|
1
|
Phan A, Joshi P, Kadelka C, Friedberg I. A longitudinal analysis of function annotations of the human proteome reveals consistently high biases. Database (Oxford) 2025; 2025:baaf036. [PMID: 40338520 PMCID: PMC12060720 DOI: 10.1093/database/baaf036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2024] [Revised: 02/28/2025] [Accepted: 04/08/2025] [Indexed: 05/09/2025]
Abstract
The resources required to study gene function are limited, especially when considering the number of genes in the human genome and the complexity of their function. Therefore, genes are prioritized for experimental studies based on many different considerations, including, but not limited to, perceived biomedical importance, such as disease-associated genes, or the understanding of biological processes, such as cell signalling pathways. At the same time, most genes are not studied or are under-characterized, which hampers our understanding of their function and potential effects on human health and wellness. Understanding function annotation disparity is a necessary first step toward understanding how much functional knowledge is gained from the human genome, and toward guidelines for better targeting future studies of the genes in the human genome effectively. Here, we present a comprehensive longitudinal analysis of the human proteome utilizing data analysis tools from economics and information theory. Specifically, we view the human proteome as a population of proteins within a knowledge economy: we treat the quantified knowledge of the protein's function as the analogue of wealth and examine the distribution of information in a population of proteins in the proteome in the same manner distribution of wealth is studied in societies. Our results show a highly skewed distribution of information about human proteins over the last decade, in which the inequality in the annotations given to the proteins remains high. Additionally, we examine the correlation between the knowledge about protein function as captured in databases and the interest in proteins as reflected by mentions in the scientific literature. We show a large gap between knowledge and interest and dissect the factors leading to this gap. In conclusion, our study shows that research efforts should be redirected to less studied proteins to mitigate the disparity among human proteins both in databases and literature.
Collapse
Affiliation(s)
- An Phan
- Program in Bioinformatics and Computational Biology, Iowa State University, Ames, IA, United States
- Department of Mathematics, Iowa State University, Ames, IA, United States
| | - Parnal Joshi
- Program in Bioinformatics and Computational Biology, Iowa State University, Ames, IA, United States
- Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, United States
| | - Claus Kadelka
- Department of Mathematics, Iowa State University, Ames, IA, United States
| | - Iddo Friedberg
- Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, United States
| |
Collapse
|
|
2
|
Tzotzos G. Properties of "Stable" Mosquito Cytochrome P450 Enzymes. INSECTS 2025; 16:184. [PMID: 40003814 PMCID: PMC11855896 DOI: 10.3390/insects16020184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2024] [Revised: 01/24/2025] [Accepted: 02/03/2025] [Indexed: 02/27/2025]
Abstract
The use of insecticides is widespread in the control of debilitating mosquito-borne diseases. P450 enzymes (CYPs) play essential roles in mosquito physiological function but also in the enzymatic detoxification of xenobiotics. Broadly speaking, CYPs can be classified as "stable", meaning those that have no or very few paralogs, and "labile", constituting gene families with many paralogous members. The evolutionary dichotomy between "stable" and "labile" P450 genes is fuzzy and there is not a clear phylogenetic demarcation between P450s involved in detoxification and P450s involved in essential metabolic processes. In this study, bioinformatic methods were used to explore differences in the sequences of "stable" and "labile" P450s that may facilitate their functional classification. Genomic and sequence data of Anopheles gambiae (Agam), Aedes aegypti (Aaeg), and Culex quinquefasciatus (Cqui) CYPs were obtained from public databases. The results of this study show that "stable" CYPs are encoded by longer genes, have longer introns and more exons, and contain a higher proportion of hydrophobic amino acids than "labile" CYPs. Compared to "labile" CYPs, a significantly higher proportion of "stable" CYPs are associated with biosynthetic and developmental processes.
Collapse
Affiliation(s)
- George Tzotzos
- Visiting Research Fellow, Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60100 Ancona, Italy
| |
Collapse
|
|
3
|
Nanda S, Pandey R, Sardar R, Panda A, Naorem A, Gupta D, Malhotra P. Comparative genomics of two protozoans Dictyostelium discoideum and Plasmodium falciparum reveals conserved as well as distinct regulatory pathways crucial for exploring novel therapeutic targets for Malaria. Heliyon 2024; 10:e38500. [PMID: 39391471 PMCID: PMC11466611 DOI: 10.1016/j.heliyon.2024.e38500] [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: 06/08/2024] [Revised: 09/23/2024] [Accepted: 09/25/2024] [Indexed: 10/12/2024] Open
Abstract
Plasmodium falciparum, which causes life-threatening cerebral malaria has rapidly gained resistance against most frontline anti-malarial drugs, thereby generating an urgent need to develop novel therapeutic approaches. Conducting in-depth investigations on Plasmodium in its native form is challenging, thereby necessitating the requirement of an efficient model system. In line, mounting evidence suggests that Dictyostelium discoideum retains both conformational and functional properties of Plasmodium proteins, however, the true potential of Dictyostelium as a host system is not fully explored. In the present study, we have exploited comparative genomics as a tool to extract, compare, and curate the extensive data available on the organism-specific databases to evaluate if D. discoideum can be established as a prime model system for functional characterization of P. falciparum genes. Through comprehensive in silico analysis, we report that despite the presence of adaptation-specific genes, the two display noteworthy conservation in the housekeeping genes, signaling pathway components, transcription regulators, and post-translational modulators. Furthermore, through orthologue analysis, the known, potential, and novel drug target genes of P. falciparum were found to be significantly conserved in D. discoideum. Our findings advocate that D. discoideum can be employed to express and functionally characterize difficult-to-express P. falciparum genes.
Collapse
Affiliation(s)
- Shivam Nanda
- Department of Genetics, University of Delhi, South Campus, New Delhi, 110 021, India
| | - Rajan Pandey
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110 067, India
| | - Rahila Sardar
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110 067, India
| | - Ashutosh Panda
- Malaria Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110 067, India
| | - Aruna Naorem
- Department of Genetics, University of Delhi, South Campus, New Delhi, 110 021, India
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110 067, India
| | - Pawan Malhotra
- Malaria Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110 067, India
| |
Collapse
|
|
4
|
Khoroshkin M, Buyan A, Dodel M, Navickas A, Yu J, Trejo F, Doty A, Baratam R, Zhou S, Lee SB, Joshi T, Garcia K, Choi B, Miglani S, Subramanyam V, Modi H, Carpenter C, Markett D, Corces MR, Mardakheh FK, Kulakovskiy IV, Goodarzi H. Systematic identification of post-transcriptional regulatory modules. Nat Commun 2024; 15:7872. [PMID: 39251607 PMCID: PMC11385195 DOI: 10.1038/s41467-024-52215-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 08/27/2024] [Indexed: 09/11/2024] Open
Abstract
In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms.
Collapse
Affiliation(s)
- Matvei Khoroshkin
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Andrey Buyan
- Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia
| | - Martin Dodel
- Centre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
- Department of Biochemistry, University of Oxford, Oxford, UK
| | - Albertas Navickas
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Institut Curie, UMR3348 CNRS, Inserm, Orsay, France
| | - Johnny Yu
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Fathima Trejo
- College of Arts and Sciences, University of San Francisco, San Francisco, CA, USA
| | - Anthony Doty
- College of Arts and Sciences, University of San Francisco, San Francisco, CA, USA
| | - Rithvik Baratam
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Shaopu Zhou
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Sean B Lee
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Tanvi Joshi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Kristle Garcia
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Benedict Choi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Sohit Miglani
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Vishvak Subramanyam
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Hailey Modi
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Christopher Carpenter
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel Markett
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - M Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Faraz K Mardakheh
- Centre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, London, UK.
- Department of Biochemistry, University of Oxford, Oxford, UK.
| | - Ivan V Kulakovskiy
- Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia.
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.
| | - Hani Goodarzi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
| |
Collapse
|
|
5
|
Hu Y, Comjean A, Attrill H, Antonazzo G, Thurmond J, Chen W, Li F, Chao T, Mohr SE, Brown NH, Perrimon N. PANGEA: a new gene set enrichment tool for Drosophila and common research organisms. Nucleic Acids Res 2023; 51:W419-W426. [PMID: 37125646 PMCID: PMC10320058 DOI: 10.1093/nar/gkad331] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/28/2023] [Accepted: 04/29/2023] [Indexed: 05/02/2023] Open
Abstract
Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an 'omics' study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualizations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species.
Collapse
Affiliation(s)
- Yanhui Hu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Aram Comjean
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Helen Attrill
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| | - Giulia Antonazzo
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| | - Jim Thurmond
- Department of Biology, Indiana University, Bloomington, IN 47405, USA
| | - Weihang Chen
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Fangge Li
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Tiffany Chao
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Stephanie E Mohr
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Nicholas H Brown
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| | - Norbert Perrimon
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston, MA 02138, USA
| |
Collapse
|
|
6
|
Aleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, Ebert D, Feuermann M, Gaudet P, Harris NL, Hill DP, Lee R, Mi H, Moxon S, Mungall CJ, Muruganugan A, Mushayahama T, Sternberg PW, Thomas PD, Van Auken K, Ramsey J, Siegele DA, Chisholm RL, Fey P, Aspromonte MC, Nugnes MV, Quaglia F, Tosatto S, Giglio M, Nadendla S, Antonazzo G, Attrill H, Dos Santos G, Marygold S, Strelets V, Tabone CJ, Thurmond J, Zhou P, Ahmed SH, Asanitthong P, Luna Buitrago D, Erdol MN, Gage MC, Ali Kadhum M, Li KYC, Long M, Michalak A, Pesala A, Pritazahra A, Saverimuttu SCC, Su R, Thurlow KE, Lovering RC, Logie C, Oliferenko S, Blake J, Christie K, Corbani L, Dolan ME, Drabkin HJ, Hill DP, Ni L, Sitnikov D, Smith C, Cuzick A, Seager J, Cooper L, Elser J, Jaiswal P, Gupta P, Jaiswal P, Naithani S, Lera-Ramirez M, Rutherford K, Wood V, De Pons JL, Dwinell MR, Hayman GT, Kaldunski ML, Kwitek AE, Laulederkind SJF, Tutaj MA, Vedi M, Wang SJ, D'Eustachio P, Aimo L, Axelsen K, Bridge A, Hyka-Nouspikel N, Morgat A, Aleksander SA, Cherry JM, Engel SR, Karra K, Miyasato SR, Nash RS, Skrzypek MS, Weng S, Wong ED, Bakker E, Berardini TZ, et alAleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, Ebert D, Feuermann M, Gaudet P, Harris NL, Hill DP, Lee R, Mi H, Moxon S, Mungall CJ, Muruganugan A, Mushayahama T, Sternberg PW, Thomas PD, Van Auken K, Ramsey J, Siegele DA, Chisholm RL, Fey P, Aspromonte MC, Nugnes MV, Quaglia F, Tosatto S, Giglio M, Nadendla S, Antonazzo G, Attrill H, Dos Santos G, Marygold S, Strelets V, Tabone CJ, Thurmond J, Zhou P, Ahmed SH, Asanitthong P, Luna Buitrago D, Erdol MN, Gage MC, Ali Kadhum M, Li KYC, Long M, Michalak A, Pesala A, Pritazahra A, Saverimuttu SCC, Su R, Thurlow KE, Lovering RC, Logie C, Oliferenko S, Blake J, Christie K, Corbani L, Dolan ME, Drabkin HJ, Hill DP, Ni L, Sitnikov D, Smith C, Cuzick A, Seager J, Cooper L, Elser J, Jaiswal P, Gupta P, Jaiswal P, Naithani S, Lera-Ramirez M, Rutherford K, Wood V, De Pons JL, Dwinell MR, Hayman GT, Kaldunski ML, Kwitek AE, Laulederkind SJF, Tutaj MA, Vedi M, Wang SJ, D'Eustachio P, Aimo L, Axelsen K, Bridge A, Hyka-Nouspikel N, Morgat A, Aleksander SA, Cherry JM, Engel SR, Karra K, Miyasato SR, Nash RS, Skrzypek MS, Weng S, Wong ED, Bakker E, Berardini TZ, Reiser L, Auchincloss A, Axelsen K, Argoud-Puy G, Blatter MC, Boutet E, Breuza L, Bridge A, Casals-Casas C, Coudert E, Estreicher A, Livia Famiglietti M, Feuermann M, Gos A, Gruaz-Gumowski N, Hulo C, Hyka-Nouspikel N, Jungo F, Le Mercier P, Lieberherr D, Masson P, Morgat A, Pedruzzi I, Pourcel L, Poux S, Rivoire C, Sundaram S, Bateman A, Bowler-Barnett E, Bye-A-Jee H, Denny P, Ignatchenko A, Ishtiaq R, Lock A, Lussi Y, Magrane M, Martin MJ, Orchard S, Raposo P, Speretta E, Tyagi N, Warner K, Zaru R, Diehl AD, Lee R, Chan J, Diamantakis S, Raciti D, Zarowiecki M, Fisher M, James-Zorn C, Ponferrada V, Zorn A, Ramachandran S, Ruzicka L, Westerfield M. The Gene Ontology knowledgebase in 2023. Genetics 2023; 224:iyad031. [PMID: 36866529 PMCID: PMC10158837 DOI: 10.1093/genetics/iyad031] [Show More Authors] [Citation(s) in RCA: 851] [Impact Index Per Article: 425.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/10/2023] [Accepted: 02/11/2023] [Indexed: 03/04/2023] Open
Abstract
The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.
Collapse
|
|
7
|
Hu Y, Comjean A, Attrill H, Antonazzo G, Thurmond J, Li F, Chao T, Mohr SE, Brown NH, Perrimon N. PANGEA: A New Gene Set Enrichment Tool for Drosophila and Common Research Organisms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.20.529262. [PMID: 36865134 PMCID: PMC9980003 DOI: 10.1101/2023.02.20.529262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an 'omics' study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/ ), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualisations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species.
Collapse
Affiliation(s)
- Yanhui Hu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Aram Comjean
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Helen Attrill
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| | - Giulia Antonazzo
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| | - Jim Thurmond
- Department of Biology, Indiana University, Bloomington, IN 47405, USA
| | - Fangge Li
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Tiffany Chao
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Stephanie E. Mohr
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Nicholas H. Brown
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| | - Norbert Perrimon
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston, MA 02138, USA
| |
Collapse
|
|
8
|
Joshi P, Banerjee S, Hu X, Khade PM, Friedberg I. GOThresher: a program to remove annotation biases from protein function annotation datasets. Bioinformatics 2023; 39:6998200. [PMID: 36688705 DOI: 10.1093/bioinformatics/btad048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 11/30/2022] [Accepted: 01/20/2023] [Indexed: 01/24/2023] Open
Abstract
MOTIVATION Advances in sequencing technologies have led to a surge in genomic data, although the functions of many gene products coded by these genes remain unknown. While in-depth, targeted experiments that determine the functions of these gene products are crucial and routinely performed, they fail to keep up with the inflow of novel genomic data. In an attempt to address this gap, high-throughput experiments are being conducted in which a large number of genes are investigated in a single study. The annotations generated as a result of these experiments are generally biased towards a small subset of less informative Gene Ontology (GO) terms. Identifying and removing biases from protein function annotation databases is important since biases impact our understanding of protein function by providing a poor picture of the annotation landscape. Additionally, as machine learning methods for predicting protein function are becoming increasingly prevalent, it is essential that they are trained on unbiased datasets. Therefore, it is not only crucial to be aware of biases, but also to judiciously remove them from annotation datasets. RESULTS We introduce GOThresher, a Python tool that identifies and removes biases in function annotations from protein function annotation databases. AVAILABILITY AND IMPLEMENTATION GOThresher is written in Python and released via PyPI https://pypi.org/project/gothresher/ and on the Bioconda Anaconda channel https://anaconda.org/bioconda/gothresher. The source code is hosted on GitHub https://github.com/FriedbergLab/GOThresher and distributed under the GPL 3.0 license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Parnal Joshi
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA.,Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA 50011, USA
| | - Sagnik Banerjee
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA.,Department of Statistics, Iowa State University, Ames, IA 50011, USA
| | - Xiao Hu
- Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA 50011, USA
| | - Pranav M Khade
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA.,Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA
| | - Iddo Friedberg
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA.,Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA 50011, USA
| |
Collapse
|
|
9
|
Feng JL, Hou D, Zhao C, Bao BH, Huang SY, Deng S, Meng FC, Zhao Q, Wang B, Li HS, Wang JS. A rat study model of depression-driven chronic prostatitis by modulating the PI3K/Akt/mTOR network. Andrologia 2022; 54:e14488. [PMID: 35727683 DOI: 10.1111/and.14488] [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: 01/04/2022] [Revised: 05/09/2022] [Accepted: 05/12/2022] [Indexed: 11/27/2022] Open
Abstract
Depression and chronic prostatitis (CP) are two common diseases that affect the human population worldwide. Clinically, it has been demonstrated that andrological patients often simultaneously suffer from depression and CP. Prior investigations have established that depression acts as an independent risk factor for CP. Herein, we explored the correlation between depression and CP using bioinformatics tools and through animal experiments. The potential targets and signalling pathways involved in depression and CP were predicted using bioinformatics tool, while depression in the rat model was established through chronic restraint stress. The expression of the related proteins and mRNA was assessed by Western blotting and real-time fluorescence quantitative reverse transcription-polymerase chain reaction (RT-qPCR). Relative to those in the control rats, the protein contents of PI3K, p-Akt, and p-mTOR were lower in the model rats (p < 0.05). Similarly, the transcript levels of PI3K, Akt, and mTOR was also relatively lower in the model rats (p < 0.05). And PI3K/Akt agonists reduced inflammation in rat prostate tissue, accompanied by significant increases in the transcript and protein expression levels of PI3K, Akt, and mTOR. Thus, we proposed that depression model rats may induce CP as a result of mediation by the negative regulation of the PI3K/Akt/mTOR signalling network.
Collapse
Affiliation(s)
- Jun-Long Feng
- Beijing University of Chinese Medicine, Beijing, China.,Department of Andrology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Dan Hou
- Beijing University of Chinese Medicine, Beijing, China.,Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
| | - Cong Zhao
- Beijing University of Chinese Medicine, Beijing, China.,Department of Andrology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Bing-Hao Bao
- Beijing University of Chinese Medicine, Beijing, China
| | - Shuai-Yang Huang
- Beijing University of Chinese Medicine, Beijing, China.,Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
| | - Sheng Deng
- Beijing University of Chinese Medicine, Beijing, China.,Department of Andrology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Fan-Chao Meng
- Beijing University of Chinese Medicine, Beijing, China.,Department of Andrology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Qi Zhao
- Beijing University of Chinese Medicine, Beijing, China.,Department of Andrology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Bin Wang
- Department of Andrology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Hai-Song Li
- Department of Andrology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Ji-Sheng Wang
- Department of Andrology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| |
Collapse
|
|
10
|
Silvestro S, Diomede F, Chiricosta L, Zingale VD, Marconi GD, Pizzicannella J, Valeri A, Avanzini MA, Calcaterra V, Pelizzo G, Mazzon E. The Role of Hypoxia in Improving the Therapeutic Potential of Mesenchymal Stromal Cells. A Comparative Study From Healthy Lung and Congenital Pulmonary Airway Malformations in Infants. Front Bioeng Biotechnol 2022; 10:868486. [PMID: 35774062 PMCID: PMC9237219 DOI: 10.3389/fbioe.2022.868486] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 05/20/2022] [Indexed: 11/17/2022] Open
Abstract
Mesenchymal stromal cells (MSCs) play an important role in the field of regenerative medicine thanks to their immunomodulatory properties and their ability to secrete paracrine factors. The use of MSCs has also been tested in children with congenital lung diseases inducing fibrosis and a decrease in lung function. Congenital malformations of the pulmonary airways (CPAM) are the most frequently encountered lung lesion that results from defects in early development of airways. Despite the beneficial properties of MSCs, interventions aimed at improving the outcome of cell therapy are needed. Hypoxia may be an approach aimed to ameliorate the therapeutic potential of MSCs. In this regard, we evaluated the transcriptomic profile of MSCs collected from pediatric patients with CPAM, analyzing similarities and differences between healthy tissue (MSCs-lung) and cystic tissue (MSCs-CPAM) both in normoxia and in cells preconditioned with hypoxia (0.2%) for 24 h. Study results showed that hypoxia induces cell cycle activation, increasing in such a way the cell proliferation ability, and enhancing cell anaerobic metabolism in both MSCs-lung and MSCs-CPAM-lung. Additionally, hypoxia downregulated several pro-apoptotic genes preserving MSCs from apoptosis and, at the same time, improving their viability in both comparisons. Finally, data obtained indicates that hypoxia leads to a greater expression of genes involved in the regulation of the cytoskeleton in MSCs-lung than MSCs-CPAM.
Collapse
Affiliation(s)
| | - Francesca Diomede
- Department of Innovative Technologies in Medicine and Dentistry, University “G. D’Annunzio” Chieti-Pescara, Chieti, Italy
| | | | | | - Guya Diletta Marconi
- Department of Medical, Oral and Biotechnological Sciences, University “G. D’Annunzio” Chieti-Pescara, Chieti, Italy
| | | | - Andrea Valeri
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Messina, Italy
| | - Maria Antonietta Avanzini
- Cell Factory, Pediatric Hematology Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Valeria Calcaterra
- Pediatrics and Adolescentology Unit, Department of Internal Medicine, University of Pavia, Pavia, Italy
- Pediatric Department, Children’s Hospital “Vittore Buzzi”, Milano, Italy
| | - Gloria Pelizzo
- Pediatric Surgery Department, Children’s Hospital “Vittore Buzzi”, Milano, Italy
- Department of Biomedical and Clinical Sciences-L. Sacco, University of Milan, Milan, Italy
| | | |
Collapse
|
|
11
|
Mesbah NM. Industrial Biotechnology Based on Enzymes From Extreme Environments. Front Bioeng Biotechnol 2022; 10:870083. [PMID: 35480975 PMCID: PMC9036996 DOI: 10.3389/fbioe.2022.870083] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 03/21/2022] [Indexed: 12/22/2022] Open
Abstract
Biocatalysis is crucial for a green, sustainable, biobased economy, and this has driven major advances in biotechnology and biocatalysis over the past 2 decades. There are numerous benefits to biocatalysis, including increased selectivity and specificity, reduced operating costs and lower toxicity, all of which result in lower environmental impact of industrial processes. Most enzymes available commercially are active and stable under a narrow range of conditions, and quickly lose activity at extremes of ion concentration, temperature, pH, pressure, and solvent concentrations. Extremophilic microorganisms thrive under extreme conditions and produce robust enzymes with higher activity and stability under unconventional circumstances. The number of extremophilic enzymes, or extremozymes, currently available are insufficient to meet growing industrial demand. This is in part due to difficulty in cultivation of extremophiles in a laboratory setting. This review will present an overview of extremozymes and their biotechnological applications. Culture-independent and genomic-based methods for study of extremozymes will be presented.
Collapse
Affiliation(s)
- Noha M Mesbah
- Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt
| |
Collapse
|
|
12
|
Duan Y, Han H, Qi J, Gao JM, Xu Z, Wang P, Zhang J, Liu C. Genome sequencing of Inonotus obliquus reveals insights into candidate genes involved in secondary metabolite biosynthesis. BMC Genomics 2022; 23:314. [PMID: 35443619 PMCID: PMC9020118 DOI: 10.1186/s12864-022-08511-x] [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: 10/11/2021] [Accepted: 03/27/2022] [Indexed: 11/10/2022] Open
Abstract
Background Inonotus obliquus is an important edible and medicinal mushroom that was shown to have many pharmacological activities in preclinical trials, including anti-inflammatory, antitumor, immunomodulatory, and antioxidant effects. However, the biosynthesis of these pharmacological components has rarely been reported. The lack of genomic information has hindered further molecular characterization of this mushroom. Results In this study, we report the genome of I. obliquus using a combined high-throughput Illumina NovaSeq with Oxford Nanopore PromethION sequencing platform. The de novo assembled 38.18 Mb I. obliquus genome was determined to harbor 12,525 predicted protein-coding genes, with 81.83% of them having detectable sequence similarities to others available in public databases. Phylogenetic analysis revealed the close evolutionary relationship of I. obliquus with Fomitiporia mediterranea and Sanghuangporus baumii in the Hymenochaetales clade. According to the distribution of reproduction-related genes, we predict that this mushroom possesses a tetrapolar heterothallic reproductive system. The I. obliquus genome was found to encode a repertoire of enzymes involved in carbohydrate metabolism, along with 135 cytochrome P450 proteins. The genome annotation revealed genes encoding key enzymes responsible for secondary metabolite biosynthesis, such as polysaccharides, polyketides, and terpenoids. Among them, we found four polyketide synthases and 20 sesquiterpenoid synthases belonging to four more types of cyclization mechanism, as well as 13 putative biosynthesis gene clusters involved in terpenoid synthesis in I. obliquus. Conclusions To the best of our knowledge, this is the first reported genome of I. obliquus; we discussed its genome characteristics and functional annotations in detail and predicted secondary metabolic biosynthesis-related genes, which provides genomic information for future studies on its associated molecular mechanism. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08511-x.
Collapse
Affiliation(s)
- Yingce Duan
- Key Laboratory for Enzyme and Enzyme-Like Material Engineering of Heilongjiang, College of Life Science, Northeast Forestry University, Harbin, 150040, Heilongjiang, China
| | - Haiyan Han
- Key Laboratory for Enzyme and Enzyme-Like Material Engineering of Heilongjiang, College of Life Science, Northeast Forestry University, Harbin, 150040, Heilongjiang, China
| | - Jianzhao Qi
- Shaanxi Key Laboratory of Natural Products & Chemical Biology, College of Chemistry & Pharmacy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Jin-Ming Gao
- Shaanxi Key Laboratory of Natural Products & Chemical Biology, College of Chemistry & Pharmacy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Zhichao Xu
- Key Laboratory for Enzyme and Enzyme-Like Material Engineering of Heilongjiang, College of Life Science, Northeast Forestry University, Harbin, 150040, Heilongjiang, China
| | - Pengchao Wang
- Key Laboratory for Enzyme and Enzyme-Like Material Engineering of Heilongjiang, College of Life Science, Northeast Forestry University, Harbin, 150040, Heilongjiang, China
| | - Jie Zhang
- Key Laboratory for Enzyme and Enzyme-Like Material Engineering of Heilongjiang, College of Life Science, Northeast Forestry University, Harbin, 150040, Heilongjiang, China
| | - Chengwei Liu
- Key Laboratory for Enzyme and Enzyme-Like Material Engineering of Heilongjiang, College of Life Science, Northeast Forestry University, Harbin, 150040, Heilongjiang, China.
| |
Collapse
|
|
13
|
Han A, Chua V, Baqai U, Purwin TJ, Bechtel N, Hunter E, Tiago M, Seifert E, Speicher DW, Schug ZT, Harbour JW, Aplin AE. Pyruvate dehydrogenase inactivation causes glycolytic phenotype in BAP1 mutant uveal melanoma. Oncogene 2022; 41:1129-1139. [PMID: 35046531 PMCID: PMC9066178 DOI: 10.1038/s41388-021-02154-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 12/02/2021] [Accepted: 12/10/2021] [Indexed: 12/12/2022]
Abstract
Effective therapeutic options are still lacking for uveal melanoma (UM) patients who develop metastasis. Metastatic traits of UM are linked to BRCA1-associated protein 1 (BAP1) mutations. Cell metabolism is re-programmed in UM with BAP1 mutant UM, but the underlying mechanisms and opportunities for therapeutic intervention remain unclear. BAP1 mutant UM tumors have an elevated glycolytic gene expression signature, with increased expression of pyruvate dehydrogenase (PDH) complex and PDH kinase (PDHK1). Furthermore, BAP1 mutant UM cells showed higher levels of phosphorylated PDHK1 and PDH that was associated with an upregulated glycolytic profile compared to BAP1 wild-type UM cells. Suppressing PDHK1-PDH phosphorylation decreased glycolytic capacity and cell growth, and induced cell cycle arrest of BAP1 mutant UM cells. Our results suggest that PDHK1-PDH phosphorylation is a causative factor of glycolytic phenotypes found in BAP1 mutant UM and propose a therapeutic opportunity for BAP1 mutant UM patients.
Collapse
Affiliation(s)
- Anna Han
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
- Department of Food Science and Human Nutrition, Jeonbuk National University, Jeonju, Jeollabuk-do, 54896, Republic of Korea
| | - Vivian Chua
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Usman Baqai
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Timothy J Purwin
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Nelisa Bechtel
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Emily Hunter
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Manoela Tiago
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Erin Seifert
- Department of Pathology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - David W Speicher
- Proteomics and Metabolomics Facility, The Wistar Institute, Philadelphia, PA, 19104, USA
- Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, 19104, USA
| | - Zachary T Schug
- Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, 19104, USA
| | - J William Harbour
- Bascom Palmer Eye Institute, Sylvester Comprehensive Cancer Center and Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL, 33146, USA
- Department of Ophthalmology, Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Andrew E Aplin
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, 19107, USA.
- Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, 19107, USA.
| |
Collapse
|
|
14
|
Makkos A, Ágg B, Varga ZV, Giricz Z, Gyöngyösi M, Lukovic D, Schulz R, Barteková M, Görbe A, Ferdinandy P. Molecular Network Approach Reveals Rictor as a Central Target of Cardiac ProtectomiRs. Int J Mol Sci 2021; 22:ijms22179539. [PMID: 34502448 PMCID: PMC8430799 DOI: 10.3390/ijms22179539] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/23/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022] Open
Abstract
Cardioprotective medications are still unmet clinical needs. We have previously identified several cardioprotective microRNAs (termed ProtectomiRs), the mRNA targets of which may reveal new drug targets for cardioprotection. Here we aimed to identify key molecular targets of ProtectomiRs and confirm their association with cardioprotection in a translational pig model of acute myocardial infarction (AMI). By using a network theoretical approach, we identified 882 potential target genes of 18 previously identified protectomiRs. The Rictor gene was the most central and it was ranked first in the protectomiR-target mRNA molecular network with the highest node degree of 5. Therefore, Rictor and its targeting microRNAs were further validated in heart samples obtained from a translational pig model of AMI and cardioprotection induced by pre- or postconditioning. Three out of five Rictor-targeting pig homologue of rat ProtectomiRs showed significant upregulation in postconditioned but not in preconditioned pig hearts. Rictor was downregulated at the mRNA and protein level in ischemic postconditioning but not in ischemic preconditioning. This is the first demonstration that Rictor is the central molecular target of ProtectomiRs and that decreased Rictor expression may regulate ischemic postconditioning-, but not preconditioning-induced acute cardioprotection. We conclude that Rictor is a potential novel drug target for acute cardioprotection.
Collapse
Affiliation(s)
- András Makkos
- Cardiovascular and Metabolic Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary; (A.M.); (B.Á.); (Z.V.V.); (Z.G.); (P.F.)
- MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary
| | - Bence Ágg
- Cardiovascular and Metabolic Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary; (A.M.); (B.Á.); (Z.V.V.); (Z.G.); (P.F.)
- MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary
- Pharmahungary Group, 6722 Szeged, Hungary
| | - Zoltán V. Varga
- Cardiovascular and Metabolic Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary; (A.M.); (B.Á.); (Z.V.V.); (Z.G.); (P.F.)
- HCEMM-SU Cardiometabolic Immunology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary
| | - Zoltán Giricz
- Cardiovascular and Metabolic Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary; (A.M.); (B.Á.); (Z.V.V.); (Z.G.); (P.F.)
| | - Mariann Gyöngyösi
- Division of Cardiology, Medical University of Vienna, 1090 Vienna, Austria; (M.G.); (D.L.)
| | - Dominika Lukovic
- Division of Cardiology, Medical University of Vienna, 1090 Vienna, Austria; (M.G.); (D.L.)
| | - Rainer Schulz
- Institute of Physiology, Justus Liebig University Giessen, 35392 Giessen, Germany;
| | - Monika Barteková
- Institute for Heart Research, Centre of Experimental Medicine, Slovak Academy of Sciences, Dúbravská cesta 9, 84104 Bratislava, Slovakia;
- Institute of Physiology, Comenius University in Bratislava, 81108 Bratislava, Slovakia
| | - Anikó Görbe
- Cardiovascular and Metabolic Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary; (A.M.); (B.Á.); (Z.V.V.); (Z.G.); (P.F.)
- MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary
- Pharmahungary Group, 6722 Szeged, Hungary
- Correspondence:
| | - Péter Ferdinandy
- Cardiovascular and Metabolic Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary; (A.M.); (B.Á.); (Z.V.V.); (Z.G.); (P.F.)
- MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary
- Pharmahungary Group, 6722 Szeged, Hungary
| |
Collapse
|
|
15
|
Weng CY, Zhu MH, Dai KL, Mi ZY, Wang YS, Liu ZQ, Zheng YG. Gene Cascade Shift and Pathway Enrichment in Rat Kidney Induced by Acarbose Through Comparative Analysis. Front Bioeng Biotechnol 2021; 9:659700. [PMID: 34095098 PMCID: PMC8176958 DOI: 10.3389/fbioe.2021.659700] [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: 01/29/2021] [Accepted: 04/12/2021] [Indexed: 01/02/2023] Open
Abstract
Acarbose is an effective anti-diabetic drug to treat type 2 diabetes mellitus (T2DM), a chronic degenerative metabolic disease caused by insulin resistance. The beneficial effects of acarbose on blood sugar control in T2DM patients have been confirmed by many studies. However, the effect of acarbose on patient kidney has yet to be fully elucidated. In this study, we report in detail the gene expression cascade shift, pathway and module enrichment, and interrelation network in acarbose-treated Rattus norvegicus kidneys based on the in-depth analysis of the GSE59913 microarray dataset. The significantly differentially expressed genes (DEGs) in the kidneys of acarbose-treated rats were initially screened out by comparative analysis. The enriched pathways for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were further identified. The protein-protein interaction (PPI) analysis for DEGs was achieved through the STRING database mining. Pathway interrelation and hub genes for enriched pathways were further examined to uncover key biological effects of acarbose. Results revealed 44 significantly up-regulated genes and 86 significantly down-regulated genes (130 significant differential genes in total) in acarbose-treated rat kidneys. Lipid metabolism pathways were considerably improved by acarbose, and the physical conditions in chronic kidney disease (CKD) patients were improved possibly through the increase of the level of high-density lipoprotein (HDL) by lecithin-cholesterol acyl-transferase (LCAT). These findings suggested that acarbose may serve as an ideal drug for CKD patients, since it not only protects the kidney, but also may relieve the complications caused by CKD.
Collapse
Affiliation(s)
- Chun-Yue Weng
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China.,Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China.,Engineering Research Center of Bioconversion and Biopurification, Ministry of Education, Zhejiang University of Technology, Hangzhou, China
| | - Mo-Han Zhu
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China.,Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China.,Engineering Research Center of Bioconversion and Biopurification, Ministry of Education, Zhejiang University of Technology, Hangzhou, China
| | - Ke-Lei Dai
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China.,Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China.,Engineering Research Center of Bioconversion and Biopurification, Ministry of Education, Zhejiang University of Technology, Hangzhou, China
| | - Zhe-Yan Mi
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China.,Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China.,Engineering Research Center of Bioconversion and Biopurification, Ministry of Education, Zhejiang University of Technology, Hangzhou, China
| | - Yuan-Shan Wang
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China.,Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China.,Engineering Research Center of Bioconversion and Biopurification, Ministry of Education, Zhejiang University of Technology, Hangzhou, China
| | - Zhi-Qiang Liu
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China.,Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China.,Engineering Research Center of Bioconversion and Biopurification, Ministry of Education, Zhejiang University of Technology, Hangzhou, China
| | - Yu-Guo Zheng
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China.,Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China.,Engineering Research Center of Bioconversion and Biopurification, Ministry of Education, Zhejiang University of Technology, Hangzhou, China
| |
Collapse
|
|
16
|
Wilfinger WW, Miller R, Eghbalnia HR, Mackey K, Chomczynski P. Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data. BMC Genomics 2021; 22:322. [PMID: 33941086 PMCID: PMC8091537 DOI: 10.1186/s12864-021-07563-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 03/29/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND RNA sequencing analysis focus on the detection of differential gene expression changes that meet a two-fold minimum change between groups. The variability present in RNA sequencing data may obscure the detection of valuable information when specific genes within certain samples display large expression variability. This paper develops methods that apply variance and dispersion estimates to intra-group data to identify genes with expression values that diverge from the group envelope. STRING database analysis of the identified genes characterize gene affiliations involved in physiological regulatory networks that contribute to biological variability. Individuals with divergent gene groupings within network pathways can thereby be identified and judiciously evaluated prior to standard differential analysis. RESULTS A three-step process is presented for evaluating biological variability within a group in RNA sequencing data in which gene counts were: (1) scaled to minimize heteroscedasticity; (2) rank-ordered to detect potentially divergent "trendlines" for every gene in the data set; and (3) tested with the STRING database to identify statistically significant pathway associations among the genes displaying marked trendline variability and dispersion. This approach was used to identify the "trendline" profile of every gene in three test data sets. Control data from an in-house data set and two archived samples revealed that 65-70% of the sequenced genes displayed trendlines with minimal variation and dispersion across the sample group after rank-ordering the samples; this is referred to as a linear trendline. Smaller subsets of genes within the three data sets displayed markedly skewed trendlines, wide dispersion and variability. STRING database analysis of these genes identified interferon-mediated response networks in 11-20% of the individuals sampled at the time of blood collection. For example, in the three control data sets, 14 to 26 genes in the defense response to virus pathway were identified in 7 individuals at false discovery rates ≤1.92 E-15. CONCLUSIONS This analysis provides a rationale for identifying and characterizing notable gene expression variability within a study group. The identification of highly variable genes and their network associations within specific individuals empowers more judicious inspection of the sample group prior to differential gene expression analysis.
Collapse
Affiliation(s)
| | | | - Hamid R. Eghbalnia
- University of Wisconsin-Madison, Madison, USA
- University of Cincinnati, Cincinnati, USA
| | - Karol Mackey
- Molecular Research Center, Inc., Cincinnati, USA
| | | |
Collapse
|
|
17
|
Sysoev M, Grötzinger SW, Renn D, Eppinger J, Rueping M, Karan R. Bioprospecting of Novel Extremozymes From Prokaryotes-The Advent of Culture-Independent Methods. Front Microbiol 2021; 12:630013. [PMID: 33643258 PMCID: PMC7902512 DOI: 10.3389/fmicb.2021.630013] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 01/21/2021] [Indexed: 12/20/2022] Open
Abstract
Extremophiles are remarkable organisms that thrive in the harshest environments on Earth, such as hydrothermal vents, hypersaline lakes and pools, alkaline soda lakes, deserts, cold oceans, and volcanic areas. These organisms have developed several strategies to overcome environmental stress and nutrient limitations. Thus, they are among the best model organisms to study adaptive mechanisms that lead to stress tolerance. Genetic and structural information derived from extremophiles and extremozymes can be used for bioengineering other nontolerant enzymes. Furthermore, extremophiles can be a valuable resource for novel biotechnological and biomedical products due to their biosynthetic properties. However, understanding life under extreme conditions is challenging due to the difficulties of in vitro cultivation and observation since > 99% of organisms cannot be cultivated. Consequently, only a minor percentage of the potential extremophiles on Earth have been discovered and characterized. Herein, we present a review of culture-independent methods, sequence-based metagenomics (SBM), and single amplified genomes (SAGs) for studying enzymes from extremophiles, with a focus on prokaryotic (archaea and bacteria) microorganisms. Additionally, we provide a comprehensive list of extremozymes discovered via metagenomics and SAGs.
Collapse
Affiliation(s)
- Maksim Sysoev
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Stefan W. Grötzinger
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Dominik Renn
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Jörg Eppinger
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Institute for Experimental Molecular Imaging, University Clinic, RWTH Aachen University, Aachen, Germany
| | - Magnus Rueping
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Institute for Experimental Molecular Imaging, University Clinic, RWTH Aachen University, Aachen, Germany
| | - Ram Karan
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| |
Collapse
|
|
18
|
IFN- γ Mediates the Development of Systemic Lupus Erythematosus. BIOMED RESEARCH INTERNATIONAL 2020; 2020:7176515. [PMID: 33123584 PMCID: PMC7586164 DOI: 10.1155/2020/7176515] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/17/2020] [Accepted: 08/25/2020] [Indexed: 12/11/2022]
Abstract
Objective Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can affect all organs in the body. It is characterized by overexpression of antibodies against autoantigen. Although previous bioinformatics analyses have identified several genetic factors underlying SLE, they did not discriminate between naive and individuals exposed to anti-SLE drugs. Here, we evaluated specific genes and pathways in active and recently diagnosed SLE population. Methods GSE46907 matrix downloaded from Gene Expression Omnibus (GEO) was analyzed using R, Metascape, STRING, and Cytoscape to identify differentially expressed genes (DEGs), enrichment pathways, protein-protein interaction (PPI), and hub genes between naive SLE individuals and healthy controls. Results A total of 134 DEGs were identified, in which 29 were downregulated, whereas 105 were upregulated in active and newly diagnosed SLE cases. GO term analysis revealed that transcriptional induction of the DEGs was particularly enhanced in response to secretion of interferon-γ and interferon-α and regulation of cytokine production innate immune responses among others. KEGG pathway analysis showed that the expression of DEGs was particularly enhanced in interferon signaling, IFN antiviral responses by activated genes, class I major histocompatibility complex (MHC-I) mediated antigen processing and presentation, and amyloid fiber formation. STAT1, IRF7, MX1, OASL, ISG15, IFIT3, IFIH1, IFIT1, OAS2, and GBP1 were the top 10 DEGs. Conclusions Our findings suggest that interferon-related gene expression and pathways are common features for SLE pathogenesis, and IFN-γ and IFN-γ-inducible GBP1 gene in naive SLE were emphasized. Together, the identified genes and cellular pathways have expanded our understanding on the mechanism underlying development of SLE. They have also opened a new frontier on potential biomarkers for diagnosis, biotherapy, and prognosis for SLE.
Collapse
|
|
19
|
Ricard-Blum S, Miele AE. Omic approaches to decipher the molecular mechanisms of fibrosis, and design new anti-fibrotic strategies. Semin Cell Dev Biol 2020; 101:161-169. [DOI: 10.1016/j.semcdb.2019.12.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 12/16/2019] [Accepted: 12/16/2019] [Indexed: 12/17/2022]
|
|
20
|
Lock A, Harris MA, Rutherford K, Hayles J, Wood V. Community curation in PomBase: enabling fission yeast experts to provide detailed, standardized, sharable annotation from research publications. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5827230. [PMID: 32353878 PMCID: PMC7192550 DOI: 10.1093/database/baaa028] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 02/28/2020] [Accepted: 03/22/2020] [Indexed: 11/22/2022]
Abstract
Maximizing the impact and value of scientific research requires efficient knowledge distribution, which increasingly depends on the integration of standardized published data into online databases. To make data integration more comprehensive and efficient for fission yeast research, PomBase has pioneered a community curation effort that engages publication authors directly in FAIR-sharing of data representing detailed biological knowledge from hypothesis-driven experiments. Canto, an intuitive online curation tool that enables biologists to describe their detailed functional data using shared ontologies, forms the core of PomBase’s system. With 8 years’ experience, and as the author response rate reaches 50%, we review community curation progress and the insights we have gained from the project. We highlight incentives and nudges we deploy to maximize participation, and summarize project outcomes, which include increased knowledge integration and dissemination as well as the unanticipated added value arising from co-curation by publication authors and professional curators.
Collapse
Affiliation(s)
- Antonia Lock
- Department of Genetics, Evolution and Environment, University College London, Gower street, London WC1E 6BT, UK
| | - Midori A Harris
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Kim Rutherford
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Jacqueline Hayles
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Valerie Wood
- Cell Cycle Laboratory, The Francis Crick Institute, Midland Rd, London NW1 1AT, UK
| |
Collapse
|
|
21
|
Naba A, Ricard-Blum S. The Extracellular Matrix Goes -Omics: Resources and Tools. EXTRACELLULAR MATRIX OMICS 2020. [DOI: 10.1007/978-3-030-58330-9_1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|