1
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Shouib R, Eitzen G. Inflammatory gene regulation by Cdc42 in airway epithelial cells. Cell Signal 2024; 122:111321. [PMID: 39067837 DOI: 10.1016/j.cellsig.2024.111321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 07/16/2024] [Accepted: 07/23/2024] [Indexed: 07/30/2024]
Abstract
Cytokine release from airway epithelial cells is a key immunological process that coordinates an immune response in the lungs. We propose that the Rho GTPase, Cdc42, regulates both transcription and trafficking of cytokines, ultimately affecting the essential process of cytokine release and subsequent inflammation in the lungs. Here, we examined the pro-inflammatory transcriptional profile that occurs in bronchial epithelial cells (BEAS-2B) in response to TNF-α using RNA-Seq and differential gene expression analysis. To interrogate the role of Cdc42 in inflammatory gene expression, we used a pharmacological inhibitor of Cdc42, ML141, and determined changes in the transcriptomic profile induced by Cdc42 inhibition. Our results indicated that Cdc42 inhibition with ML141 resulted in a unique inflammatory phenotype concomitant with increased gene expression of ER stress genes, Golgi membrane and vesicle transport genes. To further interrogate the inflammatory pathways regulated by Cdc42, we made BEAS-2B knockdown strains for the signaling targets TRIB3, DUSP5, SESN2 and BMP4, which showed high differential expression in response to Cdc42 inhibition. Depletion of DUSP5 and TRIB3 reduced the pro-inflammatory response triggered by Cdc42 inhibition as shown by a reduction in cytokine transcript levels. Depletion of SESN2 and BMP4 did not affect cytokine transcript level, however, Golgi fragmentation was reduced. These results provide further evidence that in airway epithelial cells, Cdc42 is part of a signaling network that controls inflammatory gene expression and secretion by regulating Golgi integrity. Summary sentence:We define the Cdc42-regulated gene networks for inflammatory signaling in airway epithelial cells which includes regulation of ER stress response and vesicle trafficking pathways.
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Affiliation(s)
- Rowayna Shouib
- Department of Cell Biology, University of Alberta, Edmonton, AB, Canada
| | - Gary Eitzen
- Department of Cell Biology, University of Alberta, Edmonton, AB, Canada.
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2
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Nguyen H, Pham VD, Nguyen H, Tran B, Petereit J, Nguyen T. CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome. Brief Bioinform 2024; 25:bbae222. [PMID: 39041916 PMCID: PMC11264295 DOI: 10.1093/bib/bbae222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/15/2024] [Accepted: 04/25/2024] [Indexed: 07/24/2024] Open
Abstract
This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.
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Affiliation(s)
- Ha Nguyen
- Department of Computer Science and Software Engineering, Auburn University, AL 36849, USA
| | - Van-Dung Pham
- Department of Computer Science and Software Engineering, Auburn University, AL 36849, USA
| | - Hung Nguyen
- Department of Computer Science and Software Engineering, Auburn University, AL 36849, USA
| | - Bang Tran
- Department of Computer Science, California State University, Sacramento, CA 95819, USA
| | - Juli Petereit
- Nevada Bioinformatics Center, University of Nevada, Reno, NV 89557, USA
| | - Tin Nguyen
- Department of Computer Science and Software Engineering, Auburn University, AL 36849, USA
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3
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Utjés D, Boggavarapu NR, Rasul MF, Koberg I, Zulliger A, Ponandai-Srinivasan S, von Grothusen C, Lalitkumar PG, Papaikonomou K, Alkasalias T, Gemzell-Danielsson K. Transcriptomic Profile of Breast Tissue of Premenopausal Women Following Treatment with Progesterone Receptor Modulator: Secondary Outcomes of a Randomized Controlled Trial. Int J Mol Sci 2024; 25:7590. [PMID: 39062832 PMCID: PMC11277027 DOI: 10.3390/ijms25147590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 06/19/2024] [Accepted: 06/22/2024] [Indexed: 07/28/2024] Open
Abstract
Progesterone receptor antagonism is gaining attention due to progesterone's recognized role as a major mitogen in breast tissue. Limited but promising data suggest the potential efficacy of antiprogestins in breast cancer prevention. The present study presents secondary outcomes from a randomized controlled trial and examines changes in breast mRNA expression following mifepristone treatment in healthy premenopausal women. We analyzed 32 paired breast biopsies from 16 women at baseline and after two months of mifepristone treatment. In total, 27 differentially expressed genes were identified, with enriched biological functions related to extracellular matrix remodeling. Notably, the altered gene signature induced by mifepristone in vivo was rather similar to the in vitro signature. Furthermore, this gene expression signature was linked to breast carcinogenesis and notably linked with progesterone receptor expression status in breast cancer, as validated in The Cancer Genome Atlas dataset using the R2 platform. The present study is the first to explore the breast transcriptome following mifepristone treatment in normal breast tissue in vivo, enhancing the understanding of progesterone receptor antagonism and its potential protective effect against breast cancer.
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Affiliation(s)
- Deborah Utjés
- Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; (D.U.); (N.R.B.); (M.F.R.); (I.K.); (A.Z.); (S.P.-S.); (C.v.G.); (P.G.L.); (K.P.); (K.G.-D.)
- Department of Gynecology and Reproductive Medicine, Karolinska University Hospital, 141 57 Stockholm, Sweden
| | - Nageswara Rao Boggavarapu
- Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; (D.U.); (N.R.B.); (M.F.R.); (I.K.); (A.Z.); (S.P.-S.); (C.v.G.); (P.G.L.); (K.P.); (K.G.-D.)
| | - Mohammed Fatih Rasul
- Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; (D.U.); (N.R.B.); (M.F.R.); (I.K.); (A.Z.); (S.P.-S.); (C.v.G.); (P.G.L.); (K.P.); (K.G.-D.)
- Department of Pharmaceutical Basic Science, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
| | - Isabelle Koberg
- Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; (D.U.); (N.R.B.); (M.F.R.); (I.K.); (A.Z.); (S.P.-S.); (C.v.G.); (P.G.L.); (K.P.); (K.G.-D.)
| | - Alexander Zulliger
- Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; (D.U.); (N.R.B.); (M.F.R.); (I.K.); (A.Z.); (S.P.-S.); (C.v.G.); (P.G.L.); (K.P.); (K.G.-D.)
| | - Sakthivignesh Ponandai-Srinivasan
- Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; (D.U.); (N.R.B.); (M.F.R.); (I.K.); (A.Z.); (S.P.-S.); (C.v.G.); (P.G.L.); (K.P.); (K.G.-D.)
| | - Carolina von Grothusen
- Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; (D.U.); (N.R.B.); (M.F.R.); (I.K.); (A.Z.); (S.P.-S.); (C.v.G.); (P.G.L.); (K.P.); (K.G.-D.)
| | - Parameswaran Grace Lalitkumar
- Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; (D.U.); (N.R.B.); (M.F.R.); (I.K.); (A.Z.); (S.P.-S.); (C.v.G.); (P.G.L.); (K.P.); (K.G.-D.)
| | - Kiriaki Papaikonomou
- Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; (D.U.); (N.R.B.); (M.F.R.); (I.K.); (A.Z.); (S.P.-S.); (C.v.G.); (P.G.L.); (K.P.); (K.G.-D.)
- Department of Gynecology and Reproductive Medicine, Karolinska University Hospital, 141 57 Stockholm, Sweden
| | - Twana Alkasalias
- Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; (D.U.); (N.R.B.); (M.F.R.); (I.K.); (A.Z.); (S.P.-S.); (C.v.G.); (P.G.L.); (K.P.); (K.G.-D.)
- General Directorate of Scientific Research Center, Salahaddin University-Erbil, Erbil 44001, Iraq
| | - Kristina Gemzell-Danielsson
- Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden; (D.U.); (N.R.B.); (M.F.R.); (I.K.); (A.Z.); (S.P.-S.); (C.v.G.); (P.G.L.); (K.P.); (K.G.-D.)
- Department of Gynecology and Reproductive Medicine, Karolinska University Hospital, 141 57 Stockholm, Sweden
- WHO Collaborating Centre, Division of Gynecology and Reproduction, Karolinska University Hospital, 171 76 Stockholm, Sweden
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Muthuramalingam P, Jeyasri R, Varadharajan V, Priya A, Dhanapal AR, Shin H, Thiruvengadam M, Ramesh M, Krishnan M, Omosimua RO, Sathyaseelan DD, Venkidasamy B. Network pharmacology: an efficient but underutilized approach in oral, head and neck cancer therapy-a review. Front Pharmacol 2024; 15:1410942. [PMID: 39035991 PMCID: PMC11257993 DOI: 10.3389/fphar.2024.1410942] [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: 04/02/2024] [Accepted: 06/05/2024] [Indexed: 07/23/2024] Open
Abstract
The application of network pharmacology (NP) has advanced our understanding of the complex molecular mechanisms underlying diseases, including neck, head, and oral cancers, as well as thyroid carcinoma. This review aimed to explore the therapeutic potential of natural network pharmacology using compounds and traditional Chinese medicines for combating these malignancies. NP serves as a pivotal tool that provides a comprehensive view of the interactions among compounds, genes, and diseases, thereby contributing to the advancement of disease treatment and management. In parallel, this review discusses the significance of publicly accessible databases in the identification of oral, head, and neck cancer-specific genes. These databases, including those for head and neck oral cancer, head and neck cancer, oral cancer, and genomic variants of oral cancer, offer valuable insights into the genes, miRNAs, drugs, and genetic variations associated with these cancers. They serve as indispensable resources for researchers, clinicians, and drug developers, contributing to the pursuit of precision medicine and improved treatment of these challenging malignancies. In summary, advancements in NP could improve the globalization and modernization of traditional medicines and prognostic targets as well as aid in the development of innovative drugs. Furthermore, this review will be an eye-opener for researchers working on drug development from traditional medicines by applying NP approaches.
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Affiliation(s)
- Pandiyan Muthuramalingam
- Division of Horticultural Science, College of Agriculture and Life Sciences, Gyeongsang National University, Jinju, Republic of Korea
| | - Rajendran Jeyasri
- Department of Biotechnology, Science Campus, Alagappa University, Karaikudi, India
| | | | - Arumugam Priya
- Department of Medicine, Division of Gastroenterology and Hepatology, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Anand Raj Dhanapal
- Chemistry and Bioprospecting Division, Institute of Forest Genetics and Tree Breeding (IFGTB), Coimbatore, India
| | - Hyunsuk Shin
- Division of Horticultural Science, College of Agriculture and Life Sciences, Gyeongsang National University, Jinju, Republic of Korea
| | - Muthu Thiruvengadam
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, Republic of Korea
| | - Manikandan Ramesh
- Department of Biotechnology, Science Campus, Alagappa University, Karaikudi, India
| | - Murugesan Krishnan
- Department of Oral and Maxillofacial Surgery, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India
| | | | - Divyan Devasir Sathyaseelan
- Department of General Surgery, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India
| | - Baskar Venkidasamy
- Department of Oral and Maxillofacial Surgery, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India
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5
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Wang Y, Liu M, Jafari M, Tang J. A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery. Front Pharmacol 2024; 15:1303693. [PMID: 38738181 PMCID: PMC11082401 DOI: 10.3389/fphar.2024.1303693] [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: 10/01/2023] [Accepted: 04/15/2024] [Indexed: 05/14/2024] Open
Abstract
Traditional Chinese Medicine (TCM) has been used for thousands of years to treat human diseases. Recently, many databases have been devoted to studying TCM pharmacology. Most of these databases include information about the active ingredients of TCM herbs and their disease indications. These databases enable researchers to interrogate the mechanisms of action of TCM systematically. However, there is a need for comparative studies of these databases, as they are derived from various resources with different data processing methods. In this review, we provide a comprehensive analysis of the existing TCM databases. We found that the information complements each other by comparing herbs, ingredients, and herb-ingredient pairs in these databases. Therefore, data harmonization is vital to use all the available information fully. Moreover, different TCM databases may contain various annotation types for herbs or ingredients, notably for the chemical structure of ingredients, making it challenging to integrate data from them. We also highlight the latest TCM databases on symptoms or gene expressions, suggesting that using multi-omics data and advanced bioinformatics approaches may provide new insights for drug discovery in TCM. In summary, such a comparative study would help improve the understanding of data complexity that may ultimately motivate more efficient and more standardized strategies towards the digitalization of TCM.
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Affiliation(s)
- Yinyin Wang
- School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Minxia Liu
- Faculty of Life Science, Anhui Medical University, Hefei, China
| | - Mohieddin Jafari
- Department Biochemistry and Developmental Biology, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Department Biochemistry and Developmental Biology, University of Helsinki, Helsinki, Finland
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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6
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Siddharth T, Lewis NE. Predicting pathways for old and new metabolites through clustering. J Theor Biol 2024; 578:111684. [PMID: 38048983 PMCID: PMC11139542 DOI: 10.1016/j.jtbi.2023.111684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 12/06/2023]
Abstract
The diverse metabolic pathways are fundamental to all living organisms, as they harvest energy, synthesize biomass components, produce molecules to interact with the microenvironment, and neutralize toxins. While the discovery of new metabolites and pathways continues, the prediction of pathways for new metabolites can be challenging. It can take vast amounts of time to elucidate pathways for new metabolites; thus, according to HMDB (Human Metabolome Database), only 60% of metabolites get assigned to pathways. Here, we present an approach to identify pathways based on metabolite structure. We extracted 201 features from SMILES annotations and identified new metabolites from PubMed abstracts and HMDB. After applying clustering algorithms to both groups of features, we quantified correlations between metabolites, and found the clusters accurately linked 92% of known metabolites to their respective pathways. Thus, this approach could be valuable for predicting metabolic pathways for new metabolites.
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Affiliation(s)
- Thiru Siddharth
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Bhopal, MP 462003, India
| | - Nathan E Lewis
- Department of Pediatrics and Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.
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7
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Huckvale ED, Powell CD, Jin H, Moseley HNB. Benchmark Dataset for Training Machine Learning Models to Predict the Pathway Involvement of Metabolites. Metabolites 2023; 13:1120. [PMID: 37999216 PMCID: PMC10673125 DOI: 10.3390/metabo13111120] [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: 10/10/2023] [Revised: 10/25/2023] [Accepted: 10/30/2023] [Indexed: 11/25/2023] Open
Abstract
Metabolic pathways are a human-defined grouping of life sustaining biochemical reactions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic interpretation. To address these shortcomings, various machine learning models, including those trained on data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), have been developed to predict the pathway involvement of metabolites based on their chemical descriptions; however, these prior models are based on old metabolite KEGG-based datasets, including one benchmark dataset that is invalid due to the presence of over 1500 duplicate entries. Therefore, we have developed a new benchmark dataset derived from the KEGG following optimal standards of scientific computational reproducibility and including all source code needed to update the benchmark dataset as KEGG changes. We have used this new benchmark dataset with our atom coloring methodology to develop and compare the performance of Random Forest, XGBoost, and multilayer perceptron with autoencoder models generated from our new benchmark dataset. Best overall weighted average performance across 1000 unique folds was an F1 score of 0.8180 and a Matthews correlation coefficient of 0.7933, which was provided by XGBoost binary classification models for 11 KEGG-defined pathway categories.
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Affiliation(s)
- Erik D. Huckvale
- Markey Cancer Center, University of Kentucky, Lexington, KY 40506, USA
- Superfund Research Center, University of Kentucky, Lexington, KY 40506, USA
| | - Christian D. Powell
- Markey Cancer Center, University of Kentucky, Lexington, KY 40506, USA
- Superfund Research Center, University of Kentucky, Lexington, KY 40506, USA
- Department of Computer Science (Data Science Program), University of Kentucky, Lexington, KY 40506, USA
| | - Huan Jin
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40536, USA
| | - Hunter N. B. Moseley
- Markey Cancer Center, University of Kentucky, Lexington, KY 40506, USA
- Superfund Research Center, University of Kentucky, Lexington, KY 40506, USA
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40536, USA
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40506, USA
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY 40506, USA
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8
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Zhao A, Varady S, O'Kelley-Bangsberg M, Deng V, Platenkamp A, Wijngaard P, Bern M, Gormley W, Kushkowski E, Thompson K, Tibbetts L, Conner AT, Noeckel D, Teran A, Ritz A, Applewhite DA. From network analysis to experimental validation: identification of regulators of non-muscle myosin II contractility using the folded-gastrulation signaling pathway. BMC Mol Cell Biol 2023; 24:32. [PMID: 37821823 PMCID: PMC10568788 DOI: 10.1186/s12860-023-00492-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/29/2023] [Indexed: 10/13/2023] Open
Abstract
The morphogenetic process of apical constriction, which relies on non-muscle myosin II (NMII) generated constriction of apical domains of epithelial cells, is key to the development of complex cellular patterns. Apical constriction occurs in almost all multicellular organisms, but one of the most well-characterized systems is the Folded-gastrulation (Fog)-induced apical constriction that occurs in Drosophila. The binding of Fog to its cognizant receptors Mist/Smog results in a signaling cascade that leads to the activation of NMII-generated contractility. Despite our knowledge of key molecular players involved in Fog signaling, we sought to explore whether other proteins have an undiscovered role in its regulation. We developed a computational method to predict unidentified candidate NMII regulators using a network of pairwise protein-protein interactions called an interactome. We first constructed a Drosophila interactome of over 500,000 protein-protein interactions from several databases that curate high-throughput experiments. Next, we implemented several graph-based algorithms that predicted 14 proteins potentially involved in Fog signaling. To test these candidates, we used RNAi depletion in combination with a cellular contractility assay in Drosophila S2R + cells, which respond to Fog by contracting in a stereotypical manner. Of the candidates we screened using this assay, two proteins, the serine/threonine phosphatase Flapwing and the putative guanylate kinase CG11811 were demonstrated to inhibit cellular contractility when depleted, suggestive of their roles as novel regulators of the Fog pathway.
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Affiliation(s)
- Andy Zhao
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Sophia Varady
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | | | - Vicki Deng
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Amy Platenkamp
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Petra Wijngaard
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Miriam Bern
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Wyatt Gormley
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Elaine Kushkowski
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Kat Thompson
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Logan Tibbetts
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - A Tamar Conner
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - David Noeckel
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Aidan Teran
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Anna Ritz
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA.
| | - Derek A Applewhite
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA.
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9
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Huckvale ED, Powell CD, Jin H, Moseley HN. Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.03.560715. [PMID: 37873272 PMCID: PMC10592640 DOI: 10.1101/2023.10.03.560715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Metabolic pathways are a human-defined grouping of life sustaining biochemical reactions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic interpretation. To address these shortcomings, various machine learning models, including those trained on data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), have been developed to predict the pathway involvement of metabolites based on their chemical descriptions; however, these prior models are based on old metabolite KEGG-based datasets, including one benchmark dataset that is invalid due to the presence of over 1500 duplicate entries. Therefore, we have developed a new benchmark dataset derived from the KEGG following optimal standards of scientific computational reproducibility and including all source code needed to update the benchmark dataset as KEGG changes. We have used this new benchmark dataset with our atom coloring methodology to develop and compare the performance of Random Forest, XGBoost, and multilayer perceptron with autoencoder models generated from our new benchmark dataset. Best overall weighted average performance across 1000 unique folds was an F1-score of 0.8180 and Matthews correlation coefficient of 0.7933, which was provided by XGBoost binary classification models for 11 KEGG-defined pathway categories.
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Affiliation(s)
- Erik D. Huckvale
- Department of Computer Science (Data Science Program), University of Kentucky, Lexington, KY 40506, USA
| | - Christian D. Powell
- Department of Computer Science (Data Science Program), University of Kentucky, Lexington, KY 40506, USA
- Markey Cancer Center, University of Kentucky, Lexington, KY 40506, USA
- Superfund Research Center, University of Kentucky, Lexington, KY 40506, USA
| | - Huan Jin
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40536, USA
| | - Hunter N.B. Moseley
- Markey Cancer Center, University of Kentucky, Lexington, KY 40506, USA
- Superfund Research Center, University of Kentucky, Lexington, KY 40506, USA
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40506, USA
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY 40506, USA
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10
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Sung HY, Lee S, Han M, An WJ, Ryu H, Kang E, Park YS, Lee SE, Ahn C, Oh KH, Park SK, Ahn JH. Epigenome-wide association study of diabetic chronic kidney disease progression in the Korean population: the KNOW-CKD study. Sci Rep 2023; 13:8175. [PMID: 37210443 DOI: 10.1038/s41598-023-35485-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/18/2023] [Indexed: 05/22/2023] Open
Abstract
Since the etiology of diabetic chronic kidney disease (CKD) is multifactorial, studies on DNA methylation for kidney function deterioration have rarely been performed despite the need for an epigenetic approach. Therefore, this study aimed to identify epigenetic markers associated with CKD progression based on the decline in the estimated glomerular filtration rate in diabetic CKD in Korea. An epigenome-wide association study was performed using whole blood samples from 180 CKD recruited from the KNOW-CKD cohort. Pyrosequencing was also performed on 133 CKD participants as an external replication analysis. Functional analyses, including the analysis of disease-gene networks, reactome pathways, and protein-protein interaction networks, were conducted to identify the biological mechanisms of CpG sites. A phenome-wide association study was performed to determine the associations between CpG sites and other phenotypes. Two epigenetic markers, cg10297223 on AGTR1 and cg02990553 on KRT28 indicated a potential association with diabetic CKD progression. Based on the functional analyses, other phenotypes (blood pressure and cardiac arrhythmia for AGTR1) and biological pathways (keratinization and cornified envelope for KRT28) related to CKD were also identified. This study suggests a potential association between the cg10297223 and cg02990553 and the progression of diabetic CKD in Koreans. Nevertheless, further validation is needed through additional studies.
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Affiliation(s)
- Hye Youn Sung
- Department of Biochemistry, Ewha Womans University College of Medicine, 25 Magokdong‑ro 2‑gil, Gangseo‑gu, Seoul, 07804, South Korea
| | - Sangjun Lee
- Department of Preventive Medicine, Seoul National University College of Medicine, 103, Daehak-ro, Jongro-gu, Seoul, 03080, South Korea
- Cancer Research Institute, Seoul National University, Seoul, South Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, South Korea
| | - Miyeun Han
- Department of Internal Medicine, National Medical Center, Seoul, South Korea
| | - Woo Ju An
- Department of Preventive Medicine, Seoul National University College of Medicine, 103, Daehak-ro, Jongro-gu, Seoul, 03080, South Korea
- Cancer Research Institute, Seoul National University, Seoul, South Korea
- Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyunjin Ryu
- Department of Internal Medicine, Seoul National University Hospital, 103, Daehak-ro, Jongro-gu, Seoul, 03080, Republic of Korea
| | - Eunjeong Kang
- Department of Internal Medicine, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, South Korea
| | - Yong Seek Park
- Department of Microbiology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Seung Eun Lee
- Department of Microbiology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Curie Ahn
- Department of Internal Medicine, National Medical Center, Seoul, South Korea
- Department of Internal Medicine, Seoul National University Hospital, 103, Daehak-ro, Jongro-gu, Seoul, 03080, Republic of Korea
| | - Kook-Hwan Oh
- Department of Internal Medicine, Seoul National University Hospital, 103, Daehak-ro, Jongro-gu, Seoul, 03080, Republic of Korea.
| | - Sue K Park
- Department of Preventive Medicine, Seoul National University College of Medicine, 103, Daehak-ro, Jongro-gu, Seoul, 03080, South Korea.
- Cancer Research Institute, Seoul National University, Seoul, South Korea.
- Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, South Korea.
| | - Jung-Hyuck Ahn
- Department of Biochemistry, Ewha Womans University College of Medicine, 25 Magokdong‑ro 2‑gil, Gangseo‑gu, Seoul, 07804, South Korea.
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11
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Wysocka M, Wysocki O, Zufferey M, Landers D, Freitas A. A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data. BMC Bioinformatics 2023; 24:198. [PMID: 37189058 PMCID: PMC10186658 DOI: 10.1186/s12859-023-05262-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 03/30/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings. METHODS This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. For this, we retrieved and analyzed 42 studies focusing on emerging architectural and methodological advances, the encoding of biological domain knowledge and the integration of explainability methods. RESULTS We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We introduce a concept of bio-centric interpretability and according to its taxonomy, we discuss representational methodologies for the integration of domain prior knowledge in such models. CONCLUSIONS The paper provides a critical outlook into contemporary methods for explainability and interpretability used in DL for cancer. The analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability. We introduce bio-centric interpretability which is an important step towards formalisation of biological interpretability of DL models and developing methods that are less problem- or application-specific.
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Affiliation(s)
- Magdalena Wysocka
- Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, University of Manchester, Oxford Rd, Manchester, M13 9 PL UK
- Department of Computer Science, University of Manchester, Oxford Rd, Manchester, M13 9 PL UK
| | - Oskar Wysocki
- Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, University of Manchester, Oxford Rd, Manchester, M13 9 PL UK
- Department of Computer Science, University of Manchester, Oxford Rd, Manchester, M13 9 PL UK
- Idiap Research Institute, National University of Sciences, Rue Marconi 19, CH - 1920 Martigny, Switzerland
| | - Marie Zufferey
- Idiap Research Institute, National University of Sciences, Rue Marconi 19, CH - 1920 Martigny, Switzerland
| | - Dónal Landers
- DeLondra Oncology Ltd, 38 Carlton Avenue, Wilmslow, SK9 4EP UK
| | - André Freitas
- Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, University of Manchester, Oxford Rd, Manchester, M13 9 PL UK
- Department of Computer Science, University of Manchester, Oxford Rd, Manchester, M13 9 PL UK
- Idiap Research Institute, National University of Sciences, Rue Marconi 19, CH - 1920 Martigny, Switzerland
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12
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Kwanten B, Deconick T, Walker C, Wang F, Landesman Y, Daelemans D. E3 ubiquitin ligase ASB8 promotes selinexor-induced proteasomal degradation of XPO1. Biomed Pharmacother 2023; 160:114305. [PMID: 36731340 DOI: 10.1016/j.biopha.2023.114305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 02/04/2023] Open
Abstract
Selinexor (KPT-330), a small-molecule inhibitor of exportin-1 (XPO1, CRM1) with potent anticancer activity, has recently been granted FDA approval for treatment of relapsed/refractory multiple myeloma and diffuse large B-cell lymphoma (DLBCL), with a number of additional indications currently under clinical investigation. Since selinexor has often demonstrated synergy when used in combination with other drugs, notably bortezomib and dexamethasone, a more comprehensive approach to uncover new beneficial interactions would be of great value. Moreover, stratifying patients, personalizing therapeutics and improving clinical outcomes requires a better understanding of the genetic vulnerabilities and resistance mechanisms underlying drug response. Here, we used CRISPR-Cas9 loss-of-function chemogenetic screening to identify drug-gene interactions with selinexor in chronic myeloid leukemia, multiple myeloma and DLBCL cell lines. We identified the TGFβ-SMAD4 pathway as an important mediator of resistance to selinexor in multiple myeloma cells. Moreover, higher activity of this pathway correlated with prolonged progression-free survival in multiple myeloma patients treated with selinexor, indicating that the TGFβ-SMAD4 pathway is a potential biomarker predictive of therapeutic outcome. In addition, we identified ASB8 (ankyrin repeat and SOCS box containing 8) as a shared modulator of selinexor sensitivity across all tested cancer types, with both ASB8 knockout and overexpression resulting in selinexor hypersensitivity. Mechanistically, we showed that ASB8 promotes selinexor-induced proteasomal degradation of XPO1. This study provides insight into the genetic factors that influence response to selinexor treatment and could support both the development of predictive biomarkers as well as new drug combinations.
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Affiliation(s)
- Bert Kwanten
- KU Leuven Department of Microbiology, Immunology and Transplantation, Laboratory of Virology and Chemotherapy (Rega Institute), Leuven, Belgium
| | - Tine Deconick
- KU Leuven Department of Microbiology, Immunology and Transplantation, Laboratory of Virology and Chemotherapy (Rega Institute), Leuven, Belgium
| | | | - Feng Wang
- Karyopharm Therapeutics, Newton, MA 02459, USA
| | | | - Dirk Daelemans
- KU Leuven Department of Microbiology, Immunology and Transplantation, Laboratory of Virology and Chemotherapy (Rega Institute), Leuven, Belgium.
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13
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Lyon A, Tripathi R, Meeks C, He D, Wu Y, Liu J, Wang C, Chen J, Zhu H, Mukherjee S, Ganguly S, Plattner R. ABL1/2 and DDR1 Drive MEKi Resistance in NRAS-Mutant Melanomas by Stabilizing RAF/MYC/ETS1 and Promoting RAF Homodimerization. Cancers (Basel) 2023; 15:954. [PMID: 36765910 PMCID: PMC9913232 DOI: 10.3390/cancers15030954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/06/2023] [Accepted: 01/11/2023] [Indexed: 02/05/2023] Open
Abstract
Melanomas harboring NRAS mutations are a particularly aggressive and deadly subtype. If patients cannot tolerate or the melanomas are insensitive to immune checkpoint blockade, there are no effective 2nd-line treatment options. Drugs targeting the RAF/MEK/ERK pathway, which are used for BRAF-mutant melanomas, do little to increase progression-free survival (PFS). Here, using both loss-of-function and gain-of-function approaches, we show that ABL1/2 and DDR1 are critical nodes during NRAS-mutant melanoma intrinsic and acquired MEK inhibitor (MEKi) resistance. In some acquired resistance cells, ABL1/2 and DDR1 cooperate to stabilize RAF proteins, activate ERK cytoplasmic and nuclear signaling, repress p27/KIP1 expression, and drive RAF homodimerization. In contrast, other acquired resistance cells depend solely on ABL1/2 for their survival, and are sensitive to highly specific allosteric ABL1/2 inhibitors, which prevent β-catenin nuclear localization and destabilize MYC and ETS1 in an ERK-independent manner. Significantly, targeting ABL1/2 and DDR1 with an FDA-approved anti-leukemic drug, reverses intrinsic MEKi resistance, delays acquisition of acquired resistance, and doubles the survival time in a NRAS-mutant mouse model. These data indicate that repurposing FDA-approved drugs targeting ABL1/2 and DDR1 may be a novel and effective strategy for treating patients with treatment-refractory NRAS-driven melanomas.
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Affiliation(s)
- Anastasia Lyon
- Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
| | - Rakshamani Tripathi
- Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
| | - Christina Meeks
- Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
| | - Daheng He
- Biostatistics and Bioinformatics Shared Resource Facility, College of Medicine, Markey Cancer Center, University of Kentucky, Lexington, KY 40508, USA
| | - Yuanyuan Wu
- Biostatistics and Bioinformatics Shared Resource Facility, College of Medicine, Markey Cancer Center, University of Kentucky, Lexington, KY 40508, USA
| | - Jinpeng Liu
- Biostatistics and Bioinformatics Shared Resource Facility, College of Medicine, Markey Cancer Center, University of Kentucky, Lexington, KY 40508, USA
| | - Chi Wang
- Biostatistics and Bioinformatics Shared Resource Facility, College of Medicine, Markey Cancer Center, University of Kentucky, Lexington, KY 40508, USA
| | - Jing Chen
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Haining Zhu
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Sujata Mukherjee
- Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
| | - Saptadwipa Ganguly
- Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
| | - Rina Plattner
- Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
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14
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Maghsoudi Z, Nguyen H, Tavakkoli A, Nguyen T. A comprehensive survey of the approaches for pathway analysis using multi-omics data integration. Brief Bioinform 2022; 23:6761962. [PMID: 36252928 PMCID: PMC9677478 DOI: 10.1093/bib/bbac435] [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: 04/28/2022] [Revised: 08/26/2022] [Accepted: 09/08/2022] [Indexed: 02/07/2023] Open
Abstract
Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.
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Affiliation(s)
- Zeynab Maghsoudi
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Ha Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Alireza Tavakkoli
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Tin Nguyen
- Corresponding author: Tin Nguyen, Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA. Tel.: +1-775-784-6619;
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15
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Xue S, Rogers LR, Zheng M, He J, Piermarocchi C, Mias GI. Applying differential network analysis to longitudinal gene expression in response to perturbations. Front Genet 2022; 13:1026487. [PMID: 36324501 PMCID: PMC9618823 DOI: 10.3389/fgene.2022.1026487] [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: 08/23/2022] [Accepted: 10/03/2022] [Indexed: 11/17/2022] Open
Abstract
Differential Network (DN) analysis is a method that has long been used to interpret changes in gene expression data and provide biological insights. The method identifies the rewiring of gene networks in response to external perturbations. Our study applies the DN method to the analysis of RNA-sequencing (RNA-seq) time series datasets. We focus on expression changes: (i) in saliva of a human subject after pneumococcal vaccination (PPSV23) and (ii) in primary B cells treated ex vivo with a monoclonal antibody drug (Rituximab). The DN method enabled us to identify the activation of biological pathways consistent with the mechanisms of action of the PPSV23 vaccine and target pathways of Rituximab. The community detection algorithm on the DN revealed clusters of genes characterized by collective temporal behavior. All saliva and some B cell DN communities showed characteristic time signatures, outlining a chronological order in pathway activation in response to the perturbation. Moreover, we identified early and delayed responses within network modules in the saliva dataset and three temporal patterns in the B cell data.
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Affiliation(s)
- Shuyue Xue
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - Lavida R.K. Rogers
- Department of Biological Sciences, University of the Virgin Islands, St Thomas, US Virgin Islands
| | - Minzhang Zheng
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - Jin He
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
| | - Carlo Piermarocchi
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
| | - George I. Mias
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
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16
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Sedivy-Haley K, Blimkie T, Falsafi R, Lee AHY, Hancock REW. A transcriptomic analysis of the effects of macrophage polarization and endotoxin tolerance on the response to Salmonella. PLoS One 2022; 17:e0276010. [PMID: 36240188 PMCID: PMC9565388 DOI: 10.1371/journal.pone.0276010] [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: 11/28/2021] [Accepted: 09/27/2022] [Indexed: 11/19/2022] Open
Abstract
Salmonella is an intracellular pathogen causing significant morbidity and mortality. Its ability to grow inside macrophages is important to virulence, and is dependent on the activation state of the macrophages. Classically activated M1 macrophages are non-permissive for Salmonella growth, while alternatively activated M2 macrophages are permissive for Salmonella growth. Here we showed that endotoxin-primed macrophages (MEP), such as those associated with sepsis, showed similar levels of Salmonella resistance to M1 macrophages after 2 hr of intracellular infection, but at the 4 hr and 24 hr time points were susceptible like M2 macrophages. To understand this mechanistically, transcriptomic sequencing, RNA-Seq, was performed. This showed that M1 and MEP macrophages that had not been exposed to Salmonella, demonstrated a process termed here as primed activation, in expressing relatively higher levels of particular anti-infective genes and pathways, including the JAK-STAT (Janus kinase-signal transducer and activator of transcription) pathway. In contrast, in M2 macrophages these genes and pathways were largely expressed only in response to infection. Conversely, in response to infection, M1 macrophages, but not MEP macrophages, modulated additional genes known to be associated with susceptibility to Salmonella infection, possibly contributing to the differences in resistance at later time points. Application of the JAK inhibitor Ruxolitinib before infection reduced resistance in M1 macrophages, supporting the importance of early JAK-STAT signalling in M1 resistance to Salmonella.
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Affiliation(s)
- Katharine Sedivy-Haley
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Travis Blimkie
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Reza Falsafi
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Amy Huei-Yi Lee
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Robert E W Hancock
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada
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17
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Zheng J, Qiu Y, Wu Z, Wang X, Jiang X. Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network. Front Immunol 2022; 13:944030. [PMID: 36105808 PMCID: PMC9464945 DOI: 10.3389/fimmu.2022.944030] [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: 05/14/2022] [Accepted: 08/12/2022] [Indexed: 11/19/2022] Open
Abstract
Glioblastoma multiforme (GBM) is the most malignant brain cancer with great heterogeneities in many aspects, such as prognosis, clinicopathological features, immune landscapes, and immunotherapeutic responses. Considering that gene interaction network is relatively stable in a healthy state but widely perturbed in cancers, we sought to explore the multidimensional heterogeneities of GBM through evaluating the degree of network perturbations. The gene interaction network perturbations of GBM samples (TCGA cohort) and normal samples (GTEx database) were characterized by edge perturbations, which were quantized through evaluating the change in relative gene expression value. An unsupervised consensus clustering analysis was performed to identify edge perturbation-based clusters of GBM samples. Results revealed that the edge perturbation of GBM samples was stronger than that of normal samples. Four edge perturbation-based clusters of GBM samples were identified and showed prominent heterogeneities in prognosis, clinicopathological features, somatic genomic alterations, immune landscapes, and immunotherapeutic responses. In addition, a sample-specific perturbation of gene interaction score (SPGIScore) was constructed based on the differently expressed genes (DEGs) among four clusters, and exhibited a robust ability to predict prognosis. In conclusion, the bioinformatics approach based on sample-specific edge perturbation in gene interaction network provided a new perspective to understanding the multidimensional heterogeneities of GBM.
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Affiliation(s)
- Jianglin Zheng
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yue Qiu
- Department of Otolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhipeng Wu
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xuan Wang
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Xuan Wang, ; Xiaobing Jiang,
| | - Xiaobing Jiang
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Xuan Wang, ; Xiaobing Jiang,
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18
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Alduais A, Almaghlouth S, Alfadda H, Qasem F. Biolinguistics: A Scientometric Analysis of Research on (Children's) Molecular Genetics of Speech and Language (Disorders). CHILDREN (BASEL, SWITZERLAND) 2022; 9:1300. [PMID: 36138610 PMCID: PMC9497240 DOI: 10.3390/children9091300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 08/13/2022] [Accepted: 08/23/2022] [Indexed: 11/21/2022]
Abstract
There are numerous children and adolescents throughout the world who are either diagnosed with speech and language disorders or manifest any of them as a result of another disorder. Meanwhile, since the emergence of language as an innate capability, the question of whether it constitutes a behaviour or an innate ability has been debated for decades. There have been several theories developed that support and demonstrate the biological foundations of human language. Molecular evidence of the biological basis of language came from the FOXP2 gene, also known as the language gene. Taking a closer look at both human language and biology, biolinguistics is at the core of these inquiries-attempting to understand the aetiologies of the genetics of speech and language disorders in children and adolescents. This paper presents empirical evidence based on both scientometrics and bibliometrics. We collected data between 1935 and 2022 from Scopus, WOS, and Lens. A total of 1570 documents were analysed from Scopus, 1440 from the WOS, and 5275 from Lens. Bibliometric analysis was performed using Excel based on generated reports from these three databases. CiteSpace 5.8.R3 and VOSviewer 1.6.18 were used to conduct the scientometric analysis. Eight bibliometric and eight scientometric indicators were used to measure the development of the field of biolinguistics, including but not limited to the production size of knowledge, the most examined topics, and the most frequent concepts and variables. A major finding of our study is identifying the most examined topics in the genetics of speech and language disorders. These included: gestural communication, structural design, cultural evolution, neural network, language tools, human language faculty, evolutionary biology, molecular biology, and theoretical perspective on language evolution.
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Affiliation(s)
- Ahmed Alduais
- Department of Human Sciences, University of Verona, 37129 Verona, Italy
| | - Shrouq Almaghlouth
- Department of English, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Hind Alfadda
- Department of Curriculum and Instruction, King Saud University, Riyadh 11362, Saudi Arabia
| | - Fawaz Qasem
- Department of English, University of Bisha, Al-Namas 67714, Saudi Arabia
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19
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Välikangas T, Junttila S, Rytkönen KT, Kukkonen-Macchi A, Suomi T, Elo LL. COVID-19-specific transcriptomic signature detectable in blood across multiple cohorts. Front Genet 2022; 13:929887. [PMID: 35991542 PMCID: PMC9388772 DOI: 10.3389/fgene.2022.929887] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/27/2022] [Indexed: 01/08/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels.
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Affiliation(s)
- Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Kalle T. Rytkönen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Anu Kukkonen-Macchi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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20
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Mekedem M, Ravel P, Colinge J. Application of modular response analysis to medium- to large-size biological systems. PLoS Comput Biol 2022; 18:e1009312. [PMID: 35442961 PMCID: PMC9060349 DOI: 10.1371/journal.pcbi.1009312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 05/02/2022] [Accepted: 03/31/2022] [Indexed: 11/18/2022] Open
Abstract
The development of high-throughput genomic technologies associated with recent genetic perturbation techniques such as short hairpin RNA (shRNA), gene trapping, or gene editing (CRISPR/Cas9) has made it possible to obtain large perturbation data sets. These data sets are invaluable sources of information regarding the function of genes, and they offer unique opportunities to reverse engineer gene regulatory networks in specific cell types. Modular response analysis (MRA) is a well-accepted mathematical modeling method that is precisely aimed at such network inference tasks, but its use has been limited to rather small biological systems so far. In this study, we show that MRA can be employed on large systems with almost 1,000 network components. In particular, we show that MRA performance surpasses general-purpose mutual information-based algorithms. Part of these competitive results was obtained by the application of a novel heuristic that pruned MRA-inferred interactions a posteriori. We also exploited a block structure in MRA linear algebra to parallelize large system resolutions.
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Affiliation(s)
- Meriem Mekedem
- Université de Montpellier, Montpellier, France
- Institut de Recherche en Cancérologie de Montpellier, Inserm U1194, Montpellier, France
- Institut régional du Cancer Montpellier, Montpellier, France
| | - Patrice Ravel
- Université de Montpellier, Montpellier, France
- Institut de Recherche en Cancérologie de Montpellier, Inserm U1194, Montpellier, France
- Institut régional du Cancer Montpellier, Montpellier, France
- Faculté de Pharmacie, Université de Montpellier, Montpellier, France
| | - Jacques Colinge
- Université de Montpellier, Montpellier, France
- Institut de Recherche en Cancérologie de Montpellier, Inserm U1194, Montpellier, France
- Institut régional du Cancer Montpellier, Montpellier, France
- Faculté de Médecine, Université de Montpellier, Montpellier, France
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21
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Wright AJ, Orlic-Milacic M, Rothfels K, Weiser J, Trinh QM, Jassal B, Haw RA, Stein LD. Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase. Database (Oxford) 2022; 2022:6555052. [PMID: 35348650 PMCID: PMC9216552 DOI: 10.1093/database/baac009] [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: 09/21/2021] [Revised: 01/04/2022] [Accepted: 02/15/2022] [Indexed: 11/14/2022]
Abstract
Abstract Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we compared predictions of perturbation effects based on Reactome pathways against published empirical observations. Ten cancer-relevant Reactome pathways, representing diverse biological processes such as signal transduction, cell division, DNA repair and transcriptional regulation, were selected for testing. For each pathway, root input nodes and key pathway outputs were defined. We then used pathway-diagram-derived logic graphs to predict, either by inspection by biocurators or using a novel algorithm MP-BioPath, the effects of bidirectional perturbations (upregulation/activation or downregulation/inhibition) of single root inputs on the status of key outputs. These predictions were then compared to published empirical tests. In total, 4968 test cases were analyzed across 10 pathways, of which 847 were supported by published empirical findings. Out of the 847 test cases, curators’ predictions agreed with the experimental evidence in 670 and disagreed in 177 cases, resulting in ∼81% overall accuracy. MP-BioPath predictions agreed with experimental evidence for 625 and disagreed for 222 test cases, resulting in ∼75% overall accuracy. The expected accuracy of random guessing was 33%. Per-pathway accuracy did not correlate with the number of pathway edges nor the number of pathway nodes but varied across pathways, ranging from 56% (curator)/44% (MP-BioPath) for ‘Mitotic G1 phase and G1/S transition’ to 100% (curator)/94% (MP-BioPath) for ‘RAF/MAP kinase cascade’. This study highlights the potential of pathway databases such as Reactome in modeling genetic perturbations, promoting standardization of experimental pathway activity readout and supporting hypothesis-driven research by revealing relationships between pathway inputs and outputs that have not yet been directly experimentally tested. Database URL www.reactome.org
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Affiliation(s)
- Adam J Wright
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Marija Orlic-Milacic
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Karen Rothfels
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Joel Weiser
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Quang M Trinh
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Bijay Jassal
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Robin A Haw
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Lincoln D Stein
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
- Department of Molecular Genetics, University of Toronto, Room 4396, Medical Sciences Building, 1 King’s College Circle, Toronto, ON M5S 1A1, Canada
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22
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Xiang J, Meng X, Zhao Y, Wu FX, Li M. HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure. Brief Bioinform 2022; 23:6547263. [PMID: 35275996 DOI: 10.1093/bib/bbac072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/18/2022] [Accepted: 02/13/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. RESULTS We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. CONCLUSIONS The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.
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Affiliation(s)
- Ju Xiang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China; Department of Basic Medical Sciences & Academician Workstation, Changsha Medical University, Changsha, Hunan 410219, China
| | - Xiangmao Meng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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23
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Cho JW, Shim HS, Lee CY, Park SY, Hong MH, Lee I, Kim HR. The importance of enhancer methylation for epigenetic regulation of tumorigenesis in squamous lung cancer. Exp Mol Med 2022; 54:12-22. [PMID: 34987166 PMCID: PMC8813945 DOI: 10.1038/s12276-021-00718-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 09/23/2021] [Accepted: 10/29/2021] [Indexed: 01/01/2023] Open
Abstract
Lung squamous cell carcinoma (LUSC) is a subtype of non-small cell lung cancer (NSCLC). LUSC occurs at the bronchi, shows a squamous appearance, and often occurs in smokers. To determine the epigenetic regulatory mechanisms of tumorigenesis, we performed a genome-wide analysis of DNA methylation in tumor and adjacent normal tissues from LUSC patients. With the Infinium Methylation EPIC Array, > 850,000 CpG sites, including ~350,000 CpG sites for enhancer regions, were profiled, and the differentially methylated regions (DMRs) overlapping promoters (pDMRs) and enhancers (eDMRs) between tumor and normal tissues were identified. Dimension reduction based on DMR profiles revealed that eDMRs alone and not pDMRs alone can differentiate tumors from normal tissues with the equivalent performance of total DMRs. We observed a stronger negative correlation of LUSC-specific gene expression with methylation for enhancers than promoters. Target genes of eDMRs rather than pDMRs were found to be enriched for tumor-associated genes and pathways. Furthermore, DMR methylation associated with immune infiltration was more frequently observed among enhancers than promoters. Our results suggest that methylation of enhancer regions rather than promoters play more important roles in epigenetic regulation of tumorigenesis and immune infiltration in LUSC.
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Affiliation(s)
- Jae-Won Cho
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Hyo Sup Shim
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Chang Young Lee
- Department of Thoracic and Cardiovascular Surgery, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Seong Yong Park
- Department of Thoracic and Cardiovascular Surgery, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Min Hee Hong
- Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea.
| | - Hye Ryun Kim
- Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
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24
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Xu Y, Wang J, Li F, Zhang C, Zheng X, Cao Y, Shang D, Hu C, Xu Y, Mi W, Li X, Cao Y, Zhang Y. Identifying individualized risk subpathways reveals pan-cancer molecular classification based on multi-omics data. Comput Struct Biotechnol J 2022; 20:838-849. [PMID: 35222843 PMCID: PMC8842010 DOI: 10.1016/j.csbj.2022.01.022] [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: 08/30/2021] [Revised: 01/18/2022] [Accepted: 01/18/2022] [Indexed: 11/24/2022] Open
Abstract
Cancer is a highly heterogeneous disease with different functional disorders among individuals. The initiation and progression of cancer is usually related to dysregulation of local regions within pathways. Identification of individualized risk pathways is crucial for revealing the mechanisms of tumorigenesis and heterogeneity. However, approach that focused on mining patient-specific risk subpathway regions is still lacking. Here, we developed an individualized cancer risk subpathway identification method that was referred as InCRiS by integrating multi-omics data. Then, the method was applied to nearly 3000 samples across 9 TCGA cancer types and its robustness and reliability were evaluated. Dissecting dysregulated subpathways in these tumor samples revealed several key regions which may play oncogenic roles in cancer. The construction of risk subpathway dysregulation profile of pan-cancers revealed 11 pan-cancer molecular classification (InCRiS subtypes) with significantly different clinical outcomes. Moreover, subpathway regions that tend to be disordered in individuals of specific subtypes were examined for understanding the pathogenesis of tumor and some key genes such as CTNNB1, EP300 and PRKDC were nominated in different subtypes. In summary, the proposed method and resulting data presented useful resources to study the mechanism of tumor heterogeneity and to discovery novel therapeutic targets for precise treatment of cancer.
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25
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Cai Z, Hu W, Wu R, Zheng S, Wu K. Bioinformatic analyses of hydroxylated polybrominated diphenyl ethers toxicities on impairment of adrenocortical secretory function. Environ Health Prev Med 2022; 27:38. [PMID: 36198577 PMCID: PMC9556975 DOI: 10.1265/ehpm.22-00023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 09/11/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Polybrominated diphenyl ethers (PBDEs) and their metabolites have severe impact on human health, but few studies focus on their nephrotoxicity. This study was conceived to explore hub genes that may be involved in two hydroxylated polybrominated diphenyl ethers toxicities on impairment of adrenocortical secretory function. METHODS Gene dataset was obtained from Gene Expression Omnibus (GEO). Principal component analysis and correlation analysis were used to classify the samples. Differentially expressed genes (DEGs) were screened using the limma package in RStudio (version 4.1.0). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome enrichment analyses of DEGs were conducted. Protein-protein interaction (PPI) network was established using STRING network, and genes were filtered by Cytoscape (version 3.8.2). Finally, the hub genes were integrated by plug-in CytoHubba and RobustRankAggreg, and were preliminarily verified by the Comparative Toxicogenomics Database (CTD). RESULTS GSE8588 dataset was selected in this study. About 190 upregulated and 224 downregulated DEGs in 2-OH-BDE47 group, and 244 upregulated and 276 downregulated DEGs in 2-OH-BDE85 group. Functional enrichment analyses in the GO, KEGG and Reactome indicated the potential involvement of DEGs in endocrine metabolism, oxidative stress mechanisms, regulation of abnormal cell proliferation, apoptosis, DNA damage and repair. 2-OH-BDE85 is more cytotoxic in a dose-dependent manner than 2-OH-BDE47. A total of 98 hub genes were filtered, and 91 nodes and 359 edges composed the PPI network. Besides, 9 direct-acting genes were filtered for the intersection of hub genes by CTD. CONCLUSIONS OH-PBDEs may induce H295R adrenocortical cancer cells in the disorder of endocrine metabolism, regulation of abnormal cell proliferation, DNA damage and repair. The screened hub genes may play an important role in this dysfunction.
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Affiliation(s)
- Zemin Cai
- Department of Preventive Medicine, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Wei Hu
- Chronic Disease Control Center of Shenzhen, Shenzhen 518020, Guangdong, China
| | - Ruotong Wu
- School of Life Science, Xiamen University, Xiamen 361102, Fujian, China
| | - Shukai Zheng
- Department of Preventive Medicine, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Kusheng Wu
- Department of Preventive Medicine, Shantou University Medical College, Shantou 515041, Guangdong, China
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26
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Lee S, Ono T, Aoki-Kinoshita K. RDFizing the biosynthetic pathway of E.coli O-antigen to enable semantic sharing of microbiology data. BMC Microbiol 2021; 21:325. [PMID: 34809564 PMCID: PMC8607589 DOI: 10.1186/s12866-021-02384-y] [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: 05/13/2021] [Accepted: 11/02/2021] [Indexed: 11/25/2022] Open
Abstract
Background The abundance of glycomics data that have accumulated has led to the development of many useful databases to aid in the understanding of the function of the glycans and their impact on cellular activity. At the same time, the endeavor for data sharing between glycomics databases with other biological databases have contributed to the creation of new knowledgebases. However, different data types in data description have impeded the data sharing for knowledge integration. To solve this matter, Semantic Web techniques including Resource Description Framework (RDF) and ontology development have been adopted by various groups to standardize the format for data exchange. These semantic data have contributed to the expansion of knowledgebases and hold promises of providing data that can be intelligently processed. On the other hand, bench biologists who are experts in experimental finding are end users and data producers. Therefore, it is indispensable to reduce the technical barrier required for bench biologists to manipulate their experimental data to be compatible with standard formats for data sharing. Results There are many essential concepts and practical techniques for data integration but there is no method to enable researchers to easily apply Semantic Web techniques to their experimental data. We implemented our procedure on unformatted information of E.coli O-antigen structures collected from the web and show how this information can be expressed as formatted data applicable to Semantic Web standards. In particular, we described the E-coli O-antigen biosynthesis pathway using the BioPAX ontology developed to support data exchange between pathway databases. Conclusions The method we implemented to semantically describe O-antigen biosynthesis should be helpful for biologists to understand how glycan information, including relevant pathway reaction data, can be easily shared. We hope this method can contribute to lower the technical barrier that is required when experimental findings are formulated into formal representations and can lead bench scientists to readily participate in the construction of new knowledgebases that are integrated with existing ones. Such integration over the Semantic Web will enable future work in artificial intelligence and machine learning to enable computers to infer new relationships and hypotheses in the life sciences. Supplementary Information The online version contains supplementary material available at 10.1186/s12866-021-02384-y.
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Affiliation(s)
- Sunmyoung Lee
- Graduate School of Engineering; Glycan and Life Systems Integration Center, Soka University, Hachioji, Tokyo, 192-8577, Japan
| | - Tamiko Ono
- Graduate School of Engineering; Glycan and Life Systems Integration Center, Soka University, Hachioji, Tokyo, 192-8577, Japan
| | - Kiyoko Aoki-Kinoshita
- Graduate School of Engineering; Glycan and Life Systems Integration Center, Soka University, Hachioji, Tokyo, 192-8577, Japan.
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27
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A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing. Nat Commun 2021; 12:6512. [PMID: 34764240 PMCID: PMC8586238 DOI: 10.1038/s41467-021-26788-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 09/23/2021] [Indexed: 12/20/2022] Open
Abstract
Recent studies have reported that genome editing by CRISPR–Cas9 induces a DNA damage response mediated by p53 in primary cells hampering their growth. This could lead to a selection of cells with pre-existing p53 mutations. In this study, employing an integrated computational and experimental framework, we systematically investigated the possibility of selection of additional cancer driver mutations during CRISPR-Cas9 gene editing. We first confirm the previous findings of the selection for pre-existing p53 mutations by CRISPR-Cas9. We next demonstrate that similar to p53, wildtype KRAS may also hamper the growth of Cas9-edited cells, potentially conferring a selective advantage to pre-existing KRAS-mutant cells. These selective effects are widespread, extending across cell-types and methods of CRISPR-Cas9 delivery and the strength of selection depends on the sgRNA sequence and the gene being edited. The selection for pre-existing p53 or KRAS mutations may confound CRISPR-Cas9 screens in cancer cells and more importantly, calls for monitoring patients undergoing CRISPR-Cas9-based editing for clinical therapeutics for pre-existing p53 and KRAS mutations. CRISPR-Cas9 gene editing can induce a p53 mediated damage response. Here the authors investigate the possibility of selection of pre-existing cancer driver mutations during CRISPR-Cas9 knockout based gene editing and identify KRAS mutants that may confer a selected advantage to edited cells.
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28
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Wang L, Xie W, Li K, Wang Z, Li X, Feng W, Li J. DysPIA: A Novel Dysregulated Pathway Identification Analysis Method. Front Genet 2021; 12:647653. [PMID: 34290733 PMCID: PMC8287415 DOI: 10.3389/fgene.2021.647653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 04/20/2021] [Indexed: 11/13/2022] Open
Abstract
Differential co-expression-based pathway analysis is still limited and not widely used. In most current methods, the pathways were considered as gene sets, but the gene regulation relationships were not considered, and the computational speed was slow. In this article, we proposed a novel Dysregulated Pathway Identification Analysis (DysPIA) method to overcome these shortcomings. We adopted the idea of Correlation by Individual Level Product into analysis and performed a fast enrichment analysis. We constructed a combined gene-pair background which was much more sufficient than the background used in Edge Set Enrichment Analysis. In simulation study, DysPIA was able to identify the causal pathways with high AUC (0.9584 to 0.9896). In p53 mutation data, DysPIA obtained better performance than other methods. It obtained more potential dysregulated pathways that could be literature verified, and it ran much faster (∼1,700-8,000 times faster than other methods when 10,000 permutations). DysPIA was also applied to breast cancer relapse dataset and breast cancer subtype dataset. The results show that DysPIA is effective and has a great biological significance. R packages "DysPIA" and "DysPIAData" are constructed and freely available on R CRAN (https://cran.r-project.org/web/packages/DysPIA/index.html and https://cran.r-project.org/web/packages/DysPIAData/index.html), and on GitHub (https://github.com/lemonwang2020).
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Affiliation(s)
- Limei Wang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.,Key Laboratory of Tropical Translational Medicine, Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China.,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Weixin Xie
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Kongning Li
- Key Laboratory of Tropical Translational Medicine, Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China
| | - Zhenzhen Wang
- Key Laboratory of Tropical Translational Medicine, Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China
| | - Xia Li
- Key Laboratory of Tropical Translational Medicine, Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China.,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Weixing Feng
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Jin Li
- Key Laboratory of Tropical Translational Medicine, Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China.,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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29
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Multiple allelic associations from genes involved in energy metabolism were identified in celiac disease. J Biosci 2021. [DOI: 10.1007/s12038-021-00184-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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30
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Mullegama SV, Klein SD, Williams SR, Innis JW, Probst FJ, Haldeman-Englert C, Martinez-Agosto JA, Yang Y, Tian Y, Elsea SH, Ezashi T. Transcriptome analysis of MBD5-associated neurodevelopmental disorder (MAND) neural progenitor cells reveals dysregulation of autism-associated genes. Sci Rep 2021; 11:11295. [PMID: 34050248 PMCID: PMC8163803 DOI: 10.1038/s41598-021-90798-z] [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: 06/24/2020] [Accepted: 04/20/2021] [Indexed: 11/30/2022] Open
Abstract
MBD5-associated neurodevelopmental disorder (MAND) is an autism spectrum disorder (ASD) characterized by intellectual disability, motor delay, speech impairment and behavioral problems; however, the biological role of methyl-CpG-binding domain 5, MBD5, in neurodevelopment and ASD remains largely undefined. Hence, we created neural progenitor cells (NPC) derived from individuals with chromosome 2q23.1 deletion and conducted RNA-seq to identify differentially expressed genes (DEGs) and the biological processes and pathways altered in MAND. Primary skin fibroblasts from three unrelated individuals with MAND and four unrelated controls were converted into induced pluripotent stem cell (iPSC) lines, followed by directed differentiation of iPSC to NPC. Transcriptome analysis of MAND NPC revealed 468 DEGs (q < 0.05), including 20 ASD-associated genes. Comparison of DEGs in MAND with SFARI syndromic autism genes revealed a striking significant overlap in biological processes commonly altered in neurodevelopmental phenotypes, with TGFβ, Hippo signaling, DNA replication, and cell cycle among the top enriched pathways. Overall, these transcriptome deviations provide potential connections to the overlapping neurocognitive and neuropsychiatric phenotypes associated with key high-risk ASD genes, including chromatin modifiers and epigenetic modulators, that play significant roles in these disease states.
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Affiliation(s)
- Sureni V Mullegama
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
- Department of Molecular and Cellular Biology, College of Osteopathic Medicine, Sam Houston State University, Conroe, TX, 77304, USA
| | - Steven D Klein
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Medical Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | | | - Jeffrey W Innis
- Departments of Human Genetics, Pediatrics and Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Frank J Probst
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | | | - Julian A Martinez-Agosto
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ying Yang
- Department of Molecular Pharmacology and Physiology, University of South Florida, Tampa, FL, 33612, USA
| | - Yuchen Tian
- Division of Animal Sciences and Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Sarah H Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Toshihiko Ezashi
- Division of Animal Sciences and Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA.
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Spatial Distribution of Private Gene Mutations in Clear Cell Renal Cell Carcinoma. Cancers (Basel) 2021; 13:cancers13092163. [PMID: 33946379 PMCID: PMC8124666 DOI: 10.3390/cancers13092163] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/02/2021] [Accepted: 04/27/2021] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Tumours consist of multiple groups of similar cells resulting from differing evolutionary trajectories, i.e., subclones. These subclones are prevalent in clear cell renal cell carcinoma (ccRCC). The aim of this study is to determine how similar or dissimilar the subclones in 89 ccRCC tumours are from one another regarding their gene mutations and expression profiles, i.e., the extent of intra-tumour heterogeneity. The implications of these alterations with respect to signalling pathways is also assessed. Deep sequencing allows for the identification of mutations with low-allele frequencies, providing a more comprehensive view of the heterogeneity present in the tumours. With an average of 62% of mutations having been identified in only one of the two biopsies, some of which in turn are found to impact gene expression, the complex makeup of ccRCC tumours is evident, and this can drastically influence treatment outcome. Abstract Intra-tumour heterogeneity is the molecular hallmark of renal cancer, and the molecular tumour composition determines the treatment outcome of renal cancer patients. In renal cancer tumourigenesis, in general, different tumour clones evolve over time. We analysed intra-tumour heterogeneity and subclonal mutation patterns in 178 tumour samples obtained from 89 clear cell renal cell carcinoma patients. In an initial discovery phase, whole-exome and transcriptome sequencing data from paired tumour biopsies from 16 ccRCC patients were used to design a gene panel for follow-up analysis. In this second phase, 826 selected genes were targeted at deep coverage in an extended cohort of 89 patients for a detailed analysis of tumour heterogeneity. On average, we found 22 mutations per patient. Pairwise comparison of the two biopsies from the same tumour revealed that on average, 62% of the mutations in a patient were detected in one of the two samples. In addition to commonly mutated genes (VHL, PBRM1, SETD2 and BAP1), frequent subclonal mutations with low variant allele frequency (<10%) were observed in TP53 and in mucin coding genes MUC6, MUC16, and MUC3A. Of the 89 ccRCC tumours, 87 (~98%) harboured private mutations, occurring in only one of the paired tumour samples. Clonally exclusive pathway pairs were identified using the WES data set from 16 ccRCC patients. Our findings imply that shared and private mutations significantly contribute to the complexity of differential gene expression and pathway interaction and might explain the clonal evolution of different molecular renal cancer subgroups. Multi-regional sequencing is central for the identification of subclones within ccRCC.
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Hughes RE, Elliott RJR, Dawson JC, Carragher NO. High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need. Cell Chem Biol 2021; 28:338-355. [PMID: 33740435 DOI: 10.1016/j.chembiol.2021.02.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/10/2020] [Accepted: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Conventional thinking in modern drug discovery postulates that the design of highly selective molecules which act on a single disease-associated target will yield safer and more effective drugs. However, high clinical attrition rates and the lack of progress in developing new effective treatments for many important diseases of unmet therapeutic need challenge this hypothesis. This assumption also impinges upon the efficiency of target agnostic phenotypic drug discovery strategies, where early target deconvolution is seen as a critical step to progress phenotypic hits. In this review we provide an overview of how emerging phenotypic and pathway-profiling technologies integrate to deconvolute the mechanism-of-action of phenotypic hits. We propose that such in-depth mechanistic profiling may support more efficient phenotypic drug discovery strategies that are designed to more appropriately address complex heterogeneous diseases of unmet need.
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Affiliation(s)
- Rebecca E Hughes
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Richard J R Elliott
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - John C Dawson
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK.
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Cui Y, Hunt A, Li Z, Birkin E, Lane J, Ruge F, Jiang WG. Lead DEAD/H box helicase biomarkers with the therapeutic potential identified by integrated bioinformatic approaches in lung cancer. Comput Struct Biotechnol J 2020; 19:261-278. [PMID: 33425256 PMCID: PMC7779375 DOI: 10.1016/j.csbj.2020.12.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 12/02/2020] [Accepted: 12/08/2020] [Indexed: 02/07/2023] Open
Abstract
DEAD/H box helicases are implicated in lung cancer but have not been systematically investigated for their clinical significance and function. In this study, we aimed to evaluate the potential of DEAD/H box helicases as prognostic biomarkers and therapeutic targets in lung cancer by integrated bioinformatic analysis of multivariate large-scale databases. Survival and differential expression analysis of these helicases enabled us to identify four biomarkers with the most significant alterations. These were found to be the negative prognostic factors DDX11, DDX55 and DDX56, and positive prognostic factor DDX5. Pathway enrichment analysis indicates that MYC signalling is negatively associated with expression levels of the DDX5 gene while positively associated with that of DDX11, DDX55 and DDX56. High expression levels of the DDX5 gene is associated with low mutation levels of TP53 and MUC16, the two most frequently mutated genes in lung cancer. In contrast, high expression levels of DDX11, DDX55 and DDX56 genes are associated with high levels of TP53 and MUC16 mutation. The tumour-infiltrated CD8 + T and B cells positively correlate with levels of DDX5 gene expression, while negatively correlate with that of the other three DEAD box helicases, respectively. Moreover, the DDX5-associated miRNA profile is distinguished from the miRNA profiles of DDX11, DDX55 and DDX56, although each DDX has a different miRNA signature. The identification of these four DDX helicases as biomarkers will be valuable for prognostic prediction and targeted therapeutic development in lung cancer.
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Affiliation(s)
- Yuxin Cui
- Cardiff China Research Collaborative, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Adam Hunt
- Cardiff China Research Collaborative, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Zhilei Li
- Department of Pharmacy, Zhujiang Hospital of Southern Medical University, Guangzhou 510282, PR China
| | - Emily Birkin
- Cardiff & Vale University Health Board, University Hospital of Wales, Heath Park, Cardiff CF14 4XW, UK
| | - Jane Lane
- Cardiff China Research Collaborative, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Fiona Ruge
- Cardiff China Research Collaborative, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Wen G Jiang
- Cardiff China Research Collaborative, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
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Baldwin MR, Pollack LR, Friedman RA, Norris SP, Javaid A, O'Donnell MR, Cummings MJ, Needham DM, Colantuoni E, Maurer MS, Lederer DJ. Frailty subtypes and recovery in older survivors of acute respiratory failure: a pilot study. Thorax 2020; 76:350-359. [PMID: 33298583 DOI: 10.1136/thoraxjnl-2020-214998] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 10/30/2020] [Accepted: 11/05/2020] [Indexed: 01/29/2023]
Abstract
BACKGROUND Identifying subtypes of acute respiratory failure survivors may facilitate patient selection for post-intensive care unit (ICU) follow-up clinics and trials. METHODS We conducted a single-centre prospective cohort study of 185 acute respiratory failure survivors, aged ≥ 65 years. We applied latent class modelling to identify frailty subtypes using frailty phenotype and cognitive impairment measurements made during the week before hospital discharge. We used Fine-Gray competing risks survival regression to test associations between frailty subtypes and recovery, defined as returning to a basic Activities of Daily Living disability count less than or equal to the pre-hospitalisation count within 6 months. We characterised subtypes by pre-ICU frailty (Clinical Frailty Scale score ≥ 5), the post-ICU frailty phenotype, and serum inflammatory cytokines, hormones and exosome proteomics during the week before hospital discharge. RESULTS We identified five frailty subtypes. The recovery rate decreased 49% across each subtype independent of age, sex, pre-existing disability, comorbidity and Acute Physiology and Chronic Health Evaluation II score (recovery rate ratio: 0.51, 95% CI 0.41 to 0.63). Post-ICU frailty phenotype prevalence increased across subtypes, but pre-ICU frailty prevalence did not. In the subtype with the slowest recovery, all had cognitive impairment. The three subtypes with the slowest recovery had higher interleukin-6 levels (p=0.03) and a higher prevalence of ≥ 2 deficiencies in insulin growth factor-1, dehydroepiandrostersone-sulfate, or free-testosterone (p=0.02). Exosome proteomics revealed impaired innate immunity in subtypes with slower recovery. CONCLUSIONS Frailty subtypes varied by prehospitalisation frailty and cognitive impairment at hospital discharge. Subtypes with the slowest recovery were similarly characterised by greater systemic inflammation and more anabolic hormone deficiencies at hospital discharge.
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Affiliation(s)
- Matthew R Baldwin
- Pulmonary, Allergy, and Critical Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Lauren R Pollack
- Medicine, University of Washington School of Medicine, Seattle, Washington, USA
| | - Richard A Friedman
- Bioinformatics, Columbia University Irving Medical Center, New York, New York, USA
| | - Simone P Norris
- Pulmonary, Allergy, and Critical Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Azka Javaid
- Pulmonary, Allergy, and Critical Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Max R O'Donnell
- Pulmonary, Allergy, and Critical Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Matthew J Cummings
- Pulmonary, Allergy, and Critical Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Dale M Needham
- Outcomes After Critical Illness and Surgery Group, Johns Hopkins University, Baltimore, Maryland, USA.,Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Elizabeth Colantuoni
- Outcomes After Critical Illness and Surgery Group, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Biostatistics, Johns Hopkins University-Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Mathew S Maurer
- Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - David J Lederer
- Pulmonary, Allergy, and Critical Care, Columbia University Irving Medical Center, New York, New York, USA.,Regeneron Pharmaceuticals, Tarrytown, New York, USA
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Canzler S, Hackermüller J. multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data. BMC Bioinformatics 2020; 21:561. [PMID: 33287694 PMCID: PMC7720482 DOI: 10.1186/s12859-020-03910-x] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 11/25/2020] [Indexed: 01/08/2023] Open
Abstract
Background Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well. In recent years the call for a combined analysis of multiple omics layers became prominent, giving rise to a few multi-omics enrichment tools. Each of these has its own drawbacks and restrictions regarding its universal application. Results Here, we present the multiGSEA package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layers. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. multiGSEA supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs. Conclusions With multiGSEA we introduce a highly versatile tool for multi-omics pathway integration that minimizes previous restrictions in terms of omics layer selection, pathway database availability, organism selection and the mapping of omics feature identifiers. multiGSEA is publicly available under the GPL-3 license at https://github.com/yigbt/multiGSEA and at bioconductor: https://bioconductor.org/packages/multiGSEA.
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Affiliation(s)
- Sebastian Canzler
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ, Permoserstraße 15, 04318, Leipzig, Germany.
| | - Jörg Hackermüller
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ, Permoserstraße 15, 04318, Leipzig, Germany
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Kandemir B, Gulfidan G, Arga KY, Yilmaz B, Kurnaz IA. Transcriptomic profile of Pea3 family members reveal regulatory codes for axon outgrowth and neuronal connection specificity. Sci Rep 2020; 10:18162. [PMID: 33097800 PMCID: PMC7584614 DOI: 10.1038/s41598-020-75089-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 09/29/2020] [Indexed: 12/13/2022] Open
Abstract
PEA3 transcription factor subfamily is present in a variety of tissues with branching morphogenesis, and play a particularly significant role in neural circuit formation and specificity. Many target genes in axon guidance and cell-cell adhesion pathways have been identified for Pea3 transcription factor (but not for Erm or Er81); however it was not so far clear whether all Pea3 subfamily members regulate same target genes, or whether there are unique targets for each subfamily member that help explain the exclusivity and specificity of these proteins in neuronal circuit formation. In this study, using transcriptomics and qPCR analyses in SH-SY5Y neuroblastoma cells, hypothalamic and hippocampal cell line, we have identified cell type-specific and subfamily member-specific targets for PEA3 transcription factor subfamily. While Pea3 upregulates transcription of Sema3D and represses Sema5B, for example, Erm and Er81 upregulate Sema5A and Er81 regulates Unc5C and Sema4G while repressing EFNB3 in SH-SY5Y neuroblastoma cells. We furthermore present a molecular model of how unique sites within the ETS domain of each family member can help recognize specific target motifs. Such cell-context and member-specific combinatorial expression profiles help identify cell-cell and cell-extracellular matrix communication networks and how they establish specific connections.
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Affiliation(s)
- Başak Kandemir
- Institute of Biotechnology, Gebze Technical University, Kocaeli, Turkey
- Biotechnology Graduate Program, Yeditepe University, Kayisdagi, Istanbul, Turkey
- Department of Molecular Biology and Genetics, Baskent University, Ankara, Turkey
| | - Gizem Gulfidan
- Department of Bioengineering, Marmara University, Goztepe, Istanbul, Turkey
| | - Kazim Yalcin Arga
- Department of Bioengineering, Marmara University, Goztepe, Istanbul, Turkey
| | - Bayram Yilmaz
- Biotechnology Graduate Program, Yeditepe University, Kayisdagi, Istanbul, Turkey
- Faculty of Medicine, Yeditepe University, Kayisdagi, Istanbul, Turkey
| | - Isil Aksan Kurnaz
- Institute of Biotechnology, Gebze Technical University, Kocaeli, Turkey.
- Department of Molecular Biology and Genetics, Gebze Technical University, Kocaeli, Turkey.
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Rahem SM, Epsi NJ, Coffman FD, Mitrofanova A. Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer. EBioMedicine 2020; 61:103047. [PMID: 33099086 PMCID: PMC7585053 DOI: 10.1016/j.ebiom.2020.103047] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 09/02/2020] [Accepted: 09/18/2020] [Indexed: 01/10/2023] Open
Abstract
Background Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes. Methods In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer. Findings We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients. Interpretation We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens. Funding This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.
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Affiliation(s)
- Sarra M Rahem
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA
| | - Nusrat J Epsi
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA
| | - Frederick D Coffman
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA; Department of Physician Assistant Studies and Practice, USA; Department of Pathology & Laboratory Medicine, New Jersey Medical School, Newark, New Jersey 07107, USA
| | - Antonina Mitrofanova
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, USA.
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Xu P, Wu Q, Lu D, Yu J, Rao Y, Kou Z, Fang G, Liu W, Han H. A systematic study of critical miRNAs on cells proliferation and apoptosis by the shortest path. BMC Bioinformatics 2020; 21:396. [PMID: 32894041 PMCID: PMC7487489 DOI: 10.1186/s12859-020-03732-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 09/01/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND MicroRNAs are a class of important small noncoding RNAs, which have been reported to be involved in the processes of tumorigenesis and development by targeting a few genes. Existing studies show that the imbalance between cell proliferation and apoptosis is closely related to the initiation and development of cancers. However, the impact of miRNAs on this imbalance has not been studied systematically. RESULTS In this study, we first construct a cell fate miRNA-gene regulatory network. Then, we propose a systematical method for calculating the global impact of miRNAs on cell fate genes based on the shortest path. Results on breast cancer and liver cancer datasets show that most of the cell fate genes are perturbed by the differentially expressed miRNAs. Most of the top-identified miRNAs are verified in the Human MicroRNA Disease Database (HMDD) and are related to breast and liver cancers. Function analysis shows that the top 20 miRNAs regulate multiple cell fate related function modules and interact tightly based on their functional similarity. Furthermore, more than half of them can promote sensitivity or induce resistance to some anti-cancer drugs. Besides, survival analysis demonstrates that the top-ranked miRNAs are significantly related to the overall survival time in the breast and liver cancers group. CONCLUSION In sum, this study can help to systematically study the important role of miRNAs on proliferation and apoptosis and thereby uncover the key miRNAs during the process of tumorigenesis. Furthermore, the results of this study will contribute to the development of clinical therapy based miRNAs for cancers.
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Affiliation(s)
- Peng Xu
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China.,School of computer science of information technology, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China
| | - Qian Wu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Deyang Lu
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Jian Yu
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Yongsheng Rao
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Zheng Kou
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Gang Fang
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Wenbin Liu
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China.
| | - Henry Han
- Department of Computer and Information Science, Fordham University, New York, NY, 10023, USA.
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Chen C, Weng J, Fang D, Chen J, Chen M. Transcriptomic study of lipopolysaccharide-induced sepsis damage in a mouse heart model. Exp Ther Med 2020; 20:3782-3790. [PMID: 32855727 PMCID: PMC7444370 DOI: 10.3892/etm.2020.9086] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 04/29/2020] [Indexed: 02/07/2023] Open
Abstract
Sepsis is an emergency systemic illness caused by pathogen infection and the combined result of the underactivity and overactivity of a patient's own immune system. However, the molecular mechanism of this illness remains largely unknown. Lipopolysaccharide (LPS) was injected to establish a sepsis model, and heart tissue was used to analyze transcriptome changes in mice. LPS injection was used to develop a sepsis model, which resulted in cardiac tissue rearrangement and inflammatory response activation. An RNA-sequencing-based transcriptome assay using mouse heart tissue with or without LPS injection showed that 3,326 and 1,769 genes were upregulated and downregulated, respectively (>2-fold changes; P<0.05). Furthermore, these differentially expressed genes were classified into 20 pathways, including ‘Wnt signaling pathway’, ‘VEGF signaling pathway’ and ‘TGF-β signaling pathway’, and these altered genes were enriched in 41 Gene Ontology terms. The application of Wnt3a inhibited the activation of the LPS-induced inflammatory response and activated Wnt signaling, as well as protecting against LPS-mediated cardiac tissue damage in mice. In contrast, inhibition of Wnt signaling by injection of its inhibitor IWR induced plasminogen activator inhibitor-1 expression and resulted in cardiac structure derangement, which was similar to the symptoms caused by injection of LPS, suggesting that LPS-induced damage to heart tissue may be via inhibition of Wnt signaling. The present analyses showed that Wnt signaling serves a pivotal role in sepsis development and may improve our understanding of the molecular basis underlying sepsis.
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Affiliation(s)
- Cunrong Chen
- Department of Critical Care Medicine, Union Hospital Affiliated to Fujian Medical University, Fuzhou, Fujian 350000, P.R. China
| | - Junting Weng
- Department of Critical Care Medicine, Affiliated Hospital of Putian University, Putian, Fujian 351100, P.R. China
| | - Dexiang Fang
- Department of Critical Care Medicine, Affiliated Hospital of Putian University, Putian, Fujian 351100, P.R. China
| | - Jianfei Chen
- Department of Critical Care Medicine, Affiliated Hospital of Putian University, Putian, Fujian 351100, P.R. China
| | - Min Chen
- Department of Critical Care Medicine, Affiliated Hospital of Putian University, Putian, Fujian 351100, P.R. China
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Selmansberger M, Michna A, Braselmann H, Höfig I, Schorpp K, Weber P, Anastasov N, Zitzelsberger H, Hess J, Unger K. Transcriptome network of the papillary thyroid carcinoma radiation marker CLIP2. Radiat Oncol 2020; 15:182. [PMID: 32727620 PMCID: PMC7392692 DOI: 10.1186/s13014-020-01620-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 07/15/2020] [Indexed: 11/29/2022] Open
Abstract
Background We present a functional gene association network of the CLIP2 gene, generated by de-novo reconstruction from transcriptomic microarray data. CLIP2 was previously identified as a potential marker for radiation induced papillary thyroid carcinoma (PTC) of young patients in the aftermath of the Chernobyl reactor accident. Considering the rising thyroid cancer incidence rates in western societies, potentially related to medical radiation exposure, the functional characterization of CLIP2 is of relevance and contributes to the knowledge about radiation-induced thyroid malignancies. Methods We generated a transcriptomic mRNA expression data set from a CLIP2-perturbed thyroid cancer cell line (TPC-1) with induced CLIP2 mRNA overexpression and siRNA knockdown, respectively, followed by gene-association network reconstruction using the partial correlation-based approach GeneNet. Furthermore, we investigated different approaches for prioritizing differentially expressed genes for network reconstruction and compared the resulting networks with existing functional interaction networks from the Reactome, Biogrid and STRING databases. The derived CLIP2 interaction partners were validated on transcript and protein level. Results The best reconstructed network with regard to selection parameters contained a set of 20 genes in the 1st neighborhood of CLIP2 and suggests involvement of CLIP2 in the biological processes DNA repair/maintenance, chromosomal instability, promotion of proliferation and metastasis. Peptidylprolyl Isomerase Like 3 (PPIL3), previously identified as a potential direct interaction partner of CLIP2, was confirmed in this study by co-expression at the transcript and protein level. Conclusion In our study we present an optimized preselection approach for genes subjected to gene-association network reconstruction, which was applied to CLIP2 perturbation transcriptome data of a thyroid cancer cell culture model. Our data support the potential carcinogenic role of CLIP2 overexpression in radiation-induced PTC and further suggest potential interaction partners of the gene.
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Affiliation(s)
- Martin Selmansberger
- Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany
| | - Agata Michna
- Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany
| | - Herbert Braselmann
- Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany
| | - Ines Höfig
- Institute of Radiation Biology, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany
| | - Kenji Schorpp
- Institute for Molecular Toxicology and Pharmacology, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany
| | - Peter Weber
- Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany
| | - Natasa Anastasov
- Institute of Radiation Biology, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany
| | - Horst Zitzelsberger
- Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany.,Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Clinical Cooperation Group 'Personalized Radiotherapy in Head and Neck Cancer', Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany
| | - Julia Hess
- Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany.,Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Clinical Cooperation Group 'Personalized Radiotherapy in Head and Neck Cancer', Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany
| | - Kristian Unger
- Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany. .,Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany. .,Clinical Cooperation Group 'Personalized Radiotherapy in Head and Neck Cancer', Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany.
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Friedrich M, Wiedemann K, Reiche K, Puppel SH, Pfeifer G, Zipfel I, Binder S, Köhl U, Müller GA, Engeland K, Aigner A, Füssel S, Fröhner M, Peitzsch C, Dubrovska A, Rade M, Christ S, Schreiber S, Hackermüller J, Lehmann J, Toma MI, Muders MH, Sommer U, Baretton GB, Wirth M, Horn F. The Role of lncRNAs TAPIR-1 and -2 as Diagnostic Markers and Potential Therapeutic Targets in Prostate Cancer. Cancers (Basel) 2020; 12:E1122. [PMID: 32365858 PMCID: PMC7280983 DOI: 10.3390/cancers12051122] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 04/13/2020] [Accepted: 04/21/2020] [Indexed: 01/17/2023] Open
Abstract
In search of new biomarkers suitable for the diagnosis and treatment of prostate cancer, genome-wide transcriptome sequencing was carried out with tissue specimens from 40 prostate cancer (PCa) and 8 benign prostate hyperplasia patients. We identified two intergenic long non-coding transcripts, located in close genomic proximity, which are highly expressed in PCa. Microarray studies on a larger cohort comprising 155 patients showed a profound diagnostic potential of these transcripts (AUC~0.94), which we designated as tumor associated prostate cancer increased lncRNA (TAPIR-1 and -2). To test their therapeutic potential, knockdown experiments with siRNA were carried out. The knockdown caused an increase in the p53/TP53 tumor suppressor protein level followed by downregulation of a large number of cell cycle- and DNA-damage repair key regulators. Furthermore, in radiation therapy resistant tumor cells, the knockdown leads to a renewed sensitization of these cells to radiation treatment. Accordingly, in a preclinical PCa xenograft model in mice, the systemic application of nanoparticles loaded with siRNA targeting TAPIR-1 significantly reduced tumor growth. These findings point to a crucial role of TAPIR-1 and -2 in PCa.
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Affiliation(s)
- Maik Friedrich
- Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Johannisallee 30, D-04103 Leipzig, Germany; (K.W.); (K.R.); (G.P.); (I.Z.); (S.B.); (U.K.); (F.H.)
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, RIBOLUTION Biomarker Center Perlickstr. 1, D-04103 Leipzig, Germany; (S.-H.P.); (M.R.); (S.C.)
| | - Karolin Wiedemann
- Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Johannisallee 30, D-04103 Leipzig, Germany; (K.W.); (K.R.); (G.P.); (I.Z.); (S.B.); (U.K.); (F.H.)
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, RIBOLUTION Biomarker Center Perlickstr. 1, D-04103 Leipzig, Germany; (S.-H.P.); (M.R.); (S.C.)
| | - Kristin Reiche
- Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Johannisallee 30, D-04103 Leipzig, Germany; (K.W.); (K.R.); (G.P.); (I.Z.); (S.B.); (U.K.); (F.H.)
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, RIBOLUTION Biomarker Center Perlickstr. 1, D-04103 Leipzig, Germany; (S.-H.P.); (M.R.); (S.C.)
| | - Sven-Holger Puppel
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, RIBOLUTION Biomarker Center Perlickstr. 1, D-04103 Leipzig, Germany; (S.-H.P.); (M.R.); (S.C.)
| | - Gabriele Pfeifer
- Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Johannisallee 30, D-04103 Leipzig, Germany; (K.W.); (K.R.); (G.P.); (I.Z.); (S.B.); (U.K.); (F.H.)
| | - Ivonne Zipfel
- Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Johannisallee 30, D-04103 Leipzig, Germany; (K.W.); (K.R.); (G.P.); (I.Z.); (S.B.); (U.K.); (F.H.)
| | - Stefanie Binder
- Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Johannisallee 30, D-04103 Leipzig, Germany; (K.W.); (K.R.); (G.P.); (I.Z.); (S.B.); (U.K.); (F.H.)
| | - Ulrike Köhl
- Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Johannisallee 30, D-04103 Leipzig, Germany; (K.W.); (K.R.); (G.P.); (I.Z.); (S.B.); (U.K.); (F.H.)
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, RIBOLUTION Biomarker Center Perlickstr. 1, D-04103 Leipzig, Germany; (S.-H.P.); (M.R.); (S.C.)
| | - Gerd A. Müller
- Molecular Oncology, Medical School University of Leipzig, Semmelweisstr. 14, D-04103 Leipzig, Germany; (G.A.M.); (K.E.)
- Department of Chemistry and Biochemistry, University of California at Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA
| | - Kurt Engeland
- Molecular Oncology, Medical School University of Leipzig, Semmelweisstr. 14, D-04103 Leipzig, Germany; (G.A.M.); (K.E.)
| | - Achim Aigner
- Clinical Pharmacology, Rudolf-Boehm-Institute for Pharmacology and Toxicology, Faculty of Medicine, Leipzig University, Härtelstr. 16–18, D-04107 Leipzig, Germany;
| | - Susanne Füssel
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Fetscherstr. 74, D-01307 Dresden, Germany; (S.F.); (M.F.); (M.W.)
| | - Michael Fröhner
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Fetscherstr. 74, D-01307 Dresden, Germany; (S.F.); (M.F.); (M.W.)
- Zeisigwaldklinik BETHANIEN, Zeisigwaldstraße 101, D-09130 Chemnitz, Germany
| | - Claudia Peitzsch
- National Center for Tumor Diseases (NCT), Partner Site Dresden, German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany;
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, D-01307 Dresden, Germany;
- German Cancer Consortium (DKTK), Partner Site Dresden, German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany
| | - Anna Dubrovska
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, D-01307 Dresden, Germany;
- German Cancer Consortium (DKTK), Partner Site Dresden, German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany
- Helmholtz-Zentrum Dresden—Rossendorf, Institute of Radiooncology—OncoRay, D-01328 Dresden, Germany
| | - Michael Rade
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, RIBOLUTION Biomarker Center Perlickstr. 1, D-04103 Leipzig, Germany; (S.-H.P.); (M.R.); (S.C.)
| | - Sabina Christ
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, RIBOLUTION Biomarker Center Perlickstr. 1, D-04103 Leipzig, Germany; (S.-H.P.); (M.R.); (S.C.)
| | - Stephan Schreiber
- Helmholtz Centre for Environmental Research—UFZ, Young Investigators Group Bioinformatics & Transcriptomics, Permoserstr. 15, D-04318 Leipzig, Germany; (S.S.); (J.H.)
| | - Jörg Hackermüller
- Helmholtz Centre for Environmental Research—UFZ, Young Investigators Group Bioinformatics & Transcriptomics, Permoserstr. 15, D-04318 Leipzig, Germany; (S.S.); (J.H.)
| | - Jörg Lehmann
- Department of Therapy Validation, Fraunhofer Institute for Cell Therapy and Immunology, GLP Test Facility, Perlickstr. 1, D-04103 Leipzig, Germany;
| | - Marieta I. Toma
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Fetscherstraße 74, D-01307 Dresden, Germany; (M.I.T.); (M.H.M.); (U.S.); (G.B.B.)
- Institute of Pathology, Universitätsklinikum Bonn, Venusberg-Campus 1, D-53127 Bonn, Germany
| | - Michael H. Muders
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Fetscherstraße 74, D-01307 Dresden, Germany; (M.I.T.); (M.H.M.); (U.S.); (G.B.B.)
- Rudolf-Becker-Laboratory for Prostate Cancer Research, Institute of Pathology, Universitätsklinikum Bonn, Venusberg-Campus 1, D-53127 Bonn, Germany
| | - Ulrich Sommer
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Fetscherstraße 74, D-01307 Dresden, Germany; (M.I.T.); (M.H.M.); (U.S.); (G.B.B.)
| | - Gustavo B. Baretton
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Fetscherstraße 74, D-01307 Dresden, Germany; (M.I.T.); (M.H.M.); (U.S.); (G.B.B.)
| | - Manfred Wirth
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Fetscherstr. 74, D-01307 Dresden, Germany; (S.F.); (M.F.); (M.W.)
| | - Friedemann Horn
- Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Johannisallee 30, D-04103 Leipzig, Germany; (K.W.); (K.R.); (G.P.); (I.Z.); (S.B.); (U.K.); (F.H.)
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, RIBOLUTION Biomarker Center Perlickstr. 1, D-04103 Leipzig, Germany; (S.-H.P.); (M.R.); (S.C.)
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Hu X, Go YM, Jones DP. Omics Integration for Mitochondria Systems Biology. Antioxid Redox Signal 2020; 32:853-872. [PMID: 31891667 PMCID: PMC7074923 DOI: 10.1089/ars.2019.8006] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 12/30/2019] [Indexed: 12/13/2022]
Abstract
Significance: Elucidation of the central importance of mitophagy in homeostasis of cells and organisms emphasizes that mitochondrial functions extend far beyond short-term needs for energy production. In mitochondria systems biology, the mitochondrial genome, proteome, and metabolome operate as a functional network in coordination of cell activities. Organization occurs through subnetworks that are interconnected by membrane potential, transport activities, allosteric and cooperative interactions, redox signaling mechanisms, rheostatic control by post-translational modifications, and metal ion homeostasis. These subnetworks enable use of varied energy precursors, defense against environmental stressors, and macromolecular rewiring to titrate energy production, biosynthesis, and detoxification according to cell-specific needs. Rewiring mechanisms, termed mitochondrial reprogramming, enhance fitness to respond to metabolic resources and challenges from the environment. Maladaptive responses can cause cell death. Maladaptive rewiring can cause disease. In cancer, adaptive rewiring can interfere with effective treatment. Recent Advances: Many recent advances have been facilitated by the development of new omics tools, which create opportunities to use data-driven analysis of omics data to address these complex adaptive and maladaptive mechanisms of mitochondrial reprogramming in human disease. Critical Issues: Application of omics integration to model systems reveals a critical role for metal ion homeostasis broadly impacting mitochondrial reprogramming. Importantly, data show that trans-omics associations are more robust and biologically relevant than single omics associations. Future Directions: Application of omics integration to mitophagy research creates new opportunities to link the complex, interactive functions of mitochondrial form and function in mitochondria systems biology.
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Affiliation(s)
- Xin Hu
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, Georgia
| | - Young-Mi Go
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, Georgia
| | - Dean P. Jones
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, Georgia
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Eide CA, Kurtz SE, Kaempf A, Long N, Agarwal A, Tognon CE, Mori M, Druker BJ, Chang BH, Danilov AV, Tyner JW. Simultaneous kinase inhibition with ibrutinib and BCL2 inhibition with venetoclax offers a therapeutic strategy for acute myeloid leukemia. Leukemia 2020; 34:2342-2353. [PMID: 32094466 DOI: 10.1038/s41375-020-0764-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 02/07/2020] [Accepted: 02/12/2020] [Indexed: 12/12/2022]
Abstract
Acute myeloid leukemia (AML) results from the enhanced proliferation and impaired differentiation of hematopoietic stem and progenitor cells. Using an ex vivo functional screening assay, we identified that the combination of the BTK inhibitor ibrutinib and BCL2 inhibitor venetoclax (IBR + VEN), currently in clinical trials for chronic lymphocytic leukemia (CLL), demonstrated enhanced efficacy on primary AML patient specimens, AML cell lines, and in a mouse xenograft model of AML. Expanded analyses among a large cohort of hematologic malignancies (n = 651 patients) revealed that IBR + VEN sensitivity associated with selected genetic and phenotypic features in both CLL and AML specimens. Among AML samples, 11q23 MLL rearrangements were highly sensitive to IBR + VEN. Analysis of differentially expressed genes with respect to IBR + VEN sensitivity indicated pathways preferentially enriched in patient samples with reduced ex vivo sensitivity, including IL-10 signaling. These findings suggest that IBR + VEN may represent an effective therapeutic option for patients with AML.
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Affiliation(s)
- Christopher A Eide
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.,Howard Hughes Medical Institute, Portland, OR, USA
| | - Stephen E Kurtz
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Andy Kaempf
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Nicola Long
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Anupriya Agarwal
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Cristina E Tognon
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.,Howard Hughes Medical Institute, Portland, OR, USA
| | - Motomi Mori
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.,Portland State University and Oregon Health & Science University School of Public Health, Portland, OR, USA
| | - Brian J Druker
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.,Howard Hughes Medical Institute, Portland, OR, USA
| | - Bill H Chang
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Alexey V Danilov
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Jeffrey W Tyner
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA. .,Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR, USA.
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Cripps MJ, Bagnati M, Jones TA, Ogunkolade BW, Sayers SR, Caton PW, Hanna K, Billacura MP, Fair K, Nelson C, Lowe R, Hitman GA, Berry MD, Turner MD. Identification of a subset of trace amine-associated receptors and ligands as potential modulators of insulin secretion. Biochem Pharmacol 2020; 171:113685. [DOI: 10.1016/j.bcp.2019.113685] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 10/24/2019] [Indexed: 12/19/2022]
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Sajic T, Liu Y, Arvaniti E, Surinova S, Williams EG, Schiess R, Hüttenhain R, Sethi A, Pan S, Brentnall TA, Chen R, Blattmann P, Friedrich B, Niméus E, Malander S, Omlin A, Gillessen S, Claassen M, Aebersold R. Similarities and Differences of Blood N-Glycoproteins in Five Solid Carcinomas at Localized Clinical Stage Analyzed by SWATH-MS. Cell Rep 2019; 23:2819-2831.e5. [PMID: 29847809 DOI: 10.1016/j.celrep.2018.04.114] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 03/30/2018] [Accepted: 04/26/2018] [Indexed: 02/07/2023] Open
Abstract
Cancer is mostly incurable when diagnosed at a metastatic stage, making its early detection via blood proteins of immense clinical interest. Proteomic changes in tumor tissue may lead to changes detectable in the protein composition of circulating blood plasma. Using a proteomic workflow combining N-glycosite enrichment and SWATH mass spectrometry, we generate a data resource of 284 blood samples derived from patients with different types of localized-stage carcinomas and from matched controls. We observe whether the changes in the patient's plasma are specific to a particular carcinoma or represent a generic signature of proteins modified uniformly in a common, systemic response to many cancers. A quantitative comparison of the resulting N-glycosite profiles discovers that proteins related to blood platelets are common to several cancers (e.g., THBS1), whereas others are highly cancer-type specific. Available proteomics data, including a SWATH library to study N-glycoproteins, will facilitate follow-up biomarker research into early cancer detection.
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Affiliation(s)
- Tatjana Sajic
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland.
| | - Yansheng Liu
- Department of Pharmacology, Cancer Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA
| | - Eirini Arvaniti
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland; PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Evan G Williams
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | | | - Ruth Hüttenhain
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Atul Sethi
- Department of Biomedicine, University of Basel/University Hospital Basel, and Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Sheng Pan
- The Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, 1825 Pressler, Houston, TX 77030, USA
| | - Teresa A Brentnall
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Ru Chen
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Peter Blattmann
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Betty Friedrich
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland; PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Emma Niméus
- Department of Clinical Sciences Lund, Surgery, Oncology and Pathology, Lund University, and Skåne University Hospital, Department of Surgery, Lund, Sweden
| | - Susanne Malander
- Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, and Skåne University Hospital, Department of Oncology, Lund, Sweden
| | - Aurelius Omlin
- Department of Oncology and Hematology, Kantonsspital St. Gallen, St. Gallen, Switzerland; Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Silke Gillessen
- Department of Oncology and Hematology, Kantonsspital St. Gallen, St. Gallen, Switzerland; Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Manfred Claassen
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland; Faculty of Science, University of Zurich, 8057 Zurich, Switzerland.
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McPherson R. 2018 George Lyman Duff Memorial Lecture: Genetics and Genomics of Coronary Artery Disease: A Decade of Progress. Arterioscler Thromb Vasc Biol 2019; 39:1925-1937. [PMID: 31462092 PMCID: PMC6766359 DOI: 10.1161/atvbaha.119.311392] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Accepted: 08/06/2019] [Indexed: 11/16/2022]
Abstract
Recent studies have led to a broader understanding of the genetic architecture of coronary artery disease and demonstrate that it largely derives from the cumulative effect of multiple common risk alleles individually of small effect size rather than rare variants with large effects on coronary artery disease risk. The tools applied include genome-wide association studies encompassing over 200 000 individuals complemented by bioinformatic approaches including imputation from whole-genome data sets, expression quantitative trait loci analyses, and interrogation of ENCODE (Encyclopedia of DNA Elements), Roadmap Epigenetic Project, and other data sets. Over 160 genome-wide significant loci associated with coronary artery disease risk have been identified using the genome-wide association studies approach, 90% of which are situated in intergenic regions. Here, I will describe, in part, our research over the last decade performed in collaboration with a series of bright trainees and an extensive number of groups and individuals around the world as it applies to our understanding of the genetic basis of this complex disease. These studies include computational approaches to better understand missing heritability and identify causal pathways, experimental approaches, and progress in understanding at the molecular level the function of the multiple risk loci identified and potential applications of these genomic data in clinical medicine and drug discovery.
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Affiliation(s)
- Ruth McPherson
- From the Division of Cardiology, Atherogenomics Laboratory, Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, ON, Canada
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Hoang G, Udupa S, Le A. Application of metabolomics technologies toward cancer prognosis and therapy. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2019; 347:191-223. [PMID: 31451214 DOI: 10.1016/bs.ircmb.2019.07.003] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Altered metabolism is one of the defining features of cancer. Since the discovery of the Warburg effect in 1924, research into the metabolic aspects of cancer has been reinvigorated over the past decade. Metabolomics is an invaluable tool for gaining insights into numerous biochemical processes including those related to cancer metabolism and metabolic aspects of other diseases. The combination of untargeted and targeted metabolomics approaches has greatly facilitated the discovery of many cancer biomarkers with prognostic potential. Using mass spectrometry-based stable isotope-resolved metabolomics (SIRM) with isotopic labeling, a powerful tool used in pathway analysis, researchers have discovered novel cancer metabolic pathways and metabolic targets for therapeutic application. Metabolomics technologies provide invaluable metabolic insights reflecting cancer progression in coordination with genomics and proteomics aspects. The systematic study of metabolite levels in the metabolome and their dynamics within a biological organism has been, in recent years, applied across a wide range of fields. Metabolomics technologies have been applied to both early clinical trials and pre-clinical research in several essential aspects of human health. This chapter will give an overview of metabolomics technologies and their application in the discovery of novel pathways using isotopic labeled and non-labeled metabolomics.
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Affiliation(s)
- Giang Hoang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States
| | - Sunag Udupa
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Anne Le
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
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Michalovicz LT, Kelly KA, Vashishtha S, Ben‐Hamo R, Efroni S, Miller JV, Locker AR, Sullivan K, Broderick G, Miller DB, O’Callaghan JP. Astrocyte-specific transcriptome analysis using the ALDH1L1 bacTRAP mouse reveals novel biomarkers of astrogliosis in response to neurotoxicity. J Neurochem 2019; 150:420-440. [PMID: 31222732 PMCID: PMC6771645 DOI: 10.1111/jnc.14800] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 06/13/2019] [Accepted: 06/17/2019] [Indexed: 12/21/2022]
Abstract
Neurotoxicology is hampered by the inability to predict regional and cellular targets of toxicant-induced damage. Evaluating astrogliosis overcomes this problem because reactive astrocytes highlight the location of toxicant-induced damage. While enhanced expression of glial fibrillary acidic protein is a hallmark of astrogliosis, few other biomarkers have been identified. However, bacterial artificial chromosome - translating ribosome affinity purification (bacTRAP) technology allows for characterization of the actively translating transcriptome of a particular cell type; use of this technology in aldehyde dehydrogenase 1 family member L1 (ALDH1L1) bacTRAP mice can identify genes selectively expressed in astrocytes. The aim of this study was to characterize additional biomarkers of neurotoxicity-induced astrogliosis using ALDH1L1 bacTRAP mice. The known dopaminergic neurotoxicant 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP; 12.5 mg/kg s.c.) was used to induce astrogliosis. Striatal tissue was obtained 12, 24, and 48 h following exposure for the isolation of actively translating RNA. Subsequently, MPTP-induced changes in this RNA pool were analyzed by microarray and 184 statistically significant, differentially expressed genes were identified. The dataset was interrogated by gene ontology, pathway, and co-expression network analyses, which identified novel genes, as well as those with known immune and inflammatory functions. Using these analyses, we were directed to several genes associated with reactive astrocytes. Of these, TIMP1 and miR-147 were identified as candidate biomarkers because of their robust increased expression following both MPTP and trimethyl tin exposures. Thus, we have demonstrated that bacTRAP can be used to identify new biomarkers of astrogliosis and aid in the characterization of astrocyte phenotypes induced by toxicant exposures. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* because it provided all relevant information to reproduce the study in the manuscript. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/. Cover Image for this issue: doi: 10.1111/jnc.14518.
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Affiliation(s)
- Lindsay T. Michalovicz
- Health Effects Laboratory Division, Centers for Disease Control and PreventionNational Institute for Occupational Safety and HealthMorgantownWest VirginiaUSA
| | - Kimberly A. Kelly
- Health Effects Laboratory Division, Centers for Disease Control and PreventionNational Institute for Occupational Safety and HealthMorgantownWest VirginiaUSA
| | - Saurabh Vashishtha
- Center for Clinical Systems BiologyRochester General Hospital Research InstituteRochesterNew YorkUSA
| | - Rotem Ben‐Hamo
- The Mina and Everard Goodman Faculty of Life SciencesBar‐Ilan UniversityRamat‐GanIsrael
| | - Sol Efroni
- The Mina and Everard Goodman Faculty of Life SciencesBar‐Ilan UniversityRamat‐GanIsrael
| | - Julie V. Miller
- Health Effects Laboratory Division, Centers for Disease Control and PreventionNational Institute for Occupational Safety and HealthMorgantownWest VirginiaUSA
| | - Alicia R. Locker
- Health Effects Laboratory Division, Centers for Disease Control and PreventionNational Institute for Occupational Safety and HealthMorgantownWest VirginiaUSA
| | | | - Gordon Broderick
- Center for Clinical Systems BiologyRochester General Hospital Research InstituteRochesterNew YorkUSA
| | - Diane B. Miller
- Health Effects Laboratory Division, Centers for Disease Control and PreventionNational Institute for Occupational Safety and HealthMorgantownWest VirginiaUSA
| | - James P. O’Callaghan
- Health Effects Laboratory Division, Centers for Disease Control and PreventionNational Institute for Occupational Safety and HealthMorgantownWest VirginiaUSA
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Shafi A, Nguyen T, Peyvandipour A, Nguyen H, Draghici S. A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures. Front Genet 2019; 10:159. [PMID: 30941158 PMCID: PMC6434849 DOI: 10.3389/fgene.2019.00159] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 02/14/2019] [Indexed: 12/20/2022] Open
Abstract
Although massive amounts of condition-specific molecular profiles are being accumulated in public repositories every day, meaningful interpretation of these data remains a major challenge. In an effort to identify the biomarkers that describe the key biological phenomena for a given condition, several approaches have been developed over the past few years. However, the majority of these approaches either (i) do not consider the known intermolecular interactions, or (ii) do not integrate molecular data of multiple types (e.g., genomics, transcriptomics, proteomics, epigenomics, etc.), and thus potentially fail to capture the true biological changes responsible for complex diseases (e.g., cancer). In addition, these approaches often ignore the heterogeneity and study bias present in independent molecular cohorts. In this manuscript, we propose a novel multi-cohort and multi-omics meta-analysis framework that overcomes all three limitations mentioned above in order to identify robust molecular subnetworks that capture the key dynamic nature of a given biological condition. Our framework integrates multiple independent gene expression studies, unmatched DNA methylation studies, and protein-protein interactions to identify methylation-driven subnetworks. We demonstrate the proposed framework by constructing subnetworks related to two complex diseases: glioblastoma and low-grade gliomas. We validate the identified subnetworks by showing their ability to predict patients' clinical outcome on multiple independent validation cohorts.
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Affiliation(s)
- Adib Shafi
- Department of Computer Science, Wayne State University, Detroit, MI, United States
| | - Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Azam Peyvandipour
- Department of Computer Science, Wayne State University, Detroit, MI, United States
| | - Hung Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, United States.,Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, United States
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Hosokawa M, Takeuchi A, Tanihata J, Iida K, Takeda S, Hagiwara M. Loss of RNA-Binding Protein Sfpq Causes Long-Gene Transcriptopathy in Skeletal Muscle and Severe Muscle Mass Reduction with Metabolic Myopathy. iScience 2019; 13:229-242. [PMID: 30870781 PMCID: PMC6416966 DOI: 10.1016/j.isci.2019.02.023] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 12/27/2018] [Accepted: 02/22/2019] [Indexed: 12/13/2022] Open
Abstract
Growing evidences are suggesting that extra-long genes in mammals are vulnerable for full-gene length transcription and dysregulation of long genes is a mechanism underlying human genetic disorders. How long-distance transcription is achieved is a fundamental question to be elucidated. In previous study, we had discovered that RNA-binding protein SFPQ preferentially binds to long pre-mRNAs and specifically regulates the cluster of neuronal genes >100 kbp. Here we investigated the roles of SFPQ for long gene expression, target specificities, and also physiological functions in skeletal muscle. Loss of Sfpq selectively downregulated genes >100 kbp including Dystrophin, which is 2.26 Mbp in length. Sfpq knockout (KO) mice showed progressive muscle mass reduction and metabolic myopathy characterized by glycogen accumulation and decreased abundance of mitochondrial oxidative phosphorylation complexes. Functional clustering analysis identified energy metabolism pathway genes as SFPQ's targets. These findings indicate target gene specificities and tissue-specific physiological functions of SFPQ in skeletal muscle. SFPQ is essential for long gene expression, including Dystrophin, in skeletal muscle Disruption of Sfpq caused severe muscle mass reduction and premature death SFPQ is required for metabolic pathway gene expression in skeletal muscle Loss of Sfpq decreased OXPHOS complexes and caused glycogen accumulation
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Affiliation(s)
- Motoyasu Hosokawa
- Department of Anatomy and Developmental Biology, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan; Department of Molecular Therapy, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8502, Japan
| | - Akihide Takeuchi
- Department of Anatomy and Developmental Biology, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan.
| | - Jun Tanihata
- Department of Molecular Therapy, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8502, Japan; Department of Cell Physiology, The Jikei University School of Medicine, Minato-ku, Tokyo 105-8461, Japan
| | - Kei Iida
- Medical Research Support Center, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
| | - Shin'ichi Takeda
- Department of Molecular Therapy, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8502, Japan
| | - Masatoshi Hagiwara
- Department of Anatomy and Developmental Biology, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan.
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