Supplementary Table 2. Bioinformatic Ingenuity pathway analyses of RNA-seq data presented in Fig. 2 c,d


) Ingenuity Canonical Pathways z-score (>2) TREM1 Signaling 3 Colorectal Cancer Metastasis Signaling 2.828 Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses 2.646 IL-6 Signaling 2.53 Role of RIG1-like Receptors in Antiviral Innate Immunity 2.449 Interferon Signaling 2.449 

b. Bioinformatic prediction of hyper-activated pathways and upstream regulators after TNF treatment (Fig. 3d) 
Ingenuity

Supplementary Table 5. Blood cytokine analysis of patients with XLPDR patients and control subjects (cytokines with aberrant level of secretion (p<0.05) are highlighted in yellow)

  
	Supplementary				
	Cytokine		WT ± SEM, pg/mL	XLPDR ± SEM, pg/mL	T-Test
	sCD40L	8234.25 ± 1579.33	7226.33 ± 2148.47	0.38
	EGF			27.52 ± 12.05	275.50 ± 88.97	0.02
	Eotaxin		135.17 ± 34.61	163.00 ± 19.85	0.3
	FGF-2			114.85 ± 32.20	135.64 ± 45.23	0.38
	Flt-3L			35.15 ± 17.23	0.97 ± 0.75	0.1
	Fractalkine		107.25 ± 35.58	30.22 ± 10.01	0.09
	G-CSF			19.62 ± 5.85	18.97 ± 3.16	0.47
	GM-CSF		11.38 ± 4.09	38.49 ± 20.19	0.14
	GRO			228.25 ± 53.54	1126.33 ± 232.83	0.01
	IFN2			12.45 ± 7.87	46.25 ± 13.73	0.04
	IFN			37.61 ± 7.78	12.84 ± 3.89	0.04
	IL-10			2.42 ± 1.25	6.60 ± 1.19	0.05
	IL-12P40		7.99 ± 4.84	8.13 ± 1.42	0.49
	IL-12P70		23.35 ± 8.56	3.65 ± 1.33	0.07
	IL-13			21.55 ± 18.94	0.99 ± 0.77	0.22
	IL-15			0.98 ± 0.30	1.84 ± 0.41	0.1
	IL-17A			19.96 ± 1.46	6.68 ± 2.12	0.003
	IL-1			35.70 ± 31.10	1.64 ± 1.27	0.22
	IL-1			1.14 ± 0.67	90.73 ± 24.40	0.0003
	IL-1RA			79.73 ± 28.49	144.16 ± 53.30	0.2
	IL-2			0.51 ± 0.45	3.48 ± 2.70	0.18
	IL-3		Canonical Pathways 15.52 ± 13.88	z-score (>2) 0	0.22
	Role of Pattern Recognition Receptors in Recognition of IL-4 0 Bacteria and Viruses Colorectal Cancer Metastasis Signaling IL-5 6.94 ± 3.82 IL-6 Signaling IL-6 1.46 ± 1.31	2.449 0 0 2.449 2.333 41.50 ± 11.49	-0.11 0.001
	IL-7		Interferon Signaling 0.88 ± 0.60	2.236 3.33 ± 1.05	0.07
	IL-8 IL-9 IP-10	IL-8 Signaling IL-1 Signaling 16.57 ± 2.93 Toll-like Receptor Signaling 0.55 ± 0.49 TREM1 Signaling 500.50 ± 141.61	2.236 9636.50 ± 2489.88 2 0.75 ± 0.58 2 2 583.00 ± 260.28	-6 0.41 8•10 0.41
	MCP-1		Gαs Signaling 152.11 ± 54.39	2 1937.67 ± 923.18	0.03
	MCP-3		HMGB1 Signaling 26.00 ± 23.26	2 251.07 ± 126.29	0.08
	MDC		Upstream Regulator 1116.25 ± 89.40	p-value (<E-9) 1184.00 ± 164.50	0.38
	MIP-1	IRF7	7.66 ± 1.69	3.96E-41 118.41 ± 47.59	0.01
	MIP-1 TGF-	STAT3 IRF1 STAT1	46.21 ± 9.47 2.94 ± 1.11	7.73E-35 70.57 ± 26.91 4.17E-33 6.97E-31 7.53 ± 3.02	0.24 0.13
	TNF		RELA	13.68 ± 2.89	3.71E-27 18.15 ± 4.32	0.25
	LTA		IRF3	52.39 ± 44.32	4.72E-24 1.87 ± 1.45	0.21
	VEGF		IRF5 CEBPB	201.13 ± 46.18	4.24E-23 1.96E-17 575.00 ± 243.32	0.11
			HMGB1	2.85E-12
			FOXO1		5.53E-11
			CREB1		8.42E-10

Table 6 . Primers for quantitative RT-PCR

 6 
	Species	Gene	Sense primer	Antisense primer
		ACTB	GCGGGAAATCGTGCGTGACATT	GATGGAGTTGAAGGTAGTTTCGTG
		CCL5	ATCCTCATTGCTACTGCCCTC	GCCACTGGTGTAGAAATACTCC
		CXCL1	GAAAGCTTGCCTCAATCCTG	CTTCCTCCTCCCTTCTGGTC
		IFI27	TGCTCTCACCTCATCAGCAGT	CACAACTCCTCCAATCACAACT
		IFI44L	AGCCGTCAGGGATGTACTATAAC	AGGGAATCATTTGGCTCTGTAGA
		IFIT1	TTGATGACGATGAAATGCCTGA	CAGGTCACCAGACTCCTCAC
	Human	IFIT2 IL8	AAGCACCTCAAAGGGCAAAAC ATGACTTCCAAGCTGGCCGTGGCT	TCGGCCCATGTGATAGTAGAC TCTCAGCCCTCTTCAAAAACTTCTC
		ISG15	CGCAGATCACCCAGAAGATCG	TTCGTCGCATTTGTCCACCA
		OASL	CTGATGCAGGAACTGTATAGCAC	CACAGCGTCTAGCACCTCTT
		POLA1	AGTACGCTGCTCTGGTTGTTGA	CGCATAGGCCCTCTGACTT
		RSAD2	TGGGTGCTTACACCTGCTG	GAAGTGATAGTTGACGCTGGTT
		SIGLEC1	CCTCGGGGAGGAACATCCTT	AGGCGTACCCCATCCTTGA
		TNF	CCTCTCTCTAATCAGCCCTCTG	GAGGACCTGGGAGTAGATGAG
		Actb	GGCTGTATTCCCCTCCATCG	CCAGTTGGTAACAATGCCATGT
		Cxcl10	GGGATCCCTCTCGCAAGGACGGTCC ACGCTTTCATTAAATTCTTGATGGT
		Ifit1	GCCTATCGCCAAGATTTAGATGA	TTCTGGATTTAACCGGACAGC
	Mouse	Ifit2 Ifit3	ATGAGTTTCAGAACAGTGAGTTTAA AACTGGCCCATGTGATAGTAGACCC TGGCCTACATAAAGCACCTAGATGG CGCAAACTTTTGGCAAACTTGTCT
		Pola1	ACAGATAAAGCAGTAGACTTGTCC	ATTCTGGCTGAGCCAGCTCTCC
		Rsad2	AACCTGCTCATCGAAGCTGT	ATAGTGAGCAATGGCAGCCT
		Usp18	GTGTCCGTGATCTGGTCCTT	CTGCAGAAATACAACGTGCC
Supplementary Table 2. Bioinformatic Ingenuity pathway analyses of RNA-seq data presented in Fig. 2 c,d