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- Klionsky, Daniel J., et al.
(author)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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In: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Research review (peer-reviewed)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
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- Zhang, Chengyu, et al.
(author)
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Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community
- 2024
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In: Molecular Systems Biology. - 1744-4292. ; In Press
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Journal article (peer-reviewed)abstract
- Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains’ growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.
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3. |
- Zhang, Sulin, et al.
(author)
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SNX10 (sorting nexin 10) inhibits colorectal cancer initiation and progression by controlling autophagic degradation of SRC
- 2020
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In: Autophagy. - Philadelphia : Taylor & Francis. - 1554-8627 .- 1554-8635. ; 16:4, s. 735-749
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Journal article (peer-reviewed)abstract
- The non-receptor tyrosine kinase SRC is a key mediator of cellular protumorigenic signals. SRC is aberrantly over-expressed and activated in more than 80% of colorectal cancer (CRC) patients, therefore regulation of its stability and activity is essential. Here, we report a significant down regulation of SNX10 (sorting nexin 10) in human CRC tissues, which is closely related to tumor differentiation, TNM stage, lymph node metastasis and survival period. SNX10 deficiency in normal and neoplastic colorectal epithelial cells promotes initiation and progression of CRC in mice. SNX10 controls SRC levels by mediating autophagosome-lysosome fusion and SRC recruitment for autophagic degradation. These mechanisms ensure proper controlling of the activities of SRC-STAT3 and SRC-CTNNB1 signaling pathways by up-regulating SNX10 expression under stress conditions. These findings suggest that SNX10 acts as a tumor suppressor in CRC and it could be a potential therapeutic target for future development.Abbreviations: ACTB: actin beta; ATG5: autophagy related 5; ATG12: autophagy related 12; CQ: chloroquine; CRC: colorectal cancer; CTNNB1: catenin beta 1; EBSS: Earle's balanced salt solution; KO: knockout; LAMP1: lysosomal associated membrane protein 1; LAMP2: lysosomal associated membrane protein 2; MAP1LC3: microtubule associated protein 1 light chain 3; MKI67: marker of proliferation Ki-67; mRNA: messenger RNA; PX: phox homology; RT-qPCR: real time quantitative polymerase chain reaction; siRNA: small interfering RNA; SNX10: sorting nexin 10; SQSTM1: sequestosome 1; SRC: SRC proto-oncogene, non-receptor tyrosine kinase; STAT3: signal transducer and activator of transcription 3; WT: wild type. © 2019 Informa UK Limited, trading as Taylor & Francis Group
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