Time-resolved functional genomics using deep learning reveals global hierarchical control of autophagy.
| Title: | Time-resolved functional genomics using deep learning reveals global hierarchical control of autophagy. |
|---|---|
| Authors: | Chica N; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway. nathac@uio.no.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. nathac@uio.no.; Andersen AN; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.; Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.; Orellana-Muñoz S; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.; Garcia I; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.; P AN; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.; Nakken S; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Ayuda-Durán P; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.; Håkensbakken L; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.; Schultz SW; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.; Rødningen E; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.; Putnam CD; Ludwig Institute for Cancer Research, La Jolla, CA, USA.; Department of Medicine, University of California, San Diego, La Jolla, CA, USA.; Zucknick M; Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.; Faculty of Medicine, University of Oslo, Oslo, Norway.; Rusten TE; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.; Faculty of Medicine, University of Oslo, Oslo, Norway.; Enserink JM; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway. j.m.enserink@ibv.uio.no.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. j.m.enserink@ibv.uio.no.; Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway. j.m.enserink@ibv.uio.no. |
| Source: | Nature cell biology [Nat Cell Biol] 2026 Mar; Vol. 28 (3), pp. 465-479. Date of Electronic Publication: 2026 Mar 13. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Macmillan Magazines Ltd Country of Publication: England NLM ID: 100890575 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-4679 (Electronic) Linking ISSN: 14657392 NLM ISO Abbreviation: Nat Cell Biol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Macmillan Magazines Ltd., [1999- |
| MeSH Terms: | Autophagy*/genetics ; Genomics*/methods ; Saccharomyces cerevisiae*/genetics ; Saccharomyces cerevisiae*/metabolism ; Deep Learning*; Autophagosomes/metabolism ; Saccharomyces cerevisiae Proteins/genetics ; Saccharomyces cerevisiae Proteins/metabolism ; Nitrogen/metabolism ; Gene Regulatory Networks ; Mutation ; Signal Transduction |
| Abstract: | Recycling of cellular components through autophagy maintains homeostasis in changing nutrient environments. Although its core mechanisms are extensively studied, understanding of its systems-wide dynamic regulation remains limited, particularly regarding how autophagy is inactivated once nutrients are restored. Here we mapped the genetic network that controls activation and inactivation of autophagy during nitrogen changes by combining time-resolved high-content imaging, deep learning and latent feature analysis. This dataset, termed AutoDRY, categorizes 5,919 mutants based on nutrient response kinetics and their contributions to autophagosome formation and clearance. Integrating these profiles with functional and genetic network data uncovered hierarchical and multilayered control of autophagy and revealed multiple new regulatory pathways. Notably, we identified the retrograde pathway as a pivotal time-varying modulator that tunes the expression of core autophagy genes and plays a central role in autophagy inactivation. Together, this study establishes a systems-level resource to guide future investigations of autophagy.; (© 2026. The Author(s).) |
| Competing Interests: | Competing interests: The authors declare no competing interests. |
| References: | He, C. Balancing nutrient and energy demand and supply via autophagy. Curr. Biol. 32, R684–R696 (2022). (PMID: 35728554965277310.1016/j.cub.2022.04.071); Hansen, M., Rubinsztein, D. C. & Walker, D. W. Autophagy as a promoter of longevity: insights from model organisms. Nat. Rev. Mol. Cell Biol. 19, 579–593 (2018). (PMID: 30006559642459110.1038/s41580-018-0033-y); Galluzzi, L., Pietrocola, F., Levine, B. & Kroemer, G. Metabolic control of autophagy. Cell 159, 1263–1276 (2014). (PMID: 25480292450093610.1016/j.cell.2014.11.006); Aman, Y. et al. Autophagy in healthy aging and disease. Nat. Aging 1, 634–650 (2021). (PMID: 34901876865915810.1038/s43587-021-00098-4); Feng, Y., He, D., Yao, Z. & Klionsky, D. J. The machinery of macroautophagy. Cell Res. 24, 24–41 (2014). (PMID: 2436633910.1038/cr.2013.168); González, A. & Hall, M. N. Nutrient sensing and TOR signaling in yeast and mammals. EMBO J. 36, 397–408 (2017). (PMID: 28096180569494410.15252/embj.201696010); Tsukada, M. & Ohsumi, Y. Isolation and characterization of autophagy-defective mutants of Saccharomyces cerevisiae. FEBS Lett. 333, 169–174 (1993). (PMID: 822416010.1016/0014-5793(93)80398-E); Mizushima, N. et al. A protein conjugation system essential for autophagy. Nature 395, 395–398 (1998). (PMID: 975973110.1038/26506); Kirisako, T. et al. Formation process of autophagosome is traced with Apg8/Aut7p in yeast. J. Cell Biol. 147, 435–446 (1999). (PMID: 10525546217422310.1083/jcb.147.2.435); Galluzzi, L., Bravo-San Pedro, J. M., Levine, B., Green, D. R. & Kroemer, G. Pharmacological modulation of autophagy: therapeutic potential and persisting obstacles. Nat. Rev. Drug Discov. 16, 487–511 (2017). (PMID: 28529316571364010.1038/nrd.2017.22); Behrends, C., Sowa, M. E., Gygi, S. P. & Harper, J. W. Network organization of the human autophagy system. Nature 466, 68–76 (2010). (PMID: 20562859290199810.1038/nature09204); Kramer, M. H. et al. Active interaction mapping reveals the hierarchical organization of autophagy. Mol. Cell 65, 761–774 (2017). (PMID: 28132844543930510.1016/j.molcel.2016.12.024); Hu, Z. et al. Multilayered control of protein turnover by TORC1 and Atg1. Cell Rep. 28, 3486–3496 (2019). (PMID: 3155391610.1016/j.celrep.2019.08.069); Diehl, V. et al. Minimized combinatorial CRISPR screens identify genetic interactions in autophagy. Nucleic Acids Res. 49, 5684–5704 (2021). (PMID: 33956155819180110.1093/nar/gkab309); Türei, D. et al. Autophagy regulatory network—a systems-level bioinformatics resource for studying the mechanism and regulation of autophagy. Autophagy 11, 155–165 (2015). (PMID: 25635527450265110.4161/15548627.2014.994346); Mizushima, N. & Murphy, L. O. Autophagy assays for biological discovery and therapeutic development. Trends Biochem. Sci. 45, 1080–1093 (2020). (PMID: 3283909910.1016/j.tibs.2020.07.006); Loos, B., du Toit, A. & Hofmeyr, J.-H. S. Defining and measuring autophagosome flux—concept and reality. Autophagy 10, 2087–2096 (2014). (PMID: 25484088450279010.4161/15548627.2014.973338); Barth, H. & Thumm, M. A genomic screen identifies AUT8 as a novel gene essential for autophagy in the yeast Saccharomyces cerevisiae. Gene 274, 151–156 (2001). (PMID: 1167500710.1016/S0378-1119(01)00614-X); Kira, S. et al. Reciprocal conversion of Gtr1 and Gtr2 nucleotide-binding states by Npr2–Npr3 inactivates TORC1 and induces autophagy. Autophagy 10, 1565–1578 (2014). (PMID: 25046117420653510.4161/auto.29397); Beesabathuni, N. S., Park, S. & Shah, P. S. Quantitative and temporal measurement of dynamic autophagy rates. Autophagy 19, 1164–1183 (2023). (PMID: 3602649210.1080/15548627.2022.2117515); Martin, K. R. et al. Computational model for autophagic vesicle dynamics in single cells. Autophagy 9, 74–92 (2013). (PMID: 2319689810.4161/auto.22532); Kholodenko, B. N. Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol. 7, 165–176 (2006). (PMID: 16482094167990510.1038/nrm1838); Di-Bella, J. P., Colman-Lerner, A. & Ventura, A. C. Properties of cell signaling pathways and gene expression systems operating far from steady-state. Sci. Rep. 8, 17035 (2018). (PMID: 30451879624290310.1038/s41598-018-34766-0); Binda, M. et al. The Vam6 GEF controls TORC1 by activating the EGO complex. Mol. Cell 35, 563–573 (2009). (PMID: 1974835310.1016/j.molcel.2009.06.033); Ostrowicz, C. W. et al. Defined subunit arrangement and rab interactions are required for functionality of the HOPS tethering complex. Traffic 11, 1334–1346 (2010). (PMID: 2060490210.1111/j.1600-0854.2010.01097.x); Chen, Y. et al. A Vps21 endocytic module regulates autophagy. Mol. Biol. Cell 25, 3166–3177 (2014). (PMID: 25143401419686710.1091/mbc.e14-04-0917); Caglar, M. U., Teufel, A. I. & Wilke, C. O. Sicegar: R package for sigmoidal and double-sigmoidal curve fitting. PeerJ 6, e4251 (2018). (PMID: 29362694577430110.7717/peerj.4251); Wilson, D. J. The harmonic mean p-value for combining dependent tests. Proc. Natl Acad. Sci. USA 116, 1195–1200 (2019). (PMID: 30610179634771810.1073/pnas.1814092116); Chica, N. et al. Dataset for genome-wide profiling of autophagy dynamics under nutrient availability in Saccharomyces cerevisiae. Dryad https://doi.org/10.5061/dryad.cfxpnvxdh (2025).; Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).; Oughtred, R. et al. BioGRID: a resource for studying biological interactions in yeast. Cold Spring Harb. Protoc. https://doi.org/10.1101/pdb.top080754 (2016).; Szklarczyk, D. et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015). (PMID: 2535255310.1093/nar/gku1003); Hao, B. & Kovács, I. A. A positive statistical benchmark to assess network agreement. Nat. Commun. 14, 2988 (2023). (PMID: 372256991020920710.1038/s41467-023-38625-z); Huang, J. K. et al. Systematic evaluation of molecular networks for discovery of disease genes. Cell Syst. 6, 484–495 (2018). (PMID: 29605183592072410.1016/j.cels.2018.03.001); Baryshnikova, A. Systematic functional annotation and visualization of biological networks. Cell Syst. 2, 412–421 (2016). (PMID: 2723773810.1016/j.cels.2016.04.014); Meldal, B. H. M. et al. Analysing the yeast complexome—the Complex Portal rising to the challenge. Nucleic Acids Res. 49, 3156–3167 (2021). (PMID: 33677561803463610.1093/nar/gkab077); Collins, S. R. et al. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 446, 806–810 (2007). (PMID: 1731498010.1038/nature05649); Ulitsky, I., Shlomi, T., Kupiec, M. & Shamir, R. From E-MAPs to module maps: dissecting quantitative genetic interactions using physical interactions. Mol. Syst. Biol. 4, 209 (2008). (PMID: 18628749251636410.1038/msb.2008.42); Schuldiner, M. et al. Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123, 507–519 (2005). (PMID: 1626934010.1016/j.cell.2005.08.031); Yu, L. et al. Termination of autophagy and reformation of lysosomes regulated by mTOR. Nature 465, 942–946 (2010). (PMID: 20526321292074910.1038/nature09076); Takeda, E. et al. Vacuole-mediated selective regulation of TORC1–Sch9 signaling following oxidative stress. Mol. Biol. Cell 29, 510–522 (2018). (PMID: 2923782010.1091/mbc.E17-09-0553); Chan, T. F., Bertram, P. G., Ai, W. & Zheng, X. F. Regulation of APG14 expression by the GATA-type transcription factor Gln3p. J. Biol. Chem. 276, 6463–6467 (2001). (PMID: 1109608710.1074/jbc.M008162200); Pilauri, V., Bewley, M., Diep, C. & Hopper, J. Gal80 dimerization and the yeast GAL gene switch. Genetics 169, 1903–1914 (2005). (PMID: 15695361144959510.1534/genetics.104.036723); Albert, B. et al. A molecular titration system coordinates ribosomal protein gene transcription with ribosomal RNA synthesis. Mol. Cell 64, 720–733 (2016). (PMID: 2781814210.1016/j.molcel.2016.10.003); Eisenberg, T. et al. Induction of autophagy by spermidine promotes longevity. Nat. Cell Biol. 11, 1305–1314 (2009). (PMID: 1980197310.1038/ncb1975); Chen, X. et al. Whi2 is a conserved negative regulator of TORC1 in response to low amino acids. PLoS Genet. 14, e1007592 (2018). (PMID: 30142151612687610.1371/journal.pgen.1007592); Caligaris, M. et al. Snf1/AMPK fine-tunes TORC1 signaling in response to glucose starvation. eLife 12, e84319 (2023).; Tanigawa, M. et al. A glutamine sensor that directly activates TORC1. Commun. Biol. 4, 1093 (2021). (PMID: 34535752844883910.1038/s42003-021-02625-w); Ma, J. et al. Using deep learning to model the hierarchical structure and function of a cell. Nat. Methods 15, 290–298 (2018). (PMID: 29505029588254710.1038/nmeth.4627); Culley, C., Vijayakumar, S., Zampieri, G. & Angione, C. A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth. Proc. Natl Acad. Sci. USA 117, 18869–18879 (2020). (PMID: 32675233741414010.1073/pnas.2002959117); Kemmeren, P. et al. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Cell 157, 740–752 (2014). (PMID: 2476681510.1016/j.cell.2014.02.054); Messner, C. B. et al. The proteomic landscape of genome-wide genetic perturbations. Cell 186, 2018–2034 (2023). (PMID: 37080200761564910.1016/j.cell.2023.03.026); Mülleder, M. et al. Functional metabolomics describes the yeast biosynthetic regulome. Cell 167, 553–565 (2016). (PMID: 27693354505508310.1016/j.cell.2016.09.007); Hackett, S. R. et al. Learning causal networks using inducible transcription factors and transcriptome-wide time series. Mol. Syst. Biol. 16, e9174 (2020). (PMID: 32181581707691410.15252/msb.20199174); Natarajan, K. et al. Transcriptional profiling shows that Gcn4p is a master regulator of gene expression during amino acid starvation in yeast. Mol. Cell. Biol. 21, 4347–4368 (2001). (PMID: 113906638709510.1128/MCB.21.13.4347-4368.2001); Papinski, D. et al. Early steps in autophagy depend on direct phosphorylation of Atg9 by the Atg1 kinase. Mol. Cell 53, 471–483 (2014). (PMID: 24440502397865710.1016/j.molcel.2013.12.011); Schreiber, A. et al. Multilayered regulation of autophagy by the Atg1 kinase orchestrates spatial and temporal control of autophagosome formation. Mol. Cell 81, 5066–5081 (2021). (PMID: 34798055869386010.1016/j.molcel.2021.10.024); Jazwinski, S. M. & Kriete, A. The yeast retrograde response as a model of intracellular signaling of mitochondrial dysfunction. Front. Physiol. 3, 26575 (2012). (PMID: 10.3389/fphys.2012.00139); Mattiazzi Usaj, M. et al. Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability. Mol. Syst. Biol. 16, e9243 (2020). (PMID: 32064787702509310.15252/msb.20199243); van der Vaart, A., Griffith, J. & Reggiori, F. Exit from the Golgi is required for the expansion of the autophagosomal phagophore in yeast Saccharomyces cerevisiae. Mol. Biol. Cell 21, 2270–2284 (2010). (PMID: 20444982289399010.1091/mbc.e09-04-0345); Nair, U. et al. SNARE proteins are required for macroautophagy. Cell 146, 290–302 (2011). (PMID: 21784249314336210.1016/j.cell.2011.06.022); Bruch, A., Laguna, T., Butter, F., Schaffrath, R. & Klassen, R. Misactivation of multiple starvation responses in yeast by loss of tRNA modifications. Nucleic Acids Res. 48, 7307–7320 (2020). (PMID: 324845437367188); Buchan, J. R., Kolaitis, R.-M., Taylor, J. P. & Parker, R. Eukaryotic stress granules are cleared by autophagy and Cdc48/VCP function. Cell 153, 1461–1474 (2013). (PMID: 23791177376014810.1016/j.cell.2013.05.037); Huang, H. et al. Bulk RNA degradation by nitrogen starvation-induced autophagy in yeast. EMBO J. 34, 154–168 (2015). (PMID: 2546896010.15252/embj.201489083); Baryshnikova, A. et al. Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nat. Methods 7, 1017–1024 (2010). (PMID: 21076421311732510.1038/nmeth.1534); Klionsky, D. J. et al. Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition). Autophagy 17, 1–382 (2021). (PMID: 33634751799608710.1080/15548627.2020.1797280); Janke, C. et al. A versatile toolbox for PCR-based tagging of yeast genes: new fluorescent proteins, more markers and promoter substitution cassettes. Yeast 21, 947–962 (2004). (PMID: 1533455810.1002/yea.1142); Shaner, N. C. et al. A bright monomeric green fluorescent protein derived from Branchiostoma lanceolatum. Nat. Methods 10, 407–409 (2013). (PMID: 23524392381105110.1038/nmeth.2413); Kuzmin, E. et al. Systematic analysis of complex genetic interactions. Science 360, eaao1729 (2018).; Tong, A. H. et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364–2368 (2001). (PMID: 1174320510.1126/science.1065810); Chollet, F. & Allaire, J. J. Deep Learning with R (Pearson Professional, 2018).; McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.0342 (2018).; Sainburg, T., McInnes, L. & Gentner, T. Q. Parametric UMAP embeddings for representation and semisupervised learning. Neural Comput. 33, 2881–2907 (2021).; Loader, C., Sun, J., Lucent Technologies & Liaw, A. locfit: local regression, likelihood and density estimation. R package version 1.5-9.12. https://cran.r-project.org/web/packages/locfit/index.html (2025).; Langfelder, P., Zhang, B. & Horvath, S. dynamicTreeCut: methods for detection of clusters in hierarchical clustering dendrograms. R package version 1.63-1. https://cran.r-project.org/web/packages/dynamicTreeCut/index.html (2016).; Darsow, T., Rieder, S. E. & Emr, S. D. A multispecificity syntaxin homologue, Vam3p, essential for autophagic and biosynthetic protein transport to the vacuole. J. Cell Biol. 138, 517–529 (1997). (PMID: 9245783214163210.1083/jcb.138.3.517); Sato, T. K., Darsow, T. & Emr, S. D. Vam7p, a SNAP-25-like molecule, and Vam3p, a syntaxin homolog, function together in yeast vacuolar protein trafficking. Mol. Cell. Biol. 18, 5308–5319 (1998). (PMID: 971061510911610.1128/MCB.18.9.5308); Wang, C.-W., Stromhaug, P. E., Shima, J. & Klionsky, D. J. The Ccz1–Mon1 protein complex is required for the late step of multiple vacuole delivery pathways. J. Biol. Chem. 277, 47917–47927 (2002). (PMID: 12364329275469010.1074/jbc.M208191200); Yang, S. & Rosenwald, A. A high copy suppressor screen for autophagy defects in Saccharomyces arl1 Δ and ypt6 Δ strains. G3 7, 333–341 (2017). (PMID: 27974437529558310.1534/g3.116.035998); Ohashi, Y. & Munro, S. Membrane delivery to the yeast autophagosome from the Golgi-endosomal system. Mol. Biol. Cell 21, 3998–4008 (2010). (PMID: 20861302298210510.1091/mbc.e10-05-0457); Arlt, H. et al. The dynamin Vps1 mediates Atg9 transport to the sites of autophagosome formation. J. Biol. Chem. 299, 104712 (2023). (PMID: 370609971019687110.1016/j.jbc.2023.104712); Shimobayashi, M., Takematsu, H., Eiho, K., Yamane, Y. & Kozutsumi, Y. Identification of Ypk1 as a novel selective substrate for nitrogen starvation-triggered proteolysis requiring autophagy system and endosomal sorting complex required for transport (ESCRT) machinery components. J. Biol. Chem. 285, 36984–36994 (2010). (PMID: 20855891297862710.1074/jbc.M110.119180); Kato, M. & Wickner, W. Vam10p defines a Sec18p-independent step of priming that allows yeast vacuole tethering. Proc. Natl Acad. Sci. USA 100, 6398–6403 (2003). (PMID: 1274837716445810.1073/pnas.1132162100); Fisk, D. G. et al. Saccharomyces cerevisiae S288C genome annotation: a working hypothesis. Yeast 23, 857–865 (2006). (PMID: 17001629304012210.1002/yea.1400); Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012). (PMID: 22455463333937910.1089/omi.2011.0118); Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003). (PMID: 1459765840376910.1101/gr.1239303); Bader, G. D. & Hogue, C. W. V. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 4, 2 (2003). (PMID: 10.1186/1471-2105-4-2); Cherry, J. M. et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 40, D700–D705 (2012). (PMID: 2211003710.1093/nar/gkr1029); Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (ACM, 2016).; Duong, T. ks: kernel density estimation and kernel discriminant analysis for multivariate data in R. J. Stat. Softw. 21, 1–16 (2007). (PMID: 10.18637/jss.v021.i07); Väremo, L., Nielsen, J. & Nookaew, I. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res. 41, 4378–4391 (2013). (PMID: 23444143363210910.1093/nar/gkt111); Brachmann, C. B. et al. Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications. Yeast 14, 115–132 (1998). (PMID: 948380110.1002/(SICI)1097-0061(19980130)14:23.0.CO;2-2); Guthrie, C. & Fink, G. R. (eds) Guide to Yeast Genetics and Molecular and Cell Biology, Part C (Gulf Professional Publishing, 2002).; Gietz, R. D. & Woods, R. A. Transformation of yeast by lithium acetate/single-stranded carrier DNA/polyethylene glycol method. Methods Enzymol. 350, 87–96 (2002). (PMID: 1207333810.1016/S0076-6879(02)50957-5); Araki, Y., Kira, S. & Noda, T. Quantitative assay of macroautophagy using Pho8Δ60 assay and GFP-cleavage assay in yeast. Methods Enzymol. 588, 307–321 (2017). (PMID: 2823710710.1016/bs.mie.2016.10.027); Noda, T. & Klionsky, D. J. The quantitative Pho8Δ60 assay of nonspecific autophagy. Methods Enzymol. 451, 33–42 (2008). (PMID: 1918571110.1016/S0076-6879(08)03203-5); Suzuki, K. et al. The pre-autophagosomal structure organized by concerted functions of APG genes is essential for autophagosome formation. EMBO J. 20, 5971–5981 (2001). (PMID: 1168943712569210.1093/emboj/20.21.5971); Cheong, H. & Klionsky, D. J. Biochemical methods to monitor autophagy-related processes in yeast. Methods Enzymol. 451, 1–26 (2008). (PMID: 1918570910.1016/S0076-6879(08)03201-1); Torggler, R., Papinski, D. & Kraft, C. Assays to monitor autophagy in Saccharomyces cerevisiae. Cells 6, 23 (2017).; Chica, N., Portantier, M., Nyquist-Andersen, M., Espada-Burriel, S. & Lopez-Aviles, S. Uncoupling of mitosis and cytokinesis upon a prolonged arrest in metaphase is influenced by protein phosphatases and mitotic transcription in fission yeast. Front. Cell Dev. Biol. 10, 876810 (2022). (PMID: 35923846934047910.3389/fcell.2022.876810); Foiani, M., Marini, F., Gamba, D., Lucchini, G. & Plevani, P. The B subunit of the DNA polymerase α-primase complex in Saccharomyces cerevisiae executes an essential function at the initial stage of DNA replication. Mol. Cell. Biol. 14, 923–933 (1994). (PMID: 8289832358447); Mitchell, J. K., Fonzi, W. A., Wilkerson, J. & Opheim, D. J. A particulate form of alkaline phosphatase in the yeast, Saccharomyces cerevisiae. Biochim. Biophys. Acta 657, 482–494 (1981). (PMID: 701140310.1016/0005-2744(81)90333-8); Zhang, Y., Jenkins, D. F., Manimaran, S. & Johnson, W. E. Alternative empirical Bayes models for adjusting for batch effects in genomic studies. BMC Bioinform. 19, 262 (2018). (PMID: 10.1186/s12859-018-2263-6); Moritz, S. & Bartz-Beielstein, T. ImputeTS: time series missing value imputation in R. R J. 9, 207 (2017). (PMID: 10.32614/RJ-2017-009); Liu, Y., Just, A. & Mayer, M. SHAPforxgboost: SHAP plots for ‘XGBoost’. R package version 0.1.0. https://cran.r-project.org/web/packages/SHAPforxgboost/index.html (2023). |
| Substance Nomenclature: | 0 (Saccharomyces cerevisiae Proteins); N762921K75 (Nitrogen) |
| Entry Date(s): | Date Created: 20260314 Date Completed: 20260317 Latest Revision: 20260319 |
| Update Code: | 20260319 |
| PubMed Central ID: | PMC12992121 |
| DOI: | 10.1038/s41556-025-01837-0 |
| PMID: | 41826700 |
| Database: | MEDLINE |
Journal Article