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Vyshnavi AM H, Sankaran S, Namboori PK K, Venkidasamy B, Hirad AH, Alarfaj AA, Vinayagam R. In Silico Analysis of the Effect of Hydrastis canadensis on Controlling Breast Cancer. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1412. [PMID: 37629702 PMCID: PMC10456556 DOI: 10.3390/medicina59081412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/19/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023]
Abstract
Background and Objectives: Breast cancer is a significant type of cancer among women worldwide. Studies have reported the anti-carcinogenic activity of Hydrastis Canadensis (Goldenseal) in cancer cell lines. Hydrastis Canadensis could help eliminate toxic substances due to its anti-cancer, anti-inflammatory, and other properties. The design phase includes the identification of potential and effective molecules through modern computational techniques. Objective: This work aims to study Hydrastis Canadensis's effect in controlling hormone-independent breast cancer through in-silico analysis. Materials and Methods: The preliminary screening of reported phytochemicals includes biomolecular networking. Identifying functionally relevant phytochemicals and the respective target mutations/genes leads to selecting 3D proteins of the desired mutations being considered the target. Interaction studies have been conducted using docking. The kinetic and thermodynamic stability of complexes was studied through molecular dynamic simulation and MM-PBSA/GBSA analysis. Pharmacodynamic and pharmacokinetic features have been predicted. The mechanism-wise screening, functional enrichment, and interactional studies suggest that canadaline and Riboflavin effectively interact with the target proteins. Results: Hydrastis Canadensis has been identified as the effective formulation containing all these constituents. The phytoconstituents; Riboflavin and Canadensis showed good interaction with the targets of hormone-independent breast cancer. The complexes were found to be kinetically and thermodynamically stable. Conclusions: Hydrastis Canadensis has been identified as effective in controlling 'hormone-independent or basal-like breast cancer' followed by 'hormone-dependent breast cancer: Luminal A' and Luminal B.
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Affiliation(s)
- Hima Vyshnavi AM
- Computational Chemistry Group (CCG), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India;
| | - Sathianarayanan Sankaran
- Department of Pharmaceutical Chemistry, NGSM Institute of Pharmaceutical Sciences, Nitte (Deemed to be University), Deralakatte, Mangaluru 575018, India;
| | - Krishnan Namboori PK
- Computational Chemistry Group (CCG), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India;
| | - Baskar Venkidasamy
- Department of Oral & Maxillofacial Surgery, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai 600077, India;
| | - Abdurahman Hajinur Hirad
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia; (A.H.H.); (A.A.A.)
| | - Abdullah A. Alarfaj
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia; (A.H.H.); (A.A.A.)
| | - Ramachandran Vinayagam
- Department of Biotechnology, Institute of Biotechnology, College of Life and Applied Sciences, Yeungnam University, 280 Daehak-Ro, Gyeongsan 38541, Gyeongbuk, Republic of Korea
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Large-scale prediction of key dynamic interacting proteins in multiple cancers. Int J Biol Macromol 2022; 220:1124-1132. [PMID: 36027989 DOI: 10.1016/j.ijbiomac.2022.08.125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 08/15/2022] [Accepted: 08/17/2022] [Indexed: 11/21/2022]
Abstract
Tracking cancer dynamic protein-protein interactions (PPIs) and deciphering their pathogenesis remain a challenge. We presented a dynamic PPIs' hypothesis: permanent and transient interactions might achieve dynamic switchings from normal cells to malignancy, which could cause maintenance functions to be interrupted and transient functions to be sustained. Based on the hypothesis, we first predicted >1400 key cancer genes (KCG) by applying PPI-express we proposed to 18 cancer gene expression datasets. We then further screened out key dynamic interactions (KDI) of cancer based on KCG and transient and permanent interactions under both conditions. Two prominent functional characteristics, "Cell cycle-related" and "Immune-related", were presented for KCG, suggesting that these might be their general characteristics. We found that, compared to permanent to transient KDI pairs (P2T) in the network, transient to permanent (T2P) have significantly higher edge betweenness (EB), and P2T pairs tending to locate intra-functional modules may play roles in maintaining normal biological functions, while T2P KDI pairs tending to locate inter-modules may play roles in biological signal transduction. It was consistent with our hypothesis. Also, we analyzed network characteristics of KDI pairs and their functions. Our findings of KDI may serve to understand and explain a few hallmarks of cancer.
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Sandor C, Beer NL, Webber C. Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network. PLoS Comput Biol 2017; 13:e1005816. [PMID: 29059180 PMCID: PMC5667928 DOI: 10.1371/journal.pcbi.1005816] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2017] [Revised: 11/02/2017] [Accepted: 10/11/2017] [Indexed: 12/14/2022] Open
Abstract
Type 2 Diabetes (T2D) constitutes a global health burden. Efforts to uncover predisposing genetic variation have been considerable, yet detailed knowledge of the underlying pathogenesis remains poor. Here, we constructed a T2D phenotypic-linkage network (T2D-PLN), by integrating diverse gene functional information that highlight genes, which when disrupted in mice, elicit similar T2D-relevant phenotypes. Sensitising the network to T2D-relevant phenotypes enabled significant functional convergence to be detected between genes implicated in monogenic or syndromic diabetes and genes lying within genomic regions associated with T2D common risk. We extended these analyses to a recent multiethnic T2D case-control exome of 12,940 individuals that found no evidence of T2D risk association for rare frequency variants outside of previously known T2D risk loci. Examining associations involving protein-truncating variants (PTV), most at low population frequencies, the T2D-PLN was able to identify a convergent set of biological pathways that were perturbed within four of five independent T2D case/control ethnic sets of 2000 to 5000 exomes each. These same pathways were found to be over-represented among both known monogenic or syndromic diabetes genes and genes within T2D-associated common risk loci. Our study demonstrates convergent biology amongst variants representing different classes of T2D genetic risk. Although convergence was observed at the pathway level, few of the contributing genes were found in common between different cohorts or variant classes, most notably between the exome variant sets which suggests that future rare variant studies may be better focusing their power onto a single population of recent common ancestry.
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Affiliation(s)
- Cynthia Sandor
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Nicola L. Beer
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Caleb Webber
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
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An integrated network of Arabidopsis growth regulators and its use for gene prioritization. Sci Rep 2015; 5:17617. [PMID: 26620795 PMCID: PMC4664945 DOI: 10.1038/srep17617] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Accepted: 11/03/2015] [Indexed: 11/09/2022] Open
Abstract
Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses. In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms. The contributions of this work are twofold: first, we characterized a set of carefully selected growth regulators with respect to their connectivity patterns in the integrated network, and, subsequently, we explored to which extent these connectivity patterns can be used to suggest new growth regulators. Using a large-scale comparative study, we designed new supervised machine learning methods to prioritize growth regulators. Our results show that these methods significantly improve current state-of-the-art prioritization techniques, and are able to suggest meaningful new growth regulators. In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth.
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Network-based ranking methods for prediction of novel disease associated microRNAs. Comput Biol Chem 2015; 58:139-48. [DOI: 10.1016/j.compbiolchem.2015.07.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 06/25/2015] [Accepted: 07/09/2015] [Indexed: 12/18/2022]
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Lee PF, Soo VW. An ensemble rank learning approach for gene prioritization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:3507-10. [PMID: 24110485 DOI: 10.1109/embc.2013.6610298] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Several different computational approaches have been developed to solve the gene prioritization problem. We intend to use the ensemble boosting learning techniques to combine variant computational approaches for gene prioritization in order to improve the overall performance. In particular we add a heuristic weighting function to the Rankboost algorithm according to: 1) the absolute ranks generated by the adopted methods for a certain gene, and 2) the ranking relationship between all gene-pairs from each prioritization result. We select 13 known prostate cancer genes in OMIM database as training set and protein coding gene data in HGNC database as test set. We adopt the leave-one-out strategy for the ensemble rank boosting learning. The experimental results show that our ensemble learning approach outperforms the four gene-prioritization methods in ToppGene suite in the ranking results of the 13 known genes in terms of mean average precision, ROC and AUC measures.
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Abstract
Background We develop a new concept that reflects how genes are connected based on microarray data using the coefficient of determination (the squared Pearson correlation coefficient). Our gene rank combines a priori knowledge about gene connectivity, say, from the Gene Ontology (GO) database, and the microarray expression data at hand, called the microarray enriched gene rank, or simply gene rank (GR). GR, similarly to Google PageRank, is defined in a recursive fashion and is computed as the left maximum eigenvector of a stochastic matrix derived from microarray expression data. An efficient algorithm is devised that allows computation of GR for 50 thousand genes with 500 samples within minutes on a personal computer using the public domain statistical package R. Results Computation of GR is illustrated with several microarray data sets. In particular, we apply GR (1) to answer whether bad genes are more connected than good genes in relation with cancer patient survival, (2) to associate gene connectivity with cluster/subtypes in ovarian cancer tumors, and to determine whether gene connectivity changes (3) from organ to organ within the same organism and (4) between organisms. Conclusions We have shown by examples that findings based on GR confirm biological expectations. GR may be used for hypothesis generation on gene pathways. It may be used for a homogeneous sample or for comparison of gene connectivity among cases and controls, or in longitudinal setting.
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Affiliation(s)
- Eugene Demidenko
- Department of Biomedical Data Science, Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, 03755 NH USA
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Ono T, Kuhara S. A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms. BMC Bioinformatics 2014; 15:179. [PMID: 24917541 PMCID: PMC4068192 DOI: 10.1186/1471-2105-15-179] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Accepted: 06/02/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Understanding the molecular mechanisms involved in disease is critical for the development of more effective and individualized strategies for prevention and treatment. The amount of disease-related literature, including new genetic information on the molecular mechanisms of disease, is rapidly increasing. Extracting beneficial information from literature can be facilitated by computational methods such as the knowledge-discovery approach. Several methods for mining gene-disease relationships using computational methods have been developed, however, there has been a lack of research evaluating specific disease candidate genes. RESULTS We present a novel method for gathering and prioritizing specific disease candidate genes. Our approach involved the construction of a set of Medical Subject Headings (MeSH) terms for the effective retrieval of publications related to a disease candidate gene. Information regarding the relationships between genes and publications was obtained from the gene2pubmed database. The set of genes was prioritized using a "weighted literature score" based on the number of publications and weighted by the number of genes occurring in a publication. Using our method for the disease states of pain and Alzheimer's disease, a total of 1101 pain candidate genes and 2810 Alzheimer's disease candidate genes were gathered and prioritized. The precision was 0.30 and the recall was 0.89 in the case study of pain. The precision was 0.04 and the recall was 0.6 in the case study of Alzheimer's disease. The precision-recall curve indicated that the performance of our method was superior to that of other publicly available tools. CONCLUSIONS Our method, which involved the use of a set of MeSH terms related to disease candidate genes and a novel weighted literature score, improved the accuracy of gathering and prioritizing candidate genes by focusing on a specific disease.
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Affiliation(s)
| | - Satoru Kuhara
- Department of Genetic Resources Technology, Faculty of Agriculture, Kyushu University, 6-10-1 Hakozaki Higashi-ku, Fukuoka 812-8581, Japan.
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Abstract
Bioinformatics aids in the understanding of the biological processes of living beings and the genetic architecture of human diseases. The discovery of disease-related genes improves the diagnosis and therapy design for the disease. To save the cost and time involved in the experimental verification of the candidate genes, computational methods are employed for ranking the genes according to their likelihood of being associated with the disease. Only top-ranked genes are then verified experimentally. A variety of methods have been conceived by the researchers for the prioritization of the disease candidate genes, which differ in the data source being used or the scoring function used for ranking the genes. A review of various aspects of computational disease gene prioritization and its research issues is presented in this article. The aspects covered are gene prioritization process, data sources used, types of prioritization methods, and performance assessment methods. This article provides a brief overview and acts as a quick guide for disease gene prioritization.
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Affiliation(s)
- Nivit Gill
- 1 Punjabi University Regional Centre For IT and Management , Mohali, Punjab, India
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Jiao S, Chu Q, Wang Y, Xie Z, Hou S, Liu A, Wu H, Liu L, Geng F, Wang C, Qin C, Tan R, Huang X, Tan S, Wu M, Xu X, Liu X, Yu Y, Zhang Y. Identification of the causative gene for Simmental arachnomelia syndrome using a network-based disease gene prioritization approach. PLoS One 2013; 8:e64468. [PMID: 23696895 PMCID: PMC3655968 DOI: 10.1371/journal.pone.0064468] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2012] [Accepted: 04/15/2013] [Indexed: 12/12/2022] Open
Abstract
Arachnomelia syndrome (AS), mainly found in Brown Swiss and Simmental cattle, is a congenital lethal genetic malformation of the skeletal system. In this study, a network-based disease gene prioritization approach was implemented to rank genes in the previously reported ∼7 Mb region on chromosome 23 associated with AS in Simmental cattle. The top 6 ranked candidate genes were sequenced in four German Simmental bulls, one known AS-carrier ROMEL and a pooled sample of three known non-carriers (BOSSAG, RIFURT and HIRMER). Two suspicious mutations located in coding regions, a mis-sense mutation c.1303G>A in the bystin-like (BYSL) gene and a 2-bp deletion mutation c.1224_1225delCA in the molybdenum cofactor synthesis step 1 (MOCS1) gene were detected. Bioinformatic analysis revealed that the mutation in MOCS1 was more likely to be the causative mutation. Screening the c.1224_1225delCA site in 383 individuals from 12 cattle breeds/lines, we found that only the bull ROMEL and his 12 confirmed progeny carried the mutation. Thus, our results confirm the conclusion of Buitkamp et al. that the 2-bp deletion mutation c.1224_1225delCA in exon 11 of the MOCS1 gene is causative for AS in Simmental cattle. Furthermore, a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was developed to detect the causative mutation.
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Affiliation(s)
- Shihui Jiao
- Key Laboratory of Agricultural Animal and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Qin Chu
- Institute of Animal Husbandry and Veterinary Medicine, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Yachun Wang
- Key Laboratory of Agricultural Animal and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
- * E-mail:
| | - Zhenquan Xie
- Anshan Hengli Dairy Farm, Anshan, Liaoning, China
| | - Shiyu Hou
- Anshan Hengli Dairy Farm, Anshan, Liaoning, China
| | - Airong Liu
- Hailaer Farm Buro, Hailaer, Inner Mongolia, China
| | - Hongjun Wu
- Xiertala Breeding Farm, Hailaer Farm Buro, Hailaer, Inner Mongolia, China
| | - Lin Liu
- Beijing Dairy Cattle Centre, Beijing, China
| | - Fanjun Geng
- Dingyuan Seedstock Bulls Breeding Ltd. Company, Zhengzhou, Henan, China
| | - Congyong Wang
- Dingyuan Seedstock Bulls Breeding Ltd. Company, Zhengzhou, Henan, China
| | - Chunhua Qin
- Ningxia Sygen BioEngineering Research Center, Yinchuan, Ningxia, China
| | - Rui Tan
- Xinjiang General Livestock Service, Urumqi, Xinjiang, China
| | - Xixia Huang
- College of Animal Science, Xinjiang Agriculture University, Urumqi, Xinjiang, China
| | - Shixin Tan
- Xinjiang Tianshan Animal Husbandry Bio-engineering Co. Ltd, Urumqi, Xinjiang, China
| | - Meng Wu
- Dalian Xuelong Industry Limited Group, Dalian, Liaoning, China
| | - Xianzhou Xu
- Dalian Xuelong Industry Limited Group, Dalian, Liaoning, China
| | - Xuan Liu
- Key Laboratory of Agricultural Animal and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Ying Yu
- Key Laboratory of Agricultural Animal and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yuan Zhang
- Key Laboratory of Agricultural Animal and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
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Garaffo G, Provero P, Molineris I, Pinciroli P, Peano C, Battaglia C, Tomaiuolo D, Etzion T, Gothilf Y, Santoro M, Merlo GR. Profiling, Bioinformatic, and Functional Data on the Developing Olfactory/GnRH System Reveal Cellular and Molecular Pathways Essential for This Process and Potentially Relevant for the Kallmann Syndrome. Front Endocrinol (Lausanne) 2013; 4:203. [PMID: 24427155 PMCID: PMC3876029 DOI: 10.3389/fendo.2013.00203] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Accepted: 12/18/2013] [Indexed: 11/28/2022] Open
Abstract
During embryonic development, immature neurons in the olfactory epithelium (OE) extend axons through the nasal mesenchyme, to contact projection neurons in the olfactory bulb. Axon navigation is accompanied by migration of the GnRH+ neurons, which enter the anterior forebrain and home in the septo-hypothalamic area. This process can be interrupted at various points and lead to the onset of the Kallmann syndrome (KS), a disorder characterized by anosmia and central hypogonadotropic hypogonadism. Several genes has been identified in human and mice that cause KS or a KS-like phenotype. In mice a set of transcription factors appears to be required for olfactory connectivity and GnRH neuron migration; thus we explored the transcriptional network underlying this developmental process by profiling the OE and the adjacent mesenchyme at three embryonic ages. We also profiled the OE from embryos null for Dlx5, a homeogene that causes a KS-like phenotype when deleted. We identified 20 interesting genes belonging to the following categories: (1) transmembrane adhesion/receptor, (2) axon-glia interaction, (3) scaffold/adapter for signaling, (4) synaptic proteins. We tested some of them in zebrafish embryos: the depletion of five (of six) Dlx5 targets affected axonal extension and targeting, while three (of three) affected GnRH neuron position and neurite organization. Thus, we confirmed the importance of cell-cell and cell-matrix interactions and identified new molecules needed for olfactory connection and GnRH neuron migration. Using available and newly generated data, we predicted/prioritized putative KS-disease genes, by building conserved co-expression networks with all known disease genes in human and mouse. The results show the overall validity of approaches based on high-throughput data and predictive bioinformatics to identify genes potentially relevant for the molecular pathogenesis of KS. A number of candidate will be discussed, that should be tested in future mutation screens.
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Affiliation(s)
- Giulia Garaffo
- Department of Molecular Biotechnology and Health Science, University of Torino, Torino, Italy
| | - Paolo Provero
- Department of Molecular Biotechnology and Health Science, University of Torino, Torino, Italy
| | - Ivan Molineris
- Department of Molecular Biotechnology and Health Science, University of Torino, Torino, Italy
| | - Patrizia Pinciroli
- Department of Medical Biotechnology Translational Medicine (BIOMETRA), University of Milano, Milano, Italy
| | - Clelia Peano
- Institute of Biomedical Technology, National Research Council, ITB-CNR, Segrate, Italy
| | - Cristina Battaglia
- Department of Medical Biotechnology Translational Medicine (BIOMETRA), University of Milano, Milano, Italy
- Institute of Biomedical Technology, National Research Council, ITB-CNR, Segrate, Italy
| | - Daniela Tomaiuolo
- Department of Molecular Biotechnology and Health Science, University of Torino, Torino, Italy
| | - Talya Etzion
- The George S. Wise Faculty of Life Sciences, Department of Neurobiology, Tel-Aviv University, Tel-Aviv, Israel
| | - Yoav Gothilf
- The George S. Wise Faculty of Life Sciences, Department of Neurobiology, Tel-Aviv University, Tel-Aviv, Israel
| | - Massimo Santoro
- Department of Molecular Biotechnology and Health Science, University of Torino, Torino, Italy
| | - Giorgio R. Merlo
- Department of Molecular Biotechnology and Health Science, University of Torino, Torino, Italy
- *Correspondence: Giorgio R. Merlo, Department of Molecular Biotechnology and Health Science, University of Torino, Via Nizza 52, Torino 10126, Italy e-mail:
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