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Immune differentiation regulator p100 tunes NF-κB responses to TNF Budhaditya Chatterjee,1, 2* Payel Roy,1, # * Uday Aditya Sarkar,1 Yashika Ratra,1 Meenakshi Chawla,1 James Gomes,2 Soumen Basak1§ 1Systems Immunology Laboratory, National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi-110067, India. 2Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, India. #Current address: La Jolla Institute for Allergy and Immunology, USA *These authors contributed equally to this work. §Corresponding author. Email: [email protected] Supplementary Information I. Supplementary Fig. S1 – Fig. S4 and corresponding figure legends II. Supplementary Tables III. Detailed description of global gene expression analyses IV. A description of the mathematical model and related parameterization V. Supplementary References 1 I. Supplementary Fig. S1 – Fig. S4 and corresponding figure legends a Theoretical IKK2 activity inputs b Theoretical IKK2 activity inputs of varying peak amplitude of varying durations 60 100 50 30 NEMO-IKK2(nM) 0 NEMO-IKK2 (nM) 0 simulated NF-κB activities simulated NF-κB activities 100 100 (nM) (nM) 50 50 Bn Bn κ κ NF NF 0 0 0 2 4 6 8 0 2 4 6 8 time (hr) time (hr) c experimentally-derived IKK2 activity inputs 100 TNFc TNFp 50 NEMO-IKK2 activity(nM) 0 0 2 4 6 8 0 2 4 6 8 time (hr) Figure S1: In silico studies of the NF-κB system: a) A library of twelve theoretical IKK2 activity profiles with an invariant signal duration of 8 hr and peak amplitude uniformly varying from 10 nM to 100 nM was used as model inputs (top) for simulating NF-κBn responses in a time course (bottom). These IKK2 activity profiles had identical 0.5 hr onset time. b) Thirteen IKK2 activity profiles with an identical 60 nM peak amplitude but total duration varying from 10 min to 480 min were similarly used in our simulation studies. We additionally used two IKK2 profiles with 60 nM peak amplitude, 5 min onset time and a total 10 min of duration or 60 nM peak amplitude, 10 min onset time and 20 min of duration as inputs. c) Plots showing interpolated IKK2 activity curves generated using experimental data obtained from MEFs subjected to TNFc or TNFp stimulations (Shih et al., 2009). 2 Nfkb2-/- MEFs a b Ab Nfkbia-/- Nfkb2-/- (-) αRelA αRelB TNFc 0 .5 1 3 8 0 .5 1 3 8 hr TNFp 0 .5 1 3 8 16 0 .5 1 3 8 16 0 .5 1 3 8 16 hr NF-κBn NF-κBn Oct1 Oct1 c 30min pulse duration Nfkb2-/- MEFs RelA cRel RelB Ab (-) α α α all RelA RelB IL-1β, 8hr Figure S2: Analyzing TNF-induced NF-κB signaling in mutant cells: a) EMSA comparing NF-κBn induced in a time-course in Nfkbia-/- and Nfkb2-/- MEFs in response to TNFc. The data represents three independent experiments. b) EMSA demonstrating total, RelB-containing and RelA-containing NF-κB activities induced in a time-course in response to TNFp. Dynamical RelB activity was unmasked in EMSA by ablating RelA DNA binding with an anti-RelA antibody. Similarly, RelA activity was unravelled by ablating RelB DNA binding. The data represents two biological replicates. c) Composition of NF-κBn induced after 8 hr of IL-1β treatment in Nfkb2-/- MEFs was determined in the shift-ablation assay. The data represents two biological replicates. a b 8 hr post-TNFp stimulation 1 * p<0.05 * 3.2 Relb 2.4 * *** *** 1.6 0 0.8 * ** * * *** relative * ** 0X TNFp 5X TNFp mRNA abundance mRNA 0 standardized B tg RelB κ promoter const. -1 NF- regressioncoefficient 1 10 20 30 40 parameter groups responsive -/- Relb -/- Nfkb2 Figure S3: Investigating the mechanism underlying late-acting RelB:p50 response to TNFp in the absence of p100: a) Graph plot describing the standardized regression coefficients of the parameter groups subjected to the Variance-based multiparametric sensitivity analysis. The asterisk indicates statistically significant deviation (P <0.001) from the null sensitivity for certain parameter groups, as determined by two-tailed Student’s t test. b) qRT-PCR analysis revealing the expression of Relb mRNA after 8 hr of the -/- -/- commencement of TNFp treatment in Relb Nfkb2 MEFs stably expressing RelB from a transgene (tg) either constitutively (const.) or from an NF-κB responsive promoter (top panel). Data are means ± SEM of four biological replicates. 3 Nfkb2-/- MEFs a b 0 1 3 Nfkb2-deficient 2 ×10 time ( 4 3 4 5 hr (AU) 6 2 ) measuredat6-8 7 cell 8 harvesting nRelB u tp sp 0 dp 2 6 p<0.05 4 Relb Relb 2 1 mRNA 0 sp dp tp hr 0 total after 1 mRNA (AU) mRNA 0 RelB ×103 9 RelA st proteins 7 TNFp Actin 5 0 sp dp tp 0 1 total RelB 2 protein (AU) protein 3 0 p<0.05 4 RelB protein p<0.05 u sp dp Figure S4: Investigating the Nfkb2-deficient system in a repeated TNF pulse regime: a) Computational simulation studies predicting nRelB activity (top panel), abundances of Relb mRNA as well as RelB protein in Nfkb2-deficient system subjected to a single TNFp (sp) or successive two TNFp separated by 4 hr (dp). The activities and abundances were estimated at 6-8 hr after the commencement of the -/- first pulse. u denote untreated system. b) Nfkb2 MEFs were treated with either a single TNFp (single pulse, sp) or two successive TNFp separated by 4 hr (double pulse, dp) or three successive TNFp where these pulses were separated by 2 hr (tp). Cells were harvested 8 hr after the commencement of the first pulse and subjected to either qRT-PCR analyses of Relb mRNA abundances (top bargraph) or Western Blotting analysis with antibodies against the indicated proteins (bottom panels). Densitometric analysis of the relative abundances of RelB protein has been also presented in a bargraph (bottom). mRNA data are means ± SEM of four biological replicates, protein data are means ± SEM of three experimental repeats. 4 II. Supplementary tables Table S1: List of the primers used in our quantitative real-time PCR Table S1 – List of the primers used in our quantitative real-time PCR mRNA Forward (5’ – 3’) Reverse (5’ – 3’) Actin CCAACCGTGAAAAGATGAC GTACGACCAGAGGCATACAG Csf1 CGGGCATCATCCTAGTCTTGCTGACTGT ATAGTGGCAGTATGTGGGGGGCATCCTC C3 TCAGATAAGGAGGGGCACAA ATGAAGAGGTACCCACTCTGGA Klf5 GGTCCAGACAAGATGTGAAATGG TTTATGCTCTGAAATTATCGGAACTG Me2 TTCTTAGAAGCTGCAAAGGC TCAGTGGGGAAGCTTCTCTT Nfkbia AGACTCTCGTTCCTGCACTTG AGTCTGCTGCAGGTTGTTC Psmc4 TGGTCATCGGTCAGTTCTTG CGGTCGATGGTACTCAGGAT RelB CCGAGCTAGGGGCCTTGGGTTCC AGCTCGATGGCGGGCAGGGTCTTG Tlr2 GCAAACGCTGTTCTGCTCAG AGGCGTCTCCCTCTATTGTATT Table S2: A description of genes belonging to various gene-clusters and gene-groups presented in Figure 4. Gene symbols Cluster Group id id 1110008P14Rik, 2500002L14Rik, 5133401N09Rik, Adam17, Adrb2, Agrn, Brp17, Cd82, Creld2, Ddit4, Dennd3, Fbn1, Fndc3b, Gphn, Gpt2, H2afy, H2- M3, Hagh, Hn1l, Ifngr1, Lhfpl2, LOC100044190, LOC100044322, Mcc, Ndrl, A Gr-I Plcd1, Prkcd, Psmd10, Rab8b, Rhou, Rnaset2, Slc29a1, Snx10, Surf4, Tlr2, Vdac3, Yaf2 0610010E21Rik, Abcb1b, Ass1, Axud1, Bmper, Csf1, Ehd1, Enpp4, Ext1, Mvp, Nfkb1, Nfkbia, Nfkbib, Parp8, Plscr1, Psmb10, Rab32 B 1110018G07Rik, 1110019N10Rik, 9430029K10Rik, Actr1b, Aftph, Akr1b3, Akr1b8, Arid3a, AU019823, AU022252, B9d2, C3, Calr, Chac1, Cib1, Clic4, Cyfip1, D3Ucla1, Dhps, Dnajc15, Erich1, Fktn, Fstl1, G6pdx, Gadd45b, Ghitm, Gr-II Gnptab, Gp38, Gpc1, Hmgn3, Hsd17b12, LOC668492, Lrp11, Map3k3, Mdh2, Med11, Mocs2, Mrps12, Msi2, Nadk, Ndrg1, Nfe2l1, Nrbf2, Nudt8, Nup93, C Ostf1, P4ha2, Pdgfa, Pelo, Plekhb2, Pnpla2, Pomp, Ppp1r16a, Prdx5, Psma7, Psmb6, Psmd13, Psme1, Ralb, Rassf1, Rela, Rnf145, Rpl24, Rrbp1, Sec23ip, Sec63, Slc25a5, Slc38a10, Slmo2, Sod2, Srp54, Srpr, Tbc1d15, Tbcb, Tceb1, Tgoln1, Tm9sf4, Tmem183a, Tnfaip8, Ubl4, Uso1, Uxt, Yif1b 1110008F13Rik, Actr3, AI846148, Atp2b1, Bcl10, Bud31, Cdc42ep3, Chic2, Chuk, Ciapin1, Cmtm6, Coq10b, Cyba, D15Ertd621e, D630048P19Rik, Dcbld1, Dcun1d3, Dcun1d5, EG433923, Eif4g2, Eif5a, Eif6, Fcf1, Fscn1, Fubp1, Galk1, Grpel1, Hspd1, Hspe1, Htra2, Jtv1, Klf5, LOC100042777, Gr-III LOC100047794, LOC100048413, LOC674706, Mapkapk2, Mpp6, Mrpl12, D Mrpl16, Mrpl20, Mrpl4, Mtf2, Nomo1, Orai1, Pa2g4, Picalm, Ppm1g, Psmb5, Psmc4, Psmd14, Rab21, Rap1b, Rhob, Rhog, Rps6ka4, Ruvbl2, Sdc4, Slc11a2, Slc25a39, Slc30a7, Slc35a3, Slc35b1, Srebf2, Stip1, Tatdn2, Tmem97, Trp53, Ube2m, Ublcp1, Uck2, Zfp263, Zfpm2 2310047D13Rik, 2400010D15Rik, 2610204L23Rik, Aacs, AI480653, Alas1, Arhgap29, Cebpb, Cstf2, Ctps, Cyld, Cyp51, Dcxr, Dnajb2, Fam162a, Fdps, Hist2h3b, Hnrnpf, Hrmt1l2, Ifitm2, Iscu, Jmjd6, LOC216443, LOC677317, Mid1ip1, Mras, Mrps34, Nedd4l, Nt5c3, Nubp1, Parl, Pcgf5, Pgam1, Pgp, E Plod2, Psmb3, Rala, Rb1, Rps3a, Sec13, Slc39a11, Snrpd3, Snx13, Snx18, Spr, Sqle, Ssu72, Stard4, Stk39, Tbl2, Tmem128, Tmem38b, Tsg101, Ube1c, Vdac1, Gr-IV Vps36, Zfp131 2310008H04Rik, 4933426M11Rik, Abcb6, Arhgef2, Bbs5, Bcl3, Cd47, Dnajc3, Fam102a, Fnbp1l, Gadd45g, Itpk1, Llgl2, LOC100046120, Lxn, Map3k8, Me2, Necap2, Nub1, Osmr, Pdk1, Pdk3, Pex16, Pgm2, Pld1, Plekha2, Ppp3cc, F Samd9l, Scamp1, Sccpdh, Setdb1, Socs3, Syvn1, Tspo, Ufsp2, Ugt1a10, Zfp810 5 III. Detailed description of global scale gene expression analyses Microarray mRNA analyses For microarray mRNA analyses, labelling, hybridization to the Illumina MouseRef-8 v2.0 Expression BeadChip, data processing, and quantile normalization were performed by Sandor Pvt Ltd (Hyderabad, India). We used a rank-based method for selecting genes with essentially zero detection p-value (Li et al., 2011). Among these reproducibly expressed genes, we further considered genes whose expressions were induced at least 1.3 fold at upon 6 hr of TNFc treatment in Nfkb2-/- MEFs, but not in Rela-/-Relb-/-Rel-/- cells. Our analyses led us to a list of 304 NF-κB-dependent genes. Subsequently, we utilized
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    TNF signaling pathway all genes sink node genes ● ●● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●●● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ●●● ●●● ●●● ● ● ● ●●● ● ●●● ● maximum = 3 maximum = 2 2 1 s> 10 0 <log −1 −2 2 1 x a m s 0 10 log −1 −2 group 1 group 2 group 3 group 4 group 5 group 6 group 7 group 8 TNF_signaling_pathway genes with data CASP10 CCL2 CCL5 CCL20 CASP7 FADD CXCL1 CXCL1 CXCL1 CASP3 CASP8 CXCL5 CXCL10 CX3CL1 CSF1 CSF2 BIRC2 ITCH CFLAR FAS IL18R1 JAG1 MAP3K5 IL1B IL6 IL15 LIF LTA TAB1 MAP2K7 MAPK8 FOS BCL3 NFKBIA SOCS3 TNFAIP3 TRAF1 TRAF2 MAP3K7 TNF TNFRSF1A TRADD RIPK1 MAP2K3 MAPK14 CEBPB MAP3K8 NFKB1 RPS6KA4 CREB3 BAG4 MAP3K14 MAP2K1 MAPK1 FOS JUN JUNB MMP3 MMP9 MMP14 IKBKB NFKBIA CHUK EDN1 VEGFC IKBKG NFKB1 NOD2 RIPK1 RIPK3 MLKL PGAM5 ICAM1 SELE VCAM1 DNM1L PGAM5 PTGS2 PIK3CA AKT3 NFKB1 MAP3K14 CHUK NFKBIA TRAF1 LTA TNFRSF1B TRAF2 BIRC2 RIPK1 MAP2K3 MAPK14 TRAF3 DAB2IP MAPK8 JUN IRF1 IFNB1 TNF_signaling_pathway sink nodes CASP10 CCL2 CCL5 CCL20 CASP7 FADD CXCL1 CXCL1 CXCL1 CASP3 CASP8 CXCL5 CXCL10 CX3CL1 CSF1 CSF2 BIRC2 ITCH CFLAR FAS IL18R1 JAG1 MAP3K5 IL1B IL6 IL15 LIF LTA TAB1 MAP2K7 MAPK8 FOS BCL3 NFKBIA SOCS3 TNFAIP3 TRAF1 TRAF2 MAP3K7 TNF TNFRSF1A TRADD
  • Involvement of Inhibitor Kappa B Kinase 2 (IKK2) in the Regulation of Vascular Tone

    Involvement of Inhibitor Kappa B Kinase 2 (IKK2) in the Regulation of Vascular Tone

    Laboratory Investigation (2018) 98:1311–1319 https://doi.org/10.1038/s41374-018-0061-4 ARTICLE Involvement of inhibitor kappa B kinase 2 (IKK2) in the regulation of vascular tone 1 1 1 1 Youngin Kwon ● Soo-Kyoung Choi ● Seonhee Byeon ● Young-Ho Lee Received: 6 November 2017 / Revised: 22 March 2018 / Accepted: 23 March 2018 / Published online: 21 May 2018 © United States & Canadian Academy of Pathology 2018 Abstract Inhibitor kappa B kinase 2 (IKK2) plays an essential role in the activation of nuclear factor kappa B (NF-kB). Recently, it has been suggested that IKK2 acts as a myosin light chain kinase (MLCK) and contributes to vasoconstriction in mouse aorta. However, the underlying mechanisms are still unknown. Therefore, we investigated whether IKK2 acts as a MLCK or regulates the activity of myosin light chain phosphatase (MLCP). Pressure myograph was used to measure vascular tone in rat mesenteric arteries. Immunofluorescence staining was performed to identify phosphorylation levels of MLC (ser19), MYPT1 (thr853 and thr696) and CPI-17 (thr38). SC-514 (IKK2 inhibitor, 50 μM) induced relaxation in the mesenteric arteries pre-contracted with 70 mM high K+ solution or U-46619 (thromboxane analog, 5 μM). The relaxation induced by SC-514 + 1234567890();,: 1234567890();,: was increased in the arteries pre-contracted with U-46619 compared to arteries pre-contracted with 70 mM high K solution. U-46619-induced contraction was decreased by treatment of SC-514 in the presence of MLCK inhibitor, ML-7 (10 μM). In the absence of intracellular Ca2+, U-46619 still induced contraction, which was decreased by treatment of SC-514.
  • The Mtor Substrate S6 Kinase 1 (S6K1)

    The Mtor Substrate S6 Kinase 1 (S6K1)

    The Journal of Neuroscience, July 26, 2017 • 37(30):7079–7095 • 7079 Cellular/Molecular The mTOR Substrate S6 Kinase 1 (S6K1) Is a Negative Regulator of Axon Regeneration and a Potential Drug Target for Central Nervous System Injury X Hassan Al-Ali,1,2,3* Ying Ding,5,6* Tatiana Slepak,1* XWei Wu,5 Yan Sun,5,7 Yania Martinez,1 Xiao-Ming Xu,5 Vance P. Lemmon,1,3 and XJohn L. Bixby1,3,4 1Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, Florida 33136, 2Peggy and Harold Katz Family Drug Discovery Center, University of Miami Miller School of Medicine, Miami, Florida 33136, 3Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida 33136, 4Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida 33136, 5Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana 46202, 6Department of Histology and Embryology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China, and 7Department of Anatomy, Histology and Embryology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China Themammaliantargetofrapamycin(mTOR)positivelyregulatesaxongrowthinthemammaliancentralnervoussystem(CNS).Althoughaxon regeneration and functional recovery from CNS injuries are typically limited, knockdown or deletion of PTEN, a negative regulator of mTOR, increases mTOR activity and induces robust axon growth and regeneration. It has been suggested that inhibition of S6 kinase 1 (S6K1, gene symbol: RPS6KB1), a prominent mTOR target, would blunt mTOR’s positive effect on axon growth. In contrast to this expectation, we demon- strate that inhibition of S6K1 in CNS neurons promotes neurite outgrowth in vitro by twofold to threefold.
  • Microrna‑186‑5P Downregulation Inhibits Osteoarthritis Development by Targeting MAPK1

    Microrna‑186‑5P Downregulation Inhibits Osteoarthritis Development by Targeting MAPK1

    MOLECULAR MEDICINE REPORTS 23: 253, 2021 MicroRNA‑186‑5p downregulation inhibits osteoarthritis development by targeting MAPK1 QING LI1, MINGJIE WU1, GUOFANG FANG1, KUANGWEN LI1, WENGANG CUI1, LIANG LI1, XIA LI2, JUNSHENG WANG2 and YANHONG CANG2 1Department of Orthopedics, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong 518101; 2Department of Orthopedics, The Second People's Hospital of Huai'an, Huai'an, Jiangsu 223002, P.R. China Received February 26, 2020; Accepted September 11, 2020 DOI: 10.3892/mmr.2021.11892 Abstract. As a chronic degenerative joint disease, the char‑ expression, suggesting that miR‑186‑5p may be used as a acteristics of osteoarthritis (OA) are degeneration of articular potential therapeutic target for OA. cartilage, subchondral bone sclerosis and bone hyperplasia. It has been reported that microRNA (miR)‑186‑5p serves a key Introduction role in the development of various tumors, such as osteosar‑ coma, non‑small‑cell lung cancer cells, glioma and colorectal As a chronic degenerative joint disease, the characteristics cancer. The present study aimed to investigate the effect of of osteoarthritis (OA) are degeneration of articular cartilage, miR‑186‑5p in OA. Different concentrations of IL‑1β were subchondral bone sclerosis and bone hyperplasia (1). OA used to treat the human chondrocyte cell line CHON‑001 affects an estimated 10% of men and 18% of women >60 years to simulate inflammation, and CHON‑001 cell injury was of age, worldwide (2). OA is affected by multiple factors, such assessed by detecting cell viability, apoptosis, caspase‑3 as age, sex, trauma history, obesity, heredity and joint defor‑ activity and the levels of TNF‑α, IL‑8 and IL‑6.
  • Upregulation of SLC2A3 Gene and Prognosis in Colorectal Carcinoma

    Upregulation of SLC2A3 Gene and Prognosis in Colorectal Carcinoma

    Kim et al. BMC Cancer (2019) 19:302 https://doi.org/10.1186/s12885-019-5475-x RESEARCH ARTICLE Open Access Upregulation of SLC2A3 gene and prognosis in colorectal carcinoma: analysis of TCGA data Eunyoung Kim1†, Sohee Jung2†, Won Seo Park3, Joon-Hyop Lee4, Rumi Shin5, Seung Chul Heo5, Eun Kyung Choe6, Jae Hyun Lee7, Kwangsoo Kim2* and Young Jun Chai5* Abstract Background: Upregulation of SLC2A genes that encode glucose transporter (GLUT) protein is associated with poor prognosis in many cancers. In colorectal cancer, studies reporting the association between overexpression of GLUT and poor clinical outcomes were flawed by small sample sizes or subjective interpretation of immunohistochemical staining. Here, we analyzed mRNA expressions in all 14 SLC2A genes and evaluated the association with prognosis in colorectal cancer using data from the Cancer Genome Atlas (TCGA) database. Methods: In the present study, we analyzed the expression of SLC2A genes in colorectal cancer and their association with prognosis using data obtained from the TCGA for the discovery sample, and a dataset from the Gene Expression Omnibus for the validation sample. Results: SLC2A3 was significantly associated with overall survival (OS) and disease-free survival (DFS) in both the discovery sample (345 patients) and validation sample (501 patients). High SLC2A3 expression resulted in shorter OS and DFS. In multivariate analyses, high SLC2A3 levels predicted unfavorable OS (adjusted HR 1.95, 95% CI 1.22–3.11; P = 0.005) and were associated with poor DFS (adjusted HR 1.85, 95% CI 1.10–3.12; P = 0.02). Similar results were found in the discovery set.