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      Small Spatial Scale Drivers of Secondary Metabolite Biosynthetic Diversity in Environmental Microbiomes

      research-article
      a , a , a ,
      mSystems
      American Society for Microbiology
      antibiotics, microbiomes, nonribosomal peptides, polyketides, secondary metabolites

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          ABSTRACT

          In the search for novel drug candidates, diverse environmental microbiomes have been surveyed for their secondary metabolite biosynthesis potential, yet little is known about the biosynthetic diversity encoded by divergent microbiomes from different ecosystems, and the environmental parameters driving this diversity. Here, we used targeted amplicon sequencing of adenylation (AD) and ketosynthase (KS) domains along with 16S sequencing to delineate the unique biosynthetic potential of microbiomes from three separate habitats (soil, water, and sediments) exhibiting unique small spatial scale physicochemical gradients. The estimated richness of AD domains was highest in marine sediments with 656 ± 58 operational biosynthetic units (OBUs), while the KS domain richness was highest in soil microbiomes with 388 ± 67 OBUs. Microbiomes with rich and diverse bacterial communities displayed the highest PK potential across all ecosystems, and on a small spatial scale, pH and salinity were significantly, positively correlated to KS domain richness in soil and aquatic systems, respectively. Integrating our findings, we were able to predict the KS domain richness with a RMSE of 31 OBUs and a R 2 of 0.91, and by the use of publicly available information on bacterial richness and diversity, we identified grassland biomes as being particularly promising sites for the discovery of novel polyketides. Furthermore, a focus on acidobacterial taxa is likely to be fruitful, as these were responsible for most of the variation in biosynthetic diversity. Overall, our results highlight the importance of sampling diverse environments with high taxonomic diversity in the pursuit for novel secondary metabolites.

          IMPORTANCE To counteract the antibiotic resistance crisis, novel anti-infective agents need to be discovered and brought to market. Microbial secondary metabolites have been important sources of inspiration for small-molecule therapeutics. However, the isolation of novel antibiotics is difficult, and the risk of rediscovery is high. With the overarching purpose of identifying promising microbiomes for discovery of novel bioactivity, we mapped out the most significant drivers of biosynthetic diversity across divergent microbiomes. We found the biosynthetic potential to be unique to individual ecosystems, and to depend on bacterial taxonomic diversity. Within systems, and on small spatial scales, pH and salinity correlated positively to the biosynthetic richness of the microbiomes, Acidobacteria representing the taxa most highly associated with biosynthetic diversity. Ultimately, understanding the key drivers of the biosynthesis potential of environmental microbiomes will allow us to focus bioprospecting efforts and facilitate the discovery of novel therapeutics.

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          Cutadapt removes adapter sequences from high-throughput sequencing reads

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            VSEARCH: a versatile open source tool for metagenomics

            Background VSEARCH is an open source and free of charge multithreaded 64-bit tool for processing and preparing metagenomics, genomics and population genomics nucleotide sequence data. It is designed as an alternative to the widely used USEARCH tool (Edgar, 2010) for which the source code is not publicly available, algorithm details are only rudimentarily described, and only a memory-confined 32-bit version is freely available for academic use. Methods When searching nucleotide sequences, VSEARCH uses a fast heuristic based on words shared by the query and target sequences in order to quickly identify similar sequences, a similar strategy is probably used in USEARCH. VSEARCH then performs optimal global sequence alignment of the query against potential target sequences, using full dynamic programming instead of the seed-and-extend heuristic used by USEARCH. Pairwise alignments are computed in parallel using vectorisation and multiple threads. Results VSEARCH includes most commands for analysing nucleotide sequences available in USEARCH version 7 and several of those available in USEARCH version 8, including searching (exact or based on global alignment), clustering by similarity (using length pre-sorting, abundance pre-sorting or a user-defined order), chimera detection (reference-based or de novo), dereplication (full length or prefix), pairwise alignment, reverse complementation, sorting, and subsampling. VSEARCH also includes commands for FASTQ file processing, i.e., format detection, filtering, read quality statistics, and merging of paired reads. Furthermore, VSEARCH extends functionality with several new commands and improvements, including shuffling, rereplication, masking of low-complexity sequences with the well-known DUST algorithm, a choice among different similarity definitions, and FASTQ file format conversion. VSEARCH is here shown to be more accurate than USEARCH when performing searching, clustering, chimera detection and subsampling, while on a par with USEARCH for paired-ends read merging. VSEARCH is slower than USEARCH when performing clustering and chimera detection, but significantly faster when performing paired-end reads merging and dereplication. VSEARCH is available at https://github.com/torognes/vsearch under either the BSD 2-clause license or the GNU General Public License version 3.0. Discussion VSEARCH has been shown to be a fast, accurate and full-fledged alternative to USEARCH. A free and open-source versatile tool for sequence analysis is now available to the metagenomics community.
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              Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

              (2022)
              Summary Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen–drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. Methods We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen–drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. Findings On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62–6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911–1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9–35·3), and lowest in Australasia, at 6·5 deaths (4·3–9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000–1 270 000) deaths attributable to AMR and 3·57 million (2·62–4·78) deaths associated with AMR in 2019. One pathogen–drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000–100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. Interpretation To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen–drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mSystems
                mSystems
                msystems
                mSystems
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5077
                15 February 2023
                Mar-Apr 2023
                15 February 2023
                : 8
                : 2
                : e00724-22
                Affiliations
                [a ] Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
                University of Sao Paulo
                Author notes

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-9162-9423
                https://orcid.org/0000-0003-0905-5705
                https://orcid.org/0000-0002-7888-9845
                Article
                00724-22 msystems.00724-22
                10.1128/msystems.00724-22
                10134846
                36790187
                2cc0d45c-53f1-450a-87db-b874c662497d
                Copyright © 2023 Geers et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 19 August 2022
                : 26 January 2023
                Page count
                supplementary-material: 9, Figures: 6, Tables: 1, Equations: 0, References: 48, Pages: 15, Words: 9638
                Funding
                Funded by: Novo Nordisk Foundation, FundRef https://doi.org/10.13039/501100009708;
                Award ID: NNF21OC0070749
                Award Recipient :
                Funded by: Danish National Research Foundation, FundRef https://doi.org/10.13039/501100001732;
                Award ID: DNRF137
                Award Recipient : Award Recipient :
                Funded by: Independent Research Fund Denmark, FundRef https://doi.org/10.13039/501100004836;
                Award ID: DFF - 8048-00035B
                Award Recipient :
                Categories
                Research Article
                biotechnology, Biotechnology
                Custom metadata
                March/April 2023

                antibiotics,microbiomes,nonribosomal peptides,polyketides,secondary metabolites

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