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ICGC Chronic Myeloid Disorders Group

  1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
Competing interests
No competing interests declared
  1. Luca Malcovati, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
  2. Sudhir Tauro, Division of Medial Sciences, University of Dundee, Dundee, UK
  3. Jacqueline Boultwood, Nuffield Department of Clinical Laboratory Sciences, University of Oxford, UK

Author Details Person

Jenny Bloggs

  1. Evolutionary Studies Institute and Centre of Excellence in PalaeoSciences, University of the Witwatersrand, Johannesburg, South Africa
  2. School of Geosciences, University of the Witwatersrand, Johannesburg, South Africa
Present addresses
  1. Department of Inventive Inventions, Univertity of Wessex, Windowchester, Wessex
  2. Department of Underwater Basket Weaving, University of Somewhere
Contribution
Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents
Contributed equally with
  1. Francesca Smith
  2. Wendel Jakes III
For correspondence
  1. jenny@bloggs.com
  2. +1 555-4321-09876
Competing interests
No competing interests declared
ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3400-7927

Author Details Sub Group

ICGC Chronic Myeloid Disorders Group

  1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
Competing interests
No competing interests declared
Sub-group 1
  1. Luca Malcovati, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
  2. Sudhir Tauro, Division of Medial Sciences, University of Dundee, Dundee, UK
Sub-group 2
  1. Jacqueline Boultwood, Nuffield Department of Clinical Laboratory Sciences, University of Oxford, UK

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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.

Listed are, for each triplet of cell types, the probabilities of the four topologies for prior odds p(βi=1)p(βi=0)=0.05; the number of topologies that reach probability p(T | {giA,B,C})>0.6 for some value of p(βi=1)p(βi=0) between 10−6 and 102; the non-null topology that has the highest probability p(T | {giA,B,C}) 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 p(T | {giA,B,C}) 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.

Code

<dock_design>
  <SCOREFXNS>
    <fullatom weights=beta symmetric = 0> </fullatom> 
  </SCOREFXNS> 
  <!-- Longabadaliasametatquedoloreaqueesthicillabadaliasametatquedoloreaqueesthicillabadaliasametatquedoloreaqueesthicillolong --> 
  <FILTERS> 
    <Ddg name=ddg scorefxn=fullatom threshold = 0 jump = 1 repeats = 1 repack = 1 confidence = 1/>
    <Sasa name=sasa confidence = 0/>
    <ShapeComplementarity name=shape verbose = 1 confidence = 0 jump = 1/> 
  </FILTERS>
  <MOVERS>
    <AtomTree name=docking_tree docking_ft = 1/>
    <DockSetupMover name=setup_dock/>
    <DockingProtocol name=dock docking_score_high=fullatom low_res_protocol_only = 0 docking_local_refine = 0 dock_min = 1 />
  </MOVERS>
  <APPLY_TO_POSE>
  </APPLY_TO_POSE> 
  <PROTOCOLS> 
    <Add mover_name=docking_tree/> 
    <Add mover_name=setup_dock/> 
    <Add mover_name=dock/> 
    <Add filter_name=ddg/> 
    <Add filter_name=sasa/> 
    <Add filter_name=shape/> 
  </PROTOCOLS> 
</dock_design>

Date

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Definition List

Research focus
Pre-mRNA splicing
post-transcriptional gene regulation
Experimental organism
Human
Mouse

Definition List Inline

Research focus
Pre-mRNA splicing
post-transcriptional gene regulation
Experimental organism
Human
Mouse

Definition List Timeline

Preprint posted
January 1, 2013
Received
January 22, 2013
Accepted
May 28, 2013
Version of Record published
July 2, 2013 Go to version

Doi

https://doi.org/10.7554/eLife.16370

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https://doi.org/10.7554/eLife.16370

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https://doi.org/10.7554/eLife.16370

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doi: 10.7554/eLife.16370

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Body Mass=0.060×FSTpr+13.856,SEE=6.78,r=0.50,p=<0.001.

Math Long

(7) y=β0+β1Choice+β2SVspend+β3SVsave+β4SeqSV+β5SeqLength+β6Cueposition+β7Left/right+β8Sequenceprogress+ϵ,

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(10) SVsaven= 1mni=n+1mSVspendi,

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Table

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 η2Original effect size fReplication total sample sizeDetectable effect size f
F(24,39) = 0.8678 (interaction)0.3481200.7307699169*0.3895070
F(2,39) = 0.8075 (treatments)0.0397660.20350141690.2415459
F(12,39) = 187.6811 (hematology parameters)0.9829787.599178169*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 η2Original effect size fReplication total sample sizeDetectable effect size f
F(24,39) = 0.8678 (interaction)0.3481200.7307699169*0.3895070
F(2,39) = 0.8075 (treatments)0.0397660.2035014169*0.2415459
F(12,39) = 187.6811 (hematology parameters)0.9829787.599178169*0.3331365
  1. *

    Footnote 1.

  2. The values in parenthesis refer to the highest resolution shell.

    *Rmerge=hkli|I(hkl;i)<I(hkl)>|hkli(hkl;i) where I(hkl;i) is the intensity of an individual measurement of a reflection and <I(hkl)> is the average intensity of that reflection.

Table Code Blocks

data = {{x1,R1},{x2,R2},{x3,R3}...,{xm,Rm}};
loglog=Function[{x,y},{Log[x/[Ca2+]0],Log[y]}];
datalog=Apply[loglog,data,{1}];


peakr = Function[{A,B,C,x},Piecewise[{{A+(1/2)*Log[1-(2/3)*Bx]
+C*(1-(1-(2/3)*Bx)^(3/2),x<3/2/B,Infinity}}]];


fit = NonlinearModelFit[datalog,{peakr[A,B,C,x],{A>0,B>0,C>0}},
{{A,A0},{B,B0},{C,C0}},x,Method->NMinimize];
fit['ParameterTable']
  1. Table with Code Blocks

Table Equations

Definitions of the simulated system
TypeStatus
Protein assembly occupancyk[1,Np]Pk{unbound,bound}
Histonesi[1,N]Si{me0,me1,me2,me3}
Regions of histonesRm={Histones[1,...,NNR]whenm=NR(nucleationregion)Histones[NNR+1,...,N]whenm=B(bodyregion),NB=NNNRHistones[1,...,N]whenm=L(entireregion),NL=N
  1. Table with Equations

Table Gene Sequences

Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Recombinant DNA reagentsynthesized gene - mScarletIGenscriptactagtggtggttcaggaGTTTCAAAAGGTGAAGCCGTTATTAAAGAATTTATGAGATTCAAGGTTCACATGGAAGGAAGTATGAACGGTCATGAATTTGAGATTGAAGGAGAAGGTGAAGGTAGACCATATGAAGGCACCCAAACAGCTAAATTAAAAGTAACTAAAGGTGGTCCATTACCATTTAGTTGGGATATTTTATCTCCACAATTTATGTATGGTTCACGTGCTTTCAttAAACATCCAGCAGATATTCCAGATTATTATAAACAATCATTTCCAGAAGGTTTTAAATGGGAACGTGTCATGAACTTTGAAGATGGTGGAGCAGTTACAGTCACACAAGATACCTCATTAGAAGATGGTACATTAATATATAAAGTTAAATTACGTGGTACTAATTTTCCACCAGACGGTCCAGTAATGCAAAAAAAAACAATGGGCTGGGAAGCTAGTACAGAACGTTTATATCCTGAAGATGGTGTCCTTAAAGGCGATATAAAAATGGCCTTGAGATTAAAGGATGGTGGTAGGTATTTAGCAGATTTCAAAACCACTTATAAAGCAAAAAAACCAGTTCAAATGCCAGGTGCATATAATGTTGATAGAAAACTTGATATTACCAGTCATAATGAAGATTACACAGTTGTCGAACAATACGAACGTTCTGAAGGTCGTCATAGCACTGGTGGTATGGATGAATTATACAAATAAgctagcd
Recombinant DNA reagentsynthesized gene - mNeonGenscriptactagtggtggttcaggaGTTTCAAAAGGTGAAGCCGTTATTAAAGAATTTATGAGATTCAAGGTTCACATGGAAGGAAGTATGAACGGTCATGAATTTGAGATTGAAGGAGAAGGTGAAGGTAGACCATATGAAGGCACCCAAACAGCTAAATTAAAAGTAACTAAAGGTGGTCCATTACCATTTAGTTGGGATATTTTATCTCCACAATTTATGTATGGTTCACGTGCTTTCAttAAACATCCAGCAGATATTCCAGATTATTATAAACAATCATTTCCAGAAGGTTTTAAATGGGAACGTGTCATGAACTTTGAAGATGGTGGAGCAGTTACAGTCACACAAGATACCTCATTAGAAGATGGTACATTAATATATAAAGTTAAATTACGTGGTACTAATTTTCCACCAGACGGTCCAGTAATGCAAAAAAAAACAATGGGCTGGGAAGCTAGTACAGAACGTTTATATCCTGAAGATGGTGTCCTTAAAGGCGATATAAAAATGGCCTTGAGATTAAAGGATGGTGGTAGGTATTTAGCAGATTTCAAAACCACTTATAAAGCAAAAAAACCAGTTCAAATGCCAGGTGCATATAATGTTGATAGAAAACTTGATATTACCAGTCATAATGAAGATTACACAGTTGTCGAACAATACGAACGTTCTGAAGGTCGTCATAGCACTGGTGGTATGGATGAATTATACAAATAAgctagc
  1. Table with Gene Sequences

Table Inline Images

unwindingturnover
#Chemical drawingIC50[95%CI];µMIC50[95%CI];µM
22.2
[1.7–2.7]
3.2
[2.3–4.0]
  1. Table with inline images

Table Long Url

Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Software, algorithmMetamorphMetamorph vhttps://www.moleculardevices.com/products/cellular-imaging-systems/acquisition-and-analysis-software/metamorph-microscopy#gref
  1. Table with long url

Table Ordered List

Factors classically associated with immune functions
ProteinImmune system propertiesNervous system propertiesReferences
Antimicrobial peptides(AMPs)
  1. Secreted by epithelial and phagocytic cells

  2. Disrupt microbial membranes leading to destruction of pathogen

  1. Antimicrobial in nervous system niches

  2. Control chemotaxis of immune cells and astroglia

  3. Mediate iron homeostasis

  4. Modulate nerve impulses

  5. Implicated in aging and neurodegeneration

Hanson et al., 2019;Lezi et al., 2018;Su et al., 2010;Zasloff, 2002
  1. Table with ordered list

Table Unordered List

Factors classically associated with immune functions
ProteinImmune system propertiesNervous system propertiesReferences
Antimicrobial peptides(AMPs)
  • Secreted by epithelial and phagocyticcells

  • Disrupt microbial membranes leading to destruction of pathogen

  • Antimicrobial in nervous system niches

  • Control chemotaxis of immune cells and astroglia

  • Mediate iron homeostasis

  • Modulate nerve impulses

  • Implicated in aging and neurodegeneration

Hanson et al., 2019;Lezi et al., 2018;Su et al., 2010;Zasloff, 2002
  1. Table with unordered list

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Reference Book Chapter

Book
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Randy Scheckman

Editor-in-Chief

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Expertise
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Competing interests statement
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  1. Lee R Berger
  2. John Hawks
  3. Darryl J de Ruiter
  4. Steven E Churchill
  5. Peter Schmid
  6. Lucas K Delezene
  7. Tracy L Kivell
  8. Heather M Garvin
  9. Scott A Williams
  10. Jeremy M DeSilva
  11. Matthew M Skinner
  12. Charles M Musiba
  13. Noel Cameron
  14. Trenton W Holliday
  15. William Harcourt-Smith
  16. Rebecca R Ackermann
  17. Markus Bastir
  18. Barry Bogin
  19. Debra Bolter
  20. Juliet Brophy
  21. Zachary D Cofran
  22. Kimberly A Congdon
  23. Andrew S Deane
  24. Mana Dembo
  25. Michelle Drapeau
  26. Marina C Elliott
  27. Elen M Feuerriegel
  28. Daniel Garcia-Martinez
  29. David J Green
  30. Alia Gurtov
  31. Joel D Irish
  32. Ashley Kruger
  33. Myra F Laird
  34. Damiano Marchi
  35. Marc R Meyer
  36. Shahed Nalla
  37. Enquye W Negash
  38. Caley M Orr
  39. Davorka Radovcic
  40. Lauren Schroeder
  41. Jill E Scott
  42. Zachary Throckmorton
  43. Matthew W Tocheri
  44. Caroline VanSickle
  45. Christopher S Walker
  46. Pianpian Wei
  47. Bernhard Zipfel
(2015)
Homo naledi, a new species of the genus Homo from the Dinaledi Chamber, South Africa
eLife 4:e09560.
https://doi.org/10.7554/eLife.09560

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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.16370

Article 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.sa1

Article Section Editor Title Text

Senior editor
  1. Dominique Soldati-Favre
  2. 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 link

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.

Article Section Related Items Separator Circle

eLife assessment

Collagen is a major component of extracellular matrix. The authors have identified a high-affinity inhibitory collagen receptor LAIR-1 and a soluble decoy receptor LAIR-2 (with even higher binding affinity to collagen), which can be therapeutically targeted to block tumor progression. Dr Meyaard and colleagues have also generated a dimeric LAIR-2 human IgG1 Fc fusion protein NC410 for therapeutic use. With humanized mouse models engrafted with functional human immune systems (PBMC), they have explored the anti-cancer efficacy of NC410 and revealed its impact on modulating immune responses. Furthermore, they extended this study to identify biomarkers of predictive value for NC410-based anti-cancer therapy.

https://doi.org/10.7554/eLife.16370

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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Authors Details

Author details

  1. Jenny Bloggs

    1. Evolutionary Studies Institute and Centre of Excellence in PalaeoSciences, University of the Witwatersrand, Johannesburg, South Africa
    2. School of Geosciences, University of the Witwatersrand, Johannesburg, South Africa
    Present addresses
    1. Department of Inventive Inventions, Univertity of Wessex, Windowchester, Wessex
    Contribution
    Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents
    For correspondence
    1. jenny@bloggs.com
    2. +1 555-4321-09876
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3400-7927
  2. ICGC Chronic Myeloid Disorders Group

    1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Competing interests
    No competing interests declared
    1. Luca Malcovati, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
    2. Sudhir Tauro, Division of Medial Sciences, University of Dundee, Dundee, UK
    3. Jacqueline Boultwood, Nuffield Department of Clinical Laboratory Sciences, University of Oxford, UK

Authors

  1. Lee R Berger  Is a corresponding author
  2. John Hawks
  3. Darryl J de Ruiter
  4. Steven E Churchill
  5. Peter Schmid
  6. Lucas K Delezene
  7. Tracy L Kivell
  8. Heather M Garvin
  9. Scott A Williams
  10. Jeremy M DeSilva
  11. Matthew M Skinner
  12. Charles M Musiba
  13. Noel Cameron
  14. Trenton W Holliday
  15. William Harcourt-Smith
  16. Rebecca R Ackermann
  17. Markus Bastir
  18. Barry Bogin
  19. Debra Bolter
  20. Juliet Brophy
  21. Zachary D Cofran
  22. Kimberly A Congdon
  23. Andrew S Deane
  24. Mana Dembo
  25. Michelle Drapeau
  26. Marina C Elliott
  27. Elen M Feuerriegel
  28. Daniel Garcia-Martinez
  29. David J Green
  30. Alia Gurtov
  31. Joel D Irish
  32. Ashley Kruger
  33. Myra F Laird
  34. Damiano Marchi
  35. Marc R Meyer
  36. Shahed Nalla
  37. Enquye W Negash
  38. Caley M Orr
  39. Davorka Radovcic
  40. Lauren Schroeder
  41. Jill E Scott
  42. Zachary Throckmorton
  43. Caroline VanSickle
  44. Christopher S Walker
  45. Pianpian Wei
  46. Bernhard Zipfel  Is a corresponding author
  1. University of the Witwatersrand, South Africa
  2. University of Wisconsin-Madison, United States
  3. Texas A&M University, United States
  4. Duke University, United States
  5. University of Zurich, Switzerland
  6. University of Arkansas, United States
  7. University of Kent, United Kingdom
  8. Max Planck Institute for Evolutionary Anthropology, Germany
  9. Mercyhurst University, United States
  10. New York University, United States
  11. New York Consortium in Evolutionary Primatology, United States
  12. Dartmouth College, United States
  13. University of Colorado Denver, United States
  14. Loughborough University, United Kingdom
  15. Tulane University, United States
  16. Lehman College, United States
  17. American Museum of Natural History, United States
  18. University of Cape Town, South Africa
  19. Museo Nacional de Ciencias Naturales, Spain
  20. Modesto Junior College, United States
  21. Louisiana State University, United States
  22. Nazarbayev University, Kazakhstan
  23. University of Missouri, United States
  24. University of Kentucky College of Medicine, United States
  25. Simon Fraser University, Canada
  26. Université de Montréal, Canada
  27. Australian National University, Australia
  28. Biology Department, Universidad Autònoma de Madrid, Spain
  29. Midwestern University, United States
  30. Liverpool John Moores University, United Kingdom
  31. University of Pisa, Italy
  32. Chaffey College, United States
  33. University of Johannesburg, South Africa
  34. George Washington University, United States
  35. University of Colorado School of Medicine, United States
  36. Croatian Natural History Museum, Croatia
  37. University of Iowa, United States
  38. Lincoln Memorial University, United States
  39. Smithsonian Institution, United States

Authors Between Three And Ten Authors

  1. Lee R Berger  Is a corresponding author
  2. John Hawks
  3. Darryl J de Ruiter
  4. Steven E Churchill
  5. Peter Schmid
  1. University of the Witwatersrand, South Africa
  2. University of Wisconsin-Madison, United States
  3. Texas A&M University, United States
  4. Duke University, United States
  5. University of Zurich, Switzerland
  6. University of Arkansas, United States
  7. University of Kent, United Kingdom
  8. Max Planck Institute for Evolutionary Anthropology, Germany
  9. Mercyhurst University, United States
  10. New York University, United States
  11. New York Consortium in Evolutionary Primatology, United States
  12. Dartmouth College, United States
  13. University of Colorado Denver, United States
  14. Loughborough University, United Kingdom
  15. Tulane University, United States
  16. Lehman College, United States
  17. American Museum of Natural History, United States
  18. University of Cape Town, South Africa
  19. Museo Nacional de Ciencias Naturales, Spain
  20. Modesto Junior College, United States
  21. Louisiana State University, United States
  22. Nazarbayev University, Kazakhstan
  23. University of Missouri, United States
  24. University of Kentucky College of Medicine, United States
  25. Simon Fraser University, Canada
  26. Université de Montréal, Canada
  27. Australian National University, Australia
  28. Biology Department, Universidad Autònoma de Madrid, Spain
  29. Midwestern University, United States
  30. Liverpool John Moores University, United Kingdom
  31. University of Pisa, Italy
  32. Chaffey College, United States
  33. University of Johannesburg, South Africa
  34. George Washington University, United States
  35. University of Colorado School of Medicine, United States
  36. Croatian Natural History Museum, Croatia
  37. University of Iowa, United States
  38. Lincoln Memorial University, United States
  39. Smithsonian Institution, United States

Authors Less Then Three Authors

  1. Lee R Berger  Is a corresponding author
  2. John Hawks
  1. University of the Witwatersrand, South Africa
  2. University of Wisconsin-Madison, United States
  3. Texas A&M University, United States
  4. Duke University, United States
  5. University of Zurich, Switzerland
  6. University of Arkansas, United States
  7. University of Kent, United Kingdom
  8. Max Planck Institute for Evolutionary Anthropology, Germany
  9. Mercyhurst University, United States
  10. New York University, United States
  11. New York Consortium in Evolutionary Primatology, United States
  12. Dartmouth College, United States
  13. University of Colorado Denver, United States
  14. Loughborough University, United Kingdom
  15. Tulane University, United States
  16. Lehman College, United States
  17. American Museum of Natural History, United States
  18. University of Cape Town, South Africa
  19. Museo Nacional de Ciencias Naturales, Spain
  20. Modesto Junior College, United States
  21. Louisiana State University, United States
  22. Nazarbayev University, Kazakhstan
  23. University of Missouri, United States
  24. University of Kentucky College of Medicine, United States
  25. Simon Fraser University, Canada
  26. Université de Montréal, Canada
  27. Australian National University, Australia
  28. Biology Department, Universidad Autònoma de Madrid, Spain
  29. Midwestern University, United States
  30. Liverpool John Moores University, United Kingdom
  31. University of Pisa, Italy
  32. Chaffey College, United States
  33. University of Johannesburg, South Africa
  34. George Washington University, United States
  35. University of Colorado School of Medicine, United States
  36. Croatian Natural History Museum, Croatia
  37. University of Iowa, United States
  38. Lincoln Memorial University, United States
  39. Smithsonian Institution, United States

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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.

https://doi.org/10.7554/eLife.16370

Captioned Asset Iframe Google Map

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.

https://doi.org/10.7554/eLife.16370

Captioned Asset Iframe

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.

https://doi.org/10.7554/eLife.16370

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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.

https://doi.org/10.7554/eLife.16370

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Meaningful alt text here please.
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.

https://doi.org/10.7554/eLife.16370

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Meaningful alt text here please.
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.

https://doi.org/10.7554/eLife.16370

Captioned Asset Table

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.

https://doi.org/10.7554/eLife.16370
F(Dfn, Dfd)Partial η2Original effect size fReplication total sample sizeDetectable effect size f
F(24,39) = 0.8678 (interaction)0.3481200.7307699169*0.3895070
F(2,39) = 0.8075 (treatments)0.0397660.2035014169*0.2415459
F(12,39) = 187.6811 (hematology parameters)0.9829787.599178169*0.3331365

Captioned Asset Tables

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.

https://doi.org/10.7554/eLife.16370
F(Dfn, Dfd)Partial η2Original effect size fReplication total sample sizeDetectable effect size f
F(24,39) = 0.8678 (interaction)0.3481200.7307699169*0.3895070
F(2,39) = 0.8075 (treatments)0.0397660.2035014169*0.2415459
F(12,39) = 187.6811 (hematology parameters)0.9829787.599178169*0.3331365
F(Dfn, Dfd)Partial η2Original effect size fReplication total sample sizeDetectable effect size f
F(24,39) = 0.8678 (interaction)0.3481200.7307699169*0.3895070
F(2,39) = 0.8075 (treatments)0.0397660.2035014169*0.2415459
F(12,39) = 187.6811 (hematology parameters)0.9829787.599178169*0.3331365

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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.

https://doi.org/10.7554/eLife.16370

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    CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
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    CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
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    Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain

    Akiko Terauchi, Erin M Johnson-Venkatesh ... Hisashi Umemori
    Feedback signaling between the synapse and nucleus via FGF22 and IGF2 directs the activity-dependent stabilization of presynaptic terminals in the mouse hippocampus.

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    Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain

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    Feedback signaling between the synapse and nucleus via FGF22 and IGF2 directs the activity-dependent stabilization of presynaptic terminals in the mouse hippocampus.

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    Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain

    Akiko Terauchi, Erin M Johnson-Venkatesh ... Hisashi Umemori
    Feedback signaling between the synapse and nucleus via FGF22 and IGF2 directs the activity-dependent stabilization of presynaptic terminals in the mouse hippocampus.

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    The eLife Sciences 2015 Annual Report

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    Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain

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    Feedback signaling between the synapse and nucleus via FGF22 and IGF2 directs the activity-dependent stabilization of presynaptic terminals in the mouse hippocampus.

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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.

    https://doi.org/10.7554/eLife.14734

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    (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.

    https://doi.org/10.7554/eLife.14734

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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.

    https://doi.org/10.7554/eLife.14734
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    https://doi.org/10.7554/eLife.10181.002
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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.

    https://doi.org/10.7554/eLife.14734
    F(Dfn, Dfd)Partial η2Original effect size fReplication total sample sizeDetectable effect size f
    F(24,39) = 0.8678 (interaction)0.3481200.7307699169*0.3895070
    F(2,39) = 0.8075 (treatments)0.0397660.2035014169*0.2415459
    F(12,39) = 187.6811 (hematology parameters)0.9829787.599178169*0.3331365

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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.

    https://doi.org/10.7554/eLife.14734
    F(Dfn, Dfd)Partial η2Original effect size fReplication total sample sizeDetectable effect size f
    F(24,39) = 0.8678 (interaction)0.3481200.7307699169*0.3895070
    F(2,39) = 0.8075 (treatments)0.0397660.2035014169*0.2415459
    F(12,39) = 187.6811 (hematology parameters)0.9829787.599178169*0.3331365
    F(Dfn, Dfd)Partial η2Original effect size fReplication total sample sizeDetectable effect size f
    F(24,39) = 0.8678 (interaction)0.3481200.7307699169*0.3895070
    F(2,39) = 0.8075 (treatments)0.0397660.2035014169*0.2415459
    F(12,39) = 187.6811 (hematology parameters)0.9829787.599178169*0.3331365

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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.

    https://doi.org/10.7554/eLife.14734

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      Multicellular life, potato blight and Hepatitis B

      Episode 24
      Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris commodo consequat.
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      Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris commodo consequat.
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    1. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      What was the Mg2+ concentration?
    2. Clarinet (CLA-1), a novel active zone protein required for synaptic vesicle clustering and release
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
    3. Gene free methodology for cell fate dynamics during development
      What was the Mg2+ concentration?
    4. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      What was the Mg2+ concentration?
      This is a reply.
    5. Molecular basis of fatty acid taste in Drosophila
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      What was the Mg2+ concentration?
      Only me
    6. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      https://images.pexels.com/photos/707837/pexels-photo-707837.jpeg?w=940&h=650&auto=compress&cs=tinysrgb
    7. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      <math xmlns="http://www.w3.org/1998/Math/MathML"><mlongdiv><mn> 12 </mn><mn> 16.5 </mn> <mn> 198 </mn> <msgroup position='1' shift='-1'> <msgroup> <mn> 12</mn> <msline length='2'/> </msgroup> <msgroup> <mn> 78</mn> <mn> 72</mn> <msline length='2'/> <mn> 6.0</mn> <mn> 6.0</mn> </msgroup> <msgroup position='-1'> <!-- extra shift to move to the right of the "." --> <msline length='3'/> <mn> 0</mn> </msgroup> </msgroup> </mlongdiv> </math>
      Long division ftw.
    8. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      https://www.youtube.com/watch?v=oHg5SJYRHA0

    Listing Annotation Teasers First Page

    Annotations

    1. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      What was the Mg2+ concentration?
    2. Clarinet (CLA-1), a novel active zone protein required for synaptic vesicle clustering and release
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
    3. Gene free methodology for cell fate dynamics during development
      What was the Mg2+ concentration?
    4. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      What was the Mg2+ concentration?
      This is a reply.
    5. Molecular basis of fatty acid taste in Drosophila
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      What was the Mg2+ concentration?
      Only me
    6. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      https://images.pexels.com/photos/707837/pexels-photo-707837.jpeg?w=940&h=650&auto=compress&cs=tinysrgb
    7. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      <math xmlns="http://www.w3.org/1998/Math/MathML"><mlongdiv><mn> 12 </mn><mn> 16.5 </mn> <mn> 198 </mn> <msgroup position='1' shift='-1'> <msgroup> <mn> 12</mn> <msline length='2'/> </msgroup> <msgroup> <mn> 78</mn> <mn> 72</mn> <msline length='2'/> <mn> 6.0</mn> <mn> 6.0</mn> </msgroup> <msgroup position='-1'> <!-- extra shift to move to the right of the "." --> <msline length='3'/> <mn> 0</mn> </msgroup> </msgroup> </mlongdiv> </math>
      Long division ftw.
    8. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      https://www.youtube.com/watch?v=oHg5SJYRHA0

    Listing Annotation Teasers Pager

    Annotations

    1. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      What was the Mg2+ concentration?
    2. Clarinet (CLA-1), a novel active zone protein required for synaptic vesicle clustering and release
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
    3. Gene free methodology for cell fate dynamics during development
      What was the Mg2+ concentration?
    4. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      What was the Mg2+ concentration?
      This is a reply.
    5. Molecular basis of fatty acid taste in Drosophila
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      What was the Mg2+ concentration?
      Only me
    6. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      https://images.pexels.com/photos/707837/pexels-photo-707837.jpeg?w=940&h=650&auto=compress&cs=tinysrgb
    7. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      <math xmlns="http://www.w3.org/1998/Math/MathML"><mlongdiv><mn> 12 </mn><mn> 16.5 </mn> <mn> 198 </mn> <msgroup position='1' shift='-1'> <msgroup> <mn> 12</mn> <msline length='2'/> </msgroup> <msgroup> <mn> 78</mn> <mn> 72</mn> <msline length='2'/> <mn> 6.0</mn> <mn> 6.0</mn> </msgroup> <msgroup position='-1'> <!-- extra shift to move to the right of the "." --> <msline length='3'/> <mn> 0</mn> </msgroup> </msgroup> </mlongdiv> </math>
      Long division ftw.
    8. CRIg, a tissue-resident macrophage specific immune checkpoint molecule, promotes immunological tolerance in NOD mice, via a dual role in effector and regulatory T cells
      Retrograde fibroblast growth factor 22 (FGF22) signaling regulates insulin-like growth factor 2 (IGF2) expression for activity-dependent synapse stabilization in the mammalian brain
      https://www.youtube.com/watch?v=oHg5SJYRHA0

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      Prabhat Jha
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      1. Cell Biology
      2. Epidemiology and Global Health
      Lee R Berger
      Research article

      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.

      1. Cell Biology
      2. Epidemiology and Global Health
      Lee R Berger
      Research article

      Optimal decision-making requires balancing fast but error-prone and more accurate but slower decisions through adjustments of decision thresholds.

    Listing Read More First Page

    Further reading

      1. Cell Biology
      2. Epidemiology and Global Health
      Lee R Berger
      Research article

      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.

      1. Cell Biology
      2. Epidemiology and Global Health
      Lee R Berger
      Research article

      Optimal decision-making requires balancing fast but error-prone and more accurate but slower decisions through adjustments of decision thresholds.

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      1. Microbiology and Infectious Disease
      Meaningful alt text please.

      Quorum sensing control of Type VI secretion factors restricts the proliferation of quorum-sensing mutants

      Charlotte Majerczyk, Emily Schneider, E Peter Greenberg
      Quorum-sensing control of Burkholderia thailandensis toxin and immunity pairs serves to police quorum-sensing mutants and may represent a general strategy whereby cooperators can police mutants.
    1. Mapping global environmental suitability for Zika virus

      Jane P Messina, Moritz UG Kraemer ... Simon I Hay
    2. Evolutionary diversification of the trypanosome haptoglobin-haemoglobin receptor from an ancestral haemoglobin receptor

      Harriet Lane-Serff, Paula MacGregor ... Mark Carrington
    3. Meaningful alt text please.

      The global antigenic diversity of swine influenza A viruses

      Nicola S Lewis, Colin A Russell ... Amy L Vincent
      Swine populations worldwide are sporadically infected by influenza viruses from humans and birds leading to geographically heterogeneous swine influenza virus populations that pose epizootic and pandemic threats.

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      1. Microbiology and Infectious Disease
      Meaningful alt text please.

      Quorum sensing control of Type VI secretion factors restricts the proliferation of quorum-sensing mutants

      Charlotte Majerczyk, Emily Schneider, E Peter Greenberg
      Quorum-sensing control of Burkholderia thailandensis toxin and immunity pairs serves to police quorum-sensing mutants and may represent a general strategy whereby cooperators can police mutants.
    1. Mapping global environmental suitability for Zika virus

      Jane P Messina, Moritz UG Kraemer ... Simon I Hay
    2. Evolutionary diversification of the trypanosome haptoglobin-haemoglobin receptor from an ancestral haemoglobin receptor

      Harriet Lane-Serff, Paula MacGregor ... Mark Carrington
    3. Meaningful alt text please.

      The global antigenic diversity of swine influenza A viruses

      Nicola S Lewis, Colin A Russell ... Amy L Vincent
      Swine populations worldwide are sporadically infected by influenza viruses from humans and birds leading to geographically heterogeneous swine influenza virus populations that pose epizootic and pandemic threats.

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      1. Microbiology and Infectious Disease
      Meaningful alt text please.

      Quorum sensing control of Type VI secretion factors restricts the proliferation of quorum-sensing mutants

      Charlotte Majerczyk, Emily Schneider, E Peter Greenberg
      Quorum-sensing control of Burkholderia thailandensis toxin and immunity pairs serves to police quorum-sensing mutants and may represent a general strategy whereby cooperators can police mutants.
    1. Mapping global environmental suitability for Zika virus

      Jane P Messina, Moritz UG Kraemer ... Simon I Hay
    2. Evolutionary diversification of the trypanosome haptoglobin-haemoglobin receptor from an ancestral haemoglobin receptor

      Harriet Lane-Serff, Paula MacGregor ... Mark Carrington
    3. Meaningful alt text please.

      The global antigenic diversity of swine influenza A viruses

      Nicola S Lewis, Colin A Russell ... Amy L Vincent
      Swine populations worldwide are sporadically infected by influenza viruses from humans and birds leading to geographically heterogeneous swine influenza virus populations that pose epizootic and pandemic threats.

    Read More Item

    1. Cell Biology
    2. Epidemiology and Global Health
    Lee R Berger
    Research article

    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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    Homo naledi, a new species of the genus Homo from the Dinaledi Chamber, South Africa

    1. Lee R Berger  Is a corresponding author
    1. University of the Witwatersrand, South Africa

    Content Header Journal Article 2 Authors

    Homo naledi, a new species of the genus Homo from the Dinaledi Chamber, South Africa

    1. Lee R Berger  Is a corresponding author
    2. Bernhard Zipfel  Is a corresponding author
    1. University of the Witwatersrand, South Africa
    2. Smithsonian Institution, United States

    Content Header Journal Article

    Homo naledi, a new species of the genus Homo from the Dinaledi Chamber, South Africa

    1. Lee R Berger  Is a corresponding author
    2. John Hawks
    3. Darryl J de Ruiter
    4. Steven E Churchill
    5. Peter Schmid
    6. Lucas K Delezene
    7. Tracy L Kivell
    8. Heather M Garvin
    9. Scott A Williams
    10. Jeremy M DeSilva
    11. Matthew M Skinner
    12. Charles M Musiba
    13. Noel Cameron
    14. Trenton W Holliday
    15. William Harcourt-Smith
    16. Rebecca R Ackermann
    17. Markus Bastir
    18. Barry Bogin
    19. Debra Bolter
    20. Juliet Brophy
    21. Zachary D Cofran
    22. Kimberly A Congdon
    23. Andrew S Deane
    24. Mana Dembo
    25. Michelle Drapeau
    26. Marina C Elliott
    27. Elen M Feuerriegel
    28. Daniel Garcia-Martinez
    29. David J Green
    30. Alia Gurtov
    31. Joel D Irish
    32. Ashley Kruger
    33. Myra F Laird
    34. Damiano Marchi
    35. Marc R Meyer
    36. Shahed Nalla
    37. Enquye W Negash
    38. Caley M Orr
    39. Davorka Radovcic
    40. Lauren Schroeder
    41. Jill E Scott
    42. Zachary Throckmorton
    43. Caroline VanSickle
    44. Christopher S Walker
    45. Pianpian Wei
    46. Bernhard Zipfel  Is a corresponding author
    1. University of the Witwatersrand, South Africa
    2. University of Wisconsin-Madison, United States
    3. Texas A&M University, United States
    4. Duke University, United States
    5. University of Zurich, Switzerland
    6. University of Arkansas, United States
    7. University of Kent, United Kingdom
    8. Max Planck Institute for Evolutionary Anthropology, Germany
    9. Mercyhurst University, United States
    10. New York University, United States
    11. New York Consortium in Evolutionary Primatology, United States
    12. Dartmouth College, United States
    13. University of Colorado Denver, United States
    14. Loughborough University, United Kingdom
    15. Tulane University, United States
    16. Lehman College, United States
    17. American Museum of Natural History, United States
    18. University of Cape Town, South Africa
    19. Museo Nacional de Ciencias Naturales, Spain
    20. Modesto Junior College, United States
    21. Louisiana State University, United States
    22. Nazarbayev University, Kazakhstan
    23. University of Missouri, United States
    24. University of Kentucky College of Medicine, United States
    25. Simon Fraser University, Canada
    26. Université de Montréal, Canada
    27. Australian National University, Australia
    28. Biology Department, Universidad Autònoma de Madrid, Spain
    29. Midwestern University, United States
    30. Liverpool John Moores University, United Kingdom
    31. University of Pisa, Italy
    32. Chaffey College, United States
    33. University of Johannesburg, South Africa
    34. George Washington University, United States
    35. University of Colorado School of Medicine, United States
    36. Croatian Natural History Museum, Croatia
    37. University of Iowa, United States
    38. Lincoln Memorial University, United States
    39. Smithsonian Institution, United States

    Content Header Journal Digest No Social

    Holding the focus

    A new open source software tool makes it possible for researchers to view cells in living animals in high detail.

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    Holding the focus

    A new open source software tool makes it possible for researchers to view cells in living animals in high detail.

    Content Header Journal Has Aside

    Homo naledi, a new species of the genus Homo from the Dinaledi Chamber, South Africa

    1. Lee R Berger  Is a corresponding author
    2. John Hawks
    3. Darryl J de Ruiter
    4. Steven E Churchill
    5. Peter Schmid
    6. Lucas K Delezene
    7. Tracy L Kivell
    8. Heather M Garvin
    9. Scott A Williams
    10. Jeremy M DeSilva
    11. Matthew M Skinner
    12. Charles M Musiba
    13. Noel Cameron
    14. Trenton W Holliday
    15. William Harcourt-Smith
    16. Rebecca R Ackermann
    17. Markus Bastir
    18. Barry Bogin
    19. Debra Bolter
    20. Juliet Brophy
    21. Zachary D Cofran
    22. Kimberly A Congdon
    23. Andrew S Deane
    24. Mana Dembo
    25. Michelle Drapeau
    26. Marina C Elliott
    27. Elen M Feuerriegel
    28. Daniel Garcia-Martinez
    29. David J Green
    30. Alia Gurtov
    31. Joel D Irish
    32. Ashley Kruger
    33. Myra F Laird
    34. Damiano Marchi
    35. Marc R Meyer
    36. Shahed Nalla
    37. Enquye W Negash
    38. Caley M Orr
    39. Davorka Radovcic
    40. Lauren Schroeder
    41. Jill E Scott
    42. Zachary Throckmorton
    43. Caroline VanSickle
    44. Christopher S Walker
    45. Pianpian Wei
    46. Bernhard Zipfel  Is a corresponding author
    1. University of the Witwatersrand, South Africa
    2. University of Wisconsin-Madison, United States
    3. Texas A&M University, United States
    4. Duke University, United States
    5. University of Zurich, Switzerland
    6. University of Arkansas, United States
    7. University of Kent, United Kingdom
    8. Max Planck Institute for Evolutionary Anthropology, Germany
    9. Mercyhurst University, United States
    10. New York University, United States
    11. New York Consortium in Evolutionary Primatology, United States
    12. Dartmouth College, United States
    13. University of Colorado Denver, United States
    14. Loughborough University, United Kingdom
    15. Tulane University, United States
    16. Lehman College, United States
    17. American Museum of Natural History, United States
    18. University of Cape Town, South Africa
    19. Museo Nacional de Ciencias Naturales, Spain
    20. Modesto Junior College, United States
    21. Louisiana State University, United States
    22. Nazarbayev University, Kazakhstan
    23. University of Missouri, United States
    24. University of Kentucky College of Medicine, United States
    25. Simon Fraser University, Canada
    26. Université de Montréal, Canada
    27. Australian National University, Australia
    28. Biology Department, Universidad Autònoma de Madrid, Spain
    29. Midwestern University, United States
    30. Liverpool John Moores University, United Kingdom
    31. University of Pisa, Italy
    32. Chaffey College, United States
    33. University of Johannesburg, South Africa
    34. George Washington University, United States
    35. University of Colorado School of Medicine, United States
    36. Croatian Natural History Museum, Croatia
    37. University of Iowa, United States
    38. Lincoln Memorial University, United States
    39. Smithsonian Institution, United States

    Content Header Journal Inside Elife Article

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    Genetic Engineering: Increasing the uptake of carbon dioxide

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    Flatworms have organs called protonephridia that could be used as a model system for the study of kidney disease.
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    From its earliest days, eLife has been publishing important advances in our understanding of Cancer Biology. Here, eLife Senior Editors present a collection of great papers spanning from oncogenes and tumor suppressors.
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    Ezrin enrichment on curved membranes requires a specific conformation or interaction with a curvature-sensitive partner

    From its earliest days, eLife has been publishing important advances in our understanding of Cancer Biology. Here, eLife Senior Editors present a collection of great papers spanning from oncogenes and tumor suppressors.
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    Kasugamycin potentiates rifampicin and limits emergence of resistance in Mycobacterium tuberculosis by specifically decreasing mycobacterial mistranslation

    From its earliest days, eLife has been publishing important advances in our understanding of Cancer Biology. Here, eLife Senior Editors present a collection of great papers spanning from oncogenes and tumor suppressors.
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    A title in an image-backed content header like this should never be this long Ridiculous! However if it is, at least we can scale appropriately and prevent things from breaking in a really horrible fashion

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    eLife podcast

    Flatworms have organs called protonephridia that could be used as a model system for the study of kidney disease.

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    Annual report: 2016 in review

    A look back at eLife's achievements in 2016, notably in advocating for more open practises in sciences, building an open infrastructure for research communication and supporting early-career researchers.
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    Episode 2: July 2013

    In this issue of the eLife podcast we discuss plants performing arithmetic division, the evolutionary dynamics of cancer, single-molecule measurements in the immune system, why blood vessels don’t grow in the retina.

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