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Breimann S, Kamp F, Steiner H, Frishman D. AAontology: An ontology of amino acid scales for interpretable machine learning. J Mol Biol 2024:168717. [PMID: 39053689 DOI: 10.1016/j.jmb.2024.168717] [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: 06/03/2024] [Revised: 07/15/2024] [Accepted: 07/19/2024] [Indexed: 07/27/2024]
Abstract
Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology-a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations-using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.
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Affiliation(s)
- Stephan Breimann
- Department of Bioinformatics, School of Life Sciences, Technical University of Munich, Freising, Germany; Ludwig-Maximilians-University Munich, Biomedical Center, Division of Metabolic Biochemistry, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Frits Kamp
- Ludwig-Maximilians-University Munich, Biomedical Center, Division of Metabolic Biochemistry, Munich, Germany
| | - Harald Steiner
- Ludwig-Maximilians-University Munich, Biomedical Center, Division of Metabolic Biochemistry, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Dmitrij Frishman
- Department of Bioinformatics, School of Life Sciences, Technical University of Munich, Freising, Germany.
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2
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Wang M, Yan X, Dong Y, Li X, Gao B. From driver genes to gene families: A computational analysis of oncogenic mutations and ubiquitination anomalies in hepatocellular carcinoma. Comput Biol Chem 2024; 112:108119. [PMID: 38852361 DOI: 10.1016/j.compbiolchem.2024.108119] [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: 03/19/2024] [Revised: 05/22/2024] [Accepted: 06/06/2024] [Indexed: 06/11/2024]
Abstract
Hepatocellular carcinoma (HCC) is a widespread primary liver cancer with a high fatality rate. Despite several genes with oncogenic effects in HCC have been identified, many remain undiscovered. In this study, we conducted a comprehensive computational analysis to explore the involvement of genes within the same families as known driver genes in HCC. Specifically, we expanded the concept beyond single-gene mutations to encompass gene families sharing homologous structures, integrating various omics data to comprehensively understand gene abnormalities in cancer. Our analysis identified 74 domains with an enriched mutation burden, 404 domain mutation hotspots, and 233 dysregulated driver genes. We observed that specific low-frequency somatic mutations may contribute to HCC occurrence, potentially overlooked by single-gene algorithms. Furthermore, we systematically analyzed how abnormalities in the ubiquitinated proteasome system (UPS) impact HCC, finding that abnormal genes in E3, E2, DUB families, and Degron genes often result in HCC by affecting the stability of oncogenic or tumor suppressor proteins. In conclusion, expanding the exploration of driver genes to include gene families with homologous structures emerges as a promising strategy for uncovering additional oncogenic alterations in HCC.
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Affiliation(s)
- Meng Wang
- Faculty of Environment and Life of Beijing University of Technology, Beijing 100124, China
| | - Xinyue Yan
- Faculty of Environment and Life of Beijing University of Technology, Beijing 100124, China
| | - Yanan Dong
- Faculty of Environment and Life of Beijing University of Technology, Beijing 100124, China
| | - Xiaoqin Li
- Faculty of Environment and Life of Beijing University of Technology, Beijing 100124, China.
| | - Bin Gao
- Faculty of Environment and Life of Beijing University of Technology, Beijing 100124, China
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3
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Trexler M, Bányai L, Kerekes K, Patthy L. Arginines of the CGN codon family are Achilles' heels of cancer genes. Sci Rep 2024; 14:11715. [PMID: 38778164 PMCID: PMC11111792 DOI: 10.1038/s41598-024-62553-7] [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: 11/15/2023] [Accepted: 05/17/2024] [Indexed: 05/25/2024] Open
Abstract
Recent studies have revealed that arginine is the most favorable target of amino acid alteration in most cancer types and it has been suggested that the high preference for arginine mutations reflects the critical roles of this amino acid in the function of proteins. High rates of mutations of arginine residues in cancer, however, might also be due to increased mutability of arginine codons of the CGN family as the CpG dinucleotides of these codons may be methylated. In the present work we have analyzed spectra of single base substitutions of cancer genes (oncogenes, tumor suppressor genes) and passenger genes in cancer tissues to assess the contributions of CpG hypermutability and selection to arginine mutations. Our studies have shown that arginines encoded by the CGN codon family display higher rates of mutation in both cancer genes and passenger genes than arginine codons AGA and AGG that are devoid of CpG dinucleotide, suggesting that the predominance of arginine mutations in cancer is primarily due to CpG hypermutability, rather than selection for arginine replacement. Nevertheless, our results also suggest that CGN codons for arginines may serve as Achilles' heels of cancer genes. CpG hypermutability of key arginines of proto-oncogenes, leading to high rates of recurrence of driver mutations, contributes significantly to carcinogenesis. Similarly, our results indicate that hypermutability of the CpG dinucleotide of CGA codons (converting them to TGA stop codons) contributes significantly to recurrent truncation and inactivation of tumor suppressor genes.
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Affiliation(s)
- Mária Trexler
- Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - László Bányai
- Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Krisztina Kerekes
- Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - László Patthy
- Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest, 1117, Hungary.
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4
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Pandey M, Shah SK, Gromiha MM. Computational approaches for identifying disease-causing mutations in proteins. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2023; 139:141-171. [PMID: 38448134 DOI: 10.1016/bs.apcsb.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.
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Affiliation(s)
- Medha Pandey
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Suraj Kumar Shah
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama, Japan.
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Abstract
Cancers undergo sequential changes to proton (H+) concentration and sensing that are consequences of the disease and facilitate its further progression. The impact of protonation state on protein activity can arise from alterations to amino acids or their titration. Indeed, many cancer-initiating mutations influence pH balance, regulation or sensing in a manner that enables growth and invasion outside normal constraints as part of oncogenic transformation. These cancer-supporting effects become more prominent when tumours develop an acidic microenvironment owing to metabolic reprogramming and disordered perfusion. The ensuing intracellular and extracellular pH disturbances affect multiple aspects of tumour biology, ranging from proliferation to immune surveillance, and can even facilitate further mutagenesis. As a selection pressure, extracellular acidosis accelerates disease progression by favouring acid-resistant cancer cells, which are typically associated with aggressive phenotypes. Although acid-base disturbances in tumours often occur alongside hypoxia and lactate accumulation, there is now ample evidence for a distinct role of H+-operated responses in key events underpinning cancer. The breadth of these actions presents therapeutic opportunities to change the trajectory of disease.
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Affiliation(s)
- Pawel Swietach
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
| | - Ebbe Boedtkjer
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
| | - Stine Falsig Pedersen
- Department of Biology, University of Copenhagen, University of Copenhagen, Faculty of Science, København, Denmark.
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6
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Desai H, Ofori S, Boatner L, Yu F, Villanueva M, Ung N, Nesvizhskii AI, Backus K. Multi-omic stratification of the missense variant cysteinome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.12.553095. [PMID: 37645963 PMCID: PMC10461992 DOI: 10.1101/2023.08.12.553095] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Cancer genomes are rife with genetic variants; one key outcome of this variation is gain-ofcysteine, which is the most frequently acquired amino acid due to missense variants in COSMIC. Acquired cysteines are both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain uncharacterized. Here, we pair cysteine chemoproteomics-a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues-with genomics to reveal the hidden landscape of cysteine acquisition. For both cancer and healthy genomes, we find that cysteine acquisition is a ubiquitous consequence of genetic variation that is further elevated in the context of decreased DNA repair. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized 2-stage false discovery rate (FDR) error controlled proteomic search, further enhanced with a user-friendly FragPipe interface. Integration of CADD predictions of deleteriousness revealed marked enrichment for likely damaging variants that result in acquisition of cysteine. By deploying chemoproteogenomics across eleven cell lines, we identify 116 gain-of-cysteines, of which 10 were liganded by electrophilic druglike molecules. Reference cysteines proximal to missense variants were also found to be pervasive, 791 in total, supporting heretofore untapped opportunities for proteoform-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery.
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Affiliation(s)
- Heta Desai
- Biological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, CA, 90095, USA
- Molecular Biology Institute, UCLA, Los Angeles, CA, 90095, USA
| | - Samuel Ofori
- Biological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, CA, 90095, USA
| | - Lisa Boatner
- Biological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, CA, 90095, USA
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, 90095, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Miranda Villanueva
- Biological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, CA, 90095, USA
- Molecular Biology Institute, UCLA, Los Angeles, CA, 90095, USA
| | - Nicholas Ung
- Biological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, CA, 90095, USA
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, 90095, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA
- Molecular Biology Institute, UCLA, Los Angeles, CA, 90095, USA
- DOE Institute for Genomics and Proteomics, UCLA, Los Angeles, CA, 90095, USA
- Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, UCLA, Los Angeles, CA, 90095, USA
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Keriann Backus
- Biological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, CA, 90095, USA
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, 90095, USA
- Molecular Biology Institute, UCLA, Los Angeles, CA, 90095, USA
- DOE Institute for Genomics and Proteomics, UCLA, Los Angeles, CA, 90095, USA
- Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, UCLA, Los Angeles, CA, 90095, USA
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7
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Pandey M, Gromiha MM. MutBLESS: A tool to identify disease-prone sites in cancer using deep learning. Biochim Biophys Acta Mol Basis Dis 2023; 1869:166721. [PMID: 37105446 DOI: 10.1016/j.bbadis.2023.166721] [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: 02/23/2023] [Revised: 04/07/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023]
Abstract
Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification of driver mutations from experiments is expensive and time intensive. In the present study, we collected the data for experimentally known driver mutations in 22 different cancer types and classified them into six categories: breast cancer (BRCA), acute myeloid leukaemia (LAML), endometrial carcinoma (EC), stomach cancer (STAD), skin cancer (SKCM), and other cancer types which contains 5747 disease prone and 5514 neutral sites in 516 proteins. The analysis of amino acid distribution along mutant sites revealed that the motifs AAA and LR are preferred in disease-prone sites whereas QPP and QF are dominant in neutral sites. Further, we developed a method using deep neural networks to predict disease-prone sites with amino acid sequence-based features such as physicochemical properties, secondary structure, tri-peptide motifs and conservation scores. We obtained an average AUC of 0.97 in five cancer types BRCA, LAML, EC, STAD and SKCM in a test dataset and 0.72 in all other cancer types together. Our method showed excellent performance for identifying cancer-specific mutations with an average sensitivity, specificity, and accuracy of 96.56 %, 97.39 %, and 97.64 %, respectively. We developed a web server for identifying cancer-prone sites, and it is available at https://web.iitm.ac.in/bioinfo2/MutBLESS/index.html. We suggest that our method can serve as an effective method to identify disease-prone sites and assist to develop therapeutic strategies.
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Affiliation(s)
- Medha Pandey
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.
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8
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Rasheed S, Bouley RA, Yoder RJ, Petreaca RC. Protein Arginine Methyltransferase 5 (PRMT5) Mutations in Cancer Cells. Int J Mol Sci 2023; 24:6042. [PMID: 37047013 PMCID: PMC10094674 DOI: 10.3390/ijms24076042] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/15/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023] Open
Abstract
Arginine methylation is a form of posttranslational modification that regulates many cellular functions such as development, DNA damage repair, inflammatory response, splicing, and signal transduction, among others. Protein arginine methyltransferase 5 (PRMT5) is one of nine identified methyltransferases, and it can methylate both histone and non-histone targets. It has pleiotropic functions, including recruitment of repair machinery to a chromosomal DNA double strand break (DSB) and coordinating the interplay between repair and checkpoint activation. Thus, PRMT5 has been actively studied as a cancer treatment target, and small molecule inhibitors of its enzymatic activity have already been developed. In this report, we analyzed all reported PRMT5 mutations appearing in cancer cells using data from the Catalogue of Somatic Mutations in Cancers (COSMIC). Our goal is to classify mutations as either drivers or passengers to understand which ones are likely to promote cellular transformation. Using gold standard artificial intelligence algorithms, we uncovered several key driver mutations in the active site of the enzyme (D306H, L315P, and N318K). In silico protein modeling shows that these mutations may affect the affinity of PRMT5 for S-adenosylmethionine (SAM), which is required as a methyl donor. Electrostatic analysis of the enzyme active site shows that one of these mutations creates a tunnel in the vicinity of the SAM binding site, which may allow interfering molecules to enter the enzyme active site and decrease its activity. We also identified several non-coding mutations that appear to affect PRMT5 splicing. Our analyses provide insights into the role of PRMT5 mutations in cancer cells. Additionally, since PRMT5 single molecule inhibitors have already been developed, this work may uncover future directions in how mutations can affect targeted inhibition.
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Affiliation(s)
- Shayaan Rasheed
- James Comprehensive Cancer Center, The Ohio State University Columbus, Columbus, OH 43210, USA
- Biology Program, The Ohio State University, Columbus, OH 43210, USA
| | - Renee A. Bouley
- Department of Chemistry and Biochemistry, The Ohio State University, Marion, OH 43302, USA
| | - Ryan J. Yoder
- Department of Chemistry and Biochemistry, The Ohio State University, Marion, OH 43302, USA
| | - Ruben C. Petreaca
- James Comprehensive Cancer Center, The Ohio State University Columbus, Columbus, OH 43210, USA
- Department of Molecular Genetics, The Ohio State University, Marion, OH 43302, USA
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9
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Cerezo-Magaña M, Bång-Rudenstam A, Belting M. Proteoglycans: a common portal for SARS-CoV-2 and extracellular vesicle uptake. Am J Physiol Cell Physiol 2023; 324:C76-C84. [PMID: 36458979 PMCID: PMC9799137 DOI: 10.1152/ajpcell.00453.2022] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
As structural components of the glycocalyx, heparan sulfate proteoglycans (HSPGs) are involved in multiple pathophysiological processes at the apex of cell signaling cascades, and as endocytosis receptors for particle structures, such as lipoproteins, extracellular vesicles, and enveloped viruses, including SARS-CoV-2. Given their diversity and complex biogenesis regulation, HSPGs remain understudied. Here we compile some of the latest studies focusing on HSPGs as internalizing receptors of extracellular vesicles ("endogenous virus") and SARS-CoV-2 lipid-enclosed particles and highlight similarities in their biophysical and structural characteristics. Specifically, the similarities in their biogenesis, size, and lipid composition may explain a common dependence on HSPGs for efficient cell-surface attachment and uptake. We further discuss the relative complexity of extracellular vesicle composition and the viral mechanisms that evolve towards increased infectivity that complicate therapeutic strategies addressing blockade of their uptake.
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Affiliation(s)
| | - Anna Bång-Rudenstam
- 1Department of Clinical Sciences Lund, Oncology, Lund University, Lund, Sweden
| | - Mattias Belting
- 1Department of Clinical Sciences Lund, Oncology, Lund University, Lund, Sweden,2Department of Immunology, Genetics, and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden,3Department of Hematology, Oncology, and Radiophysics, Skåne University Hospital, Lund, Sweden
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10
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Nelakurti DD, Rossetti T, Husbands AY, Petreaca RC. Arginine Depletion in Human Cancers. Cancers (Basel) 2021; 13:6274. [PMID: 34944895 PMCID: PMC8699593 DOI: 10.3390/cancers13246274] [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: 11/05/2021] [Revised: 12/04/2021] [Accepted: 12/09/2021] [Indexed: 11/25/2022] Open
Abstract
Arginine is encoded by six different codons. Base pair changes in any of these codons can have a broad spectrum of effects including substitutions to twelve different amino acids, eighteen synonymous changes, and two stop codons. Four amino acids (histidine, cysteine, glutamine, and tryptophan) account for over 75% of amino acid substitutions of arginine. This suggests that a mutational bias, or "purifying selection", mechanism is at work. This bias appears to be driven by C > T and G > A transitions in four of the six arginine codons, a signature that is universal and independent of cancer tissue of origin or histology. Here, we provide a review of the available literature and reanalyze publicly available data from the Catalogue of Somatic Mutations in Cancer (COSMIC). Our analysis identifies several genes with an arginine substitution bias. These include known factors such as IDH1, as well as previously unreported genes, including four cancer driver genes (FGFR3, PPP6C, MAX, GNAQ). We propose that base pair substitution bias and amino acid physiology both play a role in purifying selection. This model may explain the documented arginine substitution bias in cancers.
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Affiliation(s)
- Devi D. Nelakurti
- Biomedical Science Undergraduate Program, The Ohio State University Medical School, Columbus, OH 43210, USA;
| | - Tiffany Rossetti
- Biology Undergraduate Program, The Ohio State University, Marion, OH 43302, USA;
| | - Aman Y. Husbands
- Department of Molecular Genetics, The Ohio State University, Columbus, OH 43215, USA
| | - Ruben C. Petreaca
- Department of Molecular Genetics, The Ohio State University, Marion, OH 43302, USA
- Cancer Biology Program, The Ohio State University James Comprehensive Cancer Center, Columbus, OH 43210, USA
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11
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A Genome-Wide Profiling of Glioma Patients with an IDH1 Mutation Using the Catalogue of Somatic Mutations in Cancer Database. Cancers (Basel) 2021; 13:cancers13174299. [PMID: 34503108 PMCID: PMC8428353 DOI: 10.3390/cancers13174299] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 08/20/2021] [Accepted: 08/21/2021] [Indexed: 02/08/2023] Open
Abstract
Simple Summary Glioma patients that present a somatic mutation in the isocitrate dehydrogenase 1 (IDH1) gene have a significantly better prognosis and overall survival than patients with the wild-type genotype. An IDH1 mutation is hypothesized to occur early during cellular transformation and leads to further genetic instability. A genome-wide profiling of glioma patients in the Catalogue of Somatic Mutations in Cancer (COSMIC) database was performed to classify the genetic differences in IDH1-mutant versus IDH1-wildtype patients. This classification will aid in a better understanding of how this specific mutation influences the genetic make-up of glioma and the resulting prognosis. Key differences in co-mutation and gene expression levels were identified that correlate with an improved prognosis. Abstract Gliomas are differentiated into two major disease subtypes, astrocytoma or oligodendroglioma, which are then characterized as either IDH (isocitrate dehydrogenase)-wild type or IDH-mutant due to the dramatic differences in prognosis and overall survival. Here, we investigated the genetic background of IDH1-mutant gliomas using the Catalogue of Somatic Mutations in Cancer (COSMIC) database. In astrocytoma patients, we found that IDH1 is often co-mutated with TP53, ATRX, AMBRA1, PREX1, and NOTCH1, but not CHEK2, EGFR, PTEN, or the zinc finger transcription factor ZNF429. The majority of the mutations observed in these genes were further confirmed to be either drivers or pathogenic by the Cancer-Related Analysis of Variants Toolkit (CRAVAT). Gene expression analysis showed down-regulation of DRG2 and MSN expression, both of which promote cell proliferation and invasion. There was also significant over-expression of genes such as NDRG3 and KCNB1 in IDH1-mutant astrocytoma patients. We conclude that IDH1-mutant glioma is characterized by significant genetic changes that could contribute to a better prognosis in glioma patients.
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12
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Graber M, Barta H, Wood R, Pappula A, Vo M, Petreaca RC, Escorcia W. Comprehensive Genetic Analysis of DGAT2 Mutations and Gene Expression Patterns in Human Cancers. BIOLOGY 2021; 10:714. [PMID: 34439946 PMCID: PMC8389207 DOI: 10.3390/biology10080714] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/08/2021] [Accepted: 07/20/2021] [Indexed: 12/31/2022]
Abstract
DGAT2 is a transmembrane protein encoded by the DGAT2 gene that functions in lipid metabolism, triacylglycerol synthesis, and lipid droplet regulation. Cancer cells exhibit altered lipid metabolism and mutations in DGAT2 may contribute to this state. Using data from the Catalogue of Somatic Mutations in Cancer (COSMIC), we analyzed all cancer genetic DGAT2 alterations, including mutations, copy number variations and gene expression. We find that several DGAT2 mutations fall within the catalytic site of the enzyme. Using the Variant Effect Scoring Tool (VEST), we identify multiple mutations with a high likelihood of contributing to cellular transformation. We also found that D222V is a mutation hotspot neighboring a previously discovered Y223H mutation that causes Axonal Charcot-Marie-Tooth disease. Remarkably, Y223H has not been detected in cancers, suggesting that it is inhibitory to cancer progression. We also identify several single nucleotide polymorphisms (SNP) with high VEST scores, indicating that certain alleles in human populations have a pathogenic predisposition. Most mutations do not correlate with a change in gene expression, nor is gene expression dependent on high allele copy number. However, we did identify eight alleles with high expression levels, suggesting that at least in certain cases, the excess DGAT2 gene product is not inhibitory to cellular proliferation. This work uncovers unknown functions of DGAT2 in cancers and suggests that its role may be more complex than previously appreciated.
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Affiliation(s)
- Meghan Graber
- Biology Department, Xavier University, Cincinnati, OH 45207, USA; (M.G.); (H.B.); (R.W.); (M.V.)
| | - Hayley Barta
- Biology Department, Xavier University, Cincinnati, OH 45207, USA; (M.G.); (H.B.); (R.W.); (M.V.)
| | - Ryan Wood
- Biology Department, Xavier University, Cincinnati, OH 45207, USA; (M.G.); (H.B.); (R.W.); (M.V.)
| | - Amrit Pappula
- Computer Science and Engineering Undergraduate Program, The Ohio State University, Columbus, OH 43210, USA;
| | - Martin Vo
- Biology Department, Xavier University, Cincinnati, OH 45207, USA; (M.G.); (H.B.); (R.W.); (M.V.)
| | - Ruben C. Petreaca
- Department of Molecular Genetics, The Ohio State University, Marion, OH 43302, USA
| | - Wilber Escorcia
- Biology Department, Xavier University, Cincinnati, OH 45207, USA; (M.G.); (H.B.); (R.W.); (M.V.)
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13
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The structure-based cancer-related single amino acid variation prediction. Sci Rep 2021; 11:13599. [PMID: 34193921 PMCID: PMC8245468 DOI: 10.1038/s41598-021-92793-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/16/2021] [Indexed: 11/09/2022] Open
Abstract
Single amino acid variation (SAV) is an amino acid substitution of the protein sequence that can potentially influence the entire protein structure or function, as well as its binding affinity. Protein destabilization is related to diseases, including several cancers, although using traditional experiments to clarify the relationship between SAVs and cancer uses much time and resources. Some SAV prediction methods use computational approaches, with most predicting SAV-induced changes in protein stability. In this investigation, all SAV characteristics generated from protein sequences, structures and the microenvironment were converted into feature vectors and fed into an integrated predicting system using a support vector machine and genetic algorithm. Critical features were used to estimate the relationship between their properties and cancers caused by SAVs. We describe how we developed a prediction system based on protein sequences and structure that is capable of distinguishing if the SAV is related to cancer or not. The five-fold cross-validation performance of our system is 89.73% for the accuracy, 0.74 for the Matthews correlation coefficient, and 0.81 for the F1 score. We have built an online prediction server, CanSavPre ( http://bioinfo.cmu.edu.tw/CanSavPre/ ), which is expected to become a useful, practical tool for cancer research and precision medicine.
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Identifying Cancer-Relevant Mutations in the DLC START Domain Using Evolutionary and Structure-Function Analyses. Int J Mol Sci 2020; 21:ijms21218175. [PMID: 33142932 PMCID: PMC7662654 DOI: 10.3390/ijms21218175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/22/2020] [Accepted: 10/30/2020] [Indexed: 01/05/2023] Open
Abstract
Rho GTPase signaling promotes proliferation, invasion, and metastasis in a broad spectrum of cancers. Rho GTPase activity is regulated by the deleted in liver cancer (DLC) family of bona fide tumor suppressors which directly inactivate Rho GTPases by stimulating GTP hydrolysis. In addition to a RhoGAP domain, DLC proteins contain a StAR-related lipid transfer (START) domain. START domains in other organisms bind hydrophobic small molecules and can regulate interacting partners or co-occurring domains through a variety of mechanisms. In the case of DLC proteins, their START domain appears to contribute to tumor suppressive activity. However, the nature of this START-directed mechanism, as well as the identities of relevant functional residues, remain virtually unknown. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset and evolutionary and structure-function analyses, we identify several conserved residues likely to be required for START-directed regulation of DLC-1 and DLC-2 tumor-suppressive capabilities. This pan-cancer analysis shows that conserved residues of both START domains are highly overrepresented in cancer cells from a wide range tissues. Interestingly, in DLC-1 and DLC-2, three of these residues form multiple interactions at the tertiary structural level. Furthermore, mutation of any of these residues is predicted to disrupt interactions and thus destabilize the START domain. As such, these mutations would not have emerged from traditional hotspot scans of COSMIC. We propose that evolutionary and structure-function analyses are an underutilized strategy which could be used to unmask cancer-relevant mutations within COSMIC. Our data also suggest DLC-1 and DLC-2 as high-priority candidates for development of novel therapeutics that target their START domain.
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Insights into changes in binding affinity caused by disease mutations in protein-protein complexes. Comput Biol Med 2020; 123:103829. [DOI: 10.1016/j.compbiomed.2020.103829] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/20/2020] [Accepted: 05/20/2020] [Indexed: 01/11/2023]
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Abstract
An unresolved question critical for understanding cancer is how recurring somatic mutations are retained and how selective pressures drive retention. Increased intracellular pH (pHi) is common to most cancers and is an early event in cancer development. Recent work shows that recurrent somatic mutations can confer an adaptive gain in pH sensing to mutant proteins, enhancing tumorigenic phenotypes specifically at the increased pHi of cancer. Newly identified amino acid mutation signatures in cancer suggest charge-changing mutations define and shape the mutational landscape of cancer. Taken together, these results support a new perspective on the functional significance of somatic mutations in cancer. In this review, we explore existing data and new directions for better understanding how changes in dynamic pH sensing by somatic mutation might be conferring a fitness advantage to the high pH of cancer.
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Kulandaisamy A, Zaucha J, Sakthivel R, Frishman D, Michael Gromiha M. Pred‐MutHTP: Prediction of disease‐causing and neutral mutations in human transmembrane proteins. Hum Mutat 2019; 41:581-590. [DOI: 10.1002/humu.23961] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 11/05/2019] [Accepted: 11/20/2019] [Indexed: 12/24/2022]
Affiliation(s)
- A. Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciencesIndian Institute of Technology MadrasChennai Tamilnadu India
| | - Jan Zaucha
- Department of Bioinformatics, Wissenschaftszentrum WeihenstephanTechnische Universität MünchenFreising Germany
| | - Ramasamy Sakthivel
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciencesIndian Institute of Technology MadrasChennai Tamilnadu India
| | - Dmitrij Frishman
- Department of Bioinformatics, Wissenschaftszentrum WeihenstephanTechnische Universität MünchenFreising Germany
- Department of BioinformaticsPeter the Great St. Petersburg Polytechnic UniversitySt. Petersburg Russian Federation
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciencesIndian Institute of Technology MadrasChennai Tamilnadu India
- Advanced Computational Drug Discovery Unit, Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative ResearchTokyo Institute of TechnologyYokohama Japan
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Kanakaveti V, Anoosha P, Sakthivel R, Rayala S, Gromiha M. Influence of Amino Acid Mutations and Small Molecules on Targeted Inhibition of Proteins Involved in Cancer. Curr Top Med Chem 2019; 19:457-466. [DOI: 10.2174/1568026619666190304143354] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 11/19/2018] [Accepted: 12/28/2018] [Indexed: 12/23/2022]
Abstract
Background:Protein-protein interactions (PPIs) are of crucial importance in regulating the biological processes of cells both in normal and diseased conditions. Significant progress has been made in targeting PPIs using small molecules and achieved promising results. However, PPI drug discovery should be further accelerated with better understanding of chemical space along with various functional aspects.Objective:In this review, we focus on the advancements in computational research for targeted inhibition of protein-protein interactions involved in cancer.Methods:Here, we mainly focused on two aspects: (i) understanding the key roles of amino acid mutations in epidermal growth factor receptor (EGFR) as well as mutation-specific inhibitors and (ii) design of small molecule inhibitors for Bcl-2 to disrupt PPIs.Results:The paradigm of PPI inhibition to date reflect the certainty that inclination towards novel and versatile strategies enormously dictate the success of PPI inhibition. As the chemical space highly differs from the normal drug like compounds the lead optimization process has to be given the utmost priority to ensure the clinical success. Here, we provided a broader perspective on effect of mutations in oncogene EGFR connected to Bcl-2 PPIs and focused on the potential challenges.Conclusion:Understanding and bridging mutations and altered PPIs will provide insights into the alarming signals leading to massive malfunctioning of a biological system in various diseases. Finding rational elucidations from a pharmaceutical stand point will presumably broaden the horizons in future.
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Affiliation(s)
- V. Kanakaveti
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai - 600036, Tamil Nadu, India
| | - P. Anoosha
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai - 600036, Tamil Nadu, India
| | - R. Sakthivel
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai - 600036, Tamil Nadu, India
| | - S.K. Rayala
- Molecular Oncology Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai - 600036, Tamil Nadu, India
| | - M.M. Gromiha
- Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan
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19
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Rauscher B, Heigwer F, Henkel L, Hielscher T, Voloshanenko O, Boutros M. Toward an integrated map of genetic interactions in cancer cells. Mol Syst Biol 2018; 14:e7656. [PMID: 29467179 PMCID: PMC5820685 DOI: 10.15252/msb.20177656] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Revised: 01/20/2018] [Accepted: 01/23/2018] [Indexed: 12/13/2022] Open
Abstract
Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype-dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene-background relationships. In addition to known dependencies, we identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.
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Affiliation(s)
- Benedikt Rauscher
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Florian Heigwer
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Luisa Henkel
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Thomas Hielscher
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oksana Voloshanenko
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Michael Boutros
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
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20
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Anoosha P, Sakthivel R, Gromiha MM. Investigating mutation-specific biological activities of small molecules using quantitative structure-activity relationship for epidermal growth factor receptor in cancer. Mutat Res 2017; 806:19-26. [PMID: 28938109 DOI: 10.1016/j.mrfmmm.2017.08.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 08/21/2017] [Accepted: 08/22/2017] [Indexed: 06/07/2023]
Abstract
Epidermal Growth Factor Receptor (EGFR) is a potential drug target in cancer therapy. Missense mutations play major roles in influencing the protein function, leading to abnormal cell proliferation and tumorigenesis. A number of EGFR inhibitor molecules targeting ATP binding domain were developed for the past two decades. Unfortunately, they become inactive due to resistance caused by new mutations in patients, and previous studies have also reported noticeable differences in inhibitor binding to distinct known driver mutants as well. Hence, there is a high demand for identification of EGFR mutation-specific inhibitors. In our present study, we derived a set of anti-cancer compounds with biological activities against eight typical EGFR known driver mutations and developed quantitative structure-activity relationship (QSAR) models for each separately. The compounds are grouped based on their functional scaffolds, which enhanced the correlation between compound features and respective biological activities. The models for different mutants performed well with a correlation coefficient, (r) in the range of 0.72-0.91 on jack-knife test. Further, we analyzed the selected features in different models and observed that hydrogen bond and aromaticity-related features play important roles in predicting the biological activity of a compound. This analysis is complimented with docking studies, which showed the binding patterns and interactions of ligands with EGFR mutants that could influence their activities.
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Affiliation(s)
- P Anoosha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - R Sakthivel
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India.
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21
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White KA, Ruiz DG, Szpiech ZA, Strauli NB, Hernandez RD, Jacobson MP, Barber DL. Cancer-associated arginine-to-histidine mutations confer a gain in pH sensing to mutant proteins. Sci Signal 2017; 10:10/495/eaam9931. [PMID: 28874603 DOI: 10.1126/scisignal.aam9931] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The intracellular pH (pHi) of most cancers is constitutively higher than that of normal cells and enhances proliferation and cell survival. We found that increased pHi enabled the tumorigenic behaviors caused by somatic arginine-to-histidine mutations, which are frequent in cancer and confer pH sensing not seen with wild-type proteins. Experimentally raising the pHi increased the activity of R776H mutant epidermal growth factor receptor (EGFR-R776H), thereby increasing proliferation and causing transformation in fibroblasts. An Arg-to-Gly mutation did not confer these effects. Molecular dynamics simulations of EGFR suggested that decreased protonation of His776 at high pH causes conformational changes in the αC helix that may stabilize the active form of the kinase. An Arg-to-His, but not Arg-to-Lys, mutation in the transcription factor p53 (p53-R273H) decreased its transcriptional activity and attenuated the DNA damage response in fibroblasts and breast cancer cells with high pHi. Lowering pHi attenuated the tumorigenic effects of both EGFR-R776H and p53-R273H. Our data suggest that some somatic mutations may confer a fitness advantage to the higher pHi of cancer cells.
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Affiliation(s)
- Katharine A White
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Diego Garrido Ruiz
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Zachary A Szpiech
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Nicolas B Strauli
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA.,Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA.,Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Matthew P Jacobson
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Diane L Barber
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA.
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22
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Szpiech ZA, Strauli NB, White KA, Ruiz DG, Jacobson MP, Barber DL, Hernandez RD. Prominent features of the amino acid mutation landscape in cancer. PLoS One 2017; 12:e0183273. [PMID: 28837668 PMCID: PMC5570307 DOI: 10.1371/journal.pone.0183273] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 08/01/2017] [Indexed: 01/20/2023] Open
Abstract
Cancer can be viewed as a set of different diseases with distinctions based on tissue origin, driver mutations, and genetic signatures. Accordingly, each of these distinctions have been used to classify cancer subtypes and to reveal common features. Here, we present a different analysis of cancer based on amino acid mutation signatures. Non-negative Matrix Factorization and principal component analysis of 29 cancers revealed six amino acid mutation signatures, including four signatures that were dominated by either arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. Sample-level analyses reveal that while some cancers are heterogeneous, others are largely dominated by one type of mutation. Using a non-overlapping set of samples from the COSMIC somatic mutation database, we validate five of six mutation signatures, including signatures with prominent arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. This suggests that our classification of cancers based on amino acid mutation patterns may provide avenues of inquiry pertaining to specific protein mutations that may generate novel insights into cancer biology.
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Affiliation(s)
- Zachary A. Szpiech
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States of America
- * E-mail: (RDH); (ZAS)
| | - Nicolas B. Strauli
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States of America
- Biomedical Sciences Graduate Program, University of California, San Francisco, United States of America
| | - Katharine A. White
- Department of Cell and Tissue Biology, University of California, San Francisco, United States of America
| | - Diego Garrido Ruiz
- Department of Pharmaceutical Chemistry, University of California, San Francisco, United States of America
| | - Matthew P. Jacobson
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States of America
- Department of Pharmaceutical Chemistry, University of California, San Francisco, United States of America
| | - Diane L. Barber
- Department of Cell and Tissue Biology, University of California, San Francisco, United States of America
| | - Ryan D. Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States of America
- Quantitative Biosciences Institute, University of California, San Francisco, United States of America
- Institute for Human Genetics, University of California, San Francisco, United States of America
- * E-mail: (RDH); (ZAS)
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23
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Waters AM, Bagni R, Portugal F, Hartley JL. Single Synonymous Mutations in KRAS Cause Transformed Phenotypes in NIH3T3 Cells. PLoS One 2016; 11:e0163272. [PMID: 27684555 PMCID: PMC5042562 DOI: 10.1371/journal.pone.0163272] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Accepted: 09/05/2016] [Indexed: 12/29/2022] Open
Abstract
Synonymous mutations in the KRAS gene are clustered at G12, G13, and G60 in human cancers. We constructed 9 stable NIH3T3 cell lines expressing KRAS, each with one of these synonymous mutations. Compared to the negative control cell line expressing the wild type human KRAS gene, all the synonymous mutant lines expressed more KRAS protein, grew more rapidly and to higher densities, and were more invasive in multiple assays. Three of the cell lines showed dramatic loss of contact inhibition, were more refractile under phase contrast, and their refractility was greatly reduced by treatment with trametinib. Codon usage at these glycines is highly conserved in KRAS compared to HRAS, indicating selective pressure. These transformed phenotypes suggest that synonymous mutations found in driver genes such as KRAS may play a role in human cancers.
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Affiliation(s)
- Andrew M. Waters
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, United States of America
- Biology Department, Catholic University of America, Washington, District of Columbia, United States of America
| | - Rachel Bagni
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, United States of America
| | - Franklin Portugal
- Biology Department, Catholic University of America, Washington, District of Columbia, United States of America
| | - James L. Hartley
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, United States of America
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Prediction of change in protein unfolding rates upon point mutations in two state proteins. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2016; 1864:1104-1109. [DOI: 10.1016/j.bbapap.2016.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2016] [Revised: 05/05/2016] [Accepted: 06/01/2016] [Indexed: 11/23/2022]
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25
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Mészáros B, Zeke A, Reményi A, Simon I, Dosztányi Z. Systematic analysis of somatic mutations driving cancer: uncovering functional protein regions in disease development. Biol Direct 2016; 11:23. [PMID: 27150584 PMCID: PMC4858844 DOI: 10.1186/s13062-016-0125-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Accepted: 04/20/2016] [Indexed: 11/16/2022] Open
Abstract
Background Recent advances in sequencing technologies enable the large-scale identification of genes that are affected by various genetic alterations in cancer. However, understanding tumor development requires insights into how these changes cause altered protein function and impaired network regulation in general and/or in specific cancer types. Results In this work we present a novel method called iSiMPRe that identifies regions that are significantly enriched in somatic mutations and short in-frame insertions or deletions (indels). Applying this unbiased method to the complete human proteome, by using data enriched through various cancer genome projects, we identified around 500 protein regions which could be linked to one or more of 27 distinct cancer types. These regions covered the majority of known cancer genes, surprisingly even tumor suppressors. Additionally, iSiMPRe also identified novel genes and regions that have not yet been associated with cancer. Conclusions While local somatic mutations correspond to only a subset of genetic variations that can lead to cancer, our systematic analyses revealed that they represent an accompanying feature of most cancer driver genes regardless of the primary mechanism by which they are perturbed during tumorigenesis. These results indicate that the accumulation of local somatic mutations can be used to pinpoint genes responsible for cancer formation and can also help to understand the effect of cancer mutations at the level of functional modules in a broad range of cancer driver genes. Reviewers This article was reviewed by Sándor Pongor, Michael Gromiha and Zoltán Gáspári. Electronic supplementary material The online version of this article (doi:10.1186/s13062-016-0125-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bálint Mészáros
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, 2 Magyar Tudósok krt, Budapest, H-1117, Hungary.
| | - András Zeke
- Lendület Protein Interaction Group, Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, 2 Magyar Tudósok krt, Budapest, H-1117, Hungary
| | - Attila Reményi
- Lendület Protein Interaction Group, Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, 2 Magyar Tudósok krt, Budapest, H-1117, Hungary
| | - István Simon
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, 2 Magyar Tudósok krt, Budapest, H-1117, Hungary
| | - Zsuzsanna Dosztányi
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, 11/c Pázmány Péter stny, Budapest, H-1117, Hungary.
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