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Jiang C, Ruan Y, Li J, Huang J, Xiao M, Xu H. Tissue expression and promoter activity analysis of the porcine TNFSF11 gene. Theriogenology 2024; 226:277-285. [PMID: 38954996 DOI: 10.1016/j.theriogenology.2024.06.018] [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: 04/17/2024] [Revised: 06/20/2024] [Accepted: 06/20/2024] [Indexed: 07/04/2024]
Abstract
Tumour necrosis factor (TNF) superfamily member 11 (TNFSF11), also known as RANKL, plays a crucial role in regulating several physiological and pathological activities. Additionally, it is a vital factor in bone physiology, and the sex hormone progesterone regulates the expansion of stem cells and the proliferation of mammary epithelial cells. It is essential for animal growth and reproductive physiological processes. This study aimed to evaluate the tissue-specific expression characteristics and promoter activity of the TNFSF11 gene in pigs. As a result, the study examined the presence of TNFSF11 expression in the tissues of Xiangsu pigs at 0.6 and 12 months of age. Moreover, the core promoter region of TNFSF11 was also identified by utilizing a combination of bioinformatic prediction and dual-luciferase activity tests. Finally, the effect of transcription factors on the transcriptional activity of the core promoter region was determined using site-directed mutagenesis. TNFSF11 was uniformly expressed in all tissues; however, its expression in muscles was comparatively low. The core promoter region of TNFSF11 was located in the -555 to -1 region. The prediction of the transcription start site of TNFSF11 gene-2000 ∼ + 500bp showed that there was a CpG site in 17 ∼ + 487bp. Analysis of mutations in the transcription factor binding sites revealed that mutations in the Stat5b, Myog, Trl, and EN1 binding sites had significant effects on the transcriptional activity of the TNFSF11 gene, particularly following the EN1 binding site mutation (P < 0.001). This study provides insights into both the tissue-specific expression patterns of TNFSF11 in the tissues of Xiangsu pigs and the potential regulatory effects of transcription factors on its promoter activity. These results may be helpful for future research aimed at clarifying the expression and role of the porcine TNFSF11 gene.
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Affiliation(s)
- Chuanmei Jiang
- Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou University, Guiyang, China; Guizhou Provincial Key Laboratory of Animal Genetics, Breeding and Reproduction, Guizhou University, Guiyang, China; College of Animal Science, Guizhou University, Guiyang, China
| | - Yong Ruan
- Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou University, Guiyang, China; Guizhou Provincial Key Laboratory of Animal Genetics, Breeding and Reproduction, Guizhou University, Guiyang, China; College of Animal Science, Guizhou University, Guiyang, China
| | - Jifeng Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou University, Guiyang, China; Guizhou Provincial Key Laboratory of Animal Genetics, Breeding and Reproduction, Guizhou University, Guiyang, China; College of Animal Science, Guizhou University, Guiyang, China
| | - Jiajin Huang
- Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou University, Guiyang, China; Guizhou Provincial Key Laboratory of Animal Genetics, Breeding and Reproduction, Guizhou University, Guiyang, China; College of Animal Science, Guizhou University, Guiyang, China
| | - Meimei Xiao
- Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou University, Guiyang, China; Guizhou Provincial Key Laboratory of Animal Genetics, Breeding and Reproduction, Guizhou University, Guiyang, China; College of Animal Science, Guizhou University, Guiyang, China
| | - Houqiang Xu
- Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou University, Guiyang, China; Guizhou Provincial Key Laboratory of Animal Genetics, Breeding and Reproduction, Guizhou University, Guiyang, China; College of Animal Science, Guizhou University, Guiyang, China.
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2
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Abid D, Brent MR. NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration. Bioinformatics 2023; 39:7000334. [PMID: 36692138 PMCID: PMC9912366 DOI: 10.1093/bioinformatics/btad038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 01/11/2023] [Accepted: 01/18/2023] [Indexed: 01/25/2023] Open
Abstract
MOTIVATION Many methods have been proposed for mapping the targets of transcription factors (TFs) from gene expression data. It is known that combining outputs from multiple methods can improve performance. To date, outputs have been combined by using either simplistic formulae, such as geometric mean, or carefully hand-tuned formulae that may not generalize well to new inputs. Finally, the evaluation of accuracy has been challenging due to the lack of genome-scale, ground-truth networks. RESULTS We developed NetProphet3, which combines scores from multiple analyses automatically, using a tree boosting algorithm trained on TF binding location data. We also developed three independent, genome-scale evaluation metrics. By these metrics, NetProphet3 is more accurate than other commonly used packages, including NetProphet 2.0, when gene expression data from direct TF perturbations are available. Furthermore, its integration mode can forge a consensus network from gene expression data and TF binding location data. AVAILABILITY AND IMPLEMENTATION All data and code are available at https://zenodo.org/record/7504131#.Y7Wu3i-B2x8. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dhoha Abid
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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3
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Kang Y, Jung WJ, Brent MR. Predicting which genes will respond to transcription factor perturbations. G3 (BETHESDA, MD.) 2022; 12:jkac144. [PMID: 35666184 PMCID: PMC9339286 DOI: 10.1093/g3journal/jkac144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022]
Abstract
The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene's expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer.
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Affiliation(s)
- Yiming Kang
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Computer Science and Engineering, Washington University, St. Louis, MO 63108, USA
| | - Wooseok J Jung
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Computer Science and Engineering, Washington University, St. Louis, MO 63108, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Computer Science and Engineering, Washington University, St. Louis, MO 63108, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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4
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Cho KT, Sen TZ, Andorf CM. Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach. Front Artif Intell 2022; 5:830170. [PMID: 35719692 PMCID: PMC9204276 DOI: 10.3389/frai.2022.830170] [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: 12/06/2021] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.
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Affiliation(s)
- Kyoung Tak Cho
- Department of Computer Science, Iowa State University, Ames, IA, United States
| | - Taner Z. Sen
- USDA-ARS, Crop Improvement and Genetics Research Unit, Albany, CA, United States
| | - Carson M. Andorf
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA, United States
- *Correspondence: Carson M. Andorf
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Abstract
Drug resistance and metastasis—the major complications in cancer—both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single-cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory learning is feasible in a high-dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate, restraining cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.
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Affiliation(s)
- Aseel Shomar
- Department of Chemical Engineering, Israel Institute of Technology, Haifa 32000, Israel
- Network Biology Research Laboratory, Israel Institute of Technology, Haifa 32000, Israel
| | - Omri Barak
- Network Biology Research Laboratory, Israel Institute of Technology, Haifa 32000, Israel
- Rappaport Faculty of Medicine Technion, Israel Institute of Technology, Haifa 32000, Israel
| | - Naama Brenner
- Department of Chemical Engineering, Israel Institute of Technology, Haifa 32000, Israel
- Network Biology Research Laboratory, Israel Institute of Technology, Haifa 32000, Israel
- Corresponding author
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6
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Chen Y, Li F, Nielsen J. Genome-scale modeling of yeast metabolism: retrospectives and perspectives. FEMS Yeast Res 2022; 22:foac003. [PMID: 35094064 PMCID: PMC8862083 DOI: 10.1093/femsyr/foac003] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/06/2022] [Accepted: 01/27/2022] [Indexed: 11/30/2022] Open
Abstract
Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.
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Affiliation(s)
- Yu Chen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Feiran Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
- BioInnovation Institute, DK2200 Copenhagen N, Denmark
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7
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Bergenholm D, Dabirian Y, Ferreira R, Siewers V, David F, Nielsen J. Rational gRNA design based on transcription factor binding data. Synth Biol (Oxf) 2021; 6:ysab014. [PMID: 34712839 PMCID: PMC8546606 DOI: 10.1093/synbio/ysab014] [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: 11/09/2020] [Revised: 04/21/2021] [Accepted: 06/08/2021] [Indexed: 11/14/2022] Open
Abstract
The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system has become a standard tool in many genome engineering endeavors. The endonuclease-deficient version of Cas9 (dCas9) is also a powerful programmable tool for gene regulation. In this study, we made use of Saccharomyces cerevisiae transcription factor (TF) binding data to obtain a better understanding of the interplay between TF binding and binding of dCas9 fused to an activator domain, VPR. More specifically, we targeted dCas9–VPR toward binding sites of Gcr1–Gcr2 and Tye7 present in several promoters of genes encoding enzymes engaged in the central carbon metabolism. From our data, we observed an upregulation of gene expression when dCas9–VPR was targeted next to a TF binding motif, whereas a downregulation or no change was observed when dCas9 was bound on a TF motif. This suggests a steric competition between dCas9 and the specific TF. Integrating TF binding data, therefore, proved to be useful for designing guide RNAs for CRISPR interference or CRISPR activation applications.
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Affiliation(s)
- David Bergenholm
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Yasaman Dabirian
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Raphael Ferreira
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Verena Siewers
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Florian David
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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8
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Ma CZ, Brent MR. Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data. Bioinformatics 2021; 37:1234-1245. [PMID: 33135076 PMCID: PMC8189679 DOI: 10.1093/bioinformatics/btaa947] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/26/2020] [Accepted: 10/27/2020] [Indexed: 12/20/2022] Open
Abstract
Motivation The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now. Results We systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2. Availability and implementation Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cynthia Z Ma
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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9
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Cha S, Hong CP, Kang HA, Hahn JS. Differential activation mechanisms of two isoforms of Gcr1 transcription factor generated from spliced and un-spliced transcripts in Saccharomyces cerevisiae. Nucleic Acids Res 2021; 49:745-759. [PMID: 33367825 PMCID: PMC7826247 DOI: 10.1093/nar/gkaa1221] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 11/27/2020] [Accepted: 12/03/2020] [Indexed: 12/19/2022] Open
Abstract
Gcr1, an important transcription factor for glycolytic genes in Saccharomyces cerevisiae, was recently revealed to have two isoforms, Gcr1U and Gcr1S, produced from un-spliced and spliced transcripts, respectively. In this study, by generating strains expressing only Gcr1U or Gcr1S using the CRISPR/Cas9 system, we elucidate differential activation mechanisms of these two isoforms. The Gcr1U monomer forms an active complex with its coactivator Gcr2 homodimer, whereas Gcr1S acts as a homodimer without Gcr2. The USS domain, 55 residues at the N-terminus existing only in Gcr1U, inhibits dimerization of Gcr1U and even acts in trans to inhibit Gcr1S dimerization. The Gcr1S monomer inhibits the metabolic switch from fermentation to respiration by directly binding to the ALD4 promoter, which can be restored by overexpression of the ALD4 gene, encoding a mitochondrial aldehyde dehydrogenase required for ethanol utilization. Gcr1U and Gcr1S regulate almost the same target genes, but show unique activities depending on growth phase, suggesting that these isoforms play differential roles through separate activation mechanisms depending on environmental conditions.
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Affiliation(s)
- Seungwoo Cha
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Chang Pyo Hong
- Theragen Bio Co., Ltd, 145 Gwanggyo-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16229, Republic of Korea
| | - Hyun Ah Kang
- Department of Life Science, College of Natural Science, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea
| | - Ji-Sook Hahn
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
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10
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Zrimec J, Börlin CS, Buric F, Muhammad AS, Chen R, Siewers V, Verendel V, Nielsen J, Töpel M, Zelezniak A. Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure. Nat Commun 2020; 11:6141. [PMID: 33262328 PMCID: PMC7708451 DOI: 10.1038/s41467-020-19921-4] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 11/02/2020] [Indexed: 12/31/2022] Open
Abstract
Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.
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Affiliation(s)
- Jan Zrimec
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Christoph S Börlin
- 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
| | - Filip Buric
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Azam Sheikh Muhammad
- Computer Science and Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Rhongzen Chen
- Computer Science and Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Verena Siewers
- 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
| | - Vilhelm Verendel
- Computer Science and 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
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Mats Töpel
- Department of Marine Sciences, University of Gothenburg, Box 461, SE-405 30, Gothenburg, Sweden
- Gothenburg Global Biodiversity Center (GGBC), Box 461, 40530, Gothenburg, Sweden
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden.
- Science for Life Laboratory, Tomtebodavägen 23a, SE-171 65, Stockholm, Sweden.
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11
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Srinivasan R, Walvekar AS, Rashida Z, Seshasayee A, Laxman S. Genome-scale reconstruction of Gcn4/ATF4 networks driving a growth program. PLoS Genet 2020; 16:e1009252. [PMID: 33378328 PMCID: PMC7773203 DOI: 10.1371/journal.pgen.1009252] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 11/04/2020] [Indexed: 12/13/2022] Open
Abstract
Growth and starvation are considered opposite ends of a spectrum. To sustain growth, cells use coordinated gene expression programs and manage biomolecule supply in order to match the demands of metabolism and translation. Global growth programs complement increased ribosomal biogenesis with sufficient carbon metabolism, amino acid and nucleotide biosynthesis. How these resources are collectively managed is a fundamental question. The role of the Gcn4/ATF4 transcription factor has been best studied in contexts where cells encounter amino acid starvation. However, high Gcn4 activity has been observed in contexts of rapid cell proliferation, and the roles of Gcn4 in such growth contexts are unclear. Here, using a methionine-induced growth program in yeast, we show that Gcn4/ATF4 is the fulcrum that maintains metabolic supply in order to sustain translation outputs. By integrating matched transcriptome and ChIP-Seq analysis, we decipher genome-wide direct and indirect roles for Gcn4 in this growth program. Genes that enable metabolic precursor biosynthesis indispensably require Gcn4; contrastingly ribosomal genes are partly repressed by Gcn4. Gcn4 directly binds promoter-regions and transcribes a subset of metabolic genes, particularly driving lysine and arginine biosynthesis. Gcn4 also globally represses lysine and arginine enriched transcripts, which include genes encoding the translation machinery. The Gcn4 dependent lysine and arginine supply thereby maintains the synthesis of the translation machinery. This is required to maintain translation capacity. Gcn4 consequently enables metabolic-precursor supply to bolster protein synthesis, and drive a growth program. Thus, we illustrate how growth and starvation outcomes are both controlled using the same Gcn4 transcriptional outputs that function in distinct contexts.
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Affiliation(s)
- Rajalakshmi Srinivasan
- Institute for Stem Cell Science and Regenerative Medicine (inStem), GKVK post, Bangalore, India
| | - Adhish S. Walvekar
- Institute for Stem Cell Science and Regenerative Medicine (inStem), GKVK post, Bangalore, India
| | - Zeenat Rashida
- Institute for Stem Cell Science and Regenerative Medicine (inStem), GKVK post, Bangalore, India
| | - Aswin Seshasayee
- National Centre for Biological Sciences–TIFR, GKVK post, Bellary Road, Bangalore, India
| | - Sunil Laxman
- Institute for Stem Cell Science and Regenerative Medicine (inStem), GKVK post, Bangalore, India
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12
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Börlin CS, Nielsen J, Siewers V. The transcription factor Leu3 shows differential binding behavior in response to changing leucine availability. FEMS Microbiol Lett 2020; 367:5863187. [PMID: 32589214 PMCID: PMC7351879 DOI: 10.1093/femsle/fnaa107] [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: 03/18/2020] [Accepted: 06/23/2020] [Indexed: 11/26/2022] Open
Abstract
The main transcriptional regulator of leucine biosynthesis in the yeast Saccharomyces cerevisiae is the transcription factor Leu3. It has previously been reported that Leu3 always binds to its target genes, but requires activation to induce their expression. In a recent large-scale study of high-resolution transcription factor binding site identification, we showed that Leu3 has divergent binding sites in different cultivation conditions, thereby questioning the results of earlier studies. Here, we present a follow-up study using chromatin immunoprecipitation followed by sequencing (ChIP-seq) to investigate the influence of leucine supplementation on Leu3 binding activity and strength. With this new data set we are able to show that Leu3 exhibits changes in binding activity in response to changing levels of leucine availability.
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Affiliation(s)
- Christoph S Börlin
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41296 Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-41296 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41296 Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-41296 Gothenburg, Sweden.,BioInnovation Institute, Ole Maaløes Vej 3, DK2200 Copenhagen N, Denmark
| | - Verena Siewers
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41296 Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-41296 Gothenburg, Sweden
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13
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Li N, Zeng W, Xu S, Zhou J. Obtaining a series of native gradient promoter-5'-UTR sequences in Corynebacterium glutamicum ATCC 13032. Microb Cell Fact 2020; 19:120. [PMID: 32493332 PMCID: PMC7268698 DOI: 10.1186/s12934-020-01376-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 05/25/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Corynebacterium glutamicum is an important industrial microorganism used for the production of many valuable compounds, especially amino acids and their derivatives. For fine-tuning of metabolic pathways, synthetic biological tools are largely based on the rational application of promoters. However, the limited number of promoters make it difficult. RESULTS In this study, according to the analysis of RNA-Seq data, 90 DNA fragments with lengths of 200-500 bp that may contain promoter-5'-UTR (PUTR) sequences were amplified and linked to a fluorescent protein gene. When compared with the common strong PUTR PsodUTR, 17 strong PUTRs were obtained, which maintained stable expression strengths from the early to post stationary phase. Among them, PNCgl1676UTR was the strongest and its fluorescent protein expression level was more than five times higher than that of PsodUTR. Furthermore, nine typical chemicals related to the biosynthesis of sulfur-containing amino acids (such as L-methionine, L-cysteine) were selected as stress substances to preliminarily explore the stress on these PUTRs. The results showed that the expression of PbrnFUTR was activated by L-methionine, while that of PNCgl1202UTR was severely inhibited by L-lysine. CONCLUSIONS These findings demonstrated that the selected PUTRs can stably express different genes, such as the red fluorescence protein gene, and can be useful for fine-tuning regulation of metabolic networks in C. glutamicum or for establishing high-throughput screening strategies through biosensor for the production of useful compounds.
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Affiliation(s)
- Ning Li
- Key Laboratory of Industrial Biotechnology, Ministry of Education and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China.,National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China.,Jiangsu Provisional Research Center for Bioactive Product Processing Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China
| | - Weizhu Zeng
- Key Laboratory of Industrial Biotechnology, Ministry of Education and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China.,National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China.,Jiangsu Provisional Research Center for Bioactive Product Processing Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China
| | - Sha Xu
- Key Laboratory of Industrial Biotechnology, Ministry of Education and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China.,National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China.,Jiangsu Provisional Research Center for Bioactive Product Processing Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China
| | - Jingwen Zhou
- Key Laboratory of Industrial Biotechnology, Ministry of Education and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China. .,National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China. .,Jiangsu Provisional Research Center for Bioactive Product Processing Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China.
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14
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Kang Y, Patel NR, Shively C, Recio PS, Chen X, Wranik BJ, Kim G, McIsaac RS, Mitra R, Brent MR. Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses. Genome Res 2020; 30:459-471. [PMID: 32060051 PMCID: PMC7111528 DOI: 10.1101/gr.259655.119] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 02/11/2020] [Indexed: 12/22/2022]
Abstract
A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human, but they rarely converge on a common set of direct, functional targets for a TF. Even the few genes that are both bound and responsive may not be direct functional targets. Our analysis shows that when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce dual threshold optimization (DTO), a new method for setting significance thresholds on binding and perturbation-response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that have been processed by network inference algorithms, which further improves convergence. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. These new data sets improve convergence and expand the high-confidence network synergistically.
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Affiliation(s)
- Yiming Kang
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Nikhil R Patel
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Christian Shively
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Pamela Samantha Recio
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Xuhua Chen
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Bernd J Wranik
- Calico Life Sciences LLC, South San Francisco, California 94080, USA
| | - Griffin Kim
- Calico Life Sciences LLC, South San Francisco, California 94080, USA
| | - R Scott McIsaac
- Calico Life Sciences LLC, South San Francisco, California 94080, USA
| | - Robi Mitra
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, Missouri 63130, USA
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15
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Yu R, Nielsen J. Yeast systems biology in understanding principles of physiology underlying complex human diseases. Curr Opin Biotechnol 2019; 63:63-69. [PMID: 31901548 DOI: 10.1016/j.copbio.2019.11.021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/21/2019] [Accepted: 11/26/2019] [Indexed: 12/25/2022]
Abstract
Complex human diseases commonly arise from deregulation of cell growth, metabolism, and/or gene expression. Yeast is a eukaryal model organism that is widely used to study these processes. Yeast systems biology benefits from the ability to exert fine experimental control over the cell growth rate and nutrient composition, which allows orthogonal experimental design and generation of multi-omics data at high resolution. This has led to several insights on the principles of cellular physiology, including many cellular processes associated with complex human diseases. Here we review these biological insights together with experimental and modeling approaches developed in yeast to study systems biology. The role of yeast systems biology to further advance systems and personalized therapies for complex diseases is discussed.
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Affiliation(s)
- Rosemary Yu
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark; BioInnovation Institute, Ole Måløes Vej 3, DK-2200 Copenhagen N, Denmark.
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16
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Mondeel TDGA, Holland P, Nielsen J, Barberis M. ChIP-exo analysis highlights Fkh1 and Fkh2 transcription factors as hubs that integrate multi-scale networks in budding yeast. Nucleic Acids Res 2019; 47:7825-7841. [PMID: 31299083 PMCID: PMC6736057 DOI: 10.1093/nar/gkz603] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 06/23/2019] [Accepted: 07/11/2019] [Indexed: 01/18/2023] Open
Abstract
The understanding of the multi-scale nature of molecular networks represents a major challenge. For example, regulation of a timely cell cycle must be coordinated with growth, during which changes in metabolism occur, and integrate information from the extracellular environment, e.g. signal transduction. Forkhead transcription factors are evolutionarily conserved among eukaryotes, and coordinate a timely cell cycle progression in budding yeast. Specifically, Fkh1 and Fkh2 are expressed during a lengthy window of the cell cycle, thus are potentially able to function as hubs in the multi-scale cellular environment that interlocks various biochemical networks. Here we report on a novel ChIP-exo dataset for Fkh1 and Fkh2 in both logarithmic and stationary phases, which is analyzed by novel and existing software tools. Our analysis confirms known Forkhead targets from available ChIP-chip studies and highlights novel ones involved in the cell cycle, metabolism and signal transduction. Target genes are analyzed with respect to their function, temporal expression during the cell cycle, correlation with Fkh1 and Fkh2 as well as signaling and metabolic pathways they occur in. Furthermore, differences in targets between Fkh1 and Fkh2 are presented. Our work highlights Forkhead transcription factors as hubs that integrate multi-scale networks to achieve proper timing of cell division in budding yeast.
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Affiliation(s)
- Thierry D G A Mondeel
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, Surrey, UK.,Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands
| | - Petter Holland
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE412 96, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE412 96, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, DK2800 Kgs., Denmark
| | - Matteo Barberis
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, Surrey, UK.,Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands
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17
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Yu R, Nielsen J. Big data in yeast systems biology. FEMS Yeast Res 2019; 19:5585886. [DOI: 10.1093/femsyr/foz070] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 10/09/2019] [Indexed: 12/16/2022] Open
Abstract
ABSTRACTSystems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology.
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Affiliation(s)
- Rosemary Yu
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
- BioInnovation Institute, Ole Maaløes Vej 3, DK-2200 Copenhagen N, Denmark
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