RRID:AB_10732356
DOI: 10.1016/j.celrep.2026.117610
Resource: (Thermo Fisher Scientific Cat# 25-7177-82, RRID:AB_10732356)
Curator: @scibot
SciCrunch record: RRID:AB_10732356
RRID:AB_10732356
DOI: 10.1016/j.celrep.2026.117610
Resource: (Thermo Fisher Scientific Cat# 25-7177-82, RRID:AB_10732356)
Curator: @scibot
SciCrunch record: RRID:AB_10732356
RRID:AB_536018
DOI: 10.1016/j.celrep.2026.117610
Resource: (BioLegend Cat# 506916, RRID:AB_536018)
Curator: @scibot
SciCrunch record: RRID:AB_536018
RRID:AB_2861213
DOI: 10.1016/j.celrep.2026.117610
Resource: (Beyotime Cat# AA128, RRID:AB_2861213)
Curator: @scibot
SciCrunch record: RRID:AB_2861213
RRID:AB_2782998
DOI: 10.1016/j.celrep.2026.117599
Resource: (LI-COR Biosciences Cat# 926-32351, RRID:AB_2782998)
Curator: @scibot
SciCrunch record: RRID:AB_2782998
RRID:AB_2532109
DOI: 10.1016/j.celrep.2026.117599
Resource: (Abcam Cat# ab177487, RRID:AB_2532109)
Curator: @scibot
SciCrunch record: RRID:AB_2532109
RRID:AB_2224402
DOI: 10.1016/j.celrep.2026.117599
Resource: (Abcam Cat# ab5076, RRID:AB_2224402)
Curator: @scibot
SciCrunch record: RRID:AB_2224402
RRID:AB_162542
DOI: 10.1016/j.celrep.2026.117599
Resource: (Molecular Probes Cat# A-31571, RRID:AB_162542)
Curator: @scibot
SciCrunch record: RRID:AB_162542
RRID:AB_2534102
DOI: 10.1016/j.celrep.2026.117599
Resource: (Thermo Fisher Scientific Cat# A-11055, RRID:AB_2534102)
Curator: @scibot
SciCrunch record: RRID:AB_2534102
RRID:AB_2534017
DOI: 10.1016/j.celrep.2026.117599
Resource: (Thermo Fisher Scientific Cat# A10042, RRID:AB_2534017)
Curator: @scibot
SciCrunch record: RRID:AB_2534017
AB_10975465
DOI: 10.1016/j.celrep.2026.117599
Resource: (Abcam Cat# ab125212, RRID:AB_10975465)
Curator: @scibot
SciCrunch record: RRID:AB_10975465
RRID:AB_621847
DOI: 10.1016/j.celrep.2026.117596
Resource: (LI-COR Biosciences Cat# 926-32212, RRID:AB_621847)
Curator: @scibot
SciCrunch record: RRID:AB_621847
RRID:SCR_008520
DOI: 10.1016/j.celrep.2026.117596
Resource: FlowJo (RRID:SCR_008520)
Curator: @scibot
SciCrunch record: RRID:SCR_008520
RRID:AB_11180865
DOI: 10.1016/j.celrep.2026.117596
Resource: (Thermo Fisher Scientific Cat# A10037, RRID:AB_11180865)
Curator: @scibot
SciCrunch record: RRID:AB_11180865
RRID:Addgene_56439
DOI: 10.1016/j.celrep.2026.117596
Resource: RRID:Addgene_56439
Curator: @scibot
SciCrunch record: RRID:Addgene_56439
RRID:IMSR_JAX:027929
DOI: 10.1016/j.celrep.2026.117596
Resource: (IMSR Cat# JAX_027929,RRID:IMSR_JAX:027929)
Curator: @scibot
SciCrunch record: RRID:IMSR_JAX:027929
RRID:AB_2534102
DOI: 10.1016/j.celrep.2026.117596
Resource: (Thermo Fisher Scientific Cat# A-11055, RRID:AB_2534102)
Curator: @scibot
SciCrunch record: RRID:AB_2534102
RRID:IMSR_JAX:000632
DOI: 10.1016/j.celrep.2026.117596
Resource: (IMSR Cat# JAX_000632,RRID:IMSR_JAX:000632)
Curator: @scibot
SciCrunch record: RRID:IMSR_JAX:000632
RRID:AB_2535867
DOI: 10.1016/j.celrep.2026.117596
Resource: (Thermo Fisher Scientific Cat# A-21450, RRID:AB_2535867)
Curator: @scibot
SciCrunch record: RRID:AB_2535867
RRID:IMSR_JAX:007676
DOI: 10.1016/j.celrep.2026.117596
Resource: (IMSR Cat# JAX_007676,RRID:IMSR_JAX:007676)
Curator: @scibot
SciCrunch record: RRID:IMSR_JAX:007676
RRID:AB_144696
DOI: 10.1016/j.celrep.2026.117596
Resource: (Molecular Probes Cat# A-11031, RRID:AB_144696)
Curator: @scibot
SciCrunch record: RRID:AB_144696
RRID:IMSR_JAX:000697
DOI: 10.1016/j.celrep.2026.117596
Resource: (IMSR Cat# JAX_000697,RRID:IMSR_JAX:000697)
Curator: @scibot
SciCrunch record: RRID:IMSR_JAX:000697
RRID:AB_2534017
DOI: 10.1016/j.celrep.2026.117596
Resource: (Thermo Fisher Scientific Cat# A10042, RRID:AB_2534017)
Curator: @scibot
SciCrunch record: RRID:AB_2534017
RRID:AB_2576217
DOI: 10.1016/j.celrep.2026.117596
Resource: (Thermo Fisher Scientific Cat# A-11034, RRID:AB_2576217)
Curator: @scibot
SciCrunch record: RRID:AB_2576217
RRID:SCR_014199
DOI: 10.1016/j.celrep.2026.117596
Resource: Adobe Photoshop (RRID:SCR_014199)
Curator: @scibot
SciCrunch record: RRID:SCR_014199
RRID:SCR_002798
DOI: 10.1016/j.celrep.2026.117596
Resource: GraphPad Prism (RRID:SCR_002798)
Curator: @scibot
SciCrunch record: RRID:SCR_002798
RRID:Addgene_17843
DOI: 10.1016/j.celrep.2026.117596
Resource: RRID:Addgene_17843
Curator: @scibot
SciCrunch record: RRID:Addgene_17843
RRID:AB_1279486
DOI: 10.1016/j.celrep.2026.117596
Resource: (Bethyl Cat# IHC-00352, RRID:AB_1279486)
Curator: @scibot
SciCrunch record: RRID:AB_1279486
RRID:AB_2665496
DOI: 10.1016/j.celrep.2026.117596
Resource: (Novus Cat# NBP2-15339, RRID:AB_2665496)
Curator: @scibot
SciCrunch record: RRID:AB_2665496
RRID:AB_3740766
DOI: 10.1016/j.celrep.2026.117596
Resource: RRID:AB_3740766
Curator: @scibot
SciCrunch record: RRID:AB_3740766
RRID:AB_2900574
DOI: 10.1016/j.celrep.2026.117596
Resource: RRID:AB_2900574
Curator: @scibot
SciCrunch record: RRID:AB_2900574
RRID:AB_2728773
DOI: 10.1016/j.celrep.2026.117596
Resource: (Abcam Cat# ab180714, RRID:AB_2728773)
Curator: @scibot
SciCrunch record: RRID:AB_2728773
RRID:AB_2650491
DOI: 10.1016/j.celrep.2026.117596
Resource: (Cell Signaling Technology Cat# 14074, RRID:AB_2650491)
Curator: @scibot
SciCrunch record: RRID:AB_2650491
RRID:AB_262054
DOI: 10.1016/j.celrep.2026.117596
Resource: (Sigma-Aldrich Cat# A5228, RRID:AB_262054)
Curator: @scibot
SciCrunch record: RRID:AB_262054
RRID:AB_259852
DOI: 10.1016/j.celrep.2026.117596
Resource: (Sigma-Aldrich Cat# G2654, RRID:AB_259852)
Curator: @scibot
SciCrunch record: RRID:AB_259852
RRID:AB_10695459
DOI: 10.1016/j.celrep.2026.117596
Resource: (Cell Signaling Technology Cat# 5741, RRID:AB_10695459)
Curator: @scibot
SciCrunch record: RRID:AB_10695459
RRID:AB_2619627
DOI: 10.1016/j.celrep.2026.117596
Resource: (Takara Bio Cat# M182, RRID:AB_2619627)
Curator: @scibot
SciCrunch record: RRID:AB_2619627
RRID:AB_306907
DOI: 10.1016/j.celrep.2026.117596
Resource: (Abcam Cat# ab8978, RRID:AB_306907)
Curator: @scibot
SciCrunch record: RRID:AB_306907
RRID:AB_10013624
DOI: 10.1016/j.celrep.2026.117596
Resource: (Agilent Cat# A0564, RRID:AB_10013624)
Curator: @scibot
SciCrunch record: RRID:AB_10013624
RRID:AB_2161028
DOI: 10.1016/j.celrep.2026.117596
Resource: (R and D Systems Cat# AF3628, RRID:AB_2161028)
Curator: @scibot
SciCrunch record: RRID:AB_2161028
RRID:AB_776709
DOI: 10.1016/j.celrep.2026.117596
Resource: RRID:AB_776709
Curator: @scibot
SciCrunch record: RRID:AB_776709
RRID:AB_2242334
DOI: 10.1016/j.celrep.2026.117596
Resource: (Cell Signaling Technology Cat# 3700, RRID:AB_2242334)
Curator: @scibot
SciCrunch record: RRID:AB_2242334
RRID:AB_2813833
DOI: 10.1016/j.celrep.2026.117596
Resource: (Abcam Cat# ab205270, RRID:AB_2813833)
Curator: @scibot
SciCrunch record: RRID:AB_2813833
RRID:AB_298179
DOI: 10.1016/j.celrep.2026.117596
Resource: (Abcam Cat# ab11575, RRID:AB_298179)
Curator: @scibot
SciCrunch record: RRID:AB_298179
RRID:AB_3298270
DOI: 10.1016/j.celrep.2026.117596
Resource: RRID:AB_3298270
Curator: @scibot
SciCrunch record: RRID:AB_3298270
RRID:SCR_014387
DOI: 10.1016/j.celrep.2026.117596
Resource: Integrated Islet Distribution Program (IIDP) (RRID:SCR_014387)
Curator: @scibot
SciCrunch record: RRID:SCR_014387
RRID:AB_262097
DOI: 10.1016/j.celrep.2026.117596
Resource: (Sigma-Aldrich Cat# C3865, RRID:AB_262097)
Curator: @scibot
SciCrunch record: RRID:AB_262097
RRID:AB_10953628
DOI: 10.1016/j.celrep.2026.117596
Resource: (LI-COR Biosciences Cat# 926-68072, RRID:AB_10953628)
Curator: @scibot
SciCrunch record: RRID:AB_10953628
RRID:AB_621848
DOI: 10.1016/j.celrep.2026.117596
Resource: (LI-COR Biosciences Cat# 926-32213, RRID:AB_621848)
Curator: @scibot
SciCrunch record: RRID:AB_621848
Addgene_41391
DOI: 10.1016/j.cell.2026.06.017
Resource: RRID:Addgene_41391
Curator: @scibot
SciCrunch record: RRID:Addgene_41391
Addgene_41393
DOI: 10.1016/j.cell.2026.06.017
Resource: RRID:Addgene_41393
Curator: @scibot
SciCrunch record: RRID:Addgene_41393
Addgene_12259
DOI: 10.1016/j.cell.2026.06.017
Resource: RRID:Addgene_12259
Curator: @scibot
SciCrunch record: RRID:Addgene_12259
Addgene_12260
DOI: 10.1016/j.cell.2026.06.017
Resource: RRID:Addgene_12260
Curator: @scibot
SciCrunch record: RRID:Addgene_12260
Addgene_8454
DOI: 10.1016/j.cell.2026.06.017
Resource: RRID:Addgene_8454
Curator: @scibot
SciCrunch record: RRID:Addgene_8454
RRID:AB_390913
DOI: 10.1016/j.cell.2026.06.017
Resource: (Roche Cat# 11814460001, RRID:AB_390913)
Curator: @scibot
SciCrunch record: RRID:AB_390913
RRID:AB_262051
DOI: 10.1016/j.cell.2026.06.017
Resource: (Sigma-Aldrich Cat# H3663, RRID:AB_262051)
Curator: @scibot
SciCrunch record: RRID:AB_262051
RRID:AB_439702
DOI: 10.1016/j.cell.2026.06.017
Resource: (Sigma-Aldrich Cat# A8592, RRID:AB_439702)
Curator: @scibot
SciCrunch record: RRID:AB_439702
RRID:AB_675659
DOI: 10.1016/j.cell.2026.06.017
Resource: (Santa Cruz Biotechnology Cat# sc-13119, RRID:AB_675659)
Curator: @scibot
SciCrunch record: RRID:AB_675659
RRID:AB_2818986
DOI: 10.1016/j.cell.2026.06.017
Resource: (BioLegend Cat# 422302, RRID:AB_2818986)
Curator: @scibot
SciCrunch record: RRID:AB_2818986
RRID:AB_314879
DOI: 10.1016/j.cell.2026.06.017
Resource: (BioLegend Cat# 311410, RRID:AB_314879)
Curator: @scibot
SciCrunch record: RRID:AB_314879
RRID:SCR_017797
DOI: 10.1016/j.cell.2026.06.017
Resource: Rockefeller University Proteomics Resource Center Core Facility (RRID:SCR_017797)
Curator: @scibot
SciCrunch record: RRID:SCR_017797
RRID:AB_2616449
DOI: 10.1016/j.cell.2026.06.017
Resource: (Sigma-Aldrich Cat# F2426, RRID:AB_2616449)
Curator: @scibot
SciCrunch record: RRID:AB_2616449
RRID:AB_2536982
DOI: 10.1016/j.cell.2026.06.012
Resource: (Thermo Fisher Scientific Cat# MA1-21315-1MG, RRID:AB_2536982)
Curator: @scibot
SciCrunch record: RRID:AB_2536982
RRID:AB_621842
DOI: 10.1016/j.cell.2026.06.012
Resource: (LI-COR Biosciences Cat# 926-32210, RRID:AB_621842)
Curator: @scibot
SciCrunch record: RRID:AB_621842
RRID:AB_262044
DOI: 10.1016/j.cell.2026.06.012
Resource: (Sigma-Aldrich Cat# F1804, RRID:AB_262044)
Curator: @scibot
SciCrunch record: RRID:AB_262044
RRID:CVCL_1107
DOI: 10.1016/j.cell.2026.06.010
Resource: (BCRJ Cat# 0326, RRID:CVCL_1107)
Curator: @scibot
SciCrunch record: RRID:CVCL_1107
RRID:SCR_022621
DOI: 10.1007/s12035-026-06022-4
Resource: Cincinnati Children's Hospital Animal Behavior Core Facility (RRID:SCR_022621)
Curator: @scibot
SciCrunch record: RRID:SCR_022621
RRID:AB_2722623
DOI: 10.1007/s11011-026-01917-6
Resource: (Abcam Cat# ab150079, RRID:AB_2722623)
Curator: @scibot
SciCrunch record: RRID:AB_2722623
RRID:AB_2843859
DOI: 10.1007/s11011-026-01917-6
Resource: RRID:AB_2843859
Curator: @scibot
SciCrunch record: RRID:AB_2843859
RRID:AB_2941855
DOI: 10.1007/s11011-026-01902-z
Resource: (Abways Technology Cat# AB0101, RRID:AB_2941855)
Curator: @scibot
SciCrunch record: RRID:AB_2941855
RRID:AB_10700003
DOI: 10.1007/s11011-026-01902-z
Resource: (Proteintech Cat# 20536-1-AP, RRID:AB_10700003)
Curator: @scibot
SciCrunch record: RRID:AB_10700003
BL23
DOI: 10.1007/s11011-026-01902-z
Resource: RRID:BDSC_23
Curator: @scibot
SciCrunch record: RRID:BDSC_23
RRID:AB_2289842
DOI: 10.1007/s11011-026-01902-z
Resource: (Proteintech Cat# 16001-1-AP, RRID:AB_2289842)
Curator: @scibot
SciCrunch record: RRID:AB_2289842
RRID:AB_3718139
DOI: 10.1007/s11011-026-01902-z
Resource: RRID:AB_3718139
Curator: @scibot
SciCrunch record: RRID:AB_3718139
RRID:AB_2893015
DOI: 10.1007/s11011-026-01902-z
Resource: (Beyotime Cat# A0516, RRID:AB_2893015)
Curator: @scibot
SciCrunch record: RRID:AB_2893015
RRID:AB_2861791
DOI: 10.1007/s11011-026-01902-z
Resource: RRID:AB_2861791
Curator: @scibot
SciCrunch record: RRID:AB_2861791
RRID:AB_3085549
DOI: 10.1007/s11011-026-01902-z
Resource: (Proteintech Cat# 18038-1-AP, RRID:AB_3085549)
Curator: @scibot
SciCrunch record: RRID:AB_3085549
RRID:AB_2223933
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 14550-1-AP, RRID:AB_2223933)
Curator: @scibot
SciCrunch record: RRID:AB_2223933
RRID:AB_2133325
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 10712-1-AP, RRID:AB_2133325)
Curator: @scibot
SciCrunch record: RRID:AB_2133325
RRID:AB_2195801
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 11064-1-AP, RRID:AB_2195801)
Curator: @scibot
SciCrunch record: RRID:AB_2195801
RRID:AB_2247214
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 10849-1-AP, RRID:AB_2247214)
Curator: @scibot
SciCrunch record: RRID:AB_2247214
RRID:AB_2167545
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 11263-1-AP, RRID:AB_2167545)
Curator: @scibot
SciCrunch record: RRID:AB_2167545
RRID:AB_2881210
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 28755-1-AP, RRID:AB_2881210)
Curator: @scibot
SciCrunch record: RRID:AB_2881210
RRID:AB_1607719
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 14295-1-AP, RRID:AB_1607719)
Curator: @scibot
SciCrunch record: RRID:AB_1607719
RRID:AB_2756525
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 27309-1-AP, RRID:AB_2756525)
Curator: @scibot
SciCrunch record: RRID:AB_2756525
RRID:AB_2882072
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 66721-1-Ig, RRID:AB_2882072)
Curator: @scibot
SciCrunch record: RRID:AB_2882072
RRID:AB_3670995
DOI: 10.1007/s10565-026-10194-z
Resource: RRID:AB_3670995
Curator: @scibot
SciCrunch record: RRID:AB_3670995
RRID:AB_2148585
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 10828-1-AP, RRID:AB_2148585)
Curator: @scibot
SciCrunch record: RRID:AB_2148585
RRID:AB_10640807
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 11880-1-AP, RRID:AB_10640807)
Curator: @scibot
SciCrunch record: RRID:AB_10640807
RRID:AB_2107436
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 60004-1-Ig, RRID:AB_2107436)
Curator: @scibot
SciCrunch record: RRID:AB_2107436
RRID:AB_2716755
DOI: 10.1007/s10565-026-10194-z
Resource: (Proteintech Cat# 17168-1-AP, RRID:AB_2716755)
Curator: @scibot
SciCrunch record: RRID:AB_2716755
RRID:SCR_007000
DOI: 10.1007/s10528-026-11424-z
Resource: Database of Genomic Variants (RRID:SCR_007000)
Curator: @scibot
SciCrunch record: RRID:SCR_007000
RRID:SCR_012773
DOI: 10.1007/s10528-026-11424-z
Resource: KEGG (RRID:SCR_012773)
Curator: @scibot
SciCrunch record: RRID:SCR_012773
RRID:SCR_014467
DOI: 10.1007/s10528-026-11424-z
Resource: FunRich: Functional Enrichment analysis tool (RRID:SCR_014467)
Curator: @scibot
SciCrunch record: RRID:SCR_014467
RRID:SCR_006786
DOI: 10.1007/s10528-026-11424-z
Resource: WebGestalt: WEB-based GEne SeT AnaLysis Toolkit (RRID:SCR_006786)
Curator: @scibot
SciCrunch record: RRID:SCR_006786
RRID:SCR_006552
DOI: 10.1007/s10528-026-11424-z
Resource: DECIPHER (RRID:SCR_006552)
Curator: @scibot
SciCrunch record: RRID:SCR_006552
RRID:SCR_016479
DOI: 10.1007/s10528-026-11424-z
Resource: IBM SPSS Statistics (RRID:SCR_016479)
Curator: @scibot
SciCrunch record: RRID:SCR_016479
RRID:Addgene_133773
DOI: 10.1007/s10123-026-00846-9
Resource: RRID:Addgene_133773
Curator: @scibot
SciCrunch record: RRID:Addgene_133773
RRID:SCR_022718
DOI: 10.1007/s00248-026-02806-2
Resource: University of Warsaw Center of New Technologies Genomics Core Facility (RRID:SCR_022718)
Curator: @scibot
SciCrunch record: RRID:SCR_022718
RRID:SCR_024502
DOI: 10.1002/mgg3.70255
Resource: Mutation Assessor (RRID:SCR_024502)
Curator: @scibot
SciCrunch record: RRID:SCR_024502
RRID:SCR_001905
DOI: 10.1002/ijc.70581
Resource: R Project for Statistical Computing (RRID:SCR_001905)
Curator: @scibot
SciCrunch record: RRID:SCR_001905
RRID:SCR_008567
DOI: 10.1002/ijc.70581
Resource: Statistical Analysis System (RRID:SCR_008567)
Curator: @scibot
SciCrunch record: RRID:SCR_008567
RRID:AB_615042
DOI: 10.1002/dad2.70411
Resource: (Proteintech Cat# 10782-2-AP, RRID:AB_615042)
Curator: @scibot
SciCrunch record: RRID:AB_615042
RRID:SCR_001622
DOI: 10.1002/cne.70181
Resource: MATLAB (RRID:SCR_001622)
Curator: @scibot
SciCrunch record: RRID:SCR_001622
RRID:SCR_002526
DOI: 10.1002/cne.70181
Resource: Stereo Investigator (RRID:SCR_002526)
Curator: @scibot
SciCrunch record: RRID:SCR_002526
RRID:SCR_013672
DOI: 10.1002/cne.70181
Resource: ZEISS ZEN Microscopy Software (RRID:SCR_013672)
Curator: @scibot
SciCrunch record: RRID:SCR_013672
RRID:SCR_010279
DOI: 10.1002/cne.70181
Resource: Adobe Illustrator (RRID:SCR_010279)
Curator: @scibot
SciCrunch record: RRID:SCR_010279
RRID:AB_10000320
DOI: 10.1002/cne.70181
Resource: (Swant Cat# 6B3, RRID:AB_10000320)
Curator: @scibot
SciCrunch record: RRID:AB_10000320
RRID:AB_2609696
DOI: 10.1002/cne.70181
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Be aware that relationships develop early
L'inizio è fondamentale
Utilize impression management tactics
Flattering when you agree, disagree when your idea is different
careers
Importance of career development
positive work environment
lack
All you need is links
All you need is permanent human meaningful names and structures composed of named collections and items
They imply structural links and the ability of linking
Inbound links can be used as a signal of quality
inbound.links.quality

Object metaphors are powerful
familiar new
designing tools for thought
The task for designing tools for thought
are treating human information management needs as universal problems
Yes the idea of the link is simple, simply understood but impose an complex intricate imlicate order integration a multitude of integral omni optioanl concerns
Complex rules
We need the ability to formulate intricate mutual emerging intricate rules that start simple and can be grown organically adjacent next possibilities
eventually producing complex behavior simply composed from simply clrear and distinct explicate intentional well understood interplays
rules produce
rules of composition and constructions grounded in naming conventions and implicate order and universal behaviour
simple set of primitives
but now that we have the primitives that are actually working on machines
we need to design complex holonic capabilities whose interplay can be orchestrated to give us what we want
That implies a notation that helps us formulate what we desired
such that those description are amenable to the PUN to be interpreted by complex networks of capabilities that gives uswhat we intended and formulated
in terms that human understand and machines can execute/exhiit behaviour interactions info construct present in a form that human can comprehened and interact with sepcific intents and purposes
eLife Assessment
The authors describe a valuable finding that the Streptococcus pyogenes secreted protease SpeB is expressed in response to protease activity that degrades the Vfr repressor. Proteases can be released from host neutrophils (possibly by NETosis), as well as a positive feedback mechanism by SpeB itself. The authors utilize a dual fluorescent reporter system to simultaneously read speB and capsule gene expression, providing solid evidence that demonstrates that proteases can regulate Vfr; however, the data indicating that this is physiologically relevant and that extracellular traps themselves have a functional role are incomplete. This work will be of interest to microbiologists studying the regulation of virulence factors at the host-pathogen interface.
Reviewer #1 (Public review):
Summary:
This manuscript examines how Streptococcus pyogenes regulates expression of the virulence factor SpeB in response to both bacterial and host-derived cues. The authors propose that Vfr acts as a repressor of speB expression and that degradation of Vfr by SpeB or by neutrophil-derived proteases relieves this repression. This creates a model in which S. pyogenes can sense proteolytic activity during infection and use that information to tune virulence factor expression.
Strengths:
The main strength of the study is the bacterial regulatory mechanism. The dual reporter system provides a useful way to follow speB and hasABC expression, and the genetic analysis of known regulators helps validate the system. The media-swap experiments, recombinant Vfr experiments, and SpeB-mediated degradation of Vfr support the conclusion that Vfr represses speB and that proteolysis can relieve this repression. The finding that SpeB can degrade Vfr is particularly interesting because it suggests an autoregulatory mechanism that could reinforce SpeB expression once it has been initiated.
Weaknesses:
The host side of the model is less completely supported. The authors show that neutrophil lysates and protease-containing fractions can induce the speB reporter and degrade Vfr, which supports the idea that neutrophil-derived proteases can affect this circuit. However, the in vivo interpretation relies heavily on PAD4-deficient mice to implicate neutrophil extracellular traps. PAD4 deficiency is a useful perturbation, but it does not by itself distinguish loss of extracellular trap formation from changes in neutrophil recruitment, survival, degranulation, phagocytosis, oxidative killing, or other neutrophil death pathways. As a result, the current data support a role for neutrophil-associated proteolytic activity more strongly than they support a specific role for extracellular traps. This distinction is important for interpreting the central model. The bacterial circuit is well developed, but the host-derived cue remains somewhat underdefined. If the relevant signal is extracellular protease activity more broadly, then the model is still interesting, but the conclusion should be framed around neutrophil-derived proteolytic stress rather than extracellular traps specifically. If extracellular traps are the key in vivo source of protease exposure, then additional evidence would be needed to separate that mechanism from other neutrophil effector functions that remain intact in PAD4-deficient cells.
Overall:
This is a valuable study with solid evidence for a bacterial protease-sensing regulatory mechanism controlling SpeB expression. The work should be useful to investigators interested in bacterial virulence regulation, host-pathogen interactions, and how pathogens integrate immune-derived cues during infection. The impact of the study would be stronger if the host-derived signal were defined more precisely, but the bacterial Vfr-SpeB circuit provides a compelling framework for thinking about how S. pyogenes links proteolytic activity to virulence gene expression.
Reviewer #2 (Public review):
Summary:
The study examines how Streptococcus pyogenes integrates bacterial and host-derived signals to regulate SpeB, proposing that Vfr acts as a protease-sensitive repressor whose degradation relieves repression of speB. The authors further suggest that neutrophil-derived serine proteases, including those associated with inflammatory conditions, may promote this transition, and thereby counterbalance LL-37/CovRS-associated suppression of speB. The conceptual framework is interesting and potentially important for understanding how host inflammation feeds into bacterial virulence regulation.
Strengths:
The work addresses a biologically significant question and does so using a broad and generally well-integrated experimental approach, including bacterial genetics, reporter assays, recombinant protein analyses, neutrophil-derived material, human blood infection, and mouse infection models. A particular strength is the effort to connect host inflammatory processes to bacterial regulatory behavior, which gives the study conceptual reach beyond a narrow mechanistic observation. The data support the view that Vfr is relevant to speB control and that neutrophil-associated protease activity may influence this pathway.
Weaknesses:
The main limitations are mechanistic. The physiological form, localization, and abundance of Vfr are not sufficiently defined to support the proposed model at full strength, and the evidence that Vfr functions as a SpeB-labile repressor under biologically relevant conditions remains incomplete. The relationship between Vfr and the broader RopB/SIP regulatory framework is also not yet firmly established. In addition, the reporter system is not yet benchmarked closely enough against endogenous SpeB protein output, and its growth-phase dependence is insufficiently resolved, which makes it difficult in some settings to distinguish promoter activity from mature protease production. The neutrophil protease component is likewise not defined beyond a general serine protease signal, and the potentially important LL-37/CovRS/Vfr connection is underdeveloped in the main text. Overall, the conceptual advance is promising, but several of the central mechanistic claims would benefit from more direct experimental support and more cautious framing.
Reviewer #3 (Public review):
Summary:
SpeB is a cysteine protease secreted during infection by Streptococcus pyogenes (Spy). SpeB has been extensively investigated for its role in pathogenesis, which involves proteolytic processing of both Spy virulence factors and host proteins. Regulation of speB expression is complex and includes growth phase regulation, a quorum-sensing system, the transcription factor RopB, and the global regulatory system CovRS (CsrRS). Guerra et al now attempt to refine the current model of regulation of SpeB expression, focusing on the Spy protein Vfr, which has been suggested previously to act as a negative regulator of SpeB expression. In the current study, neutrophil lysates (representing proteases released during NETosis) are shown to degrade Vfr and to relieve repression of SpeB. At high cell density, SpeB itself also degrades Vfr, which may allow autoregulation of SpeB expression. These observations are unsurprising as the broad protease activities of both neutrophil proteases and SpeB are well known. Nonetheless, the data presented fill in additional details in our understanding of the complex regulation of an important Spy virulence factor.
Strengths:
(1) Construction of a GFP reporter strain provided a facile methodology for tracking speB promoter activity in a variety of experimental setups.
(2) A Vfr deletion mutant was a useful tool to investigate the role of Vfr in SpeB regulation, and mutants in speB and ropB were important controls.
(3) Experiments using neutrophil lysates in vitro, as well as in vivo studies of mice depleted of neutrophils with anti-Ly6G or in PAD4-/- mice (that cannot form NETs) support the hypothesis that neutrophil proteases derepress speB expression by degrading Vfr.
Weaknesses:
(1) The introduction and all the experiments in Figure 1 focus on CovRS, which turns out to be largely tangential to the overall story developed by the rest of the study. On the other hand, the complex and well-studied regulation of speB expression by RopB and the SIP quorum-sensing system is only minimally described. A better framing would be a more detailed introduction to the current model of speB/RopB/SIP/quorum sensing/growth phase regulation. CovRS could be introduced later as its relevance is really just to show that neutrophil lysates or NETs do more than simply providing LL-37, which signals through CsrS, as another regulator of speB expression.
(2) Vfr, as the central focus of the paper, also deserves a more thorough introduction to provide context for the study. For example, reference 19 (Shelburne et al, 2011) showed reduced transcription of speB in a vfr mutant, an effect that could be complemented by expressing vfr or a 39-aa N-terminal fragment in trans. That study presented evidence that the N-terminal peptide binds to RopB, which may prevent RopB from upregulating SpeB expression. Do the authors concur with that model? As it stands, the discussion and model in Figure 1A imply a direct regulatory effect of Vfr on speB expression rather than an indirect one through regulation of RopB. If direct regulation of speB by Vfr is a consideration, it should be investigated more thoroughly, e.g., by promoter-binding assays, CHIP-seq, etc.
(3) Use of single-cell flow cytometry generally confirmed results observed in batch culture. The authors also comment repeatedly on the heterogeneity of individual cell fluorescence representing both speB and has operon expression. However, the reason(s) for heterogeneity in gene expression are not explored, e.g., differences in individual cell growth rate in batch culture, variable loss of reporter plasmid during infection experiments, etc).
(4) Lines 116-118 and Figure 3C: Incubation of recombinant Vfr with Spy Dvfr reduced SpeB expression, but the degree of suppression is modest compared to that seen in wild-type Spy. How does the concentration of rVfr added compare to that present in the culture fluid of wild-type Spy? (Also, the concentration of rVfr used is unclear: the figure says 3 µg/ml and the legend says 0.3 mg/ml, i.e., 300 µg/ml).
(5) Lines 125-126: "...the Vfr structure contains several potential protease SpeB cleavage sites..." The role of Vfr in degrading SpeB could be clarified by identifying the predicted cleavage products, e.g., by mass spec, after co-incubation of the two recombinant proteins.
(6) Lines 122-124: "Notably, speB expression in Spy Dvfr is unaffected by LL-37 or MgCl2, further validating its [Vfr's?] dominance over CovRS regulation." This statement is an oversimplification and is potentially misleading: LL-37 is degraded by SpeB (Nyberg et al, JBC 2004), which likely explains why the addition of LL-37 fails to signal through CovRS to repress SpeB in Spy Dvfr since SpeB is produced continuously in that strain. By contrast, SpeB is only produced during the stationary phase in the wild type, so LL-37 remains active throughout the exponential phase and represses SpeB expression. The response to the CovRS ligand MgCl2 is similar (or greater) in Spy Dvfr compared to wild type (Figure S2C).
(7) Lines 153-154 and Figure 6E: Growing wild type Spy in the presence of neutrophil lysates with or without a protease inhibitor stimulated or repressed speB expression in a manner consistent with degradation (or not) of Vfr. It would be confirmatory and informative to do the same experiment with the Spy Dvfr strain.
(8) Clarity of writing could be improved, particularly by eliminating pronouns of indefinite reference (it, its, this) in contexts in which the subject is ambiguous (examples at lines 62, 89, 111, 114, 115, 123, 183, 190, 193, 204, 205, 210, 217, 221, 222, 224).
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Cancer is associated with profound changes in metabolism and a deprivation of nutrients, including an overall reduction in amino acids. This research group has previously demonstrated that amino acid deprivation leads to ECM uptake that drives breast cancer cell migration and growth, but the mechanisms that drive this ECM scavenging are unknown. Here, the demonstrate that the collagen receptor integrin a2 is upregulated in cells following AA starvation in cells cultured in 2D and 3D. This upregulation is promoted by Ras/MEK signalling and was required for collagen uptake by cells. Furthermore, amino acid starvation was shown to promote cell adhesion to collagen-I and cell migration, highlighting the functional importance of this pathway.
The data presented and approaches used are clear and well executed with appropriate conclusions made from the data. I have a few questions and suggestions that would help clarify the findings of the study and potentially increase the impact of the study:
I think addressing all of these points is optional as they will help strengthen the study but will not alter the conclusions drastically.
The text and figures are very clear and work in the field is cited appropriately.
The findings from this study are significant to a variety of cancer types where changes in metabolism and nutrient availability are prevalent. The findings suggest that targeting a2 integrin may be a valid treatment option in pancreatic and breast tumours, particularly in relation to those with KRAS mutations which increases the significance of this study significantly and paves the way for future research that will presumably use patient-derived cells to assess some of the pathways identified.
This study will be of interest to people working in basic research, cancer research and potentially translational research.
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This well-prepared manuscript describes the results of a study interrogating the mechanisms regulating amino acid-starvation-driven ECM uptake via alpha-2-integrin. The experiments are carefully carried out and analysed, and my comments mainly concern some analyses that are in my view lacking to fully enable the conclusions drawn.
Specific comments
Fig. 1. Maybe I am just missing something, but I do not understand the collagen-I labeling procedure: pH-rodo is pH sensitive, with increasing fluorescence at acidic pH values. It can therefore be used to assess endo-lysosomal pH values - but how do you distinguish in your collagen-I uptake index between more collagen taken up, vs localization in more acidic compartments? It seems this could introduce confounding effects. Please comment, and consider validating with a non-pH sensitive fluorophore.
Fig. 2. There is a very dramatic difference between the increase in ITGA2 mRNA levels (up to 200-fold) and protein levels (max 2-fold) in starvation conditions. Would protein levels increase more substantially upon longer treatments? This deserves at least commenting, ideally testing.
Fig. 3. It is very nice that the authors emply a matrigel 3D culture, but this is still a very artificial scenario, and matrigel does not really mimic the tumor ECM. To what extent is the same mechanism required for cancer cell survival in a more realistic tumor environment - i.e. with more complex ECM and/or additional (stromal) cell types present - which would alter the nutrient landscape? It would be very valuable to conduct experiments addressing this, but at least it should be discussed in more detail.
Fig. 4. The authors convincingly show that the GCN2 pathway is not responsible for the ITGA2 regulation. This is a very nice opportunity to gain insight into the relative importance of ITGA2 for cancer cell survival under nutrient starvation - how much is growth affected by the GCN2 inhibition relative to by interfering with ITGA2?
Perhaps I missed it, but can you comment on the possible relation and/or relative importance of the mTOR-inhibition-driven and RAS-driven pathways of ITGA2 regulation? If RAS signaling is important for the starvation-driven increase in ITGA2, does that imply that starvation further activates RAS signaling in cells which already harbor an oncogenic RAS mutation and thus presumably have constitutively increased RAS/MEK/MAPK signaling? Should ITGA2 not be consitutively upregulated in these cells if it was driven by RAS? Or is RAS just necessary, not the driver? Can the effect of starvation on ITGA2 be mimicked by introducing a constitutively active RAS in cells not harboring such mutations?
Fig. 6. It would be very helpful to show whether this increased migration and spreading is dependent on the RAS-dependent increase in ITGA2 (use a RAS inhibitor) and/or can be mimicked by overexpression of ITGA2? And a suggestion: the quantified difference in F is very small (about 0.4 vs 0.45), whereas the image indicates that the gap is essentially closed in the AA condition. I would replace this with a more representative example.
Fig. 7. Similar to my concern above, can you distinguish between effects of the MEKi on endo-lysosomal pH vs on collagen uptake?
Minor
P 1, a5b1 intergin -> integrin
Maybe not a huge advance but a carefully performed and clearly relevant study which contributes new information and is clearly deserving of publication.
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Summary:
In this study, Yanes et al. used several cell lines from pancreatic and breast cancer to show that amino-acid starvation induces ECM uptake via RAS-MEK signaling pathway-dependent increase of integrin α2 expression at the RNA and the protein level, using RAS and MEK inhibitors. They also showed that this pathway enhances cell adhesion and that AA starvation promotes migration on collagen. Patient data analysis indicated a correlation between high integrin α2 expression and poor prognosis in pancreatic cancer.
Major comments:
While the key conclusions are mostly convincing, some require additional experiments to be fully supported, and some additional discussion would help to clarify some points, as explained in the comments bellow. The data are clearly presented and properly analyzed and replicated with the adequate statistical analysis. The methods are clear enough to ensure reproducibility.
1) When observing collagen uptake, it would be useful to have a staining of endosomal markers to show that collagen is internalized in endosomes. Otherwise, the observed structures can only be named "vesicles" and not "endosomes".
2) In figure 1D, the proliferation is tested only by counting cells after 4 days. We cannot exclude that the difference in cell numbers is due to a difference in cell viability or to a difference of the number of cells which initially attached (as it is shown in figure 6C). Testing proliferation with a proliferation assay such as Brdu incorporation assay or at least testing the cell viability would ensure that AA starvation improves proliferation on collagen.
3) Only one siRNA has been used to knockdown integrin α2. A second siRNA should be used to ensure that the observed effects of the knockdown are not due to off-target effects.
4) In figure 2B-F, it seems that the integrin α2 levels differ between the cell lines, both at the baseline level and after starvation. Do these differences correlate with different capacities of each cell line to internalize collagen in response to AA starvation? This should be discussed.
5) In conclusion of figure 2, it is said that "a3, a5, and a6 integrin were not affected" by AA deprivation, and this is written again in the discussion. However, Figure S1K shows that integrin α5 expression also increases in this condition in SW1990 cells, as written in the text when describing this figure. The conclusion should be changed to include this result, and it would be nice to discuss it. Would fibronectin internalization in response to starvation also happen in this cell line?
5) In figure 3 it seems from the images that integrin α2 is increased only in cell-cell adhesions but not on the edge of the spheroids in contact with the matrix. Is this something consistent and could this be quantified? If the increase is only happening inside the spheroids, it seems unlikely that it would have a role in matrix uptake in 3D. Moreover, as figure 6D does not show any difference of cell adhesion to Matrigel in complete media vs under AA starvation, doing the 3D experiments in collagen rather than Matrigel would give a better insight in the significance of the uptake mechanism in 3D. Even if the observation of integrin α2 expression changes in 3D is suggesting a similar mechanism in 3D than in 2D, observing collagen uptake in 3D would ensure that the role of this pathway is the same than in 2D. This could be done either by staining for collagen in Matrigel or by embedding the cells inside fluorescent collagen. Finally concerning the 3D data, in Figure 3 the size of the spheroids is quantified but no conclusion is drawn from the observed difference. Quantifying the number of cells would give a better readout of the differences in proliferation.
6) Figure S3 shows that the MRTX1133 KRASG12D inhibitor decreases expression of integrin α2 in SW1990 cells but not in PANC1 cells, and it is speculated that this difference is due to the heterozygous status of PANC1 cells for KRAS. Using a pan-RAS inhibitor would be useful in PANC1 cells to confirm this hypothesis.
7) The finding in Figure 6D that AA starvation does not impact adhesion on Matrigel is surprising, as we would expect Matrigel to contain collagen. This should be further discussed. As SW1990 also express higher levels of integrin α5 in response to starvation, looking at adhesion on fibronectin should be done to interrogate if this mechanism is specific to collagen binding only.
8) The functional experiments of Figure 6 show nicely that AA starvation improves cell adhesion on collagen and migration under a collagen overlay. However, this does not show if the uptake of collagen itself is involved there. Blocking endocytosis would show if collagen uptake is necessary for the observed phenotype, or if it is only due to the higher expression of integrin α2 which by itself enhances cell adhesion and migration. Using in the migration experiment RAS and MEK inhibitors is also necessary to show that the same pathway is involved in migration to exclude that AA starvation would impact these via a different pathway, as it has been done for adhesion in Figure 7A. As the migration is emphasized in the title, I would also expect to see this experiment on other cell lines.
9) While most experiments are performed on SW1990 cells, the collagen uptake experiment under MEK inhibition of Figure 7B was done only on MCF10A cells. This should be done on the SW1990 cells as well for consistency.
10) If possible, showing correlation between KRAS mutation status and integrin α2 expression in patient data would reinforce the conclusions on the clinical significance of the mechanism. As the study includes breast cancer cell lines, showing the correlation between integrin α2 expression and survival in breast cancer patients would also provide better insights on the relevance of the mechanism in different cancer types.
Minor comments:
1) A reference is missing to cite the origin of the BTT-3033 inhibitor. I would suggest to cite Nissinen et al. Journal of Biochemistry 2012 (DOI: 10.1074/jbc.M111.309450).
2) In figure 2L-M the 37kDa mark is not placed at the same height for all the GAPDH bands. I suppose it is an issue of the figure design rather than an issue on the blot itself and this should be corrected. Without the uncropped blots showing the ladder it is however not possible to assess if the bands are really at the indicated size, these should be provided.
3) In Figure 6F, the graph should indicate individual values.
This study follows previous findings by the same group and others who showed that ECM uptake promotes tumors cells survival and proliferation in a low-nutrient availability context, and that the integrin α2β1 allows collagen uptake. The new findings here link these two observations as AA starvation is shown to upregulate integrin α2, leading to pro-oncogenic phenotypes. The study provides a mechanism for this, as they show the involvement of the RAS-MAPK pathway. The point made in the discussion that depending on the ERK inhibitor used or the duration of the inhibition might lead to different effects on integrin α2 levels, added to the discrepancies between cell lines, suggests that the described mechanism might not be universal or is relevant only in specific conditions.
This is still an interesting and important conceptual and mechanistic advance, which will be of interest for researchers in the fields of cancer (here pancreas and breast cancer), cell adhesion and signaling, mostly for basic research but potential clinical implications might be of interest for a broader audiance.<br /> My expertise relevant to this study is in cancer cell biology, cell adhesion, migration and signaling.
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OK!
eLife Assessment
The study presents valuable findings of a new E. coli cell-free protein synthesis (eCFPS) system that has been simplified by reducing the number of core components from 35 to 7; furthermore, the findings communicate a simplified 'fast lysate' preparation that eliminates the need for traditional runoff and dialysis steps. It is interesting that the system's robustness is exhibited by its applicability to nanoluc, a protein that expresses readily in many systems, to more challenging proteins like the functional self-assembling vimentin and the active restriction endonuclease Bsal. Despite the study representing an advancement towards simplifying protein expression workflows, the evidence is solid and supports the main claims however minor weakness exists i.e. the efficiency claims about the new system needs to be supported by accurate comparisons with typical cell free expression systems, in addition, investigations into the mechanistic basis of the observations would provide more evidence. Despite this shortcoming, the paper remains of interest to scientists in cell and molecular biology, microbiology, biotechnology and protein synthesis.
Reviewer #1 (Public review):
Summary:
The authors presented a simplified E. coli cell-free protein synthesis (eCFPS) system reduces core reaction components from 35 to 7, improving protein expression levels. They also presented a "fast lysate" protocol that simplifies extract preparation, enhancing accessibility and robustness for diverse applications.
Strengths:
The authors present a valuable new protocol for eCFPS, which simplifies its application.
Weaknesses:
The authors provide data for optimization but offer insufficient explanation of the fundamental mechanisms underlying the phenomenon based on data.
Comments on revised version.
The authors have satisfactorily addressed the concerns raised by the reviewers. However, the mechanistic basis of the observed performance gain remains insufficiently substantiated. The attribution of this improvement to enhanced transcription is currently speculative. This point could be directly tested by quantifying mRNA levels, for example, using real-time PCR, in both the initial and optimized systems. Such analysis would significantly strengthen the mechanistic interpretation of the results.
Reviewer #2 (Public review):
Summary:
The authors have made a convincing argument that the current system of in vitro translation using E. coli extracts can be significantly optimized to work with much lesser components, while maintaining activity. They have showcased their improved activity using not only physical but also functional readouts.
Strengths:
The experiments are designed in a very logical and easy to understand manner, which makes it easier not only to follow the paper, but also reproduce the results. Functional assays with the synthesized proteins are a good way to demonstrate functionality and applicability of the system. They also benchmark their system against a commercial kit to show superior performance of their system.
Weaknesses:
The production of the lysate requires special instrumentation, limiting accessibility.
Comments on revised version:
Thank you to the authors for addressing the concerns both textually and experimentally. This work has significant value.