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      Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

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          Abstract

          Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations.

          Abstract

          Stable isotope-labelled amino acid incorporation into proteins reveals that genetically homogeneous yeast colonies contain metabolically distinct subpopulations that cross-feed each other and are phenotypically diverse.

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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            The PRIDE database and related tools and resources in 2019: improving support for quantification data

            Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
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              g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)

              Abstract Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.
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                Author and article information

                Contributors
                markus.ralser@charite.de
                Journal
                Nat Microbiol
                Nat Microbiol
                Nature Microbiology
                Nature Publishing Group UK (London )
                2058-5276
                16 February 2023
                16 February 2023
                2023
                : 8
                : 3
                : 441-454
                Affiliations
                [1 ]GRID grid.6363.0, ISNI 0000 0001 2218 4662, Department of Biochemistry, , Charité Universitätsmedizin Berlin, ; Berlin, Germany
                [2 ]GRID grid.451388.3, ISNI 0000 0004 1795 1830, Molecular Biology of Metabolism Laboratory, , The Francis Crick Institute, ; London, UK
                [3 ]GRID grid.83440.3b, ISNI 0000000121901201, Institute of Healthy Ageing and Department of Genetics, Evolution and Environment, , University College London, ; London, UK
                [4 ]GRID grid.6363.0, ISNI 0000 0001 2218 4662, Core Facility–High-Throughput Mass Spectrometry, Charité Universitätsmedizin Berlin, ; Berlin, Germany
                [5 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, , University of Oxford, ; Oxford, UK
                [6 ]GRID grid.419538.2, ISNI 0000 0000 9071 0620, Max Planck Institute for Molecular Genetics, ; Berlin, Germany
                Author information
                http://orcid.org/0000-0002-5957-4661
                http://orcid.org/0000-0003-1983-2950
                http://orcid.org/0000-0003-4036-1532
                http://orcid.org/0000-0001-9535-7413
                Article
                1304
                10.1038/s41564-022-01304-8
                9981460
                36797484
                7955ec33-2bac-4760-b800-d72796a1b53c
                © The Author(s) 2023

                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
                : 30 June 2022
                : 13 December 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100010571, Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie (Federal Ministry for Education, Science, Research and Technology);
                Award ID: 161L0221
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: INST 335/797-1
                Award ID: INST 335/797-1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000289, Cancer Research UK (CRUK);
                Award ID: FC001134
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: FC001134
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000265, RCUK | Medical Research Council (MRC);
                Award ID: FC001134
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research);
                Award ID: 031L0220
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature Limited 2023

                microbial communities,proteomics,metabolomics,metabolism

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