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| 2. |
- Klionsky, Daniel J., et al.
(författare)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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Ingår i: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Forskningsöversikt (refereegranskat)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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| 3. |
- Wang, Min, et al.
(författare)
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Apolipoprotein M induces inhibition of inflammatory responses via the S1PR1 and DHCR24 pathways
- 2019
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Ingår i: Molecular Medicine Reports. - 1791-2997. ; 19:2, s. 1272-1283
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Tidskriftsartikel (refereegranskat)abstract
- © 2019 Spandidos Publications. All rights reserved. Apolipoprotein M (ApoM) is a type of apolipoprotein. It is well known that high-density lipoprotein (HDL) decreases inflammatory responses via the apoM-sphingosine-1-phosphate (S1P) pathway. The present study further investigated the importance of ApoM in the inhibitory effects of HDL on inflammation. Mice with an apoM gene deficiency (apoM-/-) were employed to investigate the effects of ApoM on the expression of interleukin-1β (IL-1β), monocyte chemotactic protein-1 (MCP-1), S1P receptor-1 (S1PR1) and 3β-hydroxysterol Δ-24-reductase (DHCR24), as compared with in wild-type mice (apoM+/+). Furthermore, cell culture experiments were performed using a permanent human hybrid endothelial cell line (EA.hy926). Cells were cultured in the presence of recombinant human apoM (rec-apoM) or were induced to overexpress apoM (apoMTg); subsequently, cells were treated with tumor necrosis factor-α (TNF-α), in order to investigate the effects of ApoM on IL-1β and MCP-1. The results demonstrated that the mRNA expression levels of IL-1β and MCP-1 were significantly higher in the liver following administration of lipopolysaccharide in apoM-/- mice compared with in apoM+/+ mice. In cell culture experiments, when cells were pre-cultured with rec-apoM or were engineered to overexpress apoM (apoMTg), they exhibited decreased expression levels of IL-1β and MCP-1 following TNF-α treatment compared with in normal apoM-expressing cells (apoMTgN). Furthermore, the mRNA expression levels of IL-1β and MCP-1 were significantly elevated following addition of the S1PR1 inhibitor W146, but not by the scavenger receptor class B type I inhibitor, block lipid transport-1 (BLT-1), in apoMTg cells prior to TNF-α treatment. Conversely, there were no differences in these inflammatory biomarkers under the same conditions in apoMTgN cells. The mRNA expression levels of DHCR24 were significantly reduced by the addition of BLT-1 prior to TNF-α treatment in apoMTg cells; however, there was no difference in the expression of this inflammatory biomarker in apoMTgN cells. In conclusion, ApoM displayed inhibitory effects against the inflammatory response in vivo and in vitro; these effects may be induced via the S1PR1 and DHCR24 pathways.
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- Wang, Zhaoming, et al.
(författare)
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Imputation and subset-based association analysis across different cancer types identifies multiple independent risk loci in the TERT-CLPTM1L region on chromosome 5p15.33
- 2014
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Ingår i: Human Molecular Genetics. - : Oxford University Press (OUP). - 0964-6906 .- 1460-2083. ; 23:24, s. 6616-6633
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWAS) have mapped risk alleles for at least 10 distinct cancers to a small region of 63 000 bp on chromosome 5p15.33. This region harbors the TERT and CLPTM1L genes; the former encodes the catalytic subunit of telomerase reverse transcriptase and the latter may play a role in apoptosis. To investigate further the genetic architecture of common susceptibility alleles in this region, we conducted an agnostic subset-based meta-analysis (association analysis based on subsets) across six distinct cancers in 34 248 cases and 45 036 controls. Based on sequential conditional analysis, we identified as many as six independent risk loci marked by common single-nucleotide polymorphisms: five in the TERT gene (Region 1: rs7726159, P = 2.10 × 10(-39); Region 3: rs2853677, P = 3.30 × 10(-36) and PConditional = 2.36 × 10(-8); Region 4: rs2736098, P = 3.87 × 10(-12) and PConditional = 5.19 × 10(-6), Region 5: rs13172201, P = 0.041 and PConditional = 2.04 × 10(-6); and Region 6: rs10069690, P = 7.49 × 10(-15) and PConditional = 5.35 × 10(-7)) and one in the neighboring CLPTM1L gene (Region 2: rs451360; P = 1.90 × 10(-18) and PConditional = 7.06 × 10(-16)). Between three and five cancers mapped to each independent locus with both risk-enhancing and protective effects. Allele-specific effects on DNA methylation were seen for a subset of risk loci, indicating that methylation and subsequent effects on gene expression may contribute to the biology of risk variants on 5p15.33. Our results provide strong support for extensive pleiotropy across this region of 5p15.33, to an extent not previously observed in other cancer susceptibility loci.
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- Gehrmann, Sebastian, et al.
(författare)
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GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
- 2022
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Ingår i: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. - : Association for Computational Linguistics (ACL). ; , s. 266-281
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Konferensbidrag (refereegranskat)abstract
- Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. The compatibility, often facilitated through leaderboards, thus leads to outdated but standardized evaluation practices. We pose that the standardization is taking place in the wrong spot. Evaluation infrastructure should enable researchers to use the latest methods and what should be standardized instead is how to incorporate these new evaluation advances.We introduce GEMv2, the new version of the Generation, Evaluation, and Metrics Benchmark which uses a modular infrastructure for dataset, model, and metric developers to benefit from each other’s work. GEMv2 supports 40 documented datasets in 51 languages, ongoing online evaluation for all datasets, and our interactive tools make it easier to add new datasets to the living benchmark.
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