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      Exploring a general multi-pronged activation strategy for natural product discovery in Actinomycetes

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          Abstract

          Natural products possess significant therapeutic potential but remain underutilized despite advances in genomics and bioinformatics. While there are approaches to activate and upregulate natural product biosynthesis in both native and heterologous microbial strains, a comprehensive strategy to elicit production of natural products as well as a generalizable and efficient method to interrogate diverse native strains collection, remains lacking. Here, we explore a flexible and robust integrase-mediated multi-pronged activation approach to reliably perturb and globally trigger antibiotics production in actinobacteria. Across 54 actinobacterial strains, our approach yielded 124 distinct activator-strain combinations which consistently outperform wild type. Our approach expands accessible metabolite space by nearly two-fold and increases selected metabolite yields by up to >200-fold, enabling discovery of Gram-negative bioactivity in tetramic acid analogs. We envision these findings as a gateway towards a more streamlined, accelerated, and scalable strategy to unlock the full potential of Nature’s chemical repertoire.

          Abstract

          A multi-pronged activation approach applied to 54 actinobacterial strains doubles accessible metabolite space and enables discovery of gram-negative bioactivity, offering insights to harness nature’s chemical potential. The approach enhances or triggers natural product production across all 54 native actinobacterial strains, doubling total metabolite production and enabling the discovery of a new tetramic acid analog with gram-negative bioactivity. Insights from this large-scale study could lead to a more streamlined approach to unlock the full potential of nature’s chemical resources.

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          Most cited references68

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.

              K Katoh (2002)
              A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.
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                Author and article information

                Contributors
                lim_yee_hwee@isce2.a-star.edu.sg
                wongft@imcb.a-star.edu.sg
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                6 January 2024
                6 January 2024
                2024
                : 7
                : 50
                Affiliations
                [1 ]GRID grid.185448.4, ISNI 0000 0004 0637 0221, Institute of Sustainability for Chemicals, , Energy and Environment (ISCE2), Agency for Science, Technology and Research (A*STAR), ; 8 Biomedical Grove, #07-01 Neuros Building, Singapore, 138665 Republic of Singapore
                [2 ]Molecular Engineering Lab, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), ( https://ror.org/04xpsrn94) 61 Biopolis Drive, #07-06, Proteos, Singapore, 138673 Republic of Singapore
                [3 ]GRID grid.185448.4, ISNI 0000 0004 0637 0221, Singapore Institute of Food and Biotechnology Innovation (SIFBI), , Agency for Science, Technology and Research (A*STAR), ; 31 Biopolis Way, #01-02, Nanos, Singapore, 138669 Republic of Singapore
                [4 ]Bioinformatics Institute (BII), Agency of Science, Technology and Research (A*STAR), ( https://ror.org/044w3nw43) 30 Biopolis Street, #07-01, Matrix, Singapore, 138671 Republic of Singapore
                [5 ]Synthetic Biology Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, ( https://ror.org/01tgyzw49) 10 Medical Drive, Singapore, 117597 Republic of Singapore
                Author information
                http://orcid.org/0000-0002-4831-5525
                http://orcid.org/0000-0002-5739-0949
                http://orcid.org/0000-0001-9227-7186
                http://orcid.org/0000-0002-2867-087X
                http://orcid.org/0000-0002-6154-9908
                http://orcid.org/0000-0002-2387-9332
                http://orcid.org/0000-0002-7789-3893
                http://orcid.org/0000-0001-6145-5904
                Article
                5648
                10.1038/s42003-023-05648-7
                10771470
                aa350f7b-8da9-49b9-b6b3-fef191e6cfec
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 August 2023
                : 29 November 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001381, National Research Foundation Singapore (National Research Foundation-Prime Minister’s office, Republic of Singapore);
                Award ID: NRF-CRP19-2017-05-00
                Award ID: NRF-CRP19-2017-05-00
                Award ID: NRF-CRP19-2017-05-00
                Award ID: NRF-CRP19-2017-05-00
                Award ID: NRF-CRP19-2017-05-00
                Award ID: NRF-CRP19-2017-05-00
                Award ID: NRF-CRP19-2017-05-00
                Award ID: NRF-CRP19-2017-05-00
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001348, Agency for Science, Technology and Research (A*STAR);
                Award ID: C211917006
                Award ID: C233017006
                Award ID: C211917003
                Award ID: C211917006
                Award ID: C233017006
                Award ID: C211917006
                Award Recipient :
                Funded by: National Research Foundation Singapore (National Research Foundation-Prime Minister’s office, Republic of Singapore)
                Funded by: National Research Foundation Singapore (National Research Foundation-Prime Minister’s office, Republic of Singapore)
                Funded by: National Research Foundation Singapore (National Research Foundation-Prime Minister’s office, Republic of Singapore)
                Funded by: National Research Foundation Singapore (National Research Foundation-Prime Minister’s office, Republic of Singapore)
                Funded by: National Research Foundation Singapore (National Research Foundation-Prime Minister’s office, Republic of Singapore)
                Funded by: National Research Foundation Singapore (National Research Foundation-Prime Minister’s office, Republic of Singapore)
                Funded by: National Research Foundation Singapore (National Research Foundation-Prime Minister’s office, Republic of Singapore)
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                © Springer Nature Limited 2024

                applied microbiology,genetic engineering,metabolic engineering

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