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Chen XR, Cui YZ, Li BZ, Yuan YJ. Genome engineering on size reduction and complexity simplification: A review. J Adv Res 2024; 60:159-171. [PMID: 37442424 PMCID: PMC11156615 DOI: 10.1016/j.jare.2023.07.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/25/2023] [Accepted: 07/10/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND Genome simplification is an important topic in the field of life sciences that has attracted attention from its conception to the present day. It can help uncover the essential components of the genome and, in turn, shed light on the underlying operating principles of complex biological systems. This has made it a central focus of both basic and applied research in the life sciences. With the recent advancements in related technologies and our increasing knowledge of the genome, now is an opportune time to delve into this topic. AIM OF REVIEW Our review investigates the progress of genome simplification from two perspectives: genome size reduction and complexity simplification. In addition, we provide insights into the future development trends of genome simplification. KEY SCIENTIFIC CONCEPTS OF REVIEW Reducing genome size requires eliminating non-essential elements as much as possible. This process has been facilitated by advances in genome manipulation and synthesis techniques. However, we still need a better and clearer understanding of living systems to reduce genome complexity. As there is a lack of quantitative and clearly defined standards for this task, we have opted to approach the topic from various perspectives and present our findings accordingly.
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Affiliation(s)
- Xiang-Rong Chen
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China; Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin, China
| | - You-Zhi Cui
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China; Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin, China
| | - Bing-Zhi Li
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China; Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin, China.
| | - Ying-Jin Yuan
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China; Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin, China
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Payra AK, Saha B, Ghosh A. MEM-FET: Essential protein prediction using membership feature and machine learning approach. Proteins 2024; 92:60-75. [PMID: 37638618 DOI: 10.1002/prot.26577] [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: 06/20/2022] [Revised: 02/21/2023] [Accepted: 08/08/2023] [Indexed: 08/29/2023]
Abstract
Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab. The results of observation in wet lab always ensure high reliability and accuracy in the biological ground. Essential protein prediction using computational approaches is an alternative choice in research. It proves its significance rapidly in day-to-day life as well as reduces the experimental cost of wet lab effectively. Existing computational methods were implemented using Protein interaction networks (PPIN), Sequence, Gene Expression Dataset (GED), Gene Ontology (GO), Orthologous groups, and Subcellular localized datasets. Machine learning has diverse categories of features that enable to model and predict essential macromolecules of understudied organisms. A novel methodology MEM-FET (membership feature) is predicted based on features, that is, edge clustering coefficient, Average clustering coefficient, subcellular localization, and Gene Ontology within a compartment of common neighbors. The accuracy (ACC) values of the predicted true positive (TP) essential proteins are 0.79, 0.74, 0.78, and 0.71 for YHQ, YMIPS, YDIP, and YMBD datasets. An enriched set of essential proteins are also predicted using the MEM-FET algorithm. Ensemble ML also validated the proposed model with an accuracy of 60%. It has been predicted that MEM-FET algorithms outperform other existing algorithms with an ACC value of 80% for the yeast dataset.
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Affiliation(s)
- Anjan Kumar Payra
- Department of Computer Science and Engineering, Dr. Sudhir Chandra Sur Degree Engineering College, Kolkata, India
| | - Banani Saha
- Department of Computer Science and Engineering, University of Calcutta, Kolkata, India
| | - Anupam Ghosh
- Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India
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Giordano M, Falbo E, Maddalena L, Piccirillo M, Granata I. Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience. Biomolecules 2023; 14:18. [PMID: 38254618 PMCID: PMC10813179 DOI: 10.3390/biom14010018] [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: 10/18/2023] [Revised: 11/29/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this information has been used to reduce the genomes of model organisms. A growing amount of evidence highlights that essentiality is a property that depends on the context. Because of their importance in vital biological processes, recognising context-specific EGs (csEGs) could help for identifying new potential pharmacological targets and to improve precision therapeutics. Since most of the computational procedures proposed to identify and predict EGs neglect their context-specificity, we focused on this aspect, providing a theoretical and experimental overview of the literature, data and computational methods dedicated to recognising csEGs. To this end, we adapted existing computational methods to exploit a specific context (the kidney tissue) and experimented with four different prediction methods using the labels provided by four different identification approaches. The considerations derived from the analysis of the obtained results, confirmed and validated also by further experiments for a different tissue context, provide the reader with guidance on exploiting existing tools for achieving csEGs identification and prediction.
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Affiliation(s)
- Maurizio Giordano
- Institute for High-Performance Computing and Networking (ICAR), National Research Council (CNR), V. Pietro Castellino 111, 80131 Naples, Italy; (E.F.); (L.M.); (M.P.); (I.G.)
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Saxena P, Rauniyar S, Thakur P, Singh RN, Bomgni A, Alaba MO, Tripathi AK, Gnimpieba EZ, Lushbough C, Sani RK. Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria. Front Microbiol 2023; 14:1086021. [PMID: 37125195 PMCID: PMC10133479 DOI: 10.3389/fmicb.2023.1086021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/23/2023] [Indexed: 05/02/2023] Open
Abstract
The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein-protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under "persistent," inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under "shell." Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies.
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Affiliation(s)
- Priya Saxena
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- Data Driven Material Discovery Center for Bioengineering Innovation, South Dakota School of Mines and Technology, Rapid City, SD, United States
| | - Shailabh Rauniyar
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- 2-Dimensional Materials for Biofilm Engineering, Science and Technology, South Dakota School of Mines and Technology, Rapid City, SD, United States
| | - Payal Thakur
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- Data Driven Material Discovery Center for Bioengineering Innovation, South Dakota School of Mines and Technology, Rapid City, SD, United States
| | - Ram Nageena Singh
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- 2-Dimensional Materials for Biofilm Engineering, Science and Technology, South Dakota School of Mines and Technology, Rapid City, SD, United States
| | - Alain Bomgni
- Department of Biomedical Engineering, University of South Dakota, Sioux Falls, SD, United States
| | - Mathew O. Alaba
- Department of Biomedical Engineering, University of South Dakota, Sioux Falls, SD, United States
| | - Abhilash Kumar Tripathi
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- 2-Dimensional Materials for Biofilm Engineering, Science and Technology, South Dakota School of Mines and Technology, Rapid City, SD, United States
| | - Etienne Z. Gnimpieba
- Department of Biomedical Engineering, University of South Dakota, Sioux Falls, SD, United States
- *Correspondence: Etienne Z. Gnimpieba,
| | - Carol Lushbough
- Department of Biomedical Engineering, University of South Dakota, Sioux Falls, SD, United States
| | - Rajesh Kumar Sani
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- Data Driven Material Discovery Center for Bioengineering Innovation, South Dakota School of Mines and Technology, Rapid City, SD, United States
- 2-Dimensional Materials for Biofilm Engineering, Science and Technology, South Dakota School of Mines and Technology, Rapid City, SD, United States
- BuG ReMeDEE Consortium, South Dakota School of Mines and Technology, Rapid City, SD, United States
- Rajesh Kumar Sani,
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Panditrao G, Bhowmick R, Meena C, Sarkar RR. Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects. J Biosci 2022. [PMID: 36210749 PMCID: PMC9018971 DOI: 10.1007/s12038-022-00253-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.
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Affiliation(s)
- Gauri Panditrao
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Rupa Bhowmick
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
| | - Chandrakala Meena
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
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Campos TL, Korhonen PK, Hofmann A, Gasser RB, Young ND. Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications. Biotechnol Adv 2021; 54:107822. [PMID: 34461202 DOI: 10.1016/j.biotechadv.2021.107822] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 08/17/2021] [Accepted: 08/24/2021] [Indexed: 12/17/2022]
Abstract
The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.
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Affiliation(s)
- Tulio L Campos
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia; Bioinformatics Core Facility, Instituto Aggeu Magalhães, Fundação Oswaldo Cruz (IAM-Fiocruz), Recife, Pernambuco, Brazil
| | - Pasi K Korhonen
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Andreas Hofmann
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.
| | - Neil D Young
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.
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