, Benito Juarez Road, New Delhi , 110021, India.
| | - Piyush Chandna
- Natdynamics Biosciences Confederation, Gurgaon, Haryana, 122001, India
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54
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Kumar K, Bhowmik D, Mandloi S, Gautam A, Lahiri A, Biswas N, Paul S, Chakrabarti S. Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways. Methods Mol Biol 2023; 2634:139-151. [PMID: 37074577 DOI: 10.1007/978-1-0716-3008-2_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Alteration of the status of the metabolic enzymes could be a probable way to regulate metabolic reprogramming, which is a critical cellular adaptation mechanism especially for cancer cells. Coordination among biological pathways, such as gene-regulatory, signaling, and metabolic pathways is crucial for regulating metabolic adaptation. Also, incorporation of resident microbial metabolic potential in human body can influence the interplay between the microbiome and the systemic or tissue metabolic environments. Systemic framework for model-based integration of multi-omics data can ultimately improve our understanding of metabolic reprogramming at holistic level. However, the interconnectivity and novel meta-pathway regulatory mechanisms are relatively lesser explored and understood. Hence, we propose a computational protocol that utilizes multi-omics data to identify probable cross-pathway regulatory and protein-protein interaction (PPI) links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These cross-pathway links were shown to play important roles in metabolic reprogramming in cancer scenarios.
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Affiliation(s)
- Krishna Kumar
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
| | - Debaleena Bhowmik
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Sapan Mandloi
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
| | - Anupam Gautam
- Algorithms in Bioinformatics, Institute for Bioinformatics and Medical Informatics, University of Tübingen,, Tübingen, Germany
- International Max Planck Research School "From Molecules to Organisms," Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Cluster of Excellence: EXC 2124: Controlling Microbes to Fight Infection, University of Tübingen, Tübingen, Germany
| | - Abhishake Lahiri
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Nupur Biswas
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
| | - Sandip Paul
- JIS Institute of Advanced Studies and Research, JIS University, Kolkata, India.
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India.
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55
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Zhang F, Zhang Q, Zhu J, Yao B, Ma C, Qiao N, He S, Ye Z, Wang Y, Han R, Feng J, Wang Y, Qin Z, Ma Z, Li K, Zhang Y, Tian S, Chen Z, Tan S, Wu Y, Ran P, Wang Y, Ding C, Zhao Y. Integrated proteogenomic characterization across major histological types of pituitary neuroendocrine tumors. Cell Res 2022; 32:1047-1067. [PMID: 36307579 PMCID: PMC9715725 DOI: 10.1038/s41422-022-00736-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 09/30/2022] [Indexed: 02/07/2023] Open
Abstract
Pituitary neuroendocrine tumor (PitNET) is one of the most common intracranial tumors. Due to its extensive tumor heterogeneity and the lack of high-quality tissues for biomarker discovery, the causative molecular mechanisms are far from being fully defined. Therefore, more studies are needed to improve the current clinicopathological classification system, and advanced treatment strategies such as targeted therapy and immunotherapy are yet to be explored. Here, we performed the largest integrative genomics, transcriptomics, proteomics, and phosphoproteomics analysis reported to date for a cohort of 200 PitNET patients. Genomics data indicate that GNAS copy number gain can serve as a reliable diagnostic marker for hyperproliferation of the PIT1 lineage. Proteomics-based classification of PitNETs identified 7 clusters, among which, tumors overexpressing epithelial-mesenchymal transition (EMT) markers clustered into a more invasive subgroup. Further analysis identified potential therapeutic targets, including CDK6, TWIST1, EGFR, and VEGFR2, for different clusters. Immune subtyping to explore the potential for application of immunotherapy in PitNET identified an association between alterations in the JAK1-STAT1-PDL1 axis and immune exhaustion, and between changes in the JAK3-STAT6-FOS/JUN axis and immune infiltration. These identified molecular markers and alternations in various clusters/subtypes were further confirmed in an independent cohort of 750 PitNET patients. This proteogenomic analysis across traditional histological boundaries improves our current understanding of PitNET pathophysiology and suggests novel therapeutic targets and strategies.
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Affiliation(s)
- Fan Zhang
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qilin Zhang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China. .,National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Jiajun Zhu
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Boyuan Yao
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chi Ma
- grid.462338.80000 0004 0605 6769State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Science, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan China
| | - Nidan Qiao
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shiman He
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhao Ye
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yunzhi Wang
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Rui Han
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jinwen Feng
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yongfei Wang
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhaoyu Qin
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zengyi Ma
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Kai Li
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yichao Zhang
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Sha Tian
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhengyuan Chen
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Subei Tan
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yue Wu
- grid.8547.e0000 0001 0125 2443National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443Department of Radiology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Peng Ran
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ye Wang
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Yao Zhao
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China. .,National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China. .,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China. .,Shanghai Key laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China. .,Neurosurgical Institute of Fudan University, Shanghai, China. .,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
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56
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Inference of epigenetic subnetworks by Bayesian regression with the incorporation of prior information. Sci Rep 2022; 12:20224. [PMID: 36418365 PMCID: PMC9684215 DOI: 10.1038/s41598-022-19879-x] [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: 11/17/2020] [Accepted: 09/06/2022] [Indexed: 11/25/2022] Open
Abstract
Changes in gene expression have been thought to play a crucial role in various types of cancer. With the advance of high-throughput experimental techniques, many genome-wide studies are underway to analyze underlying mechanisms that may drive the changes in gene expression. It has been observed that the change could arise from altered DNA methylation. However, the knowledge about the degree to which epigenetic changes might cause differences in gene expression in cancer is currently lacking. By considering the change of gene expression as the response of altered DNA methylation, we introduce a novel analytical framework to identify epigenetic subnetworks in which the methylation status of a set of highly correlated genes is predictive of a set of gene expression. By detecting highly correlated modules as representatives of the regulatory scenario underling the gene expression and DNA methylation, the dependency between DNA methylation and gene expression is explored by a Bayesian regression model with the incorporation of g-prior followed by a strategy of an optimal predictor subset selection. The subsequent network analysis indicates that the detected epigenetic subnetworks are highly biologically relevant and contain many verified epigenetic causal mechanisms. Moreover, a survival analysis indicates that they might be effective prognostic factors associated with patient survival time.
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57
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Li J, Gao J, Feng B, Jing Y. PlagueKD: a knowledge graph-based plague knowledge database. Database (Oxford) 2022; 2022:6837306. [PMID: 36412326 PMCID: PMC10161524 DOI: 10.1093/database/baac100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/17/2022] [Accepted: 10/28/2022] [Indexed: 11/23/2022]
Abstract
Plague has been confirmed as an extremely horrific international quarantine infectious disease attributed to Yersinia pestis. It has an extraordinarily high lethal rate that poses a serious hazard to human and animal lives. With the deepening of research, there has been a considerable amount of literature related to the plague that has never been systematically integrated. Indeed, it makes researchers time-consuming and laborious when they conduct some investigation. Accordingly, integrating and excavating plague-related knowledge from considerable literature takes on a critical significance. Moreover, a comprehensive plague knowledge base should be urgently built. To solve the above issues, the plague knowledge base is built for the first time. A database is built from the literature mining based on knowledge graph, which is capable of storing, retrieving, managing and accessing data. First, 5388 plague-related abstracts that were obtained automatically from PubMed are integrated, and plague entity dictionary and ontology knowledge base are constructed by using text mining technology. Second, the scattered plague-related knowledge is correlated through knowledge graph technology. A multifactor correlation knowledge graph centered on plague is formed, which contains 9633 nodes of 33 types (e.g. disease, gene, protein, species, symptom, treatment and geographic location), as well as 9466 association relations (e.g. disease-gene, gene-protein and disease-species). The Neo4j graph database is adopted to store and manage the relational data in the form of triple. Lastly, a plague knowledge base is built, which can successfully manage and visualize a large amount of structured plague-related data. This knowledge base almost provides an integrated and comprehensive plague-related knowledge. It should not only help researchers to better understand the complex pathogenesis and potential therapeutic approaches of plague but also take on a key significance to reference for exploring potential action mechanisms of corresponding drug candidates and the development of vaccine in the future. Furthermore, it is of great significance to promote the field of plague research. Researchers are enabled to acquire data more easily for more effective research. Database URL: http://39.104.28.169:18095/.
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Affiliation(s)
- Jin Li
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot 010011, China.,Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot, Inner Mongolia Autonomous Region 010018, China
| | - Jing Gao
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot 010011, China.,Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot, Inner Mongolia Autonomous Region 010018, China
| | - Baiyang Feng
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot 010011, China.,Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot, Inner Mongolia Autonomous Region 010018, China
| | - Yi Jing
- Faculty of Science, University of New South Wales, Sydney, New Sales Wales 2020, Australia
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58
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Rossignol P, Duarte K, Bresso E, A Å, Devignes MD, Eriksson N, Girerd N, Glerup R, Jardine AG, Holdaas H, Lamiral Z, Leroy C, Massy Z, März W, Krämer B, Wu PH, Schmieder R, Soveri I, Christensen JH, Svensson M, Zannad F, Fellström B. NT-proBNP and stem cell factor plasma concentrations are independently associated with cardiovascular outcomes in end-stage renal disease hemodialysis patients. EUROPEAN HEART JOURNAL OPEN 2022; 2:oeac069. [PMID: 36600882 PMCID: PMC9797490 DOI: 10.1093/ehjopen/oeac069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 09/14/2022] [Accepted: 10/24/2022] [Indexed: 11/11/2022]
Abstract
Aims End-stage renal disease (ESRD) treated by chronic hemodialysis (HD) is associated with poor cardiovascular (CV) outcomes, with no available evidence-based therapeutics. A multiplexed proteomic approach may identify new pathophysiological pathways associated with CV outcomes, potentially actionable for precision medicine. Methods and results The AURORA trial was an international, multicentre, randomized, double-blind trial involving 2776 patients undergoing maintenance HD. Rosuvastatin vs. placebo had no significant effect on the composite primary endpoint of death from CV causes, nonfatal myocardial infarction or nonfatal stroke. We first compared CV risk-matched cases and controls (n = 410) to identify novel biomarkers using a multiplex proximity extension immunoassay (276 proteomic biomarkers assessed with OlinkTM). We replicated our findings in 200 unmatched cases and 200 controls. External validation was conducted from a multicentre real-life Danish cohort [Aarhus-Aalborg (AA), n = 331 patients] in which 92 OlinkTM biomarkers were assessed. In AURORA, only N-terminal pro-brain natriuretic peptide (NT-proBNP, positive association) and stem cell factor (SCF) (negative association) were found consistently associated with the trial's primary outcome across exploration and replication phases, independently from the baseline characteristics. Stem cell factor displayed a lower added predictive ability compared with NT-ProBNP. In the AA cohort, in multivariable analyses, BNP was found significantly associated with major CV events, while higher SCF was associated with less frequent CV deaths. Conclusions Our findings suggest that NT-proBNP and SCF may help identify ESRD patients with respectively high and low CV risk, beyond classical clinical predictors and also point at novel pathways for prevention and treatment.
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Affiliation(s)
- P Rossignol
- Corresponding author. Tel: +33383157322, Fax: +33383157324,
| | - K Duarte
- Université de Lorraine, Inserm, Centre d’Investigations Cliniques- 1433, and Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT, 4, rue du Morvan, 54500 Nancy, France
| | - E Bresso
- Université de Lorraine, Inserm, Centre d’Investigations Cliniques- 1433, and Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT, 4, rue du Morvan, 54500 Nancy, France,LORIA (CNRS, Inria NGE, Université de Lorraine), F-CRIN INI-CRCT, Vandœuvre-lès-Nancy, France
| | - Åsberg A
- Department of Transplantation Medicine Oslo University Hospital–Rikshospitalet, Oslo, Norway,Norway and Department of Pharmaceutical Biosciences, School of Pharmacy, University of Oslo, Oslo, Norway
| | - M D Devignes
- LORIA (CNRS, Inria NGE, Université de Lorraine), F-CRIN INI-CRCT, Vandœuvre-lès-Nancy, France
| | - N Eriksson
- UCR Uppsala Clinical Research Center, Uppsala Science Park, Uppsala, Sweden
| | - N Girerd
- Université de Lorraine, Inserm, Centre d’Investigations Cliniques- 1433, and Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT, 4, rue du Morvan, 54500 Nancy, France
| | - R Glerup
- Department of Nephrology, Aalborg University Hospital, Aalborg, Denmark
| | - A G Jardine
- Renal Research Group, British Heart Foundation Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | | | - Z Lamiral
- Université de Lorraine, Inserm, Centre d’Investigations Cliniques- 1433, and Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT, 4, rue du Morvan, 54500 Nancy, France
| | - C Leroy
- Université de Lorraine, Inserm, Centre d’Investigations Cliniques- 1433, and Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT, 4, rue du Morvan, 54500 Nancy, France
| | - Z Massy
- CESP, Center for Research in Epidemiology and Population Health, University Paris-Saclay, University Paris-Sud, UVSQ, Villejuif, France,Division of Nephrology, Ambroise Paré University Hospital, APHP, Boulogne, Billancourt and FCRIN INI-CRCT, Paris, France
| | - W März
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria,Mannheim Institute of Public Health, Social and Preventive Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany,SYNLAB Academy, SYNLAB Holding Deutschland GmbH, Mannheim and Augsburg, Germany
| | - B Krämer
- Medical Clinic V, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - P H Wu
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden,Division of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - R Schmieder
- Department of Nephrology and Hypertension, University Hospital Erlangen, Erlangen, Germany
| | - I Soveri
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - J H Christensen
- Department of Nephrology, Aalborg University Hospital, Aalborg, Denmark
| | - M Svensson
- Department of Nephrology, Aarhus University Hospital, Aarhus, Denmark
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59
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Sorokin M, Zolotovskaia M, Nikitin D, Suntsova M, Poddubskaya E, Glusker A, Garazha A, Moisseev A, Li X, Sekacheva M, Naskhletashvili D, Seryakov A, Wang Y, Buzdin A. Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data. BMC Cancer 2022; 22:1113. [PMCID: PMC9623986 DOI: 10.1186/s12885-022-10177-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 09/26/2022] [Indexed: 12/24/2022] Open
Abstract
Background: Overall survival of advanced colorectal cancer (CRC) patients remains poor, and gene expression analysis could potentially complement detection of clinically relevant mutations to personalize CRC treatments. Methods: We performed RNA sequencing of formalin-fixed, paraffin-embedded (FFPE) cancer tissue samples of 23 CRC patients and interpreted the data obtained using bioinformatic method Oncobox for expression-based rating of targeted therapeutics. Oncobox ranks cancer drugs according to the efficiency score calculated using target genes expression and molecular pathway activation data. The patients had primary and metastatic CRC with metastases in liver, peritoneum, brain, adrenal gland, lymph nodes and ovary. Two patients had mutations in NRAS, seven others had mutated KRAS gene. Patients were treated by aflibercept, bevacizumab, bortezomib, cabozantinib, cetuximab, crizotinib, denosumab, panitumumab and regorafenib as monotherapy or in combination with chemotherapy, and information on the success of totally 39 lines of therapy was collected. Results: Oncobox drug efficiency score was effective biomarker that could predict treatment outcomes in the experimental cohort (AUC 0.77 for all lines of therapy and 0.91 for the first line after tumor sampling). Separately for bevacizumab, it was effective in the experimental cohort (AUC 0.87) and in 3 independent literature CRC datasets, n = 107 (AUC 0.84–0.94). It also predicted progression-free survival in univariate (Hazard ratio 0.14) and multivariate (Hazard ratio 0.066) analyses. Difference in AUC scores evidences importance of using recent biosamples for the prediction quality. Conclusion: Our results suggest that RNA sequencing analysis of tumor FFPE materials may be helpful for personalizing prescriptions of targeted therapeutics in CRC. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10177-3.
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Affiliation(s)
- Maxim Sorokin
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia ,grid.18763.3b0000000092721542Moscow Institute of Physics and Technology, 141701 Moscow Region, Russia ,OmicsWay Corp, 91789 Walnut, CA USA
| | - Marianna Zolotovskaia
- grid.18763.3b0000000092721542Moscow Institute of Physics and Technology, 141701 Moscow Region, Russia
| | - Daniil Nikitin
- OmicsWay Corp, 91789 Walnut, CA USA ,grid.418853.30000 0004 0440 1573Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
| | - Maria Suntsova
- grid.448878.f0000 0001 2288 8774World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Elena Poddubskaya
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia ,Clinical Center Vitamed, 121309 Moscow, Russia
| | - Alexander Glusker
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | | | - Alexey Moisseev
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Xinmin Li
- grid.19006.3e0000 0000 9632 6718Department of Pathology and Laboratory Medicine, University of California, 90095 Los Angeles, CA USA
| | - Marina Sekacheva
- grid.448878.f0000 0001 2288 8774World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - David Naskhletashvili
- grid.466904.90000 0000 9092 133XN.N. Blokhin Russian Cancer Research Center, 115478 Moscow, Russia
| | | | - Ye Wang
- grid.410645.20000 0001 0455 0905Core Laboratory, The Affiliated Qingdao Central Hospital of Qingdao University, Qingdao, China
| | - Anton Buzdin
- grid.18763.3b0000000092721542Moscow Institute of Physics and Technology, 141701 Moscow Region, Russia ,grid.418853.30000 0004 0440 1573Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia ,grid.448878.f0000 0001 2288 8774World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
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Bio-Computational Evaluation of Compounds of Bacopa Monnieri as a Potential Treatment for Schizophrenia. Molecules 2022; 27:molecules27207050. [PMID: 36296643 PMCID: PMC9611144 DOI: 10.3390/molecules27207050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/06/2022] [Accepted: 10/10/2022] [Indexed: 11/17/2022] Open
Abstract
Schizophrenia is a horrible mental disorder characterized by distorted perceptions of reality. Investigations have not identified a single etiology for schizophrenia, and there are multiple hypotheses based on various aspects of the disease. There is no specific treatment for schizophrenia. Hence, we have tried to investigate the updated information stored in the genetic databases related to genes that could be responsible for schizophrenia and other related neuronal disorders. After implementing combined computational methodology, such as protein-protein interaction analysis led by system biology approach, in silico docking analysis was performed to explore the 3D binding pattern of Bacopa monnieri natural compounds while interacting with STXBP1. The best-identified compound was CID:5319292 based on −10.3 kcal/mol binding energy. Further, selected complexes were dynamically evaluated by MDS methods, and the output reveals that the STXBP1-CID:5281800 complex showed the lowest RMSD value, i.e., between 0.3 and 0.4 nm. Hence, identified compounds could be used to develop and treat neuronal disorders after in vivo/in vitro testing.
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DHULI KRISTJANA, BONETTI GABRIELE, ANPILOGOV KYRYLO, HERBST KARENL, CONNELLY STEPHENTHADDEUS, BELLINATO FRANCESCO, GISONDI PAOLO, BERTELLI MATTEO. Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro. JOURNAL OF PREVENTIVE MEDICINE AND HYGIENE 2022; 63:E279-E288. [PMID: 36479497 PMCID: PMC9710400 DOI: 10.15167/2421-4248/jpmh2022.63.2s3.2770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.
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Affiliation(s)
- KRISTJANA DHULI
- MAGI’S LAB, Rovereto (TN), Italy
- Correspondence: Kristjana Dhuli, MAGI’S LAB, Rovereto (TN), 38068, Italy. E-mail:
| | | | | | - KAREN L. HERBST
- Total Lipedema Care, Beverly Hills California and Tucson Arizona, USA
| | - STEPHEN THADDEUS CONNELLY
- San Francisco Veterans Affairs Health Care System, Department of Oral & Maxillofacial Surgery, University of California, San Francisco, CA, USA7
| | - FRANCESCO BELLINATO
- Section of Dermatology and Venereology, Department of Medicine, University of Verona, Verona, Italy
| | - PAOLO GISONDI
- Section of Dermatology and Venereology, Department of Medicine, University of Verona, Verona, Italy
| | - MATTEO BERTELLI
- MAGI’S LAB, Rovereto (TN), Italy
- MAGI EUREGIO, Bolzano, BZ, Italy
- MAGISNAT, Peachtree Corners (GA), USA
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Feldmann D, Bope CD, Patricios J, Chimusa ER, Collins M, September AV. A whole genome sequencing approach to anterior cruciate ligament rupture-a twin study in two unrelated families. PLoS One 2022; 17:e0274354. [PMID: 36201451 PMCID: PMC9536556 DOI: 10.1371/journal.pone.0274354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 08/25/2022] [Indexed: 11/06/2022] Open
Abstract
Predisposition to anterior cruciate ligament (ACL) rupture is multi-factorial, with variation in the genome considered a key intrinsic risk factor. Most implicated loci have been identified from candidate gene-based approach using case-control association settings. Here, we leverage a hypothesis-free whole genome sequencing in two two unrelated families (Family A and B) each with twins with a history of recurrent ACL ruptures acquired playing rugby as their primary sport, aimed to elucidate biologically relevant function-altering variants and genetic modifiers in ACL rupture. Family A monozygotic twin males (Twin 1 and Twin 2) both sustained two unilateral non-contact ACL ruptures of the right limb while playing club level touch rugby. Their male sibling sustained a bilateral non-contact ACL rupture while playing rugby union was also recruited. The father had sustained a unilateral non-contact ACL rupture on the right limb while playing professional amateur level football and mother who had participated in dancing for over 10 years at a social level, with no previous ligament or tendon injuries were both recruited. Family B monozygotic twin males (Twin 3 and Twin 4) were recruited with Twin 3 who had sustained a unilateral non-contact ACL rupture of the right limb and Twin 4 sustained three non-contact ACL ruptures (two in right limb and one in left limb), both while playing provincial level rugby union. Their female sibling participated in karate and swimming activities; and mother in hockey (4 years) horse riding (15 years) and swimming, had both reported no previous history of ligament or tendon injury. Variants with potential deleterious, loss-of-function and pathogenic effects were prioritised. Identity by descent, molecular dynamic simulation and functional partner analyses were conducted. We identified, in all nine affected individuals, including twin sets, non-synonymous SNPs in three genes: COL12A1 and CATSPER2, and KCNJ12 that are commonly enriched for deleterious, loss-of-function mutations, and their dysfunctions are known to be involved in the development of chronic pain, and represent key therapeutic targets. Notably, using Identity By Decent (IBD) analyses a long shared identical sequence interval which included the LINC01250 gene, around the telomeric region of chromosome 2p25.3, was common between affected twins in both families, and an affected brother'. Overall gene sets were enriched in pathways relevant to ACL pathophysiology, including complement/coagulation cascades (p = 3.0e-7), purine metabolism (p = 6.0e-7) and mismatch repair (p = 6.9e-5) pathways. Highlighted, is that this study fills an important gap in knowledge by using a WGS approach, focusing on potential deleterious variants in two unrelated families with a historical record of ACL rupture; and providing new insights into the pathophysiology of ACL, by identifying gene sets that contribute to variability in ACL risk.
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Affiliation(s)
- Daneil Feldmann
- Division of Physiological Sciences, Department of Human Biology, University of Cape Town, Cape Town, South Africa
| | - Christian D. Bope
- Department of Mathematics and Computer Science, Faculty of Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Jon Patricios
- Wits Sport and Health (WiSH), School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Emile R. Chimusa
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle, Tyne and Wear, United Kingdom
- Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Malcolm Collins
- Division of Physiological Sciences, Department of Human Biology, University of Cape Town, Cape Town, South Africa
- UCT Research Centre for Health Through Physical Activity, Lifestyle and Sport (HPALS), Cape Town, South Africa
- International Federation of Sports Medicine (FIMS) Collaborative Centre of Sports Medicine, Cape Town, South Africa
| | - Alison V. September
- Division of Physiological Sciences, Department of Human Biology, University of Cape Town, Cape Town, South Africa
- UCT Research Centre for Health Through Physical Activity, Lifestyle and Sport (HPALS), Cape Town, South Africa
- International Federation of Sports Medicine (FIMS) Collaborative Centre of Sports Medicine, Cape Town, South Africa
- * E-mail:
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Kramberger K, Barlič-Maganja D, Pražnikar ZJ, Režen T, Rozman D, Pražnikar J, Kenig S. Whole transcriptome expression array analysis of human colon fibroblasts culture treated with Helichrysum italicum supports its use in traditional medicine. JOURNAL OF ETHNOPHARMACOLOGY 2022; 296:115505. [PMID: 35764197 DOI: 10.1016/j.jep.2022.115505] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 06/09/2022] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Helichrysum italicum (HI) is a Mediterranean plant with well-reported use in traditional medicine for a wide range of applications, including digestive and liver disorders, intestinal parasitic infections, wound healing, stomach ache and asthma. However, little is known about the global mechanism behind its pleiotropic activity. AIM OF THE STUDY The aim of this study was to explain the mechanism behind the previously demonstrated effects of HI and to justify its use in traditional medicine. MATERIALS AND METHODS A microarray-based transcriptome analysis was used to discover the global transcriptional alterations in primary colon fibroblasts after exposure to HI infusion for 6 h and 24 h. In addition, quantitative real-time PCR was used to verify the microarray results. RESULTS Altogether we identified 217 differentially expressed genes compared to non-treated cells, and only 8 were common to both treatments. Gene ontology analysis revealed that 24 h treatment with HI infusion altered the expression of genes involved in cytoskeletal rearrangement and cell growth, whereas pathway analysis further showed the importance of interleukin signaling and transcriptional regulation by TP53. For the 6 h treatment only the process of hemostasis appeared in the results of both enrichment analyses. In functional assays, HI infusion increased cell migration and decreased blood clotting and prothrombin time. CONCLUSIONS With the careful evaluation of the role of individual genes, especially SERPING1, ARHGAP1, IL33 and CDKN1A, represented in the enriched pathways and processes, we propose the main mode of HI action, which is wound healing. In addition to its indirect prevention of diseases resulting from the impaired barrier integrity, HI also effects inflammation and metabolic processes directly, as it regulates genes such as LRPPRC, LIPA, ABCA12, PRKAR1A and ANXA6.
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Affiliation(s)
- Katja Kramberger
- Faculty of Health Sciences, University of Primorska, Polje 42, 6310, Izola, Slovenia.
| | - Darja Barlič-Maganja
- Faculty of Health Sciences, University of Primorska, Polje 42, 6310, Izola, Slovenia.
| | - Zala Jenko Pražnikar
- Faculty of Health Sciences, University of Primorska, Polje 42, 6310, Izola, Slovenia.
| | - Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia.
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia.
| | - Jure Pražnikar
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, 6000, Koper, Slovenia.
| | - Saša Kenig
- Faculty of Health Sciences, University of Primorska, Polje 42, 6310, Izola, Slovenia.
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Wang Q, Li M, Wu T, Zhan L, Li L, Chen M, Xie W, Xie Z, Hu E, Xu S, Yu G. Exploring Epigenomic Datasets by ChIPseeker. Curr Protoc 2022; 2:e585. [PMID: 36286622 DOI: 10.1002/cpz1.585] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In many aspects of life, epigenetics, or the altering of phenotype without changes in sequences, play an essential role in biological function. A vast number of epigenomic datasets are emerging as a result of the advent of next-generation sequencing. Annotation, comparison, visualization, and interpretation of epigenomic datasets remain key aspects of computational biology. ChIPseeker is a Bioconductor package for performing these analyses among variable epigenomic datasets. The fundamental functions of ChIPseeker, including data preparation, annotation, comparison, and visualization, are explained in this article. ChIPseeker is a freely available open-source package that may be found at https://www.bioconductor.org/packages/ChIPseeker. © 2022 Wiley Periodicals LLC. Basic Protocol 1: ChIPseeker and epigenomic dataset preparation Basic Protocol 2: Annotation of epigenomic datasets Basic Protocol 3: Comparison of epigenomic datasets Basic Protocol 4: Visualization of annotated results Basic Protocol 5: Functional analysis of epigenomic datasets Basic Protocol 6: Genome-wide and locus-specific distribution of epigenomic datasets Basic Protocol 7: Heatmaps and metaplots of epigenomic datasets.
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Affiliation(s)
- Qianwen Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ming Li
- 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
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Lin Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Meijun Chen
- 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
| | - Zijing Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Erqiang Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shuangbin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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Sène M, Xia Y, Kamen AA. From functional genomics of vero cells to CRISPR-based genomic deletion for improved viral production rates. Biotechnol Bioeng 2022; 119:2794-2805. [PMID: 35869699 PMCID: PMC9540595 DOI: 10.1002/bit.28190] [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: 03/22/2022] [Revised: 07/07/2022] [Accepted: 07/19/2022] [Indexed: 11/16/2022]
Abstract
Despite their wide use in the vaccine manufacturing field for over 40 years, one of the main limitations to recent efforts to develop Vero cells as high-throughput vaccine manufacturing platforms is the lack of understanding of virus-host interactions during infection and cell-based virus production in Vero cells. To overcome this limitation, this manuscript uses the recently generated reference genome for the Vero cell line to identify the factors at play during influenza A virus (IAV) and recombinant vesicular stomatitis virus (rVSV) infection and replication in Vero host cells. The best antiviral gene candidate for gene editing was selected using Differential Gene Expression analysis, Gene Set Enrichment Analysis and Network Topology-based Analysis. After selection of the ISG15 gene for targeted CRISPR genomic deletion, the ISG15 genomic sequence was isolated for CRISPR guide RNAs design and the guide RNAs with the highest knockout efficiency score were selected. The CRISPR experiment was then validated by confirmation of genomic deletion via PCR and further assessed via quantification of ISG15 protein levels by western blot. The gene deletion effect was assessed thereafter via quantification of virus production yield in the edited Vero cell line. A 70-fold and an 87-fold increase of total viral particles productions in ISG15-/- Vero cells was achieved for, respectively, IAV and rVSV while the ratio of infectious viral particles/total viral particles also significantly increased from 0.0316 to 0.653 for IAV and from 0.0542 to 0.679 for rVSV-GFP.
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Affiliation(s)
| | - Yu Xia
- Department of BioengineeringMcGill UniversityMontrealQuébecCanada
| | - Amine A. Kamen
- Department of BioengineeringMcGill UniversityMontrealQuébecCanada
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Association of androgen receptor expression with glucose metabolic features in triple-negative breast cancer. PLoS One 2022; 17:e0275279. [PMID: 36178912 PMCID: PMC9524647 DOI: 10.1371/journal.pone.0275279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 09/13/2022] [Indexed: 11/19/2022] Open
Abstract
Background
Androgen receptor (AR) is a potential therapeutic target in triple-negative breast cancer (TNBC). We aimed to elucidate the association of AR expression with glucose metabolic features in TNBC.
Methods
Two independent datasets were analyzed: FDG PET data of our institution and a public dataset of GSE135565. In PET analysis, patients with TNBC who underwent pretreatment PET between Jan 2013 and Dec 2017 were retrospectively enrolled. Clinicopathologic features and maximum standardized uptake value (SUVmax) of tumors were compared with AR expression. In GSE135565 dataset, glycolysis score was calculated by the pattern of glycolysis-related genes, and of which association with SUVmax and AR gene expression were analyzed.
Results
A total of 608 female patients were included in the PET data of our institution. SUVmax was lower in AR-positive tumors (P < 0.001) and correlated with lower AR expression (rho = –0.26, P < 0.001). In multivariate analysis, AR was a deterministic factor for low SUVmax (P = 0.012), along with other key clinicopathologic features. In the GSE135565 dataset, AR expression also exhibited a negative correlation with SUVmax (r = –0.34, P = 0.001) and the glycolysis score (r = –0.27, P = 0.013).
Conclusions
Low glucose metabolism is a signature of AR expression in TNBC. It is suggested that evaluation of AR expression status needs to be considered in clinical practice particularly in TNBC with low glucose metabolism.
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Wang Z, Tober-Lau P, Farztdinov V, Lemke O, Schwecke T, Steinbrecher S, Muenzner J, Kriedemann H, Sander LE, Hartl J, Mülleder M, Ralser M, Kurth F. The human host response to monkeypox infection: a proteomic case series study. EMBO Mol Med 2022; 14:e16643. [PMID: 36169042 PMCID: PMC9641420 DOI: 10.15252/emmm.202216643] [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: 07/25/2022] [Revised: 09/09/2022] [Accepted: 09/09/2022] [Indexed: 11/23/2022] Open
Abstract
The rapid rise of monkeypox (MPX) cases outside previously endemic areas prompts for a better understanding of the disease. We studied the plasma proteome of a group of MPX patients with a similar infection history and clinical manifestation typical for the current outbreak. We report that MPX in this case series is associated with a strong plasma proteomic response among nutritional and acute phase response proteins. Moreover, we report a correlation between plasma proteins and disease severity. Contrasting the MPX host response with that of COVID‐19, we find a range of similarities, but also important differences. For instance, CFHR1 is induced in COVID‐19, but suppressed in MPX, reflecting the different roles of the complement system in the two infectious diseases. Of note, the spatial overlap in response proteins suggested that a COVID‐19 biomarker panel assay could be repurposed for MPX. Applying a targeted protein panel assay provided encouraging results and distinguished MPX cases from healthy controls. Hence, our results provide a first proteomic characterization of the MPX human host response and encourage further research on protein‐panel assays in emerging infectious diseases.
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Affiliation(s)
- Ziyue Wang
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Pinkus Tober-Lau
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Vadim Farztdinov
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Oliver Lemke
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Torsten Schwecke
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sarah Steinbrecher
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Julia Muenzner
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Helene Kriedemann
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Leif Erik Sander
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
| | - Johannes Hartl
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany.,The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Florian Kurth
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Braun M, Shoshani S, Tabach Y. Transcriptome changes in DM1 patients’ tissues are governed by the RNA interference pathway. Front Mol Biosci 2022; 9:955753. [PMID: 36060259 PMCID: PMC9437208 DOI: 10.3389/fmolb.2022.955753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
Myotonic dystrophy type 1 (DM1) is a multisystemic disease caused by pathogenic expansions of CTG repeats. The expanded repeats are transcribed to long RNA and induce cellular toxicity. Recent studies suggest that the CUG repeats are processed by the RNA interference (RNAi) pathway to generate small interfering repeated RNA (siRNA). However, the effects of the CTG repeat-derived siRNAs remain unclear. We hypothesize that the RNAi machinery in DM1 patients generates distinct gene expression patterns that determine the disease phenotype in the individual patient. The abundance of genes with complementary repeats that are targeted by siRNAs in each tissue determines the way that the tissue is affected in DM1. We integrated and analyzed published transcriptome data from muscle, heart, and brain biopsies of DM1 patients, and revealed shared, characteristic changes that correlated with disease phenotype. These signatures are overrepresented by genes and transcription factors bearing endogenous CTG/CAG repeats and are governed by aberrant activity of the RNAi machinery, miRNAs, and a specific gain-of-function of the CTG repeats. Computational analysis of the DM1 transcriptome enhances our understanding of the complex pathophysiology of the disease and may reveal a path for cure.
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Dong L, Zhang R, Huang Q, Shen Y, Li H, Yu S, Wu Q. Construction, bioinformatics analysis, and validation of competitive endogenous RNA networks in ulcerative colitis. Front Genet 2022; 13:951243. [PMID: 36061211 PMCID: PMC9428148 DOI: 10.3389/fgene.2022.951243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Ulcerative colitis (UC) is a common chronic disease of the digestive system. Recently, competitive endogenous RNAs (ceRNAs) have been increasingly used to reveal key mechanisms for the pathogenesis and treatment of UC. However, the role of ceRNA in UC pathogenesis has not been fully clarified. This study aimed to explore the mechanism of the lncRNA-miRNA-mRNA ceRNA network in UC and identify potential biomarkers and therapeutic targets. Materials and Methods: An integrative analysis of mRNA, microRNA (miRNA), and long non-coding RNA (lncRNA) files downloaded from the Gene Expression Omnibus (GEO) was performed. Differentially expressed mRNA (DE-mRNAs), miRNA (DE-miRNAs), and lncRNA (DE-lncRNAs) were investigated between the normal and UC groups by the limma package. A weighted correlation network analysis (WGCNA) was used to identify the relative model for constructing the ceRNA network, and, concurrently, miRWalk and DIANA-LncBase databases were used for target prediction. Consecutively, the Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathway, and Reactome pathway enrichment analyses, protein-protein interaction (PPI) network, Cytohubba, and ClueGO were performed to identify hub genes. Additionally, we examined the immune infiltration characteristics of UC and the correlation between hub genes and immune cells using the immuCellAI database. Finally, the expression of potential biomarkers of ceRNA was validated via qRT-PCR in an experimental UC model induced by dextran sulfate sodium (DSS). Result: The ceRNA network was constructed by combining four mRNAs, two miRNAs, and two lncRNAs, and the receiver operating characteristic (ROC) analysis showed that two mRNAs (CTLA4 and STAT1) had high diagnostic accuracy (area under the curve [AUC] > 0.9). Furthermore, CTLA4 up-regulation was positively correlated with the infiltration of immune cells. Finally, as a result of this DSS-induced experimental UC model, CTLA4, MIAT, and several associate genes expression were consistent with the results of previous bioinformatics analysis, which proved our hypothesis. Conclusion: The investigation of the ceRNA network in this study could provide insight into UC pathogenesis. CTLA4, which has immune-related properties, can be a potential biomarker in UC, and MIAT/miR-422a/CTLA4 ceRNA networks may play important roles in UC.
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Affiliation(s)
- Longcong Dong
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ruibin Zhang
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Qin Huang
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yuan Shen
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hongying Li
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Shuguang Yu
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Qiaofeng Wu
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Chronobiology Key Laboratory of Sichuan Province, Chengdu, China
- *Correspondence: Qiaofeng Wu,
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70
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Boyle DL, Prideaux EB, Hillman J, Wang W, Firestein GS. Improving Transcriptome Fidelity Following Synovial Tissue Disaggregation. Front Med (Lausanne) 2022; 9:919748. [PMID: 36035425 PMCID: PMC9400013 DOI: 10.3389/fmed.2022.919748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Objective To improve the fidelity of the cellular transcriptome of disaggregated synovial tissue for applications such as single-cell RNA sequencing (scRNAseq) by modifying the disaggregation technique. Methods Osteoarthritis (OA) and rheumatoid arthritis (RA) synovia were collected at arthroplasty. RNA was extracted from intact or disaggregated replicate pools of tissue fragments. Disaggregation was performed with either a proprietary protease, Liberase TL (Lib) as a reference method, Liberase TL with an RNA polymerase inhibitor flavopyridol (Flavo), or a cold digestion with subtilisin A (SubA). qPCR on selected markers and RNAseq were used to compare disaggregation methods using the original intact tissue as reference. Results Disaggregated cell yield and viability were similar for all three methods with some viability improved (SubA). Candidate gene analysis showed that Lib alone dramatically increased expression of several genes involved in inflammation and immunity compared with intact tissue and was unable to differentiate RA from OA. Both alternative methods reduced the disaggregation induced changes. Unbiased analysis using bulk RNAseq and the 3 protocols confirmed the candidate gene studies and showed that disaggregation-induced changes were largely prevented. The resultant data improved the ability to distinguish RA from OA synovial transcriptomes. Conclusions Disaggregation of connective tissues such as synovia has complex and selective effects on the transcriptome. We found that disaggregation with an RNA polymerase inhibitor or using a cold enzyme tended to limit induction of some relevant transcripts during tissue processing. The resultant data in the disaggregated transcriptome better represented the in situ transcriptome. The specific method chosen can be tailored to the genes of interest and the hypotheses being tested in order to optimize the fidelity of technique for applications based on cell suspensions such as sorted populations or scRNAseq.
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Affiliation(s)
- David L. Boyle
- Department of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Edward B. Prideaux
- Department of Chemistry and Biochemistry, University of California, San Diego, San Diego, CA, United States
| | - Joshua Hillman
- Department of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Wei Wang
- Department of Chemistry and Biochemistry, University of California, San Diego, San Diego, CA, United States
| | - Gary S. Firestein
- Department of Medicine, University of California, San Diego, San Diego, CA, United States
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71
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Dall'Alba G, Casa PL, Abreu FPD, Notari DL, de Avila E Silva S. A Survey of Biological Data in a Big Data Perspective. BIG DATA 2022; 10:279-297. [PMID: 35394342 DOI: 10.1089/big.2020.0383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.
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Affiliation(s)
- Gabriel Dall'Alba
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
- Genome Science and Technology Program, Faculty of Science, The University of British Columbia, Vancouver, Canada
| | - Pedro Lenz Casa
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Fernanda Pessi de Abreu
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Daniel Luis Notari
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Scheila de Avila E Silva
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
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72
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Yuan Y, Zhang L, Long Q, Jiang H, Li M. An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases. Comput Struct Biotechnol J 2022; 20:3639-3652. [PMID: 35891796 PMCID: PMC9289819 DOI: 10.1016/j.csbj.2022.07.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 07/04/2022] [Accepted: 07/04/2022] [Indexed: 12/04/2022] Open
Abstract
Numerous pathogenic interactions are yet to be revealed efficiently due to dimension burden. Existing methods are underpowered and inaccurate to estimate pathogenic interactions. We developed an accurate bioinformatic method to predict digenic interaction effects based on gene-level features. The new method created a valuable resource of genome-wide pathogenic digenic interactions. We found that known causal genes in Mendelian and Oligogenic diseases may be enriched with interactive effects for the first time.
Increasing evidence shows that genetic interaction across the entire genome may explain a non-trivial fraction of genetic diseases. Digenic interaction is the simplest manifestation of genetic interaction among genes. However, systematic exploration of digenic interactive effects on the whole genome is often discouraged by the high dimension burden. Thus, numerous digenic interactions are yet to be identified for many diseases. Here, we propose a Digenic Interaction Effect Predictor (DIEP), an accurate machine-learning approach to identify the genome-wide pathogenic coding gene pairs with digenic interaction effects. This approach achieved high accuracy and sensitivity in independent testing datasets, outperforming another gene-level digenic predictor (DiGePred). DIEP was also able to discriminate digenic interaction effect from bi-locus effects dual molecular diagnosis (pseudo-digenic). Using DIEP, we provided a valuable resource of genome-wide digenic interactions and demonstrated the enrichment of the digenic interaction effect in Mendelian and Oligogenic diseases. Therefore, DIEP will play a useful role in facilitating the genomic mapping of interactive causal genes for human diseases.
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Affiliation(s)
- Yangyang Yuan
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Center for Disease Genome Research, Sun Yat-sen University, Guangzhou 510080, China
| | - Liubin Zhang
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Center for Disease Genome Research, Sun Yat-sen University, Guangzhou 510080, China
| | - Qihan Long
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Center for Disease Genome Research, Sun Yat-sen University, Guangzhou 510080, China
| | - Hui Jiang
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Center for Disease Genome Research, Sun Yat-sen University, Guangzhou 510080, China
| | - Miaoxin Li
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China
- Center for Disease Genome Research, Sun Yat-sen University, Guangzhou 510080, China
- Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, 519000, China
- Corresponding author at: Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
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73
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Pozuelos GL, Kagda M, Rubin MA, Goniewicz ML, Girke T, Talbot P. Transcriptomic Evidence That Switching from Tobacco to Electronic Cigarettes Does Not Reverse Damage to the Respiratory Epithelium. TOXICS 2022; 10:370. [PMID: 35878275 PMCID: PMC9321508 DOI: 10.3390/toxics10070370] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 12/13/2022]
Abstract
The health benefits of switching from tobacco to electronic cigarettes (ECs) are neither confirmed nor well characterized. To address this problem, we used RNA-seq analysis to compare the nasal epithelium transcriptome from the following groups (n = 3 for each group): (1) former smokers who completely switched to second generation ECs for at least 6 months, (2) current tobacco cigarette smokers (CS), and (3) non-smokers (NS). Group three included one former cigarette smoker. The nasal epithelial biopsies from the EC users vs. NS had a higher number of differentially expressed genes (DEGs) than biopsies from the CS vs. NS and CS vs. EC sets (1817 DEGs total for the EC vs. NS, 407 DEGs for the CS vs. NS, and 116 DEGs for the CS vs. EC comparison). In the EC vs. NS comparison, enriched gene ontology terms for the downregulated DEGs included cilium assembly and organization, whereas gene ontologies for upregulated DEGs included immune response, keratinization, and NADPH oxidase. Similarly, ontologies for cilium movement were enriched in the downregulated DEGs for the CS vs. NS group. Reactome pathway analysis gave similar results and also identified keratinization and cornified envelope in the upregulated DEGs in the EC vs. NS comparison. In the CS vs. NS comparison, the enriched Reactome pathways for upregulated DEGs included biological oxidations and several metabolic processes. Regulator effects identified for the EC vs. NS comparison were inflammatory response, cell movement of phagocytes and degranulation of phagocytes. Disease Ontology Sematic Enrichment analysis identified lung disease, mouth disease, periodontal disease and pulmonary fibrosis in the EC vs. NS comparison. Squamous metaplasia associated markers, keratin 10, keratin 13 and involucrin, were increased in the EC vs. NS comparison. Our transcriptomic analysis showed that gene expression profiles associated with EC use are not equivalent to those from non-smokers. EC use may interfere with airway epithelium recovery by promoting increased oxidative stress, inhibition of ciliogenesis, and maintaining an inflammatory response. These transcriptomic alterations may contribute to the progression of diseases with chronic EC use.
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Affiliation(s)
- Giovanna L. Pozuelos
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, CA 92521, USA; (G.L.P.); (M.K.); (M.A.R.)
| | - Meenakshi Kagda
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, CA 92521, USA; (G.L.P.); (M.K.); (M.A.R.)
| | - Matine A. Rubin
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, CA 92521, USA; (G.L.P.); (M.K.); (M.A.R.)
| | - Maciej L. Goniewicz
- Department of Health Behavior, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA;
| | - Thomas Girke
- Institute for Integrative Genome Biology, University of California, Riverside, CA 92521, USA;
| | - Prue Talbot
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, CA 92521, USA; (G.L.P.); (M.K.); (M.A.R.)
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74
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Zolotovskaia MA, Kovalenko MA, Tkachev VS, Simonov AM, Sorokin MI, Kim E, Kuzmin DV, Karademir-Yilmaz B, Buzdin AA. Next-Generation Grade and Survival Expression Biomarkers of Human Gliomas Based on Algorithmically Reconstructed Molecular Pathways. Int J Mol Sci 2022; 23:ijms23137330. [PMID: 35806337 PMCID: PMC9266372 DOI: 10.3390/ijms23137330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/24/2022] [Accepted: 06/25/2022] [Indexed: 02/04/2023] Open
Abstract
In gliomas, expression of certain marker genes is strongly associated with survival and tumor type and often exceeds histological assessments. Using a human interactome model, we algorithmically reconstructed 7494 new-type molecular pathways that are centered each on an individual protein. Each single-gene expression and gene-centric pathway activation was tested as a survival and tumor grade biomarker in gliomas and their diagnostic subgroups (IDH mutant or wild type, IDH mutant with 1p/19q co-deletion, MGMT promoter methylated or unmethylated), including the three major molecular subtypes of glioblastoma (proneural, mesenchymal, classical). We used three datasets from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, which in total include 527 glioblastoma and 1097 low grade glioma profiles. We identified 2724 such gene and 2418 pathway survival biomarkers out of total 17,717 genes and 7494 pathways analyzed. We then assessed tumor grade and molecular subtype biomarkers and with the threshold of AUC > 0.7 identified 1322/982 gene biomarkers and 472/537 pathway biomarkers. This suggests roughly two times greater efficacy of the reconstructed pathway approach compared to gene biomarkers. Thus, we conclude that activation levels of algorithmically reconstructed gene-centric pathways are a potent class of new-generation diagnostic and prognostic biomarkers for gliomas.
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Affiliation(s)
- Marianna A. Zolotovskaia
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
- Correspondence: ; Tel.: +7-9165612175
| | - Max A. Kovalenko
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
| | | | - Alexander M. Simonov
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
- Omicsway Corp., Walnut, CA 91789, USA;
| | - Maxim I. Sorokin
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
- Omicsway Corp., Walnut, CA 91789, USA;
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
| | - Ella Kim
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany;
| | - Denis V. Kuzmin
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
| | - Betul Karademir-Yilmaz
- Department of Biochemistry, School of Medicine/Genetic and Metabolic Diseases Research and Investigation Center (GEMHAM), Marmara University, Istanbul 34854, Turkey;
| | - Anton A. Buzdin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
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75
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Hoft SG, Pherson MD, DiPaolo RJ. Discovering Immune-Mediated Mechanisms of Gastric Carcinogenesis Through Single-Cell RNA Sequencing. Front Immunol 2022; 13:902017. [PMID: 35757757 PMCID: PMC9231461 DOI: 10.3389/fimmu.2022.902017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/27/2022] [Indexed: 12/17/2022] Open
Abstract
Single-cell RNA sequencing (scRNAseq) technology is still relatively new in the field of gastric cancer immunology but gaining significant traction. This technology now provides unprecedented insights into the intratumoral and intertumoral heterogeneities at the immunological, cellular, and molecular levels. Within the last few years, a volume of publications reported the usefulness of scRNAseq technology in identifying thus far elusive immunological mechanisms that may promote and impede gastric cancer development. These studies analyzed datasets generated from primary human gastric cancer tissues, metastatic ascites fluid from gastric cancer patients, and laboratory-generated data from in vitro and in vivo models of gastric diseases. In this review, we overview the exciting findings from scRNAseq datasets that uncovered the role of critical immune cells, including T cells, B cells, myeloid cells, mast cells, ILC2s, and other inflammatory stromal cells, like fibroblasts and endothelial cells. In addition, we also provide a synopsis of the initial scRNAseq findings on the interesting epithelial cell responses to inflammation. In summary, these new studies have implicated roles for T and B cells and subsets like NKT cells in tumor development and progression. The current studies identified diverse subsets of macrophages and mast cells in the tumor microenvironment, however, additional studies to determine their roles in promoting cancer growth are needed. Some groups specifically focus on the less prevalent ILC2 cell type that may contribute to early cancer development. ScRNAseq analysis also reveals that stromal cells, e.g., fibroblasts and endothelial cells, regulate inflammation and promote metastasis, making them key targets for future investigations. While evaluating the outcomes, we also highlight the gaps in the current findings and provide an assessment of what this technology holds for gastric cancer research in the coming years. With scRNAseq technology expanding rapidly, we stress the need for periodic review of the findings and assess the available scRNAseq analytical tools to guide future work on immunological mechanisms of gastric carcinogenesis.
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Affiliation(s)
- Stella G Hoft
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, United States
| | - Michelle D Pherson
- Department of Biochemistry and Molecular Biology, Saint Louis University School of Medicine, St. Louis, MO, United States.,Genomics Core Facility, Saint Louis University School of Medicine, St. Louis, MO, United States
| | - Richard J DiPaolo
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, United States
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76
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Association of FXI activity with thrombo-inflammation, extracellular matrix, lipid metabolism and apoptosis in venous thrombosis. Sci Rep 2022; 12:9761. [PMID: 35697739 PMCID: PMC9192691 DOI: 10.1038/s41598-022-13174-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 02/17/2022] [Indexed: 12/31/2022] Open
Abstract
Animal experiments and early phase human trials suggest that inhibition of factor XIa (FXIa) safely prevents venous thromboembolism (VTE), and specific murine models of sepsis have shown potential efficacy in alleviating cytokine storm. These latter findings support the role of FXI beyond coagulation. Here, we combine targeted proteomics, machine learning and bioinformatics, to discover associations between FXI activity (FXI:C) and the plasma protein profile of patients with VTE. FXI:C was measured with a modified activated partial prothrombin time (APTT) clotting time assay. Proximity extension assay-based protein profiling was performed on plasma collected from subjects from the Genotyping and Molecular Phenotyping of Venous Thromboembolism (GMP-VTE) Project, collected during an acute VTE event (n = 549) and 12-months after (n = 187). Among 444 proteins investigated, N = 21 and N = 66 were associated with FXI:C during the acute VTE event and at 12 months follow-up, respectively. Seven proteins were identified as FXI:C-associated at both time points. These FXI-related proteins were enriched in immune pathways related to causes of thrombo-inflammation, extracellular matrix interaction, lipid metabolism, and apoptosis. The results of this study offer important new avenues for future research into the multiple properties of FXI, which are of high clinical interest given the current development of FXI inhibitors.
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77
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Betge J, Rindtorff N, Sauer J, Rauscher B, Dingert C, Gaitantzi H, Herweck F, Srour-Mhanna K, Miersch T, Valentini E, Boonekamp KE, Hauber V, Gutting T, Frank L, Belle S, Gaiser T, Buchholz I, Jesenofsky R, Härtel N, Zhan T, Fischer B, Breitkopf-Heinlein K, Burgermeister E, Ebert MP, Boutros M. The drug-induced phenotypic landscape of colorectal cancer organoids. Nat Commun 2022; 13:3135. [PMID: 35668108 PMCID: PMC9170716 DOI: 10.1038/s41467-022-30722-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 05/12/2022] [Indexed: 12/14/2022] Open
Abstract
Patient-derived organoids resemble the biology of tissues and tumors, enabling ex vivo modeling of human diseases. They have heterogeneous morphologies with unclear biological causes and relationship to treatment response. Here, we use high-throughput, image-based profiling to quantify phenotypes of over 5 million individual colorectal cancer organoids after treatment with >500 small molecules. Integration of data using multi-omics modeling identifies axes of morphological variation across organoids: Organoid size is linked to IGF1 receptor signaling, and cystic vs. solid organoid architecture is associated with LGR5 + stemness. Treatment-induced organoid morphology reflects organoid viability, drug mechanism of action, and is biologically interpretable. Inhibition of MEK leads to cystic reorganization of organoids and increases expression of LGR5, while inhibition of mTOR induces IGF1 receptor signaling. In conclusion, we identify shared axes of variation for colorectal cancer organoid morphology, their underlying biological mechanisms, and pharmacological interventions with the ability to move organoids along them. The heterogeneity underlying cancer organoid phenotypes is not yet well understood. Here, the authors develop an imaging analysis assay for high throughput phenotypic screening of colorectal organoids that allows to define specific morphological changes that occur following different drug treatments.
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Affiliation(s)
- Johannes Betge
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany.,Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,German Cancer Research Center (DKFZ), Junior Clinical Cooperation Unit Translational Gastrointestinal Oncology and Preclinical Models, Heidelberg, Germany.,DKFZ-Hector Cancer Institute at University Medical Center Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Niklas Rindtorff
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Jan Sauer
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany.,German Cancer Research Center (DKFZ), Computational Genome Biology Group, Heidelberg, Germany
| | - Benedikt Rauscher
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Clara Dingert
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Haristi Gaitantzi
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Frank Herweck
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Kauthar Srour-Mhanna
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,German Cancer Research Center (DKFZ), Junior Clinical Cooperation Unit Translational Gastrointestinal Oncology and Preclinical Models, Heidelberg, Germany.,DKFZ-Hector Cancer Institute at University Medical Center Mannheim, Mannheim, Germany
| | - Thilo Miersch
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Erica Valentini
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Kim E Boonekamp
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Veronika Hauber
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Tobias Gutting
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany.,Department of Internal Medicine IV, Heidelberg University, Heidelberg, Germany
| | - Larissa Frank
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany
| | - Sebastian Belle
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Timo Gaiser
- Mannheim Cancer Center, Mannheim, Germany.,Heidelberg University, Institute of Pathology, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany
| | - Inga Buchholz
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Ralf Jesenofsky
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Nicolai Härtel
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Tianzuo Zhan
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany.,Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Bernd Fischer
- German Cancer Research Center (DKFZ), Computational Genome Biology Group, Heidelberg, Germany
| | - Katja Breitkopf-Heinlein
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Elke Burgermeister
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.,Mannheim Cancer Center, Mannheim, Germany
| | - Matthias P Ebert
- Heidelberg University, Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany. .,DKFZ-Hector Cancer Institute at University Medical Center Mannheim, Mannheim, Germany. .,Mannheim Cancer Center, Mannheim, Germany.
| | - Michael Boutros
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, and Heidelberg University, Medical Faculty Mannheim, Department of Cell and Molecular Biology, Heidelberg, Germany. .,German Cancer Consortium (DKTK), Heidelberg, Germany.
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78
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Identification of the Potential Molecular Mechanisms Linking RUNX1 Activity with Nonalcoholic Fatty Liver Disease, by Means of Systems Biology. Biomedicines 2022; 10:biomedicines10061315. [PMID: 35740337 PMCID: PMC9219880 DOI: 10.3390/biomedicines10061315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/24/2022] [Accepted: 06/01/2022] [Indexed: 12/10/2022] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the most prevalent chronic hepatic disease; nevertheless, no definitive diagnostic method exists yet, apart from invasive liver biopsy, and nor is there a specific approved treatment. Runt-related transcription factor 1 (RUNX1) plays a major role in angiogenesis and inflammation; however, its link with NAFLD is unclear as controversial results have been reported. Thus, the objective of this work was to determine the proteins involved in the molecular mechanisms between RUNX1 and NAFLD, by means of systems biology. First, a mathematical model that simulates NAFLD pathophysiology was generated by analyzing Anaxomics databases and reviewing available scientific literature. Artificial neural networks established NAFLD pathophysiological processes functionally related to RUNX1: hepatic insulin resistance, lipotoxicity, and hepatic injury-liver fibrosis. Our study indicated that RUNX1 might have a high relationship with hepatic injury-liver fibrosis, and a medium relationship with lipotoxicity and insulin resistance motives. Additionally, we found five RUNX1-regulated proteins with a direct involvement in NAFLD motives, which were NFκB1, NFκB2, TNF, ADIPOQ, and IL-6. In conclusion, we suggested a relationship between RUNX1 and NAFLD since RUNX1 seems to regulate NAFLD molecular pathways, posing it as a potential therapeutic target of NAFLD, although more studies in this field are needed.
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79
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Xu N, Yao Z, Shang G, Ye D, Wang H, Zhang H, Qu Y, Xu F, Wang Y, Qin Z, Zhu J, Zhang F, Feng J, Tian S, Liu Y, Zhao J, Hou J, Guo J, Hou Y, Ding C. Integrated proteogenomic characterization of urothelial carcinoma of the bladder. J Hematol Oncol 2022; 15:76. [PMID: 35659036 PMCID: PMC9164575 DOI: 10.1186/s13045-022-01291-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 05/13/2022] [Indexed: 01/07/2023] Open
Abstract
Background Urothelial carcinoma (UC) is the most common pathological type of bladder cancer, a malignant tumor. However, an integrated multi-omics analysis of the Chinese UC patient cohort is lacking. Methods We performed an integrated multi-omics analysis, including whole-exome sequencing, RNA-seq, proteomic, and phosphoproteomic analysis of 116 Chinese UC patients, comprising 45 non-muscle-invasive bladder cancer patients (NMIBCs) and 71 muscle-invasive bladder cancer patients (MIBCs). Result Proteogenomic integration analysis indicated that SND1 and CDK5 amplifications on chromosome 7q were associated with the activation of STAT3, which was relevant to tumor proliferation. Chromosome 5p gain in NMIBC patients was a high-risk factor, through modulating actin cytoskeleton implicating in tumor cells invasion. Phosphoproteomic analysis of tumors and morphologically normal human urothelium produced UC-associated activated kinases, including CDK1 and PRKDC. Proteomic analysis identified three groups, U-I, U-II, and U-III, reflecting distinct clinical prognosis and molecular signatures. Immune subtypes of UC tumors revealed a complex immune landscape and suggested the amplification of TRAF2 related to the increased expression of PD-L1. Additionally, increased GARS, related to subtype U-II, was validated to promote pentose phosphate pathway by inhibiting activities of PGK1 and PKM2. Conclusions This study provides a valuable resource for researchers and clinicians to further identify molecular pathogenesis and therapeutic opportunities in urothelial carcinoma of the bladder. Supplementary Information The online version contains supplementary material available at 10.1186/s13045-022-01291-7.
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Affiliation(s)
- Ning Xu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Zhenmei Yao
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Guoguo Shang
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Dingwei Ye
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Haixing Wang
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Hailiang Zhang
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Yuanyuan Qu
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Fujiang Xu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Yunzhi Wang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Zhaoyu Qin
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Jiajun Zhu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Fan Zhang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Jinwen Feng
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Yang Liu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Jianyuan Zhao
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China.,Institute for Development and Regenerative Cardiovascular Medicine, MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Jun Hou
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Jianming Guo
- Department of Urology, Zhongshan Hospital Fudan University, Shanghai, 200032, China.
| | - Yingyong Hou
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, 200433, China.
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80
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Petrov I, Alexeyenko A. Individualized discovery of rare cancer drivers in global network context. eLife 2022; 11:74010. [PMID: 35593700 PMCID: PMC9159755 DOI: 10.7554/elife.74010] [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: 09/17/2021] [Accepted: 05/20/2022] [Indexed: 11/13/2022] Open
Abstract
Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become ‘hard currency’ of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations’ co-occurrence in each tumour genome – as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to 10 largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.
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Affiliation(s)
- Iurii Petrov
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.,Science for Life Laboratory, Solna, Sweden
| | - Andrey Alexeyenko
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.,Science for Life Laboratory, Solna, Sweden.,Evi-networks, enskild konsultföretag, Huddinge, Sweden
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81
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A molecular view of amyotrophic lateral sclerosis through the lens of interaction network modules. PLoS One 2022; 17:e0268159. [PMID: 35576218 PMCID: PMC9109932 DOI: 10.1371/journal.pone.0268159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/24/2022] [Indexed: 12/15/2022] Open
Abstract
Background
Despite the discovery of familial cases with mutations in Cu/Zn-superoxide dismutase (SOD1), Guanine nucleotide exchange C9orf72, TAR DNA-binding protein 43 (TARDBP) and RNA-binding protein FUS as well as a number of other genes linked to Amyotrophic Lateral Sclerosis (ALS), the etiology and molecular pathogenesis of this devastating disease is still not understood. As proteins do not act alone, conducting an analysis of ALS at the system level may provide new insights into the molecular biology of ALS and put it into relationship to other neurological diseases.
Methods
A set of ALS-associated genes/proteins were collected from publicly available databases and text mining of scientific literature. We used these as seed proteins to build protein-protein interaction (PPI) networks serving as a scaffold for further analyses. From the collection of networks, a set of core modules enriched in seed proteins were identified. The molecular biology of the core modules was investigated, as were their associations to other diseases. To assess the core modules’ ability to describe unknown or less well-studied ALS biology, they were queried for proteins more recently associated to ALS and not involved in the primary analysis.
Results
We describe a set of 26 ALS core modules enriched in ALS-associated proteins. We show that these ALS core modules not only capture most of the current knowledge about ALS, but they also allow us to suggest biological interdependencies. In addition, new associations of ALS networks with other neurodegenerative diseases, e.g. Alzheimer’s, Huntington’s and Parkinson’s disease were found. A follow-up analysis of 140 ALS-associated proteins identified since 2014 reveals a significant overrepresentation of new ALS proteins in these 26 disease modules.
Conclusions
Using protein-protein interaction networks offers a relevant approach for broadening the understanding of the biological context of known ALS-associated genes. Using a bottom-up approach for the analysis of protein-protein interaction networks is a useful method to avoid bias caused by over-connected proteins. Our ALS-enriched modules cover most known biological functions associated with ALS. The presence of recently identified ALS-associated proteins in the core modules highlights the potential for using these as a scaffold for identification of novel ALS disease mechanisms.
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82
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OncoboxPD: human 51 672 molecular pathways database with tools for activity calculating and visualization. Comput Struct Biotechnol J 2022; 20:2280-2291. [PMID: 35615022 PMCID: PMC9120235 DOI: 10.1016/j.csbj.2022.05.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/05/2022] [Accepted: 05/05/2022] [Indexed: 12/29/2022] Open
Abstract
OncoboxPD (Oncobox pathway databank) available at https://open.oncobox.com is the collection of 51 672 uniformly processed human molecular pathways. Superposition of all pathways formed interactome graph of protein–protein interactions and metabolic reactions containing 361 654 interactions and 64 095 molecular participants. Pathways are uniformly classified by biological processes, and each pathway node is algorithmically functionally annotated by specific activator/repressor role. This enables online calculation of statistically supported pathway activation levels (PALs) with the built-in bioinformatic tool using custom RNA/protein expression profiles. Each pathway can be visualized as static or dynamic graph, where vertices are molecules participating in a pathway and edges are interactions or reactions between them. Differentially expressed nodes in a pathway can be visualized in two-color mode with user-defined color scale. For every comparison, OncoboxPD also generates a graph summarizing top up- and downregulated pathways.
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83
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Humtsoe JO, Kim HS, Jones L, Cevallos J, Boileau P, Kuo F, Morris LGT, Ha P. Development and Characterization of MYB-NFIB Fusion Expression in Adenoid Cystic Carcinoma. Cancers (Basel) 2022; 14:2263. [PMID: 35565392 PMCID: PMC9103462 DOI: 10.3390/cancers14092263] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/21/2022] [Accepted: 04/24/2022] [Indexed: 02/01/2023] Open
Abstract
Adenoid cystic carcinoma (ACC) is the second most common cancer type arising from the salivary gland. The frequent occurrence of chromosome t(6;9) translocation leading to the fusion of MYB and NFIB transcription factor genes is considered a genetic hallmark of ACC. This inter-chromosomal rearrangement may encode multiple variants of functional MYB-NFIB fusion in ACC. However, the lack of an ACC model that harbors the t(6;9) translocation has limited studies on defining the potential function and implication of chimeric MYB-NFIB protein in ACC. This report aims to establish a MYB-NFIB fusion protein expressing system in ACC cells for in vitro and in vivo studies. RNA-seq data from MYB-NFIB translocation positive ACC patients' tumors and MYB-NFIB fusion transcript in ACC patient-derived xenografts (ACCX) was analyzed to identify MYB breakpoints and their frequency of occurrence. Based on the MYB breakpoint identified, variants of MYB-NFIB fusion expression system were developed in a MYB-NFIB deficient ACC cell lines. Analysis confirmed MYB-NFIB fusion protein expression in ACC cells and ACCXs. Furthermore, recombinant MYB-NFIB fusion displayed sustained protein stability and impacted transcriptional activities of interferon-associated genes set as compared to a wild type MYB. In vivo tumor formation analysis indicated the capacity of MYB-NFIB fusion cells to grow as implanted tumors, although there were no fusion-mediated growth advantages. This expression system may be useful not only in studies to determine the functional aspects of MYB-NFIB fusion but also in evaluating effective drug response in vitro and in vivo settings.
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Affiliation(s)
- Joseph O. Humtsoe
- Department of Otolaryngology, Head and Neck Surgery, University of California-San Francisco, Helen Diller Family Comprehensive Cancer Center, San Francisco, CA 94080, USA; (J.O.H.); (H.-S.K.); (L.J.)
| | - Hyun-Su Kim
- Department of Otolaryngology, Head and Neck Surgery, University of California-San Francisco, Helen Diller Family Comprehensive Cancer Center, San Francisco, CA 94080, USA; (J.O.H.); (H.-S.K.); (L.J.)
| | - Leilani Jones
- Department of Otolaryngology, Head and Neck Surgery, University of California-San Francisco, Helen Diller Family Comprehensive Cancer Center, San Francisco, CA 94080, USA; (J.O.H.); (H.-S.K.); (L.J.)
| | - James Cevallos
- School of Medicine, University of California-San Francisco, San Francisco, CA 94080, USA;
| | - Philippe Boileau
- Graduate Group in Biostatistics, Center for Computational Biology, University of California-Berkeley, Berkeley, CA 94720, USA;
| | - Fengshen Kuo
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA; (F.K.); (L.G.T.M.)
| | - Luc G. T. Morris
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA; (F.K.); (L.G.T.M.)
| | - Patrick Ha
- Department of Otolaryngology, Head and Neck Surgery, University of California-San Francisco, Helen Diller Family Comprehensive Cancer Center, San Francisco, CA 94080, USA; (J.O.H.); (H.-S.K.); (L.J.)
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84
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Karagiannaki I, Gourlia K, Lagani V, Pantazis Y, Tsamardinos I. Learning biologically-interpretable latent representations for gene expression data: Pathway Activity Score Learning Algorithm. Mach Learn 2022; 112:4257-4287. [PMID: 37900054 PMCID: PMC10600308 DOI: 10.1007/s10994-022-06158-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 11/12/2021] [Accepted: 02/19/2022] [Indexed: 11/24/2022]
Abstract
Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (i.e., high dimensional data). However, lower-dimensional representations that retain the useful biological information do exist. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways (genesets in general) and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL's latent space has a fairly straightforward biological interpretation. PASL is shown to outperform in predictive performance the state-of-the-art method (PLIER) on two collections of breast cancer and leukemia gene expression datasets. PASL is also trained on a large corpus of 50000 gene expression samples to construct a universal dictionary of features across different tissues and pathologies. The dictionary validated on 35643 held-out samples for reconstruction error. It is then applied on 165 held-out datasets spanning a diverse range of diseases. The AutoML tool JADBio is employed to show that the predictive information in the PASL-created feature space is retained after the transformation. The code is available at https://github.com/mensxmachina/PASL.
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Affiliation(s)
- Ioulia Karagiannaki
- Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas (IESL-FORTH), Heraklion, Greece
| | | | - Vincenzo Lagani
- Institute of Chemical Biology, Ilia State University, Tbilisi, 0162 Georgia
- JADBio, Gnosis Data Analysis PC, Heraklion, Crete Greece
| | - Yannis Pantazis
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology - Hellas, Heraklion, Greece
| | - Ioannis Tsamardinos
- Department of Computer Science, University of Crete, Heraklion, Greece
- JADBio, Gnosis Data Analysis PC, Heraklion, Crete Greece
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology - Hellas, Heraklion, Greece
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85
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Karunakaran KB, Gabriel GC, Balakrishnan N, Lo CW, Ganapathiraju MK. Novel Protein-Protein Interactions Highlighting the Crosstalk between Hypoplastic Left Heart Syndrome, Ciliopathies and Neurodevelopmental Delays. Genes (Basel) 2022; 13:genes13040627. [PMID: 35456433 PMCID: PMC9032108 DOI: 10.3390/genes13040627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 02/06/2023] Open
Abstract
Hypoplastic left heart syndrome (HLHS) is a severe congenital heart disease (CHD) affecting 1 in 5000 newborns. We constructed the interactome of 74 HLHS-associated genes identified from a large-scale mouse mutagenesis screen, augmenting it with 408 novel protein-protein interactions (PPIs) using our High-Precision Protein-Protein Interaction Prediction (HiPPIP) model. The interactome is available on a webserver with advanced search capabilities. A total of 364 genes including 73 novel interactors were differentially regulated in tissue/iPSC-derived cardiomyocytes of HLHS patients. Novel PPIs facilitated the identification of TOR signaling and endoplasmic reticulum stress modules. We found that 60.5% of the interactome consisted of housekeeping genes that may harbor large-effect mutations and drive HLHS etiology but show limited transmission. Network proximity of diabetes, Alzheimer's disease, and liver carcinoma-associated genes to HLHS genes suggested a mechanistic basis for their comorbidity with HLHS. Interactome genes showed tissue-specificity for sites of extracardiac anomalies (placenta, liver and brain). The HLHS interactome shared significant overlaps with the interactomes of ciliopathy- and microcephaly-associated genes, with the shared genes enriched for genes involved in intellectual disability and/or developmental delay, and neuronal death pathways, respectively. This supported the increased burden of ciliopathy variants and prevalence of neurological abnormalities observed among HLHS patients with developmental delay and microcephaly, respectively.
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Affiliation(s)
- Kalyani B. Karunakaran
- Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India; (K.B.K.); (N.B.)
| | - George C. Gabriel
- Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15201, USA; (G.C.G.); (C.W.L.)
| | - Narayanaswamy Balakrishnan
- Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India; (K.B.K.); (N.B.)
| | - Cecilia W. Lo
- Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15201, USA; (G.C.G.); (C.W.L.)
| | - Madhavi K. Ganapathiraju
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15206, USA
- Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Correspondence:
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86
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Naik AA, Sivaramakrishnan V. Femoral Head Osteonecrosis is associated with thrombosis, fatty acid and cholesterol biosynthesis: A potential role for anti-thrombotics and statins as disease modifying agents. Med Hypotheses 2022. [DOI: 10.1016/j.mehy.2022.110808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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87
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Li H, Li M, Guo H, Lin G, Huang Q, Qiu M. Integrative Analyses of Circulating mRNA and lncRNA Expression Profile in Plasma of Lung Cancer Patients. Front Oncol 2022; 12:843054. [PMID: 35433477 PMCID: PMC9008738 DOI: 10.3389/fonc.2022.843054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
Circulating-free RNAs (cfRNAs) have been regarded as potential biomarkers for “liquid biopsy” in cancers. However, the circulating messenger RNA (mRNA) and long noncoding RNA (lncRNA) profiles of lung cancer have not been fully characterized. In this study, we profiled circulating mRNA and lncRNA profiles of 16 lung cancer patients and 4 patients with benign pulmonary nodules. Compared with benign pulmonary nodules, 806 mRNAs and 1,762 lncRNAs were differentially expressed in plasma of lung adenocarcinoma patients. For lung squamous cell carcinomas, 256 mRNAs and 946 lncRNAs were differentially expressed. A total of 231 mRNAs and 298 lncRNAs were differentially expressed in small cell lung cancer. Eleven mRNAs, 51 lncRNAs, and 207 canonical pathways were differentially expressed in lung cancer in total. Forty-five blood samples were collected to verify our findings via performing qPCR. There are plenty of meaningful mRNAs and lncRNAs that were found. MYC, a transcription regulator associated with the stemness of cancer cells, is overexpressed in lung adenocarcinoma. Transforming growth factor beta (TGFB1), which plays pleiotropic roles in cancer progression, was found to be upregulated in lung squamous carcinoma. MALAT1, a well-known oncogenic lncRNA, was also found to be upregulated in lung squamous carcinoma. Thus, this study provided a systematic resource of mRNA and lncRNA expression profiles in lung cancer plasma.
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Affiliation(s)
- Haoran Li
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, China
| | - Mingru Li
- Department of Thoracic Surgery, Aerospace 731 Hospital, Beijing, China
| | - Haifa Guo
- The First Department of Thoracic Surgery, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Guihu Lin
- Department of Thoracic Surgery, Aerospace 731 Hospital, Beijing, China
| | - Qi Huang
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Qi Huang, ; Mantang Qiu,
| | - Mantang Qiu
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, China
- *Correspondence: Qi Huang, ; Mantang Qiu,
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88
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Machlovi SI, Neuner SM, Hemmer BM, Khan R, Liu Y, Huang M, Zhu JD, Castellano JM, Cai D, Marcora E, Goate AM. APOE4 confers transcriptomic and functional alterations to primary mouse microglia. Neurobiol Dis 2022; 164:105615. [PMID: 35031484 PMCID: PMC8934202 DOI: 10.1016/j.nbd.2022.105615] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 12/09/2021] [Accepted: 01/05/2022] [Indexed: 12/12/2022] Open
Abstract
Common genetic variants in more than forty loci modulate risk for Alzheimer's disease (AD). AD risk alleles are enriched within enhancers active in myeloid cells, suggesting that microglia, the brain-resident macrophages, may play a key role in the etiology of AD. A major genetic risk factor for AD is Apolipoprotein E (APOE) genotype, with the ε4/ε4 (E4) genotype increasing risk for AD by approximately 15 fold compared to the most common ε3/ε3 (E3) genotype. However, the impact of APOE genotype on microglial function has not been thoroughly investigated. To address this, we cultured primary microglia from mice in which both alleles of the mouse Apoe gene have been humanized to encode either human APOE ε3 or APOE ε4. Relative to E3 microglia, E4 microglia exhibit altered morphology, increased endolysosomal mass, increased cytokine/chemokine production, and increased lipid and lipid droplet accumulation at baseline. These changes were accompanied by decreased translation and increased phosphorylation of eIF2ɑ and eIF2ɑ-kinases that participate in the integrated stress response, suggesting that E4 genotype leads to elevated levels of cellular stress in microglia relative to E3 genotype. Using live-cell imaging and flow cytometry, we also show that E4 microglia exhibited increased phagocytic uptake of myelin and other substrates compared to E3 microglia. While transcriptomic profiling of myelin-challenged microglia revealed a largely overlapping response profile across genotypes, differential enrichment of genes in interferon signaling, extracellular matrix and translation-related pathways was identified in E4 versus E3 microglia both at baseline and following myelin challenge. Together, our results suggest E4 genotype confers several important functional alterations to microglia even prior to myelin challenge, providing insight into the molecular and cellular mechanisms by which APOE4 may increase risk for AD.
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Affiliation(s)
- Saima I Machlovi
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Friedman Brain Institute, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sarah M Neuner
- Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Friedman Brain Institute, New York, NY, USA; Department of Genetics and Genomic Sciences, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brittany M Hemmer
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Friedman Brain Institute, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Riana Khan
- Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Friedman Brain Institute, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yiyuan Liu
- Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Friedman Brain Institute, New York, NY, USA; Department of Genetics and Genomic Sciences, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Min Huang
- James J Peters VA Medical Center, Research & Development, Bronx, NY, USA; Department of Neurology, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jeffrey D Zhu
- Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Friedman Brain Institute, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph M Castellano
- Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Friedman Brain Institute, New York, NY, USA; Department of Neurology, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dongming Cai
- Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Friedman Brain Institute, New York, NY, USA; James J Peters VA Medical Center, Research & Development, Bronx, NY, USA; Department of Neurology, New York, NY, USA; Alzheimer Disease Research Center, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edoardo Marcora
- Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Friedman Brain Institute, New York, NY, USA; Department of Genetics and Genomic Sciences, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alison M Goate
- Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Friedman Brain Institute, New York, NY, USA; Department of Genetics and Genomic Sciences, New York, NY, USA; Department of Neurology, New York, NY, USA; Alzheimer Disease Research Center, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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89
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Latent Membrane Protein 1 (LMP1) from Epstein–Barr Virus (EBV) Strains M81 and B95.8 Modulate miRNA Expression When Expressed in Immortalized Human Nasopharyngeal Cells. Genes (Basel) 2022; 13:genes13020353. [PMID: 35205397 PMCID: PMC8871543 DOI: 10.3390/genes13020353] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/12/2022] [Accepted: 02/15/2022] [Indexed: 12/01/2022] Open
Abstract
The Epstein–Barr virus (EBV) is a ubiquitous γ herpesvirus strongly associated with nasopharyngeal carcinomas, and the viral oncogenicity in part relies on cellular effects of the viral latent membrane protein 1 (LMP1). It was previously described that EBV strains B95.8 and M81 differ in cell tropism and the activation of the lytic cycle. Nonetheless, it is unknown whether LMP1 from these strains have different effects when expressed in nasopharyngeal cells. Thus, herein we evaluated the effects of EBV LMP1 derived from viral strains B95.8 and M81 and expressed in immortalized nasopharyngeal cells NP69SV40T in the regulation of 91 selected cellular miRNAs. We found that cells expressing either LMP1 behave similarly in terms of NF-kB activation and cell migration. Nonetheless, the miRs 100-5p, 192-5p, and 574-3p were expressed at higher levels in cells expressing LMP1 B95.8 compared to M81. Additionally, results generated by in silico pathway enrichment analysis indicated that LMP1 M81 distinctly regulate genes involved in cell cycle (i.e., RB1), mRNA processing (i.e., NUP50), and mitochondrial biogenesis (i.e., ATF2). In conclusion, LMP1 M81 was found to distinctively regulate miRs 100-5p, 192-5p, and 574-3p, and the in silico analysis provided valuable clues to dissect the molecular effects of EBV LMP1 expressed in nasopharyngeal cells.
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90
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Hooper CM, Castleden IR, Tanz SK, Grasso SV, Millar AH. Subcellular Proteomics as a Unified Approach of Experimental Localizations and Computed Prediction Data for Arabidopsis and Crop Plants. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1346:67-89. [PMID: 35113396 DOI: 10.1007/978-3-030-80352-0_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In eukaryotic organisms, subcellular protein location is critical in defining protein function and understanding sub-functionalization of gene families. Some proteins have defined locations, whereas others have low specificity targeting and complex accumulation patterns. There is no single approach that can be considered entirely adequate for defining the in vivo location of all proteins. By combining evidence from different approaches, the strengths and weaknesses of different technologies can be estimated, and a location consensus can be built. The Subcellular Location of Proteins in Arabidopsis database ( http://suba.live/ ) combines experimental data sets that have been reported in the literature and is analyzing these data to provide useful tools for biologists to interpret their own data. Foremost among these tools is a consensus classifier (SUBAcon) that computes a proposed location for all proteins based on balancing the experimental evidence and predictions. Further tools analyze sets of proteins to define the abundance of cellular structures. Extending these types of resources to plant crop species has been complex due to polyploidy, gene family expansion and contraction, and the movement of pathways and processes within cells across the plant kingdom. The Crop Proteins of Annotated Location database ( http://crop-pal.org/ ) has developed a range of subcellular location resources including a species-specific voting consensus for 12 plant crop species that offers collated evidence and filters for current crop proteomes akin to SUBA. Comprehensive cross-species comparison of these data shows that the sub-cellular proteomes (subcellulomes) depend only to some degree on phylogenetic relationship and are more conserved in major biosynthesis than in metabolic pathways. Together SUBA and cropPAL created reference subcellulomes for plants as well as species-specific subcellulomes for cross-species data mining. These data collections are increasingly used by the research community to provide a subcellular protein location layer, inform models of compartmented cell function and protein-protein interaction network, guide future molecular crop breeding strategies, or simply answer a specific question-where is my protein of interest inside the cell?
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Affiliation(s)
- Cornelia M Hooper
- The Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, WA, Australia
| | - Ian R Castleden
- The Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, WA, Australia
| | - Sandra K Tanz
- The Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, WA, Australia
| | - Sally V Grasso
- The Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, WA, Australia
| | - A Harvey Millar
- The Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, WA, Australia.
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91
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Varga JK, Diffley K, Welker Leng KR, Fierke CA, Schueler-Furman O. Structure-based prediction of HDAC6 substrates validated by enzymatic assay reveals determinants of promiscuity and detects new potential substrates. Sci Rep 2022; 12:1788. [PMID: 35110592 PMCID: PMC8810773 DOI: 10.1038/s41598-022-05681-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 01/17/2022] [Indexed: 01/25/2023] Open
Abstract
Histone deacetylases play important biological roles well beyond the deacetylation of histone tails. In particular, HDAC6 is involved in multiple cellular processes such as apoptosis, cytoskeleton reorganization, and protein folding, affecting substrates such as ɑ-tubulin, Hsp90 and cortactin proteins. We have applied a biochemical enzymatic assay to measure the activity of HDAC6 on a set of candidate unlabeled peptides. These served for the calibration of a structure-based substrate prediction protocol, Rosetta FlexPepBind, previously used for the successful substrate prediction of HDAC8 and other enzymes. A proteome-wide screen of reported acetylation sites using our calibrated protocol together with the enzymatic assay provide new peptide substrates and avenues to novel potential functional regulatory roles of this promiscuous, multi-faceted enzyme. In particular, we propose novel regulatory roles of HDAC6 in tumorigenesis and cancer cell survival via the regulation of EGFR/Akt pathway activation. The calibration process and comparison of the results between HDAC6 and HDAC8 highlight structural differences that explain the established promiscuity of HDAC6.
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Affiliation(s)
- Julia K Varga
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of Jerusalem, Faculty of Medicine, POB 12272, 9112102, Jerusalem, Israel
| | - Kelsey Diffley
- Department of Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, MI, 48109, USA
| | - Katherine R Welker Leng
- Department of Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, MI, 48109, USA
| | - Carol A Fierke
- Department of Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, MI, 48109, USA
- Department of Biochemistry, Brandeis University, 415 South Street, Waltham, MA, 02453, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of Jerusalem, Faculty of Medicine, POB 12272, 9112102, Jerusalem, Israel.
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92
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Deciphering Biomarkers for Leptomeningeal Metastasis in Malignant Hemopathies (Lymphoma/Leukemia) Patients by Comprehensive Multipronged Proteomics Characterization of Cerebrospinal Fluid. Cancers (Basel) 2022; 14:cancers14020449. [PMID: 35053611 PMCID: PMC8773653 DOI: 10.3390/cancers14020449] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/04/2022] [Accepted: 01/06/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary The early diagnosis of leptomeningeal disease is a challenge because it is asymptomatic in the early stages. Consequently, it is important to identify a panel of biomarkers to help in its diagnosis and/or prognosis. For this purpose, we explored a multipronged proteomics approach in cerebrospinal fluid (CSF) to determine a potential panel of biomarkers. Thus, a systematic and exhaustive characterization of more than 300 CSF samples was performed by an integrated approach by Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) and functional proteomics analysis to establish protein profiles, which were useful for developing a panel of biomarkers validated by in silico approaches. Abstract In the present work, leptomeningeal disease, a very destructive form of systemic cancer, was characterized from several proteomics points of view. This pathology involves the invasion of the leptomeninges by malignant tumor cells. The tumor spreads to the central nervous system through the cerebrospinal fluid (CSF) and has a very grim prognosis; the average life expectancy of patients who suffer it does not exceed 3 months. The early diagnosis of leptomeningeal disease is a challenge because, in most of the cases, it is an asymptomatic pathology. When the symptoms are clear, the disease is already in the very advanced stages and life expectancy is low. Consequently, there is a pressing need to determine useful CSF proteins to help in the diagnosis and/or prognosis of this disease. For this purpose, a systematic and exhaustive proteomics characterization of CSF by multipronged proteomics approaches was performed to determine different protein profiles as potential biomarkers. Proteins such as PTPRC, SERPINC1, sCD44, sCD14, ANPEP, SPP1, FCGR1A, C9, sCD19, and sCD34, among others, and their functional analysis, reveals that most of them are linked to the pathology and are not detected on normal CSF. Finally, a panel of biomarkers was verified by a prediction model for leptomeningeal disease, showing new insights into the research for potential biomarkers that are easy to translate into the clinic for the diagnosis of this devastating disease.
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93
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Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO. Nat Methods 2022; 19:179-186. [PMID: 35027765 PMCID: PMC8828471 DOI: 10.1038/s41592-021-01343-9] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 11/05/2021] [Indexed: 01/04/2023]
Abstract
Factor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multimodal data, but enables the performance of spatio-temporally informed dimensionality reduction, interpolation, and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. To illustrate MEFISTO, we apply the model to different datasets with spatial or temporal resolution, including an evolutionary atlas of organ development, a longitudinal microbiome study, a single-cell multi-omics atlas of mouse gastrulation and spatially resolved transcriptomics. MEFISTO models bulk and single-cell multi-omics data with temporal or spatial dependencies for interpretable pattern discovery and integration.
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94
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Muscolino A, Di Maria A, Rapicavoli RV, Alaimo S, Bellomo L, Billeci F, Borzì S, Ferragina P, Ferro A, Pulvirenti A. NETME: on-the-fly knowledge network construction from biomedical literature. APPLIED NETWORK SCIENCE 2022; 7:1. [PMID: 35013714 PMCID: PMC8733431 DOI: 10.1007/s41109-021-00435-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 09/21/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling. RESULTS We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s41109-021-00435-x.
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Affiliation(s)
| | - Antonio Di Maria
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | | | - Salvatore Alaimo
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Lorenzo Bellomo
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Fabrizio Billeci
- Department of Maths and Computer Science, University of Catania, Catania, Italy
| | - Stefano Borzì
- Department of Maths and Computer Science, University of Catania, Catania, Italy
| | - Paolo Ferragina
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Alfredo Ferro
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Alfredo Pulvirenti
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
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95
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Yuan Y, Qu A. High-Order Joint Embedding for Multi-Level Link Prediction. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2021.2005608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Yubai Yuan
- Department of Statistics, University of California, Irvine, Irvine, CA
| | - Annie Qu
- Department of Statistics, University of California, Irvine, Irvine, CA
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96
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Gu Y, Guo Y, Gao N, Fang Y, Xu C, Hu G, Guo M, Ma Y, Zhang Y, Zhou J, Luo Y, Zhang H, Wen Q, Qiao H. The proteomic characterization of the peritumor microenvironment in human hepatocellular carcinoma. Oncogene 2022; 41:2480-2491. [PMID: 35314790 PMCID: PMC9033583 DOI: 10.1038/s41388-022-02264-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 02/17/2022] [Accepted: 02/23/2022] [Indexed: 01/29/2023]
Abstract
The tumor microenvironment (TME) was usually studied in tumor tissue and in relation to only tumor progression, with little involved in occurrence, recurrence and metastasis of tumor. Thus, a new concept "peritumor microenvironment (PME)" was proposed in the proteomic characterization of peritumor liver tissues in human hepatocellular carcinoma (HCC). The PME for occurrence (PME-O) and progression (PME-P) were almost totally different at proteome composition and function. Proteins for occurrence and progression rarely overlapped and crossed. Immunity played a central role in PME-O, whereas inflammation, angiogenesis and metabolism were critical in PME-P. Proteome profiling identified three PME subtypes with different features of HCC. Thymidine phosphorylase (TYMP) was validated as an antiangiogenic target in an orthotopic HCC mouse model. Overall, the proteomic characterization of the PME revealed that the entire processes of HCC occurrence and progression differ substantially. These findings could enable advances in cancer biology, diagnostics and therapeutics.
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Affiliation(s)
- Yuhan Gu
- grid.207374.50000 0001 2189 3846Institute of Clinical Pharmacology, Zhengzhou University, Zhengzhou, China
| | - Yuanyuan Guo
- grid.207374.50000 0001 2189 3846Institute of Clinical Pharmacology, Zhengzhou University, Zhengzhou, China ,grid.412633.10000 0004 1799 0733Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Na Gao
- grid.207374.50000 0001 2189 3846Institute of Clinical Pharmacology, Zhengzhou University, Zhengzhou, China
| | - Yan Fang
- grid.207374.50000 0001 2189 3846Institute of Clinical Pharmacology, Zhengzhou University, Zhengzhou, China
| | - Chen Xu
- grid.207374.50000 0001 2189 3846Institute of Clinical Pharmacology, Zhengzhou University, Zhengzhou, China
| | - Guiming Hu
- grid.207374.50000 0001 2189 3846Institute of Clinical Pharmacology, Zhengzhou University, Zhengzhou, China
| | - Mengxue Guo
- grid.207374.50000 0001 2189 3846Institute of Clinical Pharmacology, Zhengzhou University, Zhengzhou, China
| | - Yaxing Ma
- grid.207374.50000 0001 2189 3846Institute of Clinical Pharmacology, Zhengzhou University, Zhengzhou, China
| | - Yunfei Zhang
- grid.414008.90000 0004 1799 4638Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Jun Zhou
- grid.414011.10000 0004 1808 090XAffiliated People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanlin Luo
- grid.414008.90000 0004 1799 4638Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Haifeng Zhang
- grid.207374.50000 0001 2189 3846Institute of Clinical Pharmacology, Zhengzhou University, Zhengzhou, China
| | - Qiang Wen
- grid.207374.50000 0001 2189 3846Institute of Clinical Pharmacology, Zhengzhou University, Zhengzhou, China
| | - Hailing Qiao
- grid.207374.50000 0001 2189 3846Institute of Clinical Pharmacology, Zhengzhou University, Zhengzhou, China
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97
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Shukla R, Yadav AK, Sote WO, Junior MC, Singh TR. Systems biology and big data analytics. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00005-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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98
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Marchini M, Ashkin MR, Bellini M, Sun MMG, Workentine ML, Okuyan HM, Krawetz R, Beier F, Rolian C. A Na +/K + ATPase Pump Regulates Chondrocyte Differentiation and Bone Length Variation in Mice. Front Cell Dev Biol 2022; 9:708384. [PMID: 34970538 PMCID: PMC8712571 DOI: 10.3389/fcell.2021.708384] [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: 05/11/2021] [Accepted: 11/04/2021] [Indexed: 11/23/2022] Open
Abstract
The genetic and developmental mechanisms involved in limb formation are relatively well documented, but how these mechanisms are modulated by changes in chondrocyte physiology to produce differences in limb bone length remains unclear. Here, we used high throughput RNA sequencing (RNAseq) to probe the developmental genetic basis of variation in limb bone length in Longshanks, a mouse model of experimental evolution. We find that increased tibia length in Longshanks is associated with altered expression of a few key endochondral ossification genes such as Npr3, Dlk1, Sox9, and Sfrp1, as well reduced expression of Fxyd2, a facultative subunit of the cell membrane-bound Na+/K+ ATPase pump (NKA). Next, using murine tibia and cell cultures, we show a dynamic role for NKA in chondrocyte differentiation and in bone length regulation. Specifically, we show that pharmacological inhibition of NKA disrupts chondrocyte differentiation, by upregulating expression of mesenchymal stem cell markers (Prrx1, Serpina3n), downregulation of chondrogenesis marker Sox9, and altered expression of extracellular matrix genes (e.g., collagens) associated with proliferative and hypertrophic chondrocytes. Together, Longshanks and in vitro data suggest a broader developmental and evolutionary role of NKA in regulating limb length diversity.
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Affiliation(s)
- Marta Marchini
- Department of Anatomy and Cell Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada
| | - Mitchell R Ashkin
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Melina Bellini
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Margaret Man-Ger Sun
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Matthew Lloyd Workentine
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Hamza Malik Okuyan
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Roman Krawetz
- Department of Anatomy and Cell Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada
| | - Frank Beier
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Campbell Rolian
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada.,Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
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99
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Network Approaches for Precision Oncology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:199-213. [DOI: 10.1007/978-3-030-91836-1_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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100
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Sharaireh A, Tierney AL, Unwin RD. Global Proteomic Profiling of Embryonic Stem Cells Using iTRAQ Isobaric Tags with LC-MS/MS Quantification. Methods Mol Biol 2022; 2490:157-177. [PMID: 35486245 DOI: 10.1007/978-1-0716-2281-0_12] [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] [Indexed: 06/14/2023]
Abstract
In this methods chapter, we describe the use of isobaric tags for relative and absolute quantification (iTRAQ) for the differential expression analysis of global proteins between embryonic stem cell samples. This protocol describes how proteins are collected from cell culture, digested and prepared so that peptides are labeled with these isobaric tags. Labeled digests are pooled, fractionated offline, and quantified using liquid chromatography-mass spectrometry (LC-MS). This offline fractionation allows for a greater separation and thus increased identification/quantification of peptides. This combined method enables large-scale, deep penetration into the proteome of embryonic stem cells. During quantification, the relative intensities of label-derived reporter ions represent the relative amount of peptide in each sample. Using search algorithms that integrate the generated data for the identified and quantified peptides allows the relative quantification of proteins in the samples. The isobaric tags can be used in a 4 or 8 multiplexed manner; however, using an 8-plex experimental setup allows for the simultaneous analysis of biological and technical replicates within the same mass spectrometry run, thus minimizing experimental variation and increasing the confidence in any identified expression differences.
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Affiliation(s)
- Aseel Sharaireh
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
- Department of Conservative Dentistry, School of Dentistry, University of Jordan, Amman, Jordan.
| | - Anna L Tierney
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Richard D Unwin
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
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