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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
Listed are, for each triplet of cell types, the probabilities of the four topologies for prior odds ; the number of topologies that reach probability for some value of between 10−6 and 102; the non-null topology that has the highest probability over the range of prior odds (if the null topology is the most likely topology for the entire range of prior odds, the topology is marked ‘null’); and the value of highest probability over the range of prior odds; the correct topology and triplet length in the traditional model; and the correct topology and triplet length in the Adolfsson model.
(A) Doxycycline-inducible gam-gfp fusion construct in the E. coli chromosome. Constitutively produced TetR protein represses the PN25tetO promoter, which produces GamGFP upon doxycycline induction. oriC, origin of replication; ter, replication terminus; arrows, directions of transcription. (B) Phage λ assay for end-blocking activity by Mu Gam and GamGFP. Rolling-circle replication of phage λred gam is inhibited by E. coli RecBCD, which causes small plaques of λred gam on wild-type E. coli (Smith, 1983). Mu Gam protein binds and protects DNA ends from RecBCD exonuclease activity (Akroyd and Symonds, 1986) and so is expected to allow rolling-circle replication of λred gam and therefore allow formation of large plaques. (C) λred gam plaques are small on recB+ (WT) and large on recB-deficient cells (recB-). Plaques produced on WT cells carrying gam and gam-gfp are small when Gam and GamGFP proteins are not produced (Uninduced). (D) λred gam produce large plaques on WT cells if Gam or GamGFP are produced (Induced). (E) UV sensitivity of E. coli recB-null mutant compared with recB+(WT), and uninduced gam and gam-gfp carrying cells. WT (), recB− (
), WT GamGFP, (
); WT Gam, (
). (F) Induction of Gam or GamGFP with 200 ng/ml doxycycline causes UV sensitivity similar to that of recB-null mutant cells, indicating that Gam or GamGFP block RecBCD action on double-stranded DNA ends. WT, SMR14327; recB, SMR8350; WT GamGFP, SMR14334; WT Gam, SMR14333. Representative experiment performed three times with comparable results.
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Date Updated
Definition List
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F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | Partial η2 | Original effect size f | Replication total sample size | Detectable effect size f |
---|---|---|---|---|---|---|---|---|---|---|---|---|
F(24,39) = 0.8678 (interaction) | 0.348120 | 0.7307699 | 169* | 0.3895070 | ||||||||
F(2,39) = 0.8075 (treatments) | 0.039766 | 0.2035014 | 169 | 0.2415459 | ||||||||
F(12,39) = 187.6811 (hematology parameters) | 0.982978 | 7.599178 | 169* | 0.3331365 |
F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | F(Dfn, Dfd) | Partial η2 | Original effect size f | Replication total sample size | Detectable effect size f |
---|---|---|---|---|---|---|---|---|---|---|---|---|
F(24,39) = 0.8678 (interaction) | 0.348120 | 0.7307699 | 169* | 0.3895070 | ||||||||
F(2,39) = 0.8075 (treatments) | 0.039766 | 0.2035014 | 169* | 0.2415459 | ||||||||
F(12,39) = 187.6811 (hematology parameters) | 0.982978 | 7.599178 | 169* | 0.3331365 |
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*
Footnote 1.
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The values in parenthesis refer to the highest resolution shell.
* where is the intensity of an individual measurement of a reflection and is the average intensity of that reflection.
Table Code Blocks
data = {{},{},{}...,{}}; loglog=Function[{x,y},{Log[x/],Log[y]}]; datalog=Apply[loglog,data,{1}]; peakr = Function[{},Piecewise[{{+(1/2)*Log[1-(2/3)*] +*(1-(1-(2/3)*)^(3/2),<3/2/,Infinity}}]]; fit = NonlinearModelFit[datalog,{peakr[],{}}, {{},{},{}},x,Method->NMinimize]; fit['ParameterTable'] |
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Definitions of the simulated system | |
---|---|
Type | Status |
Protein assembly occupancy | |
Histones | |
Regions of histones |
-
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Table Gene Sequences
Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
---|---|---|---|---|
Recombinant DNA reagent | synthesized gene - mScarletI | Genscript | actagtggtggttcaggaGTTTCAAAAGGTGAAGCCGTTATTAAAGAATTTATGAGATTCAAGGTTCACATGGAAGGAAGTATGAACGGTCATGAATTTGAGATTGAAGGAGAAGGTGAAGGTAGACCATATGAAGGCACCCAAACAGCTAAATTAAAAGTAACTAAAGGTGGTCCATTACCATTTAGTTGGGATATTTTATCTCCACAATTTATGTATGGTTCACGTGCTTTCAttAAACATCCAGCAGATATTCCAGATTATTATAAACAATCATTTCCAGAAGGTTTTAAATGGGAACGTGTCATGAACTTTGAAGATGGTGGAGCAGTTACAGTCACACAAGATACCTCATTAGAAGATGGTACATTAATATATAAAGTTAAATTACGTGGTACTAATTTTCCACCAGACGGTCCAGTAATGCAAAAAAAAACAATGGGCTGGGAAGCTAGTACAGAACGTTTATATCCTGAAGATGGTGTCCTTAAAGGCGATATAAAAATGGCCTTGAGATTAAAGGATGGTGGTAGGTATTTAGCAGATTTCAAAACCACTTATAAAGCAAAAAAACCAGTTCAAATGCCAGGTGCATATAATGTTGATAGAAAACTTGATATTACCAGTCATAATGAAGATTACACAGTTGTCGAACAATACGAACGTTCTGAAGGTCGTCATAGCACTGGTGGTATGGATGAATTATACAAATAAgctagc | d |
Recombinant DNA reagent | synthesized gene - mNeon | Genscript | actagtggtggttcaggaGTTTCAAAAGGTGAAGCCGTTATTAAAGAATTTATGAGATTCAAGGTTCACATGGAAGGAAGTATGAACGGTCATGAATTTGAGATTGAAGGAGAAGGTGAAGGTAGACCATATGAAGGCACCCAAACAGCTAAATTAAAAGTAACTAAAGGTGGTCCATTACCATTTAGTTGGGATATTTTATCTCCACAATTTATGTATGGTTCACGTGCTTTCAttAAACATCCAGCAGATATTCCAGATTATTATAAACAATCATTTCCAGAAGGTTTTAAATGGGAACGTGTCATGAACTTTGAAGATGGTGGAGCAGTTACAGTCACACAAGATACCTCATTAGAAGATGGTACATTAATATATAAAGTTAAATTACGTGGTACTAATTTTCCACCAGACGGTCCAGTAATGCAAAAAAAAACAATGGGCTGGGAAGCTAGTACAGAACGTTTATATCCTGAAGATGGTGTCCTTAAAGGCGATATAAAAATGGCCTTGAGATTAAAGGATGGTGGTAGGTATTTAGCAGATTTCAAAACCACTTATAAAGCAAAAAAACCAGTTCAAATGCCAGGTGCATATAATGTTGATAGAAAACTTGATATTACCAGTCATAATGAAGATTACACAGTTGTCGAACAATACGAACGTTCTGAAGGTCGTCATAGCACTGGTGGTATGGATGAATTATACAAATAAgctagc |
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Table with Gene Sequences
Table Inline Images
unwinding | turnover | ||
---|---|---|---|
# | Chemical drawing | IC50[95%CI];µM | IC50[95%CI];µM |
2 | ![]() | 2.2 [1.7–2.7] | 3.2 [2.3–4.0] |
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Table Long Url
Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
---|---|---|---|---|
Software, algorithm | Metamorph | Metamorph v | https://www.moleculardevices.com/products/cellular-imaging-systems/acquisition-and-analysis-software/metamorph-microscopy#gref |
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Table Ordered List
Factors classically associated with immune functions | ||||
---|---|---|---|---|
Protein | Immune system properties | Nervous system properties | References | |
Antimicrobial peptides(AMPs) |
|
| Hanson et al., 2019;Lezi et al., 2018; |
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Table with ordered list
Table Unordered List
Factors classically associated with immune functions | ||||
---|---|---|---|---|
Protein | Immune system properties | Nervous system properties | References | |
Antimicrobial peptides(AMPs) |
|
| Hanson et al., 2019;Lezi et al., 2018;Su et al., 2010;Zasloff, 2002 |
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This valuable paper informs on the role of type I PRMTs in programming muscle stem cell identification. The evidence presented is mostly solid, with some weaknesses in the evidence regarding the proposed mechanism. The paper will be of particular interest to those who study skeletal muscle satellite cell biology.
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Introduction
TNF Receptor Associated Factor 2 (TRAF2) is an adaptor protein that transduces signals following ligation of certain cytokine receptors including those binding TNF. It was first identified together with TRAF1 as a component of TNF receptor-2 and then TNF receptor-1 (TNFR1) signalling complexes (Rothe et al., 1994; Shu et al., 1996). TRAF2, like most other TRAFs, contains a RING domain, several zinc fingers, a TRAF-N, and a conserved TRAF-C domain which is responsible for oligomerisation and receptor binding through its MATH region (Takeuchi et al., 1996; Uren and Vaux, 1996).
RING domains are nearly always associated with ubiquitin E3 ligase activity (Shi and Kehrl, 2003) and TRAF2 can promote ubiquitylation of RIPK1 in TNFR1 signalling complexes (TNFR1-SC) (Wertz et al., 2004). However TRAF2 recruits E3 ligases such as cIAPs to TNFR1-SC and these have also been shown to be able to ubiquitylate RIPK1 and regulate TNF signalling (Dynek et al., 2010; Mahoney et al., 2008; Varfolomeev et al., 2008; Vince et al., 2009). This makes it difficult to unambiguously determine the role of the E3 ligase activity of TRAF2.
Activation of JNK and NF-κB by TNF is reduced in cells from Traf2-/- mice while only JNK signalling was affected in lymphocytes from transgenic mice that express a dominant negative (DN) form of TRAF2 that lacks the RING domain (Lee et al., 1997; Yeh et al., 1997). Traf2-/-Traf5-/- mouse embryonic fibroblasts (MEFs) have a pronounced defect in activation of NF-κB by TNF, suggesting that absence of TRAF2 can be compensated by TRAF5 (Tada et al., 2001). Although activation of NF-κB was restored in Traf2-/-Traf5-/- cells by re-expression of wild type TRAF2, it was not restored when the cells were reconstituted with TRAF2 point mutants that could not bind cIAPs (Vince et al., 2009; Zhang et al., 2010). These data, together with a wealth of different lines of evidence showing that cIAPs are critical E3 ligases required for TNF-induced canonical NF-κB (Blackwell et al., 2013; Haas et al., 2009; Silke, 2011), support the idea that the main function of TRAF2 in TNF-induced NF-κB is to recruit cIAPs to the TNFR1-SC. However, it remains possible that the RING of TRAF2 plays another function, such as in activating JNK and protecting cells from TNF-induced cell death (Vince et al., 2009; Zhang et al., 2010). Furthermore it has been shown that TRAF2 can K48-ubiquitylate caspase-8 to set the threshold for TRAIL or Fas induced cell death (Gonzalvez et al., 2012). Moreover, TRAF2 inhibits non-canonical NF-κB signalling (Grech et al., 2004; Zarnegar et al., 2008) and this function requires the RING domain of TRAF2 to induce proteosomal degradation of NIK (Vince et al., 2009). However, structural and in vitro analyses indicate that, unlike TRAF6, the RING domain of TRAF2 is unable to bind E2 conjugating enzymes (Yin et al., 2009), and is therefore unlikely to have intrinsic E3 ligase activity.
Sphingosine-1-phosphate (S1P) is a pleiotropic sphingolipid mediator that regulates proliferation, differentiation, cell trafficking and vascular development (Pitson, 2011). S1P is generated by sphingosine kinase 1 and 2 (SPHK1 and SPHK2) (Kohama et al., 1998; Liu et al., 2000). Extracellular S1P mainly acts by binding to its five G protein-coupled receptors S1P1-5 (Hla and Dannenberg, 2012). However, some intracellular roles have been suggested for S1P, including the blocking of the histone deacetylases, HDAC1/2 (Hait et al., 2009) and the induction of apoptosis through interaction with BAK and BAX (Chipuk et al., 2012).
Recently, it was suggested that the RING domain of TRAF2 requires S1P as a co-factor for its E3 ligase activity (Alvarez et al., 2010). Alvarez and colleagues proposed that SPHK1 but not SPHK2 is activated by TNF and phosphorylates sphingosine to S1P which in turn binds to the RING domain of TRAF2 and serves as an essential co-factor that was missing in the experiments of Yin et al. Alvarez and colleagues, observed that in the absence of SPHK1, TNF-induced NF-κB activation was completely abolished.
Although we know a lot about TRAF2, there are still important gaps particularly with regard to cell type specificity and in vivo function of TRAF2. Moreover, despite the claims that SPHK1 and its product, S1P, are required for TRAF2 to function as a ubiquitin ligase, the responses of Traf2-/- and Sphk1-/- cells to TNF were not compared. Therefore, we undertook an analysis of TRAF2 and SPHK1 function in TNF signalling in a number of different tissues.
Surprisingly, we found that neither TRAF2 nor SPHK1 are required for TNF mediated canonical NF-κB and MAPK signalling in macrophages. However, MEFs, murine dermal fibroblasts (MDFs) and keratinocytes required TRAF2 but not SPHK1 for full strength TNF signalling. In these cell types, absence of TRAF2 caused a delay in TNF-induced activation of NF-κB and MAPK, and sensitivity to killing by TNF was increased. Absence of TRAF2 in keratinocytes in vivo resulted in psoriasis-like epidermal hyperplasia and skin inflammation. Unlike TNF-dependent genetic inflammatory skin conditions, such as IKK2 epidermal knock-out (Pasparakis et al., 2002) and the cpdm mutant (Gerlach et al., 2011), the onset of inflammation was only delayed, and not prevented by deletion of TNF. This early TNF-dependent inflammation is caused by excessive apoptotic but not necroptotic cell death and could be prevented by deletion of Casp8. We observed constitutive activation of NIK and non-canonical NF-κB in Traf2-/- keratinocytes which caused production of inflammatory cytokines and chemokines. We were able to reverse this inflammatory phenotype by simultaneously deleting both Tnf and Nfkb2 genes. Our results highlight the important role TRAF2 plays to protect keratinocytes from cell death and to down-regulate inflammatory responses and support the idea that intrinsic defects in keratinocytes can initiate psoriasis-like skin inflammation.
https://doi.org/10.7554/eLife.16370Article Section Editor Evaluation
Editor's evaluation
The manuscript by Rosello et al. represents a major advance in implementation of cutting-edge genome editing methodologies in the zebrafish. The study seeks to describe optimized tools for precise base editing in zebrafish and to demonstrate their effective application. Overall, this study demonstrates that cytosine base editing is an efficient and powerful method for introducing precise in vivo edits into the zebrafish genome, and will be of interest to scientists who use zebrafish and other genetic systems to model human development and disease.
https://doi.org/10.7554/eLife.62927.sa1Article Section Editor Title Text
- Dominique Soldati-Favre
- University of Geneva, Switzerland
Article Section Header Link
TNF Receptor Associated Factor 2 (TRAF2) is an adaptor protein that transduces signals following ligation of certain cytokine receptors
some text to carry a linkTNF Receptor Associated Factor 2 (TRAF2) is an adaptor protein that transduces signals following ligation of certain cytokine receptors including those binding TNF. It was first identified together with TRAF1 as a component of TNF receptor-2 and then TNF receptor-1 (TNFR1) signalling complexes (Rothe et al., 1994; Shu et al., 1996). TRAF2, like most other TRAFs, contains a RING domain, several zinc fingers, a TRAF-N, and a conserved TRAF-C domain which is responsible for oligomerisation and receptor binding through its MATH region (Takeuchi et al., 1996; Uren and Vaux, 1996).
RING domains are nearly always associated with ubiquitin E3 ligase activity (Shi and Kehrl, 2003) and TRAF2 can promote ubiquitylation of RIPK1 in TNFR1 signalling complexes (TNFR1-SC) (Wertz et al., 2004). However TRAF2 recruits E3 ligases such as cIAPs to TNFR1-SC and these have also been shown to be able to ubiquitylate RIPK1 and regulate TNF signalling (Dynek et al., 2010; Mahoney et al., 2008; Varfolomeev et al., 2008; Vince et al., 2009). This makes it difficult to unambiguously determine the role of the E3 ligase activity of TRAF2.
Activation of JNK and NF-κB by TNF is reduced in cells from Traf2-/- mice while only JNK signalling was affected in lymphocytes from transgenic mice that express a dominant negative (DN) form of TRAF2 that lacks the RING domain (Lee et al., 1997; Yeh et al., 1997). Traf2-/-Traf5-/- mouse embryonic fibroblasts (MEFs) have a pronounced defect in activation of NF-κB by TNF, suggesting that absence of TRAF2 can be compensated by TRAF5 (Tada et al., 2001). Although activation of NF-κB was restored in Traf2-/-Traf5-/- cells by re-expression of wild type TRAF2, it was not restored when the cells were reconstituted with TRAF2 point mutants that could not bind cIAPs (Vince et al., 2009; Zhang et al., 2010). These data, together with a wealth of different lines of evidence showing that cIAPs are critical E3 ligases required for TNF-induced canonical NF-κB (Blackwell et al., 2013; Haas et al., 2009; Silke, 2011), support the idea that the main function of TRAF2 in TNF-induced NF-κB is to recruit cIAPs to the TNFR1-SC. However, it remains possible that the RING of TRAF2 plays another function, such as in activating JNK and protecting cells from TNF-induced cell death (Vince et al., 2009; Zhang et al., 2010). Furthermore it has been shown that TRAF2 can K48-ubiquitylate caspase-8 to set the threshold for TRAIL or Fas induced cell death (Gonzalvez et al., 2012). Moreover, TRAF2 inhibits non-canonical NF-κB signalling (Grech et al., 2004; Zarnegar et al., 2008) and this function requires the RING domain of TRAF2 to induce proteosomal degradation of NIK (Vince et al., 2009). However, structural and in vitro analyses indicate that, unlike TRAF6, the RING domain of TRAF2 is unable to bind E2 conjugating enzymes (Yin et al., 2009), and is therefore unlikely to have intrinsic E3 ligase activity.
Sphingosine-1-phosphate (S1P) is a pleiotropic sphingolipid mediator that regulates proliferation, differentiation, cell trafficking and vascular development (Pitson, 2011). S1P is generated by sphingosine kinase 1 and 2 (SPHK1 and SPHK2) (Kohama et al., 1998; Liu et al., 2000). Extracellular S1P mainly acts by binding to its five G protein-coupled receptors S1P1-5 (Hla and Dannenberg, 2012). However, some intracellular roles have been suggested for S1P, including the blocking of the histone deacetylases, HDAC1/2 (Hait et al., 2009) and the induction of apoptosis through interaction with BAK and BAX (Chipuk et al., 2012).
Recently, it was suggested that the RING domain of TRAF2 requires S1P as a co-factor for its E3 ligase activity (Alvarez et al., 2010). Alvarez and colleagues proposed that SPHK1 but not SPHK2 is activated by TNF and phosphorylates sphingosine to S1P which in turn binds to the RING domain of TRAF2 and serves as an essential co-factor that was missing in the experiments of Yin et al. Alvarez and colleagues, observed that in the absence of SPHK1, TNF-induced NF-κB activation was completely abolished.
Although we know a lot about TRAF2, there are still important gaps particularly with regard to cell type specificity and in vivo function of TRAF2. Moreover, despite the claims that SPHK1 and its product, S1P, are required for TRAF2 to function as a ubiquitin ligase, the responses of Traf2-/- and Sphk1-/- cells to TNF were not compared. Therefore, we undertook an analysis of TRAF2 and SPHK1 function in TNF signalling in a number of different tissues.
Surprisingly, we found that neither TRAF2 nor SPHK1 are required for TNF mediated canonical NF-κB and MAPK signalling in macrophages. However, MEFs, murine dermal fibroblasts (MDFs) and keratinocytes required TRAF2 but not SPHK1 for full strength TNF signalling. In these cell types, absence of TRAF2 caused a delay in TNF-induced activation of NF-κB and MAPK, and sensitivity to killing by TNF was increased. Absence of TRAF2 in keratinocytes in vivo resulted in psoriasis-like epidermal hyperplasia and skin inflammation. Unlike TNF-dependent genetic inflammatory skin conditions, such as IKK2 epidermal knock-out (Pasparakis et al., 2002) and the cpdm mutant (Gerlach et al., 2011), the onset of inflammation was only delayed, and not prevented by deletion of TNF. This early TNF-dependent inflammation is caused by excessive apoptotic but not necroptotic cell death and could be prevented by deletion of Casp8. We observed constitutive activation of NIK and non-canonical NF-κB in Traf2-/- keratinocytes which caused production of inflammatory cytokines and chemokines. We were able to reverse this inflammatory phenotype by simultaneously deleting both Tnf and Nfkb2 genes. Our results highlight the important role TRAF2 plays to protect keratinocytes from cell death and to down-regulate inflammatory responses and support the idea that intrinsic defects in keratinocytes can initiate psoriasis-like skin inflammation.
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Article Section Without Title
TNF Receptor Associated Factor 2 (TRAF2) is an adaptor protein that transduces signals following ligation of certain cytokine receptors including those binding TNF. It was first identified together with TRAF1 as a component of TNF receptor-2 and then TNF receptor-1 (TNFR1) signalling complexes (Rothe et al., 1994; Shu et al., 1996). TRAF2, like most other TRAFs, contains a RING domain, several zinc fingers, a TRAF-N, and a conserved TRAF-C domain which is responsible for oligomerisation and receptor binding through its MATH region (Takeuchi et al., 1996; Uren and Vaux, 1996).
RING domains are nearly always associated with ubiquitin E3 ligase activity (Shi and Kehrl, 2003) and TRAF2 can promote ubiquitylation of RIPK1 in TNFR1 signalling complexes (TNFR1-SC) (Wertz et al., 2004). However TRAF2 recruits E3 ligases such as cIAPs to TNFR1-SC and these have also been shown to be able to ubiquitylate RIPK1 and regulate TNF signalling (Dynek et al., 2010; Mahoney et al., 2008; Varfolomeev et al., 2008; Vince et al., 2009). This makes it difficult to unambiguously determine the role of the E3 ligase activity of TRAF2.
Activation of JNK and NF-κB by TNF is reduced in cells from Traf2-/- mice while only JNK signalling was affected in lymphocytes from transgenic mice that express a dominant negative (DN) form of TRAF2 that lacks the RING domain (Lee et al., 1997; Yeh et al., 1997). Traf2-/-Traf5-/- mouse embryonic fibroblasts (MEFs) have a pronounced defect in activation of NF-κB by TNF, suggesting that absence of TRAF2 can be compensated by TRAF5 (Tada et al., 2001). Although activation of NF-κB was restored in Traf2-/-Traf5-/- cells by re-expression of wild type TRAF2, it was not restored when the cells were reconstituted with TRAF2 point mutants that could not bind cIAPs (Vince et al., 2009; Zhang et al., 2010). These data, together with a wealth of different lines of evidence showing that cIAPs are critical E3 ligases required for TNF-induced canonical NF-κB (Blackwell et al., 2013; Haas et al., 2009; Silke, 2011), support the idea that the main function of TRAF2 in TNF-induced NF-κB is to recruit cIAPs to the TNFR1-SC. However, it remains possible that the RING of TRAF2 plays another function, such as in activating JNK and protecting cells from TNF-induced cell death (Vince et al., 2009; Zhang et al., 2010). Furthermore it has been shown that TRAF2 can K48-ubiquitylate caspase-8 to set the threshold for TRAIL or Fas induced cell death (Gonzalvez et al., 2012). Moreover, TRAF2 inhibits non-canonical NF-κB signalling (Grech et al., 2004; Zarnegar et al., 2008) and this function requires the RING domain of TRAF2 to induce proteosomal degradation of NIK (Vince et al., 2009). However, structural and in vitro analyses indicate that, unlike TRAF6, the RING domain of TRAF2 is unable to bind E2 conjugating enzymes (Yin et al., 2009), and is therefore unlikely to have intrinsic E3 ligase activity.
Sphingosine-1-phosphate (S1P) is a pleiotropic sphingolipid mediator that regulates proliferation, differentiation, cell trafficking and vascular development (Pitson, 2011). S1P is generated by sphingosine kinase 1 and 2 (SPHK1 and SPHK2) (Kohama et al., 1998; Liu et al., 2000). Extracellular S1P mainly acts by binding to its five G protein-coupled receptors S1P1-5 (Hla and Dannenberg, 2012). However, some intracellular roles have been suggested for S1P, including the blocking of the histone deacetylases, HDAC1/2 (Hait et al., 2009) and the induction of apoptosis through interaction with BAK and BAX (Chipuk et al., 2012).
Recently, it was suggested that the RING domain of TRAF2 requires S1P as a co-factor for its E3 ligase activity (Alvarez et al., 2010). Alvarez and colleagues proposed that SPHK1 but not SPHK2 is activated by TNF and phosphorylates sphingosine to S1P which in turn binds to the RING domain of TRAF2 and serves as an essential co-factor that was missing in the experiments of Yin et al. Alvarez and colleagues, observed that in the absence of SPHK1, TNF-induced NF-κB activation was completely abolished.
Although we know a lot about TRAF2, there are still important gaps particularly with regard to cell type specificity and in vivo function of TRAF2. Moreover, despite the claims that SPHK1 and its product, S1P, are required for TRAF2 to function as a ubiquitin ligase, the responses of Traf2-/- and Sphk1-/- cells to TNF were not compared. Therefore, we undertook an analysis of TRAF2 and SPHK1 function in TNF signalling in a number of different tissues.
Surprisingly, we found that neither TRAF2 nor SPHK1 are required for TNF mediated canonical NF-κB and MAPK signalling in macrophages. However, MEFs, murine dermal fibroblasts (MDFs) and keratinocytes required TRAF2 but not SPHK1 for full strength TNF signalling. In these cell types, absence of TRAF2 caused a delay in TNF-induced activation of NF-κB and MAPK, and sensitivity to killing by TNF was increased. Absence of TRAF2 in keratinocytes in vivo resulted in psoriasis-like epidermal hyperplasia and skin inflammation. Unlike TNF-dependent genetic inflammatory skin conditions, such as IKK2 epidermal knock-out (Pasparakis et al., 2002) and the cpdm mutant (Gerlach et al., 2011), the onset of inflammation was only delayed, and not prevented by deletion of TNF. This early TNF-dependent inflammation is caused by excessive apoptotic but not necroptotic cell death and could be prevented by deletion of Casp8. We observed constitutive activation of NIK and non-canonical NF-κB in Traf2-/- keratinocytes which caused production of inflammatory cytokines and chemokines. We were able to reverse this inflammatory phenotype by simultaneously deleting both Tnf and Nfkb2 genes. Our results highlight the important role TRAF2 plays to protect keratinocytes from cell death and to down-regulate inflammatory responses and support the idea that intrinsic defects in keratinocytes can initiate psoriasis-like skin inflammation.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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BookUnderstanding the rhythm of breathing: so near, yet so farIn: Julius D, Clapham DE, editors. Annual Review of Physiology 75:423–452.https://doi.org/10.1146/annurev-physiol-040510-130049
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BookPhilosophie AnatomiqueParis: Méquignon-Marvis
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Clinical TrialScripps Wired for Health Study (Registration: 000000)Scripps Translational Science Institute (sponsor) (2015)
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ConferenceBoundary learning by optimization with topological constraintsBoundary learning by optimization with topological constraints, IEEE Conference on Computer Vision and Pattern Recognition (CVPR)https://doi.org/10.1038/365833a0
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ConferenceBoundary learning by optimization with topological constraintsBoundary learning by optimization with topological constraints, IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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ConferenceBoundary learning by optimization with topological constraintsBoundary learning by optimization with topological constraints, IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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DataNKX2-5 mutations causative for congenital heart disease retain functionality and are directed to hundreds of targetsNCBI Gene Expression Omnibus. Series accession number GSE44902
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PatentIRE-1alpha inhibitors (US20100941530)Mankind Corp, United States patent, United States
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SoftwareThe Pymol Molecular Graphics SystemDeLano Scientific, Palo Alto, CA
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Table 1 - source data 1.
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some more details here - https://doi.org/10.7554/eLife.10181.001
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Table 1 - source data 1.
Is the species name mentioned in the title, impact statement, abstract and digest of eLife papers
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Table 1 - source data 1.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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Many features are conserved between the mammalian nephron and planarian protonephridia.
(A) Image of a young adult C. elegans and schematic depicting the twelve pairs of sensory neurons in the anterior amphid individuals carrying a virus with k deleterious mutations at its endemic equilibrium. class carrying the fewest number of deleterious mutations is defined as mutation class k = 0. Inset: variation in the basic reproductive rate of infected individuals (gray histogram) and variation in the net reproductive rate R of infected individuals (brown histogram) resulting from variation in the number of deleterious mutations carried by circulating viruses.
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eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.
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Oliver HobertReviewing EditorColombia University, USA
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Oliver HobertReviewing EditorColombia University, USA
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Ramanath Hegde is a Postdoctoral Fellow at the Institute of Protein Biochemistry in Naples, Italy, where he investigates ways of preventing cells from destroying mutant proteins.
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Ramanath Hegde is a Postdoctoral Fellow at the Institute of Protein Biochemistry in Naples, Italy, where he investigates ways of preventing cells from destroying mutant proteins.
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Prabhat JhaUniversity of Toronto
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Optimal decision-making requires balancing fast but error-prone and more accurate but slower decisions through adjustments of decision thresholds. Here, we demonstrate two distinct correlates of such speed-accuracy adjustments by recording subthalamic nucleus (STN) activity and electroencephalography in 11 Parkinson’s disease patients during a perceptual decision-making task; STN low-frequency oscillatory (LFO) activity (2–8 Hz), coupled to activity at prefrontal electrode Fz, and STN beta activity (13–30 Hz) coupled to electrodes C3/C4 close to motor cortex. These two correlates differed not only in their cortical topography and spectral characteristics but also in the relative timing of recruitment and in their precise relationship with decision thresholds. Increases of STN LFO power preceding the response predicted increased thresholds only after accuracy instructions, while cue-induced reductions of STN beta power decreased thresholds irrespective of instructions. These findings indicate that distinct neural mechanisms determine whether a decision will be made in haste or with caution.
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Optimal decision-making requires balancing fast but error-prone and more accurate but slower decisions through adjustments of decision thresholds.
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Optimal decision-making requires balancing fast but error-prone and more accurate but slower decisions through adjustments of decision thresholds. Here, we demonstrate two distinct correlates of such speed-accuracy adjustments by recording subthalamic nucleus (STN) activity and electroencephalography in 11 Parkinson’s disease patients during a perceptual decision-making task; STN low-frequency oscillatory (LFO) activity (2–8 Hz), coupled to activity at prefrontal electrode Fz, and STN beta activity (13–30 Hz) coupled to electrodes C3/C4 close to motor cortex. These two correlates differed not only in their cortical topography and spectral characteristics but also in the relative timing of recruitment and in their precise relationship with decision thresholds. Increases of STN LFO power preceding the response predicted increased thresholds only after accuracy instructions, while cue-induced reductions of STN beta power decreased thresholds irrespective of instructions. These findings indicate that distinct neural mechanisms determine whether a decision will be made in haste or with caution.
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Optimal decision-making requires balancing fast but error-prone and more accurate but slower decisions through adjustments of decision thresholds.
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Optimal decision-making requires balancing fast but error-prone and more accurate but slower decisions through adjustments of decision thresholds. Here, we demonstrate two distinct correlates of such speed-accuracy adjustments by recording subthalamic nucleus (STN) activity and electroencephalography in 11 Parkinson’s disease patients during a perceptual decision-making task; STN low-frequency oscillatory (LFO) activity (2–8 Hz), coupled to activity at prefrontal electrode Fz, and STN beta activity (13–30 Hz) coupled to electrodes C3/C4 close to motor cortex. These two correlates differed not only in their cortical topography and spectral characteristics but also in the relative timing of recruitment and in their precise relationship with decision thresholds. Increases of STN LFO power preceding the response predicted increased thresholds only after accuracy instructions, while cue-induced reductions of STN beta power decreased thresholds irrespective of instructions. These findings indicate that distinct neural mechanisms determine whether a decision will be made in haste or with caution.
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