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Wen X, Lin J, Yang C, Li Y, Cheng H, Liu Y, Zhang Y, Ma H, Mao Y, Liao X, Wang M. Automated characterization and analysis of expression compatibility between regulatory sequences and metabolic genes in Escherichia coli. Synth Syst Biotechnol 2024; 9:647-657. [PMID: 38817827 PMCID: PMC11137365 DOI: 10.1016/j.synbio.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 05/11/2024] [Accepted: 05/16/2024] [Indexed: 06/01/2024] Open
Abstract
Utilizing standardized artificial regulatory sequences to fine-tuning the expression of multiple metabolic pathways/genes is a key strategy in the creation of efficient microbial cell factories. However, when regulatory sequence expression strengths are characterized using only a few reporter genes, they may not be applicable across diverse genes. This introduces great uncertainty into the precise regulation of multiple genes at multiple expression levels. To address this, our study adopted a fluorescent protein fusion strategy for a more accurate assessment of target protein expression levels. We combined 41 commonly-used metabolic genes with 15 regulatory sequences, yielding an expression dataset encompassing 520 unique combinations. This dataset highlighted substantial variation in protein expression level under identical regulatory sequences, with relative expression levels ranging from 2.8 to 176-fold. It also demonstrated that improving the strength of regulatory sequences does not necessarily lead to significant improvements in the expression levels of target proteins. Utilizing this dataset, we have developed various machine learning models and discovered that the integration of promoter regions, ribosome binding sites, and coding sequences significantly improves the accuracy of predicting protein expression levels, with a Spearman correlation coefficient of 0.72, where the promoter sequence exerts a predominant influence. Our study aims not only to provide a detailed guide for fine-tuning gene expression in the metabolic engineering of Escherichia coli but also to deepen our understanding of the compatibility issues between regulatory sequences and target genes.
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Affiliation(s)
- Xiao Wen
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Jiawei Lin
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- School of Biological Engineering, Tianjin University of Science and Technology, Tianjin, 300457, China
| | - Chunhe Yang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- School of Biological Engineering, Tianjin University of Science and Technology, Tianjin, 300457, China
| | - Ying Li
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- School of Biological Engineering, Tianjin University of Science and Technology, Tianjin, 300457, China
| | - Haijiao Cheng
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Ye Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Yue Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Hongwu Ma
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Yufeng Mao
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Xiaoping Liao
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Meng Wang
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
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2
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Lam HYI, Ong XE, Mutwil M. Large language models in plant biology. TRENDS IN PLANT SCIENCE 2024; 29:1145-1155. [PMID: 38797656 DOI: 10.1016/j.tplants.2024.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/29/2024]
Abstract
Large language models (LLMs), such as ChatGPT, have taken the world by storm. However, LLMs are not limited to human language and can be used to analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multipurpose prediction tools able to predict the state of cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom.
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Affiliation(s)
- Hilbert Yuen In Lam
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Xing Er Ong
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Marek Mutwil
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore.
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3
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Wang Z, Peng Y, Li J, Li J, Yuan H, Yang S, Ding X, Xie A, Zhang J, Wang S, Li K, Shi J, Xing G, Shi W, Yan J, Liu J. DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions. PLANT COMMUNICATIONS 2024; 5:100985. [PMID: 38859587 DOI: 10.1016/j.xplc.2024.100985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/25/2024] [Accepted: 06/05/2024] [Indexed: 06/12/2024]
Abstract
Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the effect of chromatin interactions on target gene expression, thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements. In this study, we developed a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCCs) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and both proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences. Some important motifs were identified through DeepCBA; they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity. Importantly, experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression. Moreover, promoter editing and verification of two reported genes (ZmCLE7 and ZmVTE4) demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding. DeepCBA is available at http://www.deepcba.com/ or http://124.220.197.196/.
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Affiliation(s)
- Zhenye Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yong Peng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Jie Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jiying Li
- Microsoft Corporation, Redmond, WA 98052, USA
| | - Hao Yuan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Shangpo Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Xinru Ding
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Ao Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jiangling Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Shouzhe Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China; WIMI Biotechnology Co., Ltd., Changzhou 213000, China
| | - Keqin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jiaqi Shi
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Guangjie Xing
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Weihan Shi
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Jianxiao Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
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Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024; 74:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [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: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
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Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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5
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Masoudi-Sobhanzadeh Y, Li S, Peng Y, Panchenko A. Interpretable deep residual network uncovers nucleosome positioning and associated features. Nucleic Acids Res 2024; 52:8734-8745. [PMID: 39036965 PMCID: PMC11347144 DOI: 10.1093/nar/gkae623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/31/2024] [Accepted: 07/04/2024] [Indexed: 07/23/2024] Open
Abstract
Nucleosomes represent elementary building units of eukaryotic chromosomes and consist of DNA wrapped around a histone octamer flanked by linker DNA segments. Nucleosomes are central in epigenetic pathways and their genomic positioning is associated with regulation of gene expression, DNA replication, DNA methylation and DNA repair, among other functions. Building on prior discoveries that DNA sequences noticeably affect nucleosome positioning, our objective is to identify nucleosome positions and related features across entire genome. Here, we introduce an interpretable framework based on the concepts of deep residual networks (NuPoSe). Trained on high-coverage human experimental MNase-seq data, NuPoSe is able to learn sequence and structural patterns associated with nucleosome organization in human genome. NuPoSe can be also applied to unseen data from different organisms and cell types. Our findings point to 43 informative features, most of them constitute tri-nucleotides, di-nucleotides and one tetra-nucleotide. Most features are significantly associated with the nucleosomal structural characteristics, namely, periodicity of nucleosomal DNA and its location with respect to a histone octamer. Importantly, we show that features derived from the 27 bp linker DNA flanking nucleosomes contribute up to 10% to the quality of the prediction model. This, along with the comprehensive training sets, deep-learning architecture, and feature selection method, may contribute to the NuPoSe's 80-89% classification accuracy on different independent datasets.
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Affiliation(s)
| | - Shuxiang Li
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, K7L3N6, Canada
| | - Yunhui Peng
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China
| | - Anna R Panchenko
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, K7L3N6, Canada
- Department of Biology and Molecular Sciences, Queen's University, Kingston, K7L3N6, Canada
- School of Computing, Queen's University, Kingston, K7L3N6, Canada
- Ontario Institute of Cancer Research, Toronto, M5G 0A3, Canada
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6
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Lin J, Luo R, Pinello L. EPInformer: a scalable deep learning framework for gene expression prediction by integrating promoter-enhancer sequences with multimodal epigenomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.01.606099. [PMID: 39131276 PMCID: PMC11312614 DOI: 10.1101/2024.08.01.606099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Transcriptional regulation, critical for cellular differentiation and adaptation to environmental changes, involves coordinated interactions among DNA sequences, regulatory proteins, and chromatin architecture. Despite extensive data from consortia like ENCODE, understanding the dynamics of cis-regulatory elements (CREs) in gene expression remains challenging. Deep learning is a powerful tool for learning gene expression and epigenomic signals from DNA sequences, exhibiting superior performance compared to conventional machine learning approaches. However, even the most advanced deep learning-based methods may fall short in capturing the regulatory effects of distal elements such as enhancers, limiting their predictive accuracy. In addition, these methods may require significant resources to train or to adapt to newly generated data. To address these challenges, we present EPInformer, a scalable deep-learning framework for predicting gene expression by integrating promoter-enhancer interactions with their sequences, epigenomic signals, and chromatin contacts. Our model outperforms existing gene expression prediction models in rigorous cross-chromosome validation, accurately recapitulates enhancer-gene interactions validated by CRISPR perturbation experiments, and identifies crucial transcription factor motifs within regulatory sequences. EPInformer is available as open-source software at https://github.com/pinellolab/EPInformer.
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Affiliation(s)
- Jiecong Lin
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, Massachusetts 02129, USA
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, Massachusetts 02129, USA
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Wang X, Li F, Zhang Y, Imoto S, Shen HH, Li S, Guo Y, Yang J, Song J. Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects. Brief Bioinform 2024; 25:bbae446. [PMID: 39276327 PMCID: PMC11401448 DOI: 10.1093/bib/bbae446] [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: 04/02/2024] [Revised: 08/08/2024] [Accepted: 08/27/2024] [Indexed: 09/16/2024] Open
Abstract
Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data. Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their model-based interpretation and downstream tasks. This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding variants. Additionally, we summarize the limitations of current approaches in variant effect prediction research and outline opportunities for improvement. We anticipate that our study will offer a practical and useful guide for the bioinformatic community to further advance the unraveling of genetic variant effects.
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Affiliation(s)
- Xiaoyu Wang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Fuyi Li
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Yiwen Zhang
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Seiya Imoto
- Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Hsin-Hui Shen
- Department of Materials Science and Engineering, Faculty of Engineering, Monash University, Clayton, VIC 3800, Australia
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310030, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
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8
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Gorjifard S, Jores T, Tonnies J, Mueth NA, Bubb K, Wrightsman T, Buckler ES, Fields S, Cuperus JT, Queitsch C. Arabidopsis and maize terminator strength is determined by GC content, polyadenylation motifs and cleavage probability. Nat Commun 2024; 15:5868. [PMID: 38997252 PMCID: PMC11245536 DOI: 10.1038/s41467-024-50174-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 07/03/2024] [Indexed: 07/14/2024] Open
Abstract
The 3' end of a gene, often called a terminator, modulates mRNA stability, localization, translation, and polyadenylation. Here, we adapted Plant STARR-seq, a massively parallel reporter assay, to measure the activity of over 50,000 terminators from the plants Arabidopsis thaliana and Zea mays. We characterize thousands of plant terminators, including many that outperform bacterial terminators commonly used in plants. Terminator activity is species-specific, differing in tobacco leaf and maize protoplast assays. While recapitulating known biology, our results reveal the relative contributions of polyadenylation motifs to terminator strength. We built a computational model to predict terminator strength and used it to conduct in silico evolution that generated optimized synthetic terminators. Additionally, we discover alternative polyadenylation sites across tens of thousands of terminators; however, the strongest terminators tend to have a dominant cleavage site. Our results establish features of plant terminator function and identify strong naturally occurring and synthetic terminators.
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Affiliation(s)
- Sayeh Gorjifard
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Tobias Jores
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Jackson Tonnies
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
- Graduate Program in Biology, University of Washington, Seattle, WA, 98195, USA
| | - Nicholas A Mueth
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Kerry Bubb
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Travis Wrightsman
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14853, USA
| | - Edward S Buckler
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14853, USA
- Agricultural Research Service, United States Department of Agriculture, Ithaca, NY, 14853, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Stanley Fields
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
- Department of Medicine, University of Washington, Seattle, WA, 98195, USA
| | - Josh T Cuperus
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Christine Queitsch
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA.
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Mendoza-Revilla J, Trop E, Gonzalez L, Roller M, Dalla-Torre H, de Almeida BP, Richard G, Caton J, Lopez Carranza N, Skwark M, Laterre A, Beguir K, Pierrot T, Lopez M. A foundational large language model for edible plant genomes. Commun Biol 2024; 7:835. [PMID: 38982288 PMCID: PMC11233511 DOI: 10.1038/s42003-024-06465-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 06/17/2024] [Indexed: 07/11/2024] Open
Abstract
Significant progress has been made in the field of plant genomics, as demonstrated by the increased use of high-throughput methodologies that enable the characterization of multiple genome-wide molecular phenotypes. These findings have provided valuable insights into plant traits and their underlying genetic mechanisms, particularly in model plant species. Nonetheless, effectively leveraging them to make accurate predictions represents a critical step in crop genomic improvement. We present AgroNT, a foundational large language model trained on genomes from 48 plant species with a predominant focus on crop species. We show that AgroNT can obtain state-of-the-art predictions for regulatory annotations, promoter/terminator strength, tissue-specific gene expression, and prioritize functional variants. We conduct a large-scale in silico saturation mutagenesis analysis on cassava to evaluate the regulatory impact of over 10 million mutations and provide their predicted effects as a resource for variant characterization. Finally, we propose the use of the diverse datasets compiled here as the Plants Genomic Benchmark (PGB), providing a comprehensive benchmark for deep learning-based methods in plant genomic research. The pre-trained AgroNT model is publicly available on HuggingFace at https://huggingface.co/InstaDeepAI/agro-nucleotide-transformer-1b for future research purposes.
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Rich A, Acar O, Carvunis AR. Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome. Genome Biol 2024; 25:183. [PMID: 38978079 PMCID: PMC11232214 DOI: 10.1186/s13059-024-03287-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 05/20/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Recent studies uncovered pervasive transcription and translation of thousands of noncanonical open reading frames (nORFs) outside of annotated genes. The contribution of nORFs to cellular phenotypes is difficult to infer using conventional approaches because nORFs tend to be short, of recent de novo origins, and lowly expressed. Here we develop a dedicated coexpression analysis framework that accounts for low expression to investigate the transcriptional regulation, evolution, and potential cellular roles of nORFs in Saccharomyces cerevisiae. RESULTS Our results reveal that nORFs tend to be preferentially coexpressed with genes involved in cellular transport or homeostasis but rarely with genes involved in RNA processing. Mechanistically, we discover that young de novo nORFs located downstream of conserved genes tend to leverage their neighbors' promoters through transcription readthrough, resulting in high coexpression and high expression levels. Transcriptional piggybacking also influences the coexpression profiles of young de novo nORFs located upstream of genes, but to a lesser extent and without detectable impact on expression levels. Transcriptional piggybacking influences, but does not determine, the transcription profiles of de novo nORFs emerging nearby genes. About 40% of nORFs are not strongly coexpressed with any gene but are transcriptionally regulated nonetheless and tend to form entirely new transcription modules. We offer a web browser interface ( https://carvunislab.csb.pitt.edu/shiny/coexpression/ ) to efficiently query, visualize, and download our coexpression inferences. CONCLUSIONS Our results suggest that nORF transcription is highly regulated. Our coexpression dataset serves as an unprecedented resource for unraveling how nORFs integrate into cellular networks, contribute to cellular phenotypes, and evolve.
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Affiliation(s)
- April Rich
- Joint Carnegie Mellon University-University of Pittsburgh, University of Pittsburgh Computational Biology PhD Program, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Pittsburgh Center for Evolutionary Biology and Medicine (CEBaM), University of Pittsburgh, Pittsburgh, PA, USA
| | - Omer Acar
- Joint Carnegie Mellon University-University of Pittsburgh, University of Pittsburgh Computational Biology PhD Program, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Pittsburgh Center for Evolutionary Biology and Medicine (CEBaM), University of Pittsburgh, Pittsburgh, PA, USA
| | - Anne-Ruxandra Carvunis
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
- Pittsburgh Center for Evolutionary Biology and Medicine (CEBaM), University of Pittsburgh, Pittsburgh, PA, USA.
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11
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Li T, Xu H, Teng S, Suo M, Bahitwa R, Xu M, Qian Y, Ramstein GP, Song B, Buckler ES, Wang H. Modeling 0.6 million genes for the rational design of functional cis-regulatory variants and de novo design of cis-regulatory sequences. Proc Natl Acad Sci U S A 2024; 121:e2319811121. [PMID: 38889146 PMCID: PMC11214048 DOI: 10.1073/pnas.2319811121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 05/14/2024] [Indexed: 06/20/2024] Open
Abstract
Rational design of plant cis-regulatory DNA sequences without expert intervention or prior domain knowledge is still a daunting task. Here, we developed PhytoExpr, a deep learning framework capable of predicting both mRNA abundance and plant species using the proximal regulatory sequence as the sole input. PhytoExpr was trained over 17 species representative of major clades of the plant kingdom to enhance its generalizability. Via input perturbation, quantitative functional annotation of the input sequence was achieved at single-nucleotide resolution, revealing an abundance of predicted high-impact nucleotides in conserved noncoding sequences and transcription factor binding sites. Evaluation of maize HapMap3 single-nucleotide polymorphisms (SNPs) by PhytoExpr demonstrates an enrichment of predicted high-impact SNPs in cis-eQTL. Additionally, we provided two algorithms that harnessed the power of PhytoExpr in designing functional cis-regulatory variants, and de novo creation of species-specific cis-regulatory sequences through in silico evolution of random DNA sequences. Our model represents a general and robust approach for functional variant discovery in population genetics and rational design of regulatory sequences for genome editing and synthetic biology.
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Affiliation(s)
- Tianyi Li
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Hui Xu
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Shouzhen Teng
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Mingrui Suo
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Revocatus Bahitwa
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
- Legumes Research Program, Research and Innovation Division, Tanzania Agricultural Research Institute, Ilonga, Kilosa, Morogoro67410, Tanzania
| | - Mingchi Xu
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Yiheng Qian
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Guillaume P. Ramstein
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus8000, Denmark
| | - Baoxing Song
- National Key Laboratory of Wheat Improvement, Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agriculture Sciences in Weifang, Weifang, Shandong261325, People’s Republic of China
- Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region of the Ministry of Agriculture, College of Agronomy, Northwest A&F University, Yangling, Shaanxi712100, People’s Republic of China
| | - Edward S. Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY14853
- Agricultural Research Service, United States Department of Agriculture, Ithaca, NY14853
| | - Hai Wang
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
- Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing100193, People’s Republic of China
- Sanya Institute of China Agricultural University, Sanya572025, People’s Republic of China
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12
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Gonzalez-Avalos E, Onodera A, Samaniego-Castruita D, Rao A, Ay F. Predicting gene expression state and prioritizing putative enhancers using 5hmC signal. Genome Biol 2024; 25:142. [PMID: 38825692 PMCID: PMC11145787 DOI: 10.1186/s13059-024-03273-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/11/2024] [Indexed: 06/04/2024] Open
Abstract
BACKGROUND Like its parent base 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC) is a direct epigenetic modification of cytosines in the context of CpG dinucleotides. 5hmC is the most abundant oxidized form of 5mC, generated through the action of TET dioxygenases at gene bodies of actively-transcribed genes and at active or lineage-specific enhancers. Although such enrichments are reported for 5hmC, to date, predictive models of gene expression state or putative regulatory regions for genes using 5hmC have not been developed. RESULTS Here, by using only 5hmC enrichment in genic regions and their vicinity, we develop neural network models that predict gene expression state across 49 cell types. We show that our deep neural network models distinguish high vs low expression state utilizing only 5hmC levels and these predictive models generalize to unseen cell types. Further, in order to leverage 5hmC signal in distal enhancers for expression prediction, we employ an Activity-by-Contact model and also develop a graph convolutional neural network model with both utilizing Hi-C data and 5hmC enrichment to prioritize enhancer-promoter links. These approaches identify known and novel putative enhancers for key genes in multiple immune cell subsets. CONCLUSIONS Our work highlights the importance of 5hmC in gene regulation through proximal and distal mechanisms and provides a framework to link it to genome function. With the recent advances in 6-letter DNA sequencing by short and long-read techniques, profiling of 5mC and 5hmC may be done routinely in the near future, hence, providing a broad range of applications for the methods developed here.
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Affiliation(s)
- Edahi Gonzalez-Avalos
- La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Atsushi Onodera
- La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA
- Department of Immunology, Graduate School of Medicine, Chiba University, Chiba, 260-8670, Japan
| | - Daniela Samaniego-Castruita
- La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA
- Biological Sciences Graduate Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Anjana Rao
- La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA.
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Pharmacology, University of California San Diego, La Jolla, CA, 92093, USA.
- Sanford Consortium for Regenerative Medicine, La Jolla, CA, 92093, USA.
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093, USA.
| | - Ferhat Ay
- La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA.
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, 92093, USA.
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
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13
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Hwang H, Jeon H, Yeo N, Baek D. Big data and deep learning for RNA biology. Exp Mol Med 2024; 56:1293-1321. [PMID: 38871816 PMCID: PMC11263376 DOI: 10.1038/s12276-024-01243-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.
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Affiliation(s)
- Hyeonseo Hwang
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyeonseong Jeon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Genome4me Inc., Seoul, Republic of Korea
| | - Nagyeong Yeo
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Daehyun Baek
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Genome4me Inc., Seoul, Republic of Korea.
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14
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Wang S, Wang W. Interpretable prediction of mRNA abundance from promoter sequence using contextual regression models. NAR Genom Bioinform 2024; 6:lqae055. [PMID: 38807713 PMCID: PMC11131020 DOI: 10.1093/nargab/lqae055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 04/08/2024] [Accepted: 05/12/2024] [Indexed: 05/30/2024] Open
Abstract
While machine learning models have been successfully applied to predicting gene expression from promoter sequences, it remains a great challenge to derive intuitive interpretation of the model and reveal DNA motif grammar such as motif cooperation and distance constraint between motif sites. Previous interpretation approaches are often time-consuming or have difficulty to learn the combinatory rules. In this work, we designed interpretable neural network models to predict the mRNA expression levels from DNA sequences. By applying the Contextual Regression framework we developed, we extracted weighted features to cluster samples into different groups, which have different gene expression levels. We performed motif analysis in each cluster and found motifs with active or repressive regulation on gene expression. By comparing the co-occurrence locations of discovered motifs, we also uncovered multiple grammars of motif combination including communities of cooperative motifs and distance constraints between motif pairs. These results revealed new insights of the regulatory architecture of promoter sequences.
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Affiliation(s)
- Song Wang
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359, USA
| | - Wei Wang
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093-0359, USA
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15
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Durge AR, Shrimankar DD. DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences. Curr Genomics 2024; 25:185-201. [PMID: 39087000 PMCID: PMC11288165 DOI: 10.2174/0113892029268176240125055419] [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: 08/24/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 08/02/2024] Open
Abstract
Background Analyzing genomic sequences plays a crucial role in understanding biological diversity and classifying Bamboo species. Existing methods for genomic sequence analysis suffer from limitations such as complexity, low accuracy, and the need for constant reconfiguration in response to evolving genomic datasets. Aim This study addresses these limitations by introducing a novel Dual Heuristic Feature Selection-based Ensemble Classification Model (DHFS-ECM) for the precise identification of Bamboo species from genomic sequences. Methods The proposed DHFS-ECM method employs a Genetic Algorithm to perform dual heuristic feature selection. This process maximizes inter-class variance, leading to the selection of informative N-gram feature sets. Subsequently, intra-class variance levels are used to create optimal training and validation sets, ensuring comprehensive coverage of class-specific features. The selected features are then processed through an ensemble classification layer, combining multiple stratification models for species-specific categorization. Results Comparative analysis with state-of-the-art methods demonstrate that DHFS-ECM achieves remarkable improvements in accuracy (9.5%), precision (5.9%), recall (8.5%), and AUC performance (4.5%). Importantly, the model maintains its performance even with an increased number of species classes due to the continuous learning facilitated by the Dual Heuristic Genetic Algorithm Model. Conclusion DHFS-ECM offers several key advantages, including efficient feature extraction, reduced model complexity, enhanced interpretability, and increased robustness and accuracy through the ensemble classification layer. These attributes make DHFS-ECM a promising tool for real-time clinical applications and a valuable contribution to the field of genomic sequence analysis.
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Affiliation(s)
- Aditi R Durge
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, India
| | - Deepti D Shrimankar
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, India
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16
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Harrison MC, Ubbelohde EJ, LaBella AL, Opulente DA, Wolters JF, Zhou X, Shen XX, Groenewald M, Hittinger CT, Rokas A. Machine learning enables identification of an alternative yeast galactose utilization pathway. Proc Natl Acad Sci U S A 2024; 121:e2315314121. [PMID: 38669185 PMCID: PMC11067038 DOI: 10.1073/pnas.2315314121] [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: 09/05/2023] [Accepted: 02/27/2024] [Indexed: 04/28/2024] Open
Abstract
How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy. Known structural genes involved in assimilation of these sources and presence/absence patterns of growth in other sources were important features contributing to prediction accuracy. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.2%) or growth data (82.6%) but not from isolation environment data (65.6%). Prediction accuracy was even higher (93.3%) when we combined genomic and growth data. After the GALactose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol, raising the hypothesis that several species in two orders, Serinales and Pichiales (containing the emerging pathogen Candida auris and the genus Ogataea, respectively), have an alternative galactose utilization pathway because they lack the GAL genes. Growth and biochemical assays confirmed that several of these species utilize galactose through an alternative oxidoreductive D-galactose pathway, rather than the canonical GAL pathway. Machine learning approaches are powerful for investigating the evolution of the yeast genotype-phenotype map, and their application will uncover novel biology, even in well-studied traits.
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Affiliation(s)
- Marie-Claire Harrison
- Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235
| | - Emily J Ubbelohde
- Laboratory of Genetics, Department of Energy (DOE) Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726
| | - Abigail L LaBella
- Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28262
| | - Dana A Opulente
- Laboratory of Genetics, Department of Energy (DOE) Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726
- Department of Biology, Villanova University, Villanova, PA 19085
| | - John F Wolters
- Laboratory of Genetics, Department of Energy (DOE) Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726
| | - Xiaofan Zhou
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Center, South China Agricultural University, Guangzhou 510642, China
| | - Xing-Xing Shen
- Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Institute of Insect Sciences, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
| | | | - Chris Todd Hittinger
- Laboratory of Genetics, Department of Energy (DOE) Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726
| | - Antonis Rokas
- Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235
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17
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Johnson SR, Fu X, Viknander S, Goldin C, Monaco S, Zelezniak A, Yang KK. Computational scoring and experimental evaluation of enzymes generated by neural networks. Nat Biotechnol 2024:10.1038/s41587-024-02214-2. [PMID: 38653796 DOI: 10.1038/s41587-024-02214-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 03/20/2024] [Indexed: 04/25/2024]
Abstract
In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. We evaluate a set of 20 diverse computational metrics to assess the quality of enzyme sequences produced by three contrasting generative models: ancestral sequence reconstruction, a generative adversarial network and a protein language model. Focusing on two enzyme families, we expressed and purified over 500 natural and generated sequences with 70-90% identity to the most similar natural sequences to benchmark computational metrics for predicting in vitro enzyme activity. Over three rounds of experiments, we developed a computational filter that improved the rate of experimental success by 50-150%. The proposed metrics and models will drive protein engineering research by serving as a benchmark for generative protein sequence models and helping to select active variants for experimental testing.
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Affiliation(s)
| | - Xiaozhi Fu
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Sandra Viknander
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Clara Goldin
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | | | - Aleksej Zelezniak
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden.
- Institute of Biotechnology, Life Sciences Centre, Vilnius University, Vilnius, Lithuania.
- Randall Centre for Cell & Molecular Biophysics, King's College London, Guy's Campus, London, UK.
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18
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Karollus A, Hingerl J, Gankin D, Grosshauser M, Klemon K, Gagneur J. Species-aware DNA language models capture regulatory elements and their evolution. Genome Biol 2024; 25:83. [PMID: 38566111 PMCID: PMC10985990 DOI: 10.1186/s13059-024-03221-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 03/20/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND The rise of large-scale multi-species genome sequencing projects promises to shed new light on how genomes encode gene regulatory instructions. To this end, new algorithms are needed that can leverage conservation to capture regulatory elements while accounting for their evolution. RESULTS Here, we introduce species-aware DNA language models, which we trained on more than 800 species spanning over 500 million years of evolution. Investigating their ability to predict masked nucleotides from context, we show that DNA language models distinguish transcription factor and RNA-binding protein motifs from background non-coding sequence. Owing to their flexibility, DNA language models capture conserved regulatory elements over much further evolutionary distances than sequence alignment would allow. Remarkably, DNA language models reconstruct motif instances bound in vivo better than unbound ones and account for the evolution of motif sequences and their positional constraints, showing that these models capture functional high-order sequence and evolutionary context. We further show that species-aware training yields improved sequence representations for endogenous and MPRA-based gene expression prediction, as well as motif discovery. CONCLUSIONS Collectively, these results demonstrate that species-aware DNA language models are a powerful, flexible, and scalable tool to integrate information from large compendia of highly diverged genomes.
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Affiliation(s)
- Alexander Karollus
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Johannes Hingerl
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Dennis Gankin
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Martin Grosshauser
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Kristian Klemon
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Munich, Germany.
- Institute of Human Genetics, School of Medicine and Health, Technical University of Munich, Munich, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
- Munich Data Science Institute, Technical University of Munich, Garching, Germany.
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19
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Goshisht MK. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges. ACS OMEGA 2024; 9:9921-9945. [PMID: 38463314 PMCID: PMC10918679 DOI: 10.1021/acsomega.3c05913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 03/12/2024]
Abstract
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.
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Affiliation(s)
- Manoj Kumar Goshisht
- Department of Chemistry, Natural and
Applied Sciences, University of Wisconsin—Green
Bay, Green
Bay, Wisconsin 54311-7001, United States
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20
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Guo Z, Liu J, Wang Y, Chen M, Wang D, Xu D, Cheng J. Diffusion models in bioinformatics and computational biology. NATURE REVIEWS BIOENGINEERING 2024; 2:136-154. [PMID: 38576453 PMCID: PMC10994218 DOI: 10.1038/s44222-023-00114-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/25/2023] [Indexed: 04/06/2024]
Abstract
Denoising diffusion models embody a type of generative artificial intelligence that can be applied in computer vision, natural language processing and bioinformatics. In this Review, we introduce the key concepts and theoretical foundations of three diffusion modelling frameworks (denoising diffusion probabilistic models, noise-conditioned scoring networks and score stochastic differential equations). We then explore their applications in bioinformatics and computational biology, including protein design and generation, drug and small-molecule design, protein-ligand interaction modelling, cryo-electron microscopy image data analysis and single-cell data analysis. Finally, we highlight open-source diffusion model tools and consider the future applications of diffusion models in bioinformatics.
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Affiliation(s)
- Zhiye Guo
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Yanli Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Mengrui Chen
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Duolin Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
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21
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Quinn TP, Hess JL, Marshe VS, Barnett MM, Hauschild AC, Maciukiewicz M, Elsheikh SSM, Men X, Schwarz E, Trakadis YJ, Breen MS, Barnett EJ, Zhang-James Y, Ahsen ME, Cao H, Chen J, Hou J, Salekin A, Lin PI, Nicodemus KK, Meyer-Lindenberg A, Bichindaritz I, Faraone SV, Cairns MJ, Pandey G, Müller DJ, Glatt SJ. A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Mol Psychiatry 2024; 29:387-401. [PMID: 38177352 PMCID: PMC11228968 DOI: 10.1038/s41380-023-02334-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/21/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024]
Abstract
Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.
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Affiliation(s)
- Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia
| | - Jonathan L Hess
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Victoria S Marshe
- Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Michelle M Barnett
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Anne-Christin Hauschild
- Department of Medical Informatics, Medical University Center Göttingen, Göttingen, Lower Saxony, 37075, Germany
| | - Malgorzata Maciukiewicz
- Hospital Zurich, University of Zurich, Zurich, 8091, Switzerland
- Department of Rheumatology and Immunology, University Hospital Bern, Bern, 3010, Switzerland
- Department for Biomedical Research (DBMR), University of Bern, Bern, 3010, Switzerland
| | - Samar S M Elsheikh
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Xiaoyu Men
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A1, Canada
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Yannis J Trakadis
- Department Human Genetics, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
| | - Michael S Breen
- Psychiatry, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric J Barnett
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Mehmet Eren Ahsen
- Department of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
- Department of Biomedical and Translational Sciences, Carle-Illinois School of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
| | - Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Jiahui Hou
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Asif Salekin
- Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13244, USA
| | - Ping-I Lin
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, 2052, Australia
- Mental Health Research Unit, South Western Sydney Local Health District, Liverpool, NSW, 2170, Australia
| | | | - Andreas Meyer-Lindenberg
- Clinical Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Isabelle Bichindaritz
- Biomedical and Health Informatics/Computer Science Department, State University of New York at Oswego, Oswego, NY, 13126, USA
- Intelligent Bio Systems Lab, State University of New York at Oswego, Oswego, NY, 13126, USA
| | - Stephen V Faraone
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg, 97080, Germany
| | - Stephen J Glatt
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Public Health and Preventive Medicine, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
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22
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Gorjifard S, Jores T, Tonnies J, Mueth NA, Bubb K, Wrightsman T, Buckler ES, Fields S, Cuperus JT, Queitsch C. Arabidopsis and Maize Terminator Strength is Determined by GC Content, Polyadenylation Motifs and Cleavage Probability. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.16.545379. [PMID: 37398426 PMCID: PMC10312805 DOI: 10.1101/2023.06.16.545379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The 3' end of a gene, often called a terminator, modulates mRNA stability, localization, translation, and polyadenylation. Here, we adapted Plant STARR-seq, a massively parallel reporter assay, to measure the activity of over 50,000 terminators from the plants Arabidopsis thaliana and Zea mays. We characterize thousands of plant terminators, including many that outperform bacterial terminators commonly used in plants. Terminator activity is species-specific, differing in tobacco leaf and maize protoplast assays. While recapitulating known biology, our results reveal the relative contributions of polyadenylation motifs to terminator strength. We built a computational model to predict terminator strength and used it to conduct in silico evolution that generated optimized synthetic terminators. Additionally, we discover alternative polyadenylation sites across tens of thousands of terminators; however, the strongest terminators tend to have a dominant cleavage site. Our results establish features of plant terminator function and identify strong naturally occurring and synthetic terminators.
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Affiliation(s)
- Sayeh Gorjifard
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Tobias Jores
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Jackson Tonnies
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
- Graduate Program in Biology, University of Washington, Seattle, WA 98195
| | - Nicholas A Mueth
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Kerry Bubb
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Travis Wrightsman
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853
| | - Edward S Buckler
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853
- Agricultural Research Service, United States Department of Agriculture, Ithaca, NY 14853
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853
| | - Stanley Fields
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
- Department of Medicine, University of Washington, Seattle, WA 98195
| | - Josh T Cuperus
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Christine Queitsch
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
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23
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Zeng J, Song K, Wang J, Wen H, Zhou J, Ni T, Lu H, Yu Y. Characterization and optimization of 5´ untranslated region containing poly-adenine tracts in Kluyveromyces marxianus using machine-learning model. Microb Cell Fact 2024; 23:7. [PMID: 38172836 PMCID: PMC10763412 DOI: 10.1186/s12934-023-02271-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 12/12/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The 5´ untranslated region (5´ UTR) plays a key role in regulating translation efficiency and mRNA stability, making it a favored target in genetic engineering and synthetic biology. A common feature found in the 5´ UTR is the poly-adenine (poly(A)) tract. However, the effect of 5´ UTR poly(A) on protein production remains controversial. Machine-learning models are powerful tools for explaining the complex contributions of features, but models incorporating features of 5´ UTR poly(A) are currently lacking. Thus, our goal is to construct such a model, using natural 5´ UTRs from Kluyveromyces marxianus, a promising cell factory for producing heterologous proteins. RESULTS We constructed a mini-library consisting of 207 5´ UTRs harboring poly(A) and 34 5´ UTRs without poly(A) from K. marxianus. The effects of each 5´ UTR on the production of a GFP reporter were evaluated individually in vivo, and the resulting protein abundance spanned an approximately 450-fold range throughout. The data were used to train a multi-layer perceptron neural network (MLP-NN) model that incorporated the length and position of poly(A) as features. The model exhibited good performance in predicting protein abundance (average R2 = 0.7290). The model suggests that the length of poly(A) is negatively correlated with protein production, whereas poly(A) located between 10 and 30 nt upstream of the start codon (AUG) exhibits a weak positive effect on protein abundance. Using the model as guidance, the deletion or reduction of poly(A) upstream of 30 nt preceding AUG tended to improve the production of GFP and a feruloyl esterase. Deletions of poly(A) showed inconsistent effects on mRNA levels, suggesting that poly(A) represses protein production either with or without reducing mRNA levels. CONCLUSION The effects of poly(A) on protein production depend on its length and position. Integrating poly(A) features into machine-learning models improves simulation accuracy. Deleting or reducing poly(A) upstream of 30 nt preceding AUG tends to enhance protein production. This optimization strategy can be applied to enhance the yield of K. marxianus and other microbial cell factories.
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Affiliation(s)
- Junyuan Zeng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Kunfeng Song
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Jingqi Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Haimei Wen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Jungang Zhou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Ting Ni
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Hong Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Yao Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China.
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China.
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24
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Rashid A, Al-Obeida F, Hafez W, Benakatti G, Malik RA, Koutentis C, Sharief J, Brierley J, Quraishi N, Malik ZA, Anwary A, Alkhzaimi H, Zaki SA, Khilnani P, Kadwa R, Phatak R, Schumacher M, Shaikh G, Al-Dubai A, Hussain A. ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES. Shock 2024; 61:4-18. [PMID: 37752080 DOI: 10.1097/shk.0000000000002227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
ABSTRACT Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.
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Affiliation(s)
| | | | | | | | | | | | | | - Joe Brierley
- Great Ormond Street Children's Hospital, London, UK
| | - Nasir Quraishi
- Centre for Spinal Studies & Surgery, Queen's Medical Centre. The University of Nottingham. Nottingham, UK
| | - Zainab A Malik
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences. Dubai, U.A.E
| | - Arif Anwary
- School of Computing, Edinburgh Napier University. Edinburgh, UK
| | | | | | | | | | - Rajesh Phatak
- Pediatric Intensive Care, Burjeel Hospital, Najda, Abu Dhabi
| | | | - Guftar Shaikh
- Endocrinology, Royal Hospital for Children. Glasgow, UK
| | - Ahmed Al-Dubai
- School of Computing, Edinburgh Napier University. Edinburgh, UK
| | - Amir Hussain
- School of Computing, Edinburgh Napier University. Edinburgh, UK
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25
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Barrington CL, Galindo G, Koch AL, Horton ER, Morrison EJ, Tisa S, Stasevich TJ, Rissland OS. Synonymous codon usage regulates translation initiation. Cell Rep 2023; 42:113413. [PMID: 38096059 PMCID: PMC10790568 DOI: 10.1016/j.celrep.2023.113413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 08/30/2023] [Accepted: 10/25/2023] [Indexed: 12/30/2023] Open
Abstract
Nonoptimal synonymous codons repress gene expression, but the underlying mechanisms are poorly understood. We and others have previously shown that nonoptimal codons slow translation elongation speeds and thereby trigger messenger RNA (mRNA) degradation. Nevertheless, transcript levels are often insufficient to explain protein levels, suggesting additional mechanisms by which codon usage regulates gene expression. Using reporters in human and Drosophila cells, we find that transcript levels account for less than half of the variation in protein abundance due to codon usage. This discrepancy is explained by translational differences whereby nonoptimal codons repress translation initiation. Nonoptimal transcripts are also less bound by the translation initiation factors eIF4E and eIF4G1, providing a mechanistic explanation for their reduced initiation rates. Importantly, translational repression can occur without mRNA decay and deadenylation, and it does not depend on the known nonoptimality sensor, CNOT3. Our results reveal a potent mechanism of regulation by codon usage where nonoptimal codons repress further rounds of translation.
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Affiliation(s)
- Chloe L Barrington
- Department of Biochemistry & Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045, USA; RNA Bioscience Initiative, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Gabriel Galindo
- Department of Biochemistry & Molecular Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Amanda L Koch
- Department of Biochemistry & Molecular Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Emma R Horton
- Department of Biochemistry & Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045, USA; RNA Bioscience Initiative, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Evan J Morrison
- Department of Biochemistry & Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045, USA; RNA Bioscience Initiative, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Samantha Tisa
- Department of Biochemistry & Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045, USA; RNA Bioscience Initiative, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Timothy J Stasevich
- Department of Biochemistry & Molecular Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Olivia S Rissland
- Department of Biochemistry & Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045, USA; RNA Bioscience Initiative, University of Colorado School of Medicine, Aurora, CO 80045, USA.
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26
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Liu B, Gao J, Liu XF, Rao G, Luo J, Han P, Hu W, Zhang Z, Zhao Q, Han L, Jiang Z, Zhou M. Direct prediction of carbapenem resistance in Pseudomonas aeruginosa by whole genome sequencing and metagenomic sequencing. J Clin Microbiol 2023; 61:e0061723. [PMID: 37823665 PMCID: PMC10662344 DOI: 10.1128/jcm.00617-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 08/17/2023] [Indexed: 10/13/2023] Open
Abstract
Carbapenem resistance is a major concern in the management of antibiotic-resistant Pseudomonas aeruginosa infections. The direct prediction of carbapenem-resistant phenotype from genotype in P. aeruginosa isolates and clinical samples would promote timely antibiotic therapy. The complex carbapenem resistance mechanism and the high prevalence of variant-driven carbapenem resistance in P. aeruginosa make it challenging to predict the carbapenem-resistant phenotype through the genotype. In this study, using whole genome sequencing (WGS) data of 1,622 P. aeruginosa isolates followed by machine learning, we screened 16 and 31 key gene features associated with imipenem (IPM) and meropenem (MEM) resistance in P. aeruginosa, including oprD(HIGH), and constructed the resistance prediction models. The areas under the curves of the IPM and MEM resistance prediction models were 0.906 and 0.925, respectively. For the direct prediction of carbapenem resistance in P. aeruginosa from clinical samples by the key gene features selected and prediction models constructed, 72 P. aeruginosa-positive sputum samples were collected and sequenced by metagenomic sequencing (MGS) based on next-generation sequencing (NGS) or Oxford Nanopore Technology (ONT). The prediction applicability of MGS based on NGS outperformed that of MGS based on ONT. In 72 P. aeruginosa-positive sputum samples, 65.0% (26/40) of IPM-insensitive and 63.2% (24/38) of MEM-insensitive P. aeruginosa were directly predicted by NGS-based MGS with positive predictive values of 0.897 and 0.889, respectively. By the direct detection of the key gene features associated with carbapenem resistance of P. aeruginosa, the carbapenem resistance of P. aeruginosa could be directly predicted from cultured isolates by WGS or from clinical samples by NGS-based MGS, which could assist the timely treatment and surveillance of carbapenem-resistant P. aeruginosa.
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Affiliation(s)
- Bing Liu
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China
| | - Jianpeng Gao
- Genskey Medical Technology Co., Ltd., Beijing, China
| | - Xue Fei Liu
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China
| | - Guanhua Rao
- Genskey Medical Technology Co., Ltd., Beijing, China
| | - Jiajie Luo
- Genskey Medical Technology Co., Ltd., Beijing, China
| | - Peng Han
- Genskey Medical Technology Co., Ltd., Beijing, China
| | - Weiting Hu
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China
| | - Ze Zhang
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China
| | - Qianqian Zhao
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China
| | - Lizhong Han
- Department of Clinical Microbiology,, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi Jiang
- Genskey Medical Technology Co., Ltd., Beijing, China
| | - Min Zhou
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China
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27
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Parthiban S, Vijeesh T, Gayathri T, Shanmugaraj B, Sharma A, Sathishkumar R. Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals. FRONTIERS IN PLANT SCIENCE 2023; 14:1252166. [PMID: 38034587 PMCID: PMC10684705 DOI: 10.3389/fpls.2023.1252166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.
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Affiliation(s)
- Subramanian Parthiban
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thandarvalli Vijeesh
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thashanamoorthi Gayathri
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Balamurugan Shanmugaraj
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, Mexico
| | - Ramalingam Sathishkumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
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28
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Nambiar A, Dubinkina V, Liu S, Maslov S. FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi. PLoS Comput Biol 2023; 19:e1011563. [PMID: 37971967 PMCID: PMC10653424 DOI: 10.1371/journal.pcbi.1011563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 09/30/2023] [Indexed: 11/19/2023] Open
Abstract
mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE-a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.
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Affiliation(s)
- Ananthan Nambiar
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, United States of America
- Carl R. Woese Institute for Genomic Biology, Urbana, Illinois, United States of America
| | - Veronika Dubinkina
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, United States of America
- Carl R. Woese Institute for Genomic Biology, Urbana, Illinois, United States of America
- The Gladstone Institute of Data Science and Biotechnology, San Francisco, California, United States of America
| | - Simon Liu
- Carl R. Woese Institute for Genomic Biology, Urbana, Illinois, United States of America
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois, United States of America
| | - Sergei Maslov
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, United States of America
- Carl R. Woese Institute for Genomic Biology, Urbana, Illinois, United States of America
- Department of Physics, University of Illinois Urbana-Champaign, Urbana, Illinois, United States of America
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, Illinois, United States of America
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29
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Lamoureux CR, Decker KT, Sastry AV, Rychel K, Gao Y, McConn J, Zielinski D, Palsson BO. A multi-scale expression and regulation knowledge base for Escherichia coli. Nucleic Acids Res 2023; 51:10176-10193. [PMID: 37713610 PMCID: PMC10602906 DOI: 10.1093/nar/gkad750] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/02/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023] Open
Abstract
Transcriptomic data is accumulating rapidly; thus, scalable methods for extracting knowledge from this data are critical. Here, we assembled a top-down expression and regulation knowledge base for Escherichia coli. The expression component is a 1035-sample, high-quality RNA-seq compendium consisting of data generated in our lab using a single experimental protocol. The compendium contains diverse growth conditions, including: 9 media; 39 supplements, including antibiotics; 42 heterologous proteins; and 76 gene knockouts. Using this resource, we elucidated global expression patterns. We used machine learning to extract 201 modules that account for 86% of known regulatory interactions, creating the regulatory component. With these modules, we identified two novel regulons and quantified systems-level regulatory responses. We also integrated 1675 curated, publicly-available transcriptomes into the resource. We demonstrated workflows for analyzing new data against this knowledge base via deconstruction of regulation during aerobic transition. This resource illuminates the E. coli transcriptome at scale and provides a blueprint for top-down transcriptomic analysis of non-model organisms.
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Affiliation(s)
- Cameron R Lamoureux
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Katherine T Decker
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anand V Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kevin Rychel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ye Gao
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - John Luke McConn
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Daniel C Zielinski
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs. Lyngby, Denmark
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30
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Clifton BD, Hariyani I, Kimura A, Luo F, Nguyen A, Ranz JM. Paralog transcriptional differentiation in the D. melanogaster-specific gene family Sdic across populations and spermatogenesis stages. Commun Biol 2023; 6:1069. [PMID: 37864070 PMCID: PMC10589255 DOI: 10.1038/s42003-023-05427-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 10/05/2023] [Indexed: 10/22/2023] Open
Abstract
How recently originated gene copies become stable genomic components remains uncertain as high sequence similarity of young duplicates precludes their functional characterization. The tandem multigene family Sdic is specific to Drosophila melanogaster and has been annotated across multiple reference-quality genome assemblies. Here we show the existence of a positive correlation between Sdic copy number and total expression, plus vast intrastrain differences in mRNA abundance among paralogs, using RNA-sequencing from testis of four strains with variable paralog composition. Single cell and nucleus RNA-sequencing data expose paralog expression differentiation in meiotic cell types within testis from third instar larva and adults. Additional RNA-sequencing across synthetic strains only differing in their Y chromosomes reveal a tissue-dependent trans-regulatory effect on Sdic: upregulation in testis and downregulation in male accessory gland. By leveraging paralog-specific expression information from tissue- and cell-specific data, our results elucidate the intraspecific functional diversification of a recently expanded tandem gene family.
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Affiliation(s)
- Bryan D Clifton
- Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, 92697, USA.
| | - Imtiyaz Hariyani
- Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, 92697, USA
| | - Ashlyn Kimura
- Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, 92697, USA
| | - Fangning Luo
- Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, 92697, USA
| | - Alvin Nguyen
- Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, 92697, USA
| | - José M Ranz
- Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, 92697, USA.
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31
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Wang C, Dong Y, Li C, Oberstaller J, Zhang M, Gibbons J, Pires CV, Xiao M, Zhu L, Jiang RHY, Kim K, Miao J, Otto TD, Cui L, Adams JH, Liu X. MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites. Genome Biol 2023; 24:231. [PMID: 37845769 PMCID: PMC10577899 DOI: 10.1186/s13059-023-03063-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/19/2023] [Indexed: 10/18/2023] Open
Abstract
Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria parasites remain elusive. We developed a sequence-based, ab initio deep learning framework, MalariaSED, for predicting chromatin profiles in malaria parasites. The MalariaSED performance was validated by published ChIP-qPCR and TF motifs results. Applying MalariaSED to ~ 1.3 million variants shows that geographically differentiated noncoding variants are associated with parasite invasion and drug resistance. Further analysis reveals chromatin accessibility changes at Plasmodium falciparum rings are partly associated with artemisinin resistance. MalariaSED illuminates the potential functional roles of noncoding variants in malaria parasites.
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Affiliation(s)
- Chengqi Wang
- Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA.
| | - Yibo Dong
- Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
- Present address: Florida Department of Health, Jacksonville, FL, USA
| | - Chang Li
- Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Jenna Oberstaller
- Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Min Zhang
- Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Justin Gibbons
- Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Camilla Valente Pires
- Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Mianli Xiao
- Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
- Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Lei Zhu
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Rays H Y Jiang
- Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Kami Kim
- Department of Internal Medicine, Morsani College of Medicine, Tampa, FL, USA
| | - Jun Miao
- Department of Internal Medicine, Morsani College of Medicine, Tampa, FL, USA
| | - Thomas D Otto
- School of Infection & Immunity, MVLS, University of Glasgow, Glasgow, UK
| | - Liwang Cui
- Department of Internal Medicine, Morsani College of Medicine, Tampa, FL, USA
| | - John H Adams
- Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Xiaoming Liu
- Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
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32
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Jores T, Hamm M, Cuperus JT, Queitsch C. Frontiers and techniques in plant gene regulation. CURRENT OPINION IN PLANT BIOLOGY 2023; 75:102403. [PMID: 37331209 DOI: 10.1016/j.pbi.2023.102403] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/12/2023] [Accepted: 05/19/2023] [Indexed: 06/20/2023]
Abstract
Understanding plant gene regulation has been a priority for generations of plant scientists. However, due to its complex nature, the regulatory code governing plant gene expression has yet to be deciphered comprehensively. Recently developed methods-often relying on next-generation sequencing technology and state-of-the-art computational approaches-have started to further our understanding of the gene regulatory logic used by plants. In this review, we discuss these methods and the insights into the regulatory code of plants that they can yield.
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Affiliation(s)
- Tobias Jores
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Morgan Hamm
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Josh T Cuperus
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Christine Queitsch
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
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33
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Monti R, Ohler U. Toward Identification of Functional Sequences and Variants in Noncoding DNA. Annu Rev Biomed Data Sci 2023; 6:191-210. [PMID: 37262323 DOI: 10.1146/annurev-biodatasci-122120-110102] [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] [Indexed: 06/03/2023]
Abstract
Understanding the noncoding part of the genome, which encodes gene regulation, is necessary to identify genetic mechanisms of disease and translate findings from genome-wide association studies into actionable results for treatments and personalized care. Here we provide an overview of the computational analysis of noncoding regions, starting from gene-regulatory mechanisms and their representation in data. Deep learning methods, when applied to these data, highlight important regulatory sequence elements and predict the functional effects of genetic variants. These and other algorithms are used to predict damaging sequence variants. Finally, we introduce rare-variant association tests that incorporate functional annotations and predictions in order to increase interpretability and statistical power.
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Affiliation(s)
- Remo Monti
- Max Delbrück Center for Molecular Medicine (MDC), Helmholtz Association of German Research Centers, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany;
- Digital Health-Machine Learning, Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Uwe Ohler
- Max Delbrück Center for Molecular Medicine (MDC), Helmholtz Association of German Research Centers, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany;
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Abstract
Following the widespread use of deep learning for genomics, deep generative modeling is also becoming a viable methodology for the broad field. Deep generative models (DGMs) can learn the complex structure of genomic data and allow researchers to generate novel genomic instances that retain the real characteristics of the original dataset. Aside from data generation, DGMs can also be used for dimensionality reduction by mapping the data space to a latent space, as well as for prediction tasks via exploitation of this learned mapping or supervised/semi-supervised DGM designs. In this review, we briefly introduce generative modeling and two currently prevailing architectures, we present conceptual applications along with notable examples in functional and evolutionary genomics, and we provide our perspective on potential challenges and future directions.
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Affiliation(s)
- Burak Yelmen
- Laboratoire Interdisciplinaire des Sciences du Numérique, CNRS UMR 9015, INRIA, Université Paris-Saclay, Orsay, France;
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Flora Jay
- Laboratoire Interdisciplinaire des Sciences du Numérique, CNRS UMR 9015, INRIA, Université Paris-Saclay, Orsay, France;
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35
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Zhang XE, Liu C, Dai J, Yuan Y, Gao C, Feng Y, Wu B, Wei P, You C, Wang X, Si T. Enabling technology and core theory of synthetic biology. SCIENCE CHINA. LIFE SCIENCES 2023; 66:1742-1785. [PMID: 36753021 PMCID: PMC9907219 DOI: 10.1007/s11427-022-2214-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/04/2022] [Indexed: 02/09/2023]
Abstract
Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.
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Affiliation(s)
- Xian-En Zhang
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Chenli Liu
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Junbiao Dai
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Yingjin 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, 300072, China.
| | - Caixia Gao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Bian Wu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ping Wei
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Chun You
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Tong Si
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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36
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Milito A, Aschern M, McQuillan JL, Yang JS. Challenges and advances towards the rational design of microalgal synthetic promoters in Chlamydomonas reinhardtii. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:3833-3850. [PMID: 37025006 DOI: 10.1093/jxb/erad100] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
Microalgae hold enormous potential to provide a safe and sustainable source of high-value compounds, acting as carbon-fixing biofactories that could help to mitigate rapidly progressing climate change. Bioengineering microalgal strains will be key to optimizing and modifying their metabolic outputs, and to render them competitive with established industrial biotechnology hosts, such as bacteria or yeast. To achieve this, precise and tuneable control over transgene expression will be essential, which would require the development and rational design of synthetic promoters as a key strategy. Among green microalgae, Chlamydomonas reinhardtii represents the reference species for bioengineering and synthetic biology; however, the repertoire of functional synthetic promoters for this species, and for microalgae generally, is limited in comparison to other commercial chassis, emphasizing the need to expand the current microalgal gene expression toolbox. Here, we discuss state-of-the-art promoter analyses, and highlight areas of research required to advance synthetic promoter development in C. reinhardtii. In particular, we exemplify high-throughput studies performed in other model systems that could be applicable to microalgae, and propose novel approaches to interrogating algal promoters. We lastly outline the major limitations hindering microalgal promoter development, while providing novel suggestions and perspectives for how to overcome them.
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Affiliation(s)
- Alfonsina Milito
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
| | - Moritz Aschern
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
| | - Josie L McQuillan
- Department of Chemical and Biological Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK
| | - Jae-Seong Yang
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
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37
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Smet D, Opdebeeck H, Vandepoele K. Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice. FRONTIERS IN PLANT SCIENCE 2023; 14:1212073. [PMID: 37528982 PMCID: PMC10390317 DOI: 10.3389/fpls.2023.1212073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 06/16/2023] [Indexed: 08/03/2023]
Abstract
Plants have evolved various mechanisms to adapt to adverse environmental stresses, such as the modulation of gene expression. Expression of stress-responsive genes is controlled by specific regulators, including transcription factors (TFs), that bind to sequence-specific binding sites, representing key components of cis-regulatory elements and regulatory networks. Our understanding of the underlying regulatory code remains, however, incomplete. Recent studies have shown that, by training machine learning (ML) algorithms on genomic sequence features, it is possible to predict which genes will transcriptionally respond to a specific stress. By identifying the most important features for gene expression prediction, these trained ML models allow, in theory, to further elucidate the regulatory code underlying the transcriptional response to abiotic stress. Here, we trained random forest ML models to predict gene expression in rice (Oryza sativa) in response to heat or drought stress. Apart from thoroughly assessing model performance and robustness across various input training data, the importance of promoter and gene body sequence features to train ML models was evaluated. The use of enriched promoter oligomers, complementing known TF binding sites, allowed us to gain novel insights in DNA motifs contributing to the stress regulatory code. By comparing genomic feature importance scores for drought and heat stress over time, general and stress-specific genomic features contributing to the performance of the learned models and their temporal variation were identified. This study provides a solid foundation to build and interpret ML models accurately predicting transcriptional responses and enables novel insights in biological sequence features that are important for abiotic stress responses.
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Affiliation(s)
- Dajo Smet
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium
| | - Helder Opdebeeck
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium
| | - Klaas Vandepoele
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
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38
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Zhou A, Kirkpatrick LD, Ornelas IJ, Washington LJ, Hummel NFC, Gee CW, Tang SN, Barnum CR, Scheller HV, Shih PM. A Suite of Constitutive Promoters for Tuning Gene Expression in Plants. ACS Synth Biol 2023; 12:1533-1545. [PMID: 37083366 DOI: 10.1021/acssynbio.3c00075] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
The need for convenient tools to express transgenes over a large dynamic range is pervasive throughout plant synthetic biology; however, current efforts are largely limited by the heavy reliance on a small set of strong promoters, precluding more nuanced and refined engineering endeavors in planta. To address this technical gap, we characterize a suite of constitutive promoters that span a wide range of transcriptional levels and develop a GoldenGate-based plasmid toolkit named PCONS, optimized for versatile cloning and rapid testing of transgene expression at varying strengths. We demonstrate how easy access to a stepwise gradient of expression levels can be used for optimizing synthetic transcriptional systems and the production of small molecules in planta. We also systematically investigate the potential of using PCONS as an internal standard in plant biology experimental design, establishing the best practices for signal normalization in experiments. Although our library has primarily been developed for optimizing expression in N. benthamiana, we demonstrate the translatability of our promoters across distantly related species using a multiplexed reporter assay with barcoded transcripts. Our findings showcase the advantages of the PCONS library as an invaluable toolkit for plant synthetic biology.
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Affiliation(s)
- Andy Zhou
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, California 94720, United States
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, California 94608, United States
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94705, United States
| | - Liam D Kirkpatrick
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, California 94720, United States
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, California 94608, United States
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94705, United States
| | - Izaiah J Ornelas
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, California 94608, United States
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94705, United States
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California 94720, United States
| | - Lorenzo J Washington
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, California 94720, United States
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, California 94608, United States
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94705, United States
| | - Niklas F C Hummel
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, California 94608, United States
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94705, United States
| | - Christopher W Gee
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, California 94608, United States
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94705, United States
| | - Sophia N Tang
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, California 94608, United States
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94705, United States
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California 94720, United States
| | - Collin R Barnum
- Biochemistry, Molecular, Cellular and Developmental Biology Graduate Group, University of California, Davis, California 95616, United States
| | - Henrik V Scheller
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, California 94720, United States
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, California 94608, United States
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94705, United States
| | - Patrick M Shih
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, California 94720, United States
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, California 94608, United States
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94705, United States
- Innovative Genomics Institute, University of California, Berkeley, California 94720, United States
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Orantes-Bonilla M, Wang H, Lee HT, Golicz AA, Hu D, Li W, Zou J, Snowdon RJ. Transgressive and parental dominant gene expression and cytosine methylation during seed development in Brassica napus hybrids. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:113. [PMID: 37071201 PMCID: PMC10113308 DOI: 10.1007/s00122-023-04345-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 03/12/2023] [Indexed: 05/13/2023]
Abstract
KEY MESSAGE Transcriptomic and epigenomic profiling of gene expression and small RNAs during seed and seedling development reveals expression and methylation dominance levels with implications on early stage heterosis in oilseed rape. The enhanced performance of hybrids through heterosis remains a key aspect in plant breeding; however, the underlying mechanisms are still not fully elucidated. To investigate the potential role of transcriptomic and epigenomic patterns in early expression of hybrid vigor, we investigated gene expression, small RNA abundance and genome-wide methylation in hybrids from two distant Brassica napus ecotypes during seed and seedling developmental stages using next-generation sequencing. A total of 31117, 344, 36229 and 7399 differentially expressed genes, microRNAs, small interfering RNAs and differentially methylated regions were identified, respectively. Approximately 70% of the differentially expressed or methylated features displayed parental dominance levels where the hybrid followed the same patterns as the parents. Via gene ontology enrichment and microRNA-target association analyses during seed development, we found copies of reproductive, developmental and meiotic genes with transgressive and paternal dominance patterns. Interestingly, maternal dominance was more prominent in hypermethylated and downregulated features during seed formation, contrasting to the general maternal gamete demethylation reported during gametogenesis in angiosperms. Associations between methylation and gene expression allowed identification of putative epialleles with diverse pivotal biological functions during seed formation. Furthermore, most differentially methylated regions, differentially expressed siRNAs and transposable elements were in regions that flanked genes without differential expression. This suggests that differential expression and methylation of epigenomic features may help maintain expression of pivotal genes in a hybrid context. Differential expression and methylation patterns during seed formation in an F1 hybrid provide novel insights into genes and mechanisms with potential roles in early heterosis.
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Affiliation(s)
- Mauricio Orantes-Bonilla
- Department of Plant Breeding, Land Use and Nutrition, IFZ Research Centre for Biosystems, Justus Liebig University, Giessen, Germany
| | - Hao Wang
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Huey Tyng Lee
- Department of Plant Breeding, Land Use and Nutrition, IFZ Research Centre for Biosystems, Justus Liebig University, Giessen, Germany
| | - Agnieszka A Golicz
- Department of Plant Breeding, Land Use and Nutrition, IFZ Research Centre for Biosystems, Justus Liebig University, Giessen, Germany
| | - Dandan Hu
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Wenwen Li
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Jun Zou
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Rod J Snowdon
- Department of Plant Breeding, Land Use and Nutrition, IFZ Research Centre for Biosystems, Justus Liebig University, Giessen, Germany.
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40
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Kwon MS, Adidjaja JJ, Kim HU. Predicting the effects of cultivation condition on gene regulation in Escherichia coli by using deep learning. Comput Struct Biotechnol J 2023; 21:2613-2620. [PMID: 38213890 PMCID: PMC10781998 DOI: 10.1016/j.csbj.2023.04.010] [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: 12/09/2022] [Revised: 04/02/2023] [Accepted: 04/12/2023] [Indexed: 01/13/2024] Open
Abstract
Cell's physiology is affected by cultivation conditions at varying degrees, including carbon sources and inorganic nutrients in growth medium, and the presence or absence of aeration. When examining the effects of cultivation conditions on the cell, the cell's transcriptional response is often examined first among other phenotypes (e.g., proteome and metabolome). In this regard, we developed DeepMGR, a deep learning model that predicts the effects of culture media on gene regulation in Escherichia coli. DeepMGR specifically classifies the direction of gene regulation (i.e., upregulation, no regulation, or downregulation) for an input gene in comparison with M9 minimal medium with glucose as a control condition. For this classification task, DeepMGR uses a feedforward neural network to process: i) DNA sequence of a target gene, ii) presence or absence of aeration and trace elements, and iii) concentration and structural information (SMILES) of up to ten nutrients. The complete DeepMGR showed accuracy of 0.867 and F1 score of 0.703 for a test set from the gold standard dataset. DeepMGR was further subjected to simulation studies for validation where regulation directions for groups of homologous genes were predicted, and the DeepMGR results were compared with the literature with focus on carbon sources that upregulate specific genes. DeepMGR will be useful for designing experiments to understand gene regulations, especially in the context of metabolic engineering.
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Affiliation(s)
- Mun Su Kwon
- Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Joshua Julio Adidjaja
- Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Hyun Uk Kim
- Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea
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41
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Yuan L, Lu H, Li F, Nielsen J, Kerkhoven EJ. HGTphyloDetect: facilitating the identification and phylogenetic analysis of horizontal gene transfer. Brief Bioinform 2023; 24:7031155. [PMID: 36752380 PMCID: PMC10025432 DOI: 10.1093/bib/bbad035] [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: 09/28/2022] [Revised: 12/28/2022] [Accepted: 01/17/2023] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Horizontal gene transfer (HGT) is an important driver in genome evolution, gain-of-function, and metabolic adaptation to environmental niches. Genome-wide identification of putative HGT events has become increasingly practical, given the rapid growth of genomic data. However, existing HGT analysis toolboxes are not widely used, limited by their inability to perform phylogenetic reconstruction to explore potential donors, and the detection of HGT from both evolutionarily distant and closely related species. RESULTS In this study, we have developed HGTphyloDetect, which is a versatile computational toolbox that combines high-throughput analysis with phylogenetic inference, to facilitate comprehensive investigation of HGT events. Two case studies with Saccharomyces cerevisiae and Candida versatilis demonstrate the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, HGTphyloDetect enables phylogenetic analysis to illustrate a likely path of gene transmission among the evolutionarily distant or closely related species. CONCLUSIONS The HGTphyloDetect computational toolbox is designed for ease of use and can accurately find HGT events with a very low false discovery rate in a high-throughput manner. The HGTphyloDetect toolbox and its related user tutorial are freely available at https://github.com/SysBioChalmers/HGTphyloDetect.
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Affiliation(s)
- Le Yuan
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96 Gothenburg, Sweden
| | - Hongzhong Lu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Feiran Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96 Gothenburg, Sweden
- BioInnovation Institute, Ole Måløes Vej 3 DK-2200 Copenhagen, Denmark
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96 Gothenburg, Sweden
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42
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Yasmeen E, Wang J, Riaz M, Zhang L, Zuo K. Designing artificial synthetic promoters for accurate, smart, and versatile gene expression in plants. PLANT COMMUNICATIONS 2023:100558. [PMID: 36760129 PMCID: PMC10363483 DOI: 10.1016/j.xplc.2023.100558] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/30/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
With the development of high-throughput biology techniques and artificial intelligence, it has become increasingly feasible to design and construct artificial biological parts, modules, circuits, and even whole systems. To overcome the limitations of native promoters in controlling gene expression, artificial promoter design aims to synthesize short, inducible, and conditionally controlled promoters to coordinate the expression of multiple genes in diverse plant metabolic and signaling pathways. Synthetic promoters are versatile and can drive gene expression accurately with smart responses; they show potential for enhancing desirable traits in crops, thereby improving crop yield, nutritional quality, and food security. This review first illustrates the importance of synthetic promoters, then introduces promoter architecture and thoroughly summarizes advances in synthetic promoter construction. Restrictions to the development of synthetic promoters and future applications of such promoters in synthetic plant biology and crop improvement are also discussed.
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Affiliation(s)
- Erum Yasmeen
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jin Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Muhammad Riaz
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lida Zhang
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Kaijing Zuo
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
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43
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Georgieva O. An Iterative Unsupervised Method for Gene Expression Differentiation. Genes (Basel) 2023; 14:412. [PMID: 36833339 PMCID: PMC9956932 DOI: 10.3390/genes14020412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/24/2023] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
For several decades, intensive research for understanding gene activity and its role in organism's lives is the research focus of scientists in different areas. A part of these investigations is the analysis of gene expression data for selecting differentially expressed genes. Methods that identify the interested genes have been proposed on statistical data analysis. The problem is that there is no good agreement among them, as different results are produced by distinct methods. By taking the advantage of the unsupervised data analysis, an iterative clustering procedure that finds differentially expressed genes shows promising results. In the present paper, a comparative study of the clustering methods applied for gene expression analysis is presented to explicate the choice of the clustering algorithm implemented in the method. An investigation of different distance measures is provided to reveal those that increase the efficiency of the method in finding the real data structure. Further, the method is improved by incorporating an additional aggregation measure based on the standard deviation of the expression levels. Its usage increases the gene distinction as a new amount of differentially expressed genes is found. The method is summarized in a detailed procedure. The significance of the method is proved by an analysis of two mice strain data sets. The differentially expressed genes defined by the proposed method are compared with those selected by the well-known statistical methods applied to the same data set.
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Affiliation(s)
- Olga Georgieva
- Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", 125 Tsarigradsko Shosse Blvd., bl. 2, 1113 Sofia, Bulgaria
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44
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Goulet DR, Yan Y, Agrawal P, Waight AB, Mak ANS, Zhu Y. Codon Optimization Using a Recurrent Neural Network. J Comput Biol 2023; 30:70-81. [PMID: 35727687 DOI: 10.1089/cmb.2021.0458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Codon optimization of a DNA sequence can significantly increase efficiency of protein expression, reducing the cost to manufacture biologic pharmaceuticals. Although directed methods based on such factors as codon usage bias and GC nucleotide content are often used to optimize protein expression, undirected optimization using machine learning could further improve the process by capitalizing on undiscovered patterns that exist within real DNA sequences. To explore this hypothesis, Chinese hamster DNA sequences were used to train a recurrent neural network (RNN) model of codon optimization. The model was used to generate optimized DNA sequence based on an input amino acid sequence for the example receptor programmed death-ligand 1 and for an example monoclonal antibody. When RNN-optimized sequences were transfected transiently or stably into Chinese hamster ovary cells, the resulting protein expression was as high or higher than that produced by DNA sequences optimized by conventional algorithms.
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Affiliation(s)
- Dennis R Goulet
- Department of Protein Engineering, and SysImmune, Inc., Redmond, Washington, USA
| | - Yongqi Yan
- Department of Cell Science, SysImmune, Inc., Redmond, Washington, USA
| | - Palak Agrawal
- Department of Cell Science, SysImmune, Inc., Redmond, Washington, USA
| | - Andrew B Waight
- Department of Protein Engineering, and SysImmune, Inc., Redmond, Washington, USA
| | - Amanda Nga-Sze Mak
- Department of Protein Engineering, and SysImmune, Inc., Redmond, Washington, USA.,Department of Cell Science, SysImmune, Inc., Redmond, Washington, USA
| | - Yi Zhu
- Department of Protein Engineering, and SysImmune, Inc., Redmond, Washington, USA.,Department of Cell Science, SysImmune, Inc., Redmond, Washington, USA
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45
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Kari H, Bandi SMS, Kumar A, Yella VR. DeePromClass: Delineator for Eukaryotic Core Promoters Employing Deep Neural Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:802-807. [PMID: 35353704 DOI: 10.1109/tcbb.2022.3163418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Computational promoter identification in eukaryotes is a classical biological problem that should be refurbished with the availability of an avalanche of experimental data and emerging deep learning technologies. The current knowledge indicates that eukaryotic core promoters display multifarious signals such as TATA-Box, Inr element, TCT, and Pause-button, etc., and structural motifs such as G-quadruplexes. In the present study, we combined the power of deep learning with a plethora of promoter motifs to delineate promoter and non-promoters gleaned from the statistical properties of DNA sequence arrangement. To this end, we implemented convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network architecture for five model systems with [-100 to +50] segments relative to the transcription start site being the core promoter. Unlike previous state-of-the-art tools, which furnish a binary decision of promoter or non-promoter, we classify a chunk of 151mer sequence into a promoter along with the consensus signal type or a non-promoter. The combined CNN-LSTM model; we call "DeePromClass", achieved testing accuracy of 90.6%, 93.6%, 91.8%, 86.5%, and 84.0% for S. cerevisiae, C. elegans, D. melanogaster, Mus musculus, and Homo sapiens respectively. In total, our tool provides an insightful update on next-generation promoter prediction tools for promoter biologists.
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46
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Zrimec J, Zelezniak A, Gruden K. Toward learning the principles of plant gene regulation. TRENDS IN PLANT SCIENCE 2022; 27:1206-1208. [PMID: 36100536 DOI: 10.1016/j.tplants.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 08/09/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Advanced machine learning (ML) algorithms produce highly accurate models of gene expression, uncovering novel regulatory features in nucleotide sequences involving multiple cis-regulatory regions across whole genes and structural properties. These broaden our understanding of gene regulation and point to new principles to test and adopt in the field of plant science.
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Affiliation(s)
- Jan Zrimec
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 111, 1000 Ljubljana, Slovenia.
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, 412 96, Gothenburg, Sweden
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 111, 1000 Ljubljana, Slovenia.
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47
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Durge AR, Shrimankar DD, Sawarkar AD. Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective. Curr Genomics 2022; 23:299-317. [PMID: 36778194 PMCID: PMC9878859 DOI: 10.2174/1389202923666220927105311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/29/2022] [Accepted: 09/01/2022] [Indexed: 11/22/2022] Open
Abstract
Genome sequences indicate a wide variety of characteristics, which include species and sub-species type, genotype, diseases, growth indicators, yield quality, etc. To analyze and study the characteristics of the genome sequences across different species, various deep learning models have been proposed by researchers, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Multilayer Perceptrons (MLPs), etc., which vary in terms of evaluation performance, area of application and species that are processed. Due to a wide differentiation between the algorithmic implementations, it becomes difficult for research programmers to select the best possible genome processing model for their application. In order to facilitate this selection, the paper reviews a wide variety of such models and compares their performance in terms of accuracy, area of application, computational complexity, processing delay, precision and recall. Thus, in the present review, various deep learning and machine learning models have been presented that possess different accuracies for different applications. For multiple genomic data, Repeated Incremental Pruning to Produce Error Reduction with Support Vector Machine (Ripper SVM) outputs 99.7% of accuracy, and for cancer genomic data, it exhibits 99.27% of accuracy using the CNN Bayesian method. Whereas for Covid genome analysis, Bidirectional Long Short-Term Memory with CNN (BiLSTM CNN) exhibits the highest accuracy of 99.95%. A similar analysis of precision and recall of different models has been reviewed. Finally, this paper concludes with some interesting observations related to the genomic processing models and recommends applications for their efficient use.
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Affiliation(s)
- Aditi R. Durge
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, India
| | - Deepti D. Shrimankar
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, India,Address correspondence to this author at the Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, India; Tel: 9860606477; E-mail:
| | - Ankush D. Sawarkar
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, India
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48
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Kim JI, Maguire F, Tsang KK, Gouliouris T, Peacock SJ, McAllister TA, McArthur AG, Beiko RG. Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective. Clin Microbiol Rev 2022; 35:e0017921. [PMID: 35612324 PMCID: PMC9491192 DOI: 10.1128/cmr.00179-21] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Antimicrobial resistance (AMR) is a global health crisis that poses a great threat to modern medicine. Effective prevention strategies are urgently required to slow the emergence and further dissemination of AMR. Given the availability of data sets encompassing hundreds or thousands of pathogen genomes, machine learning (ML) is increasingly being used to predict resistance to different antibiotics in pathogens based on gene content and genome composition. A key objective of this work is to advocate for the incorporation of ML into front-line settings but also highlight the further refinements that are necessary to safely and confidently incorporate these methods. The question of what to predict is not trivial given the existence of different quantitative and qualitative laboratory measures of AMR. ML models typically treat genes as independent predictors, with no consideration of structural and functional linkages; they also may not be accurate when new mutational variants of known AMR genes emerge. Finally, to have the technology trusted by end users in public health settings, ML models need to be transparent and explainable to ensure that the basis for prediction is clear. We strongly advocate that the next set of AMR-ML studies should focus on the refinement of these limitations to be able to bridge the gap to diagnostic implementation.
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Affiliation(s)
- Jee In Kim
- Faculty of Computer Science, Dalhousie University, Halifax, Canada
- Institute for Comparative Genomics, Dalhousie University, Halifax, Canada
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, Canada
| | - Finlay Maguire
- Faculty of Computer Science, Dalhousie University, Halifax, Canada
- Institute for Comparative Genomics, Dalhousie University, Halifax, Canada
- Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, Canada
- Shared Hospital Laboratory, Toronto, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Kara K. Tsang
- London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Theodore Gouliouris
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
- Clinical Microbiology and Public Health Laboratory, Public Health England, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Sharon J. Peacock
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Tim A. McAllister
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, Canada
| | - Andrew G. McArthur
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Canada
- M.G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Canada
| | - Robert G. Beiko
- Faculty of Computer Science, Dalhousie University, Halifax, Canada
- Institute for Comparative Genomics, Dalhousie University, Halifax, Canada
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49
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Controlling gene expression with deep generative design of regulatory DNA. Nat Commun 2022; 13:5099. [PMID: 36042233 PMCID: PMC9427793 DOI: 10.1038/s41467-022-32818-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 08/18/2022] [Indexed: 11/25/2022] Open
Abstract
Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue. Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Here the authors present EspressionGAN, a generative adversarial network that uses genomic and transcriptomic data to generate regulatory sequences.
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50
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Beardall WA, Stan GB, Dunlop MJ. Deep Learning Concepts and Applications for Synthetic Biology. GEN BIOTECHNOLOGY 2022; 1:360-371. [PMID: 36061221 PMCID: PMC9428732 DOI: 10.1089/genbio.2022.0017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/14/2022] [Indexed: 12/24/2022]
Abstract
Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space.
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Affiliation(s)
- William A.V. Beardall
- Department of Bioengineering, Imperial College London, London, United Kingdom
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, London, United Kingdom
| | - Guy-Bart Stan
- Department of Bioengineering, Imperial College London, London, United Kingdom
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, London, United Kingdom
| | - Mary J. Dunlop
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
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