Supplementary Information

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 ) measured at 6-8 at 6-8 measured 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 total after 1 after mRNA (AU) mRNA 0 RelB ×103 9 RelA st

proteins 7 TNFp Actin

5 0 sp dp tp

0 1

total RelB total 2 protein (AU) protein 3 0 p<0.05 4 RelBprotein 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 the partition around medoid-based algorithm, which allows for robust clustering of datasets with significant outliers (Reynolds et al., 2006). In the partition around medoid-based clustering analysis, typically the Manhattan distances, and not the Euclidean distances, between the data points are calculated. Nevertheless, we utilized the Cluster package present in R for clustering analysis and the heatmap as well as violin plots were generated in MATLAB.

Y! Right tailed test (fdr 0.001): if signiﬁcant, mean of Y is greater than mean of X! Gr-I! Gr-II! Gr-III! Gr-IV! WT! 0.0! Nfkb2-/-! Small!

Relb-/-Nfkb2-/-! 0.5! ! Rela-/-Nfkb2-/-! ) Medium! d Rela-/-Relb-/-Rel-/-! 1.0! Large! Left tailed test (fdr 0.001): if signiﬁcant, mean of Y is less than mean of X! ! 1.5!

WT! Very Large! Nfkb2-/-! 2.0! Effect Size ( Cohen’s Cohen’s Effect( Size Relb-/-Nfkb2-/-! Huge! Rela-/-Nfkb2-/-! 2.5! Rela-/-Relb-/-Rel-/-! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! -/- -/- -/- -/- -/- -/- -/- -/- -/- -/- -/- -/- -/- -/- -/- -/- WT WT WT WT Rel Rel Rel X! Rel -/- -/- -/- -/- Nfkb2 Nfkb2 Nfkb2 Nfkb2 Nfkb2 Nfkb2 Nfkb2 Nfkb2 Nfkb2 Nfkb2 Nfkb2 Nfkb2 -/- -/- -/- -/- -/- -/- -/- -/- Relb Relb Relb Relb -/- -/- -/- -/- Rela Rela Rela Rela Relb Relb Relb Relb Rela Rela Rela Rela

Analyzing microarray data for determining the significance of gene-expression differences between various genotypes: We subjected our data set to right and left tailed t-tests, considering false discovery rate < 0.001, and examined if the mean of a pair of distributions was significantly different. We also determined the effect size as a measure of the numerical significance of these differences. The data has been presented using q-value maps. Absence of a block in a q-value map indicates insignificant difference between a pair of distributions. In other words, it signifies that the expression of genes belonging to a given groups is not significantly different between the indicated pair of genotypes. The color bar captures effect sizes for statistically significant pairs.

As indicated in the main text, we catalogued 304 NF-κB-dependent genes into six distinct clusters, which were arranged further into four gene-groups. The significance of

6 gene-expression differences between various genotypes within a given gene-group was determined by combining the multiple hypotheses testing method with a stringent false discovery correction strategy (Maitra and Melnykov, 2010). We implemented individually right and left tailed t-tests for false discovery rate < 0.001, and examined if the mean of a distribution was significantly different than the mean of another distribution. The numerical significance of the difference in the mean of two distributions was captured from the effect size (Cohen’s d). The data was visualized using the q-value map (see the associated Figure), as described earlier (Sawilowsky, 2009).

7 IV. Description of the mathematical model and related parameterization In our previously published mathematical model v2.0 (Roy et al., 2017), we depicted signal-responsive activation of four NF-κB heterodimers, namely RelA:p50, RelA:p52, RelB:p50 and RelB:p52. Combinatorial association of RelA, RelB, p50, and p52 produced these heterodimers. Except for RelB:p52, these heterodimers were sequestered in the unstimulated system by IκBα, IκBβ, IκBε or IκBδ/(p100)2. IκBβ did not interact with RelB:p50. Experimentally measured or theoretical NEMO-IKK2 and NIK-IKK1 activity profiles were used as model inputs. Signal-induced degradation of inhibitory proteins led to nuclear translocation of the bound NF-κB heterodimers. On the other hand, RelB:p52 was produced upon preferential binding of RelB to p52, which was generated from p100 in response to noncanoncial signals. Once generated, RelB:p52 translocated into the nucleus. RelB also bound to p100, but the resultant RelB:p100 complex was unresponsive to canonical or noncanonical signals. As such, RelA, p50 and IκBβ were produced involving constitutive transcription reaction. Synthesis of RelB, p100/Nfkb2, IκBα and IκBε involved both NF-κB-independent (constitutive) as well as dependent transcriptions. Because of a lack of experimental evidence, we restricted RelB:p52 from mediating the expression of genes encoding NF-κB/IκB proteins in this model.

We recently observed that IκBδ/(p100)2 also sequesters RelB:p52 (Mukherjee et al., 2017). Accordingly, here we revised the model v2.0 to include the description of

IκBδ/(p100)2-mediated inhibition of RelB:p52. The corresponding rate parameters were considered to be identical to those associated with RelB:p50 and IκBδ/(p100)2 interaction. Next, it was shown that IKK2 phosphorylates RelB during TNF signaling and that IKK2-mediated phosphorylation prevents RelB:p50 binding to IκBα (Authier et al., 2014). In the model v2.0, we assumed that TNF converts pre-existing RelB or RelB:p50 into respective RelB* or RelB*p50. As such RelB and RelB* possessed identical properties except that RelB*p50 bound to IκBα and IκBε with ~100-fold lower affinities. The conversion of RelB into RelB* occurred throughout the 8 hr of the TNFc regime. Here, we further revised the model and elaborately described RelB* generation that allowed us to simulate both chronic and brief TNF treatment regimes. We first included the description of RelB*:p52, whose properties were similar to that of RelB:p52. We permitted the conversion of RelB and RelB heterodimers (RelB:p50 and RelB:p52) into corresponding RelB* and RelB* heterodimers for 8h for the TNFc regime or for stimulation regimes with the duration of the IKK2 activity

8 ≥4h. For brief TNF regimes with the duration of the IKK2 activity <4h, conversion started along with the onset of TNF signaling but continued for a time period that was double the duration of the TNF-induced transient IKK2 activity. We computed the duration of the input as the time elapsed above an arbritarily defined threshold concentration of 15nM in the corresponding kinase activity curve.

Model version 2.0 Revised model version 2.0 TNFc regime ) WT WT

nM 100 RelA:NFκB (

Bn RelB:NFκB κ 50 NF-

Nfkbia-/- Nfkbia-/- ) 100 nM ( Bn

κ 50 NF- 0 0 2 4 6 8 0 2 4 6 8 time (h) time (h) Additional Fig A: Performance of the revised model v2.0 in the TNFc regime: Simulation of the previously published (Roy et. al., 2016) mathematical model revealing TNFc-induced NF-κB activity in WT and Nfkbia-deficient systems. Activities of RelA and RelB heterodimers have been indicated.

Furthermore, we assumed that only RelB*:p50, and not RelB:p50, participated in the autoregulatory RelB synthesis. This assumption was consistent with the notion that TNF modification enhanced the transcriptional activity of RelB. Finally, we terminated RelA- mediated transcription of RelB at 2 h post-TNF stimulation; this was terminated at 1.5h post- TNF treatment in the original v2.0. Using this revised model, we attempted to recapitulate in silico experimental TNF signaling, whose dynamical control is thought to be determined by IκBα. Our simulationstudies revealed elevated RelA as well as RelB activities in the IκBα-deficient system (Additional Figure A). Of note, previous experimental analyses indicated a substantially heightened nuclear RelA, but not RelB, activity in IκBα-null cells (Roy et al., 2017). To resolve this discrepancy, we further manually refined the parameter ensemble. We considered eighteen, experimentally-derived, quantifiable constrains for the parameterization exercise (Additional Table C). Together, we altered sixteen rate parameters to arrive onto the

9 model v2.1 (Additional Table D), which satisfied all the pre-determined constraints. Actual changes in the parameter values in v2.1 were subtle and mostly within the range of ~ 2-5 fold. Importantly, the model v2.1 satisfied the biochemical constraints even upon a ~ 1.5-10 fold increase or decrease in the newly-fitted parameter values (Additional Figure B). These analyses suggested that the model v2.1 possessed a modestly broad boundary.

Additional table C: A list of biochemical constrains considered during

model fitting. # Description of the constraints Source

C1 Stimulation: TNFc, Genetic background: WT Banoth et al.,2015 RelA:NF-κBn peak is 100nM ± 30nM and peaks within 45 min C2 Stimulation: TNFc, Genetic background: WT Roy et al., 2017 early RelB:NF-κBn peak is less than 20% of RelA:NFkB peak C3 Stimulation: TNFc, Genetic background: Nfkb2-/- Roy et al., 2017 4h RelB:NF-κBn level is 1.0-2.0 times that of 0.5h level

C4 Stimulation: TNFc, Genetic background: Nfkb2-/- Roy et al., 2017 8h RelB:NF-κBn level is 1.5-3.0 times that of 0.5h level

C5 Stimulation: TNFc, Genetic background: WT Banoth et al.,2015 RelA:NF- Bn activity is greater 15nM at 240 min κ C6 Stimulation: none, Genetic background: WT O’dea et al., 2007 Basal free IκB is less than 25% of total IκB proteins

C7 Stimulation: none, Genetic background: WT Roy et al., 2017 Basal RelA:NF-κBn activity should be within 2-9 fold higher than basal RelB:NF-κBn activity

C8 Stimulation: none, Genetic background: Nfkb2-/- Roy et al., 2017 Basal RelA:NF-κBn activity is similar, i.e. within 0.5-2 fold w.r.t. basal RelB:NF-κBn activity

C9 Stimulation: none, Genetic background: WT Werner et al., 2008 Basal NF-κBn <5% of total NF-κB

C10 Stimulation: none, Genetic background: Nfkb2-/- Roy et al., 2017 Stimulation: none, Genetic background: WT The RelB:IκBα level is 1.5 to 10 fold higher in Nfkb2-/- than WT

C11 Stimulation: none, Genetic background: WT current study Stimulation: TNFc, Genetic background: WT Basal NF-κBn activity is less than 5% of NF-κBn peak activity upon TNF chronic stimulation

C12 Stimulation: none, Genetic background: Nfkbia-/- current study Stimulation: TNFc, Genetic background: Nfkb2-/- Basal NF-κBn activity in Nfkbia-/- is only 2-8 fold less than NF-κBn peak activity upon TNF chronic treatment of Nfkb2-/-

C13 Stimulation: none, Genetic background: Nfkb2-/- current study Stimulation: TNFc, Genetic background: WT Basal NF-κBn activity in Nfkb2-/- is 2-15% of NF-κBn peak activity upon TNF chronic treatment of WT ﬁbroblasts

C14 Stimulation: TNFc, Genetic background: WT Kearns et al. 2006 IκBα mRNA fold change upon TNFc stimulation: 5-100 fold

C15 Stimulation: TNFc, Genetic background: WT Banoth et al.,2015 Nfkb2 mRNA fold change upon TNFc stimulation: 1-10 fold

C16 Stimulation: TNFc, Genetic background: WT Roy et al., 2017 RelB mRNA fold change upon TNFc stimulation: 1-10 fold

C17 Stimulation: TNFc, Genetic background: Nfkb2-/- Roy et al., 2017 RelB mRNA fold change upon TNFc stimulation in Nfkb2 ko: 2-18 fold

C18 Stimulation: LTbR, Genetic background: WT Banoth et al., 2015 NF-κBn activity >10 nM within 0h up to 24 h of LTbR treatment

10 Additional table D: A list of rate parameters subjected to modification.

# processes parameterfold # change 1 Basal transcription of IκBα 1 2ê 2 Basal transcription of IκBε 5 2ê 3 Basal transcription of RelB 12 1.24ê 4 Association rate between p100 and RelB 57,59 10é 5 Degradation of RelB p100 89,90 5é 6 Association rate between IκBα and RelA:p50 93 1.2é 7 Association rate between IκBδ and RelA:NFkB 96,100 4é 8 Dissociation rate between IκBδ and RelA:NFkB 112,116 4.9ê 9 NF-κB induced maximum fold change of IκBα transcription 2 1.7é 10 NF-κB induced maximum fold change of IκBε transcription 6 2.5ê 11 NF-κB induced maximum fold change of RelB transcription 13 1.1é 12 Affinity of RelA:NF-κB for IκBα promoter 3 1.9ê 13 Affinity of RelA:NF-κB for IκBε promoter 7 2ê 14 Affinity of RelA:NF-κB for p100 promoter 10 2ê 15 Affinity of RelA:NF-κB, RelB:NF-κB for RelB promoter 14,15 1.25é, 1.6ê 16 Conversion of RelB to RelB* 208-215 1.2é

10 Additional Fig B: Estimating the boundaries for the revised model: A set of sixteen rate parameters were subjected to revisions to arrive onto the NF-κB system model v2.1. We then 1 simulated the model v2.1 iteratively using various multipliers (0.1, 0.2, 0.5, 1, 2, 5, 10) for these parameters. Following each simulation, the model parametermultiplier was compared against the list of 0.1 eighteen established constraints 1 5 10 15 presented in the Additional table C. The # ﬁtted parameters bars show the maximum and minimum multipliers for each of the rate constant groups that satisfy all the constraints.

11 When we simulated TNF signaling in the IκBα–deficient system using v2.1, we indeed noticed an improvement in the model performance. Consistent with experimental results, the model v2.1 revealed a heightened nuclear RelA activity and a low- level of nuclear RelB activity in the absence of IκBα (Additional Figure C). Prior simulation studies involving the v2.0 led to the discovery of an autoregulatory RelB pathway (Roy et al., 2017). The model v2.1 aptly reproduced key data published earlier by Roy et al., using v2.0

100 WT Additional Fig C: Performance of the Systems Model v2.1 in the 50 TNFc regime: Simulation of the revised NF-κB System model v2.1 revealing TNFc-induced NF-κB 0 100 activity in WT and IκBα-deficient Nfkbia-/- systems. Activities of RelA and RelB heterodimers have been 50 indicated.

0 0 2 4 6 8 RelA NF-κB RelB NF-κB (Additional Figure D). Our current study indicated that RelB synthesis acted as a key determinant of the late RelB activity induced upon brief TNF stimulation of p100-deficient cells. We tested if the newly-fitted parameters influenced our overall conclusion. To this end, we incrementally increased these rate parameters up to 4 fold or similarly decreased them. Subsequently, we scored the effect of parameter perturbations on the late RelB activity induced in the p100-deficient system. Our analyses assured that with the expected exception of the RelB synthesis related rate parameters (#3, #11 and #15), the majority of the newly- fitted parameters had only subtle impact on the late nuclear RelB activity (Additional Figure E). Nevertheless, the final model v2.1 consisted of 77 species, 296 reactions and 215 parameters. We used MATLAB 2012a for designing, simulating the model and for the generation of the figures.

(i) 50 (ii) 50 )

) α

nM B α nM κ B

25 κ 25 RelB-I RelB-I complex ( complex ( 0 0 -/- -/-

WT WT

Nfkb2 Nfkb2 (iii) (iv) ) ) 150 150 -/- -/- nM nM WT Nfkb2 WT Nfkb2 ( ( 100 100 Bn Bn κ κ 50 50

0 0 RelB:NF RelB:NF 0 2 4 6 8 0 2 4 6 8 time (hr) time (hr) (v) (vi) ) 150 ) 150 -/- -/- -/- -/- -/- -/- nM Nfkb2 Rela Nfkb2 nM Nfkb2 Rela Nfkb2 ( ( 100 100 Bn Bn κ κ 50 50

0 0 RelB:NF RelB:NF 0 2 4 6 8 0 2 4 6 8 time (hr) time (hr)

Key ﬁgures from Roy et. al., 2017 Key ﬁgures from Roy et. al., 2017 produced using model v 2.0 were reproduced using model v 2.1

Additional Fig D: Comparing the performance of the NF-κB systems Model v2.1 with the previously published simulation data obtained using v2.0: Computationsimulations using the previously published NF-κB systems Model v2.0 (i, iii and v) and the model v2.1(ii, iv and vi). We compared RelB binding to IκBα in WT and Nfkb2-deficient system (i and ii), and dynamical RelB activation in various knockout systems (iii to vi) in these two model versions.

Nfkb2-/- 4 1/4 3 1/3 2 1/2

activity (AU) nominal activity in v2.1 RelBn

late no activity 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # ﬁtted parameters

Additional Fig E: Examining the robustness of the conclusion with respect to the newly fitted parameter values: The values of each of the sixteen newly fitted rate parameters in the model v2.1 were increased incrementally up to 4 fold (blue circles) or decreased similarly up to 4 fold (red circles). The resulting parameter ensembles were used for simulating the TNFp regime in the Nfkb2-deficient system, and the late nRelB activities were estimated. The horizontal line parallel to the X-axis represents the late nRelB activity induced by TNFp in the Nfkb2- defiicent system using the unaltered parameter values.

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