CD8^+ T cell loss induces autoinflammation in inborn errors of cell death

Domain: biology
Published: 2026-03-11
Editorial outcome: minor_revision

Abstract
Systemic autoinflammatory diseases (SAID) with inborn errors of cell death (IECD) are caused by overactivation of programmed cell death (PCD). However, the pathogenesis by which PCD leads to autoinflammation remains unclear. Here, we identified IECD patients carrying compound heterozygous RIPK1 variants K377E/R390G with autoinflammatory manifestations. Mechanistically, K377E and R390G mutations suppress NF-κB signaling and activate RIPK1 to promote cell death. CD8+ T cells of the patients displayed overactivated RIPK1 and excessive cell death, leading to an elevated CD4/CD8 ratio, which could also be detected in patients with cleavage-resistant mutation of RIPK1 or SHARPIN deficiency. We show that the increased cell death of CD8+ T cells promotes TNF and IFN-γ secretion to activate monocytes/macrophages, which triggers overproduction of proinflammatory cytokines. In addition, disruption of the communication between T cells and monocytes/macrophages through pharmacologic blockade of TNF and IFN attenuates proinflammatory cytokine production in macrophages and relieves all the symptoms in patients. This study further clarifies the mechanism for a group of IECD with SAID. Increased CD4/CD8 ratio and augmented RIPK1 activation in T cells provide potentially additional criteria for diagnosis of RIPK1-dependent IECD and a combination of TNF/JAK inhibitor could be an effective therapy for the diagnosed patients.

Introduction
Systemic autoinflammatory diseases (SAIDs) are a group of disorders induced by overactivation of the innate immune system characterized by recurrent fever and multi-organ inflammation, but typically without high-titer antibodies or antigen-specific T cells1, 2. To date, more than 50 genes responsible for monogenic SAIDs have been identified. The list of genes whose mutations lead to SAIDs is enriched with key regulators of programmed cell death (PCD). Enhanced PCD is one of the characteristics in the SAID patients caused by the cleavage resistance mutations in RIPK13, 4, 5 or deficiencies of RIPK16, 7, TANK-binding kinase 1 (TBK1) 8, OTU deubiquitinase with linear linkage specificity (OTULIN), HOIL1-interacting protein (HOIP) 9, Heme-oxidized IRP2 ubiquitin ligase-1 (HOIL1) 10, and Shank-associated RH domain-interacting protein (SHARPIN) 11. This clinical entity is designated as ‘inborn errors of cell death (IECD)’ 11. Pathogenic variants of IECD have been shown to activate apoptosis and necroptosis in cellular or murine models12. Therapies targeting inflammatory cytokines, such as anti-IL6, anti-TNF, or anti-IL-1 antibodies, were applied to treat IECD patients, but refractory cases still showed unresponsiveness or tolerance after long-term treatment3, 5, 8, 11. Therefore, investigation of therapies targeting upstream molecules is urgently needed to offer more effective treatment on the refractory SAID with IECD.

The excessive cell death is strongly linked to autoinflammatory diseases in humans, while the pathogenesis by which cell death causes spontaneous inflammation in these conditions is unknown. The study of the pathogenesis aids the improvement of targeted therapy for refractory IECD.

RIPK1 is a key mediator of cell death and inflammation13. Overactivation of RIPK1 leads to enhanced apoptosis, necroptosis, and inflammatory response in mice and humans14. Whereas RIPK1 deficiency and heterozygous non-cleavable variants in RIPK1 cause immunodeficiency and dominantly inherited autoinflammatory diseases, respectively, a recessive autoinflammatory disease caused by RIPK1 variants has not been reported. K63-linked ubiquitination of RIPK1 on K377 is essential for NF-κB pathway activation and negative regulation of cell death. Mice bearing Ripk1K376R/K376R knockin mutation display embryonic lethality due to excessive apoptosis and necroptosis caused by RIPK1 activation15, 16, 17. Nevertheless, the function of RIPK1 ubiquitination in human disease has not been defined or studied. In this study, we identified two autoinflammatory patients carrying recessively inherited compound heterozygous RIPK1 variants K377E/R390G. We demonstrate that RIPK1 K377E/R390G variants lead to autoinflammatory disease by promoting RIPK1 activation-mediated death of CD8+ T cells and identify TNF and IFN-γ as molecules linking programmed cell death and autoinflammation.

Results
Biallelic RIPK1 mutations in patients with autoinflammation

We identified two children from one family with autoinflammatory disease characterized by recurrent fevers, lymphadenopathy, and skin rashes (Fig. 1a-d). They also developed colitis characterized by diarrhea, abdominal pain and perianal abscess. At the age of 7 months and 4 years, respectively, patient 1 (P1) and patient 2 (P2) displayed fever episodes that lasted 3-4 days and recurred every 8-9 days. Their fever episodes were accompanied by elevated serum C-reactive protein (CRP), serum amyloid A (SAA), erythrocyte sedimentation rate (ESR), D-dimer, and lactate dehydrogenase (LDH) (Fig. 1e, Supplementary Figure. 1a-c). Neither patient exhibited evidence of immunodeficiency. In addition, the levels of proinflammatory cytokines IL-6 and TNF were strongly increased in the serum of both patients during flares (Fig. 1f). Serum CRP levels and adenosine deaminase (ADA) activity in patients were higher than those of unaffected controls and parents during disease flares and even in remission (Fig. 1f). P1 had taken prednisone during flares, with resolution of fever but limited effect on inflammatory indexes (Fig. 1e, Supplementary Figure. 1a-c).

By performing whole exome sequencing (WES) and Sanger sequencing on the family, we identified that both P1 and P2 carried the RIPK1 compound heterozygous mutations (c. 1129A>G / p. K377E and c. 1168C>G / p. R390G), whereas their unaffected mother (and maternal grandmother) and father (and paternal grandmother) were heterozygous carriers of RIPK1 R390G and K377E, respectively (Supplementary Figure. 1d, e). All heterozygous family members were healthy. Therefore, this autoinflammatory disease in the siblings is recessively inherited and segregated with the inheritance of both RIPK1 variants. The lysine residue at amino acid position 377 is the site for K63-linked ubiquitination of RIPK118, while the function of residue R390 has not been characterized. Both K377 and R390 residues are conserved in RIPK1 across species (Supplementary Figure. 1f). The two RIPK1 variants were predicted to be deleterious with their CADD scores well above that of the non-cleavable RIPK1 mutations D324V, D324H, and D324N (Supplementary Figure. 1g). No other candidate variants were identified by WES.

Given that RIPK1 is a widely expressed regulator of programmed cell death, we utilized SV40-immortalized human dermal fibroblasts (SV40-HDFs) and primary T cells derived from unaffected controls and P2 to assess the sensitivity to apoptosis induced by TNF (T) with cIAP1/2 degrader SM-164 (S) as well as necroptosis induced by TNF (T) with pan-caspase inhibitor z-VAD-FMK (Z) or TSZ. HDFs from P2 showed increased susceptibility to both TZ/TSZ-induced necroptosis, which was fully rescued by RIPK1 kinase inhibitor Necrostatin-1s (Nec-1s), and to TS-induced apoptosis, which was only partially inhibited by Nec-1s (Fig. 1g, Supplementary Figure. 2a). Primary T cells isolated from patients, but not heterozygous carriers and unaffected controls, were more sensitive to TS-induced apoptosis or TSZ-induced necroptosis, which was in accordance with the recessive inheritance pattern (Fig. 1h). P2’s SV40-HDFs and P1’s primary T cells displayed higher levels of RIPK1 phosphorylation at S166 and MLKL phosphorylation at S358 under cell death stimulation, and these patterns could be inhibited by Nec-1s (Supplementary Figure. 2b, c), indicating that SV40-HDFs and T cells bearing K377E/R390G mutations were prone to RIPK1 activation-induced cell death. Thus, compound R390G and K377E mutations in RIPK1 sensitized patients’ cells to programmed cell death.

To further investigate the function of K377E and R390G mutations in RIPK1 under isogenic conditions, we established HT-29 cell models with either the K377E or R390G knockin mutation. Both mutant cell lines showed increased sensitivity to TZ-induced cell death (Supplementary Figure. 2d). The elevated cell death levels, along with enhanced RIPK1 and MLKL phosphorylation in K377E and R390G knockin HT-29 cells, could be reversed by Nec-1s (Supplementary Figure. 2e, f). Thus, both K377E and R390G mutations in RIPK1 could promote cell death. We also determined the effect of RIPK1 K377E and R390G mutations on the TNFR1 downstream NF-κB and MAPK pathways. The activation of both NF-κB and ERK signaling was partially impaired in P2 HDFs compared to unaffected controls. Consistently, NF-κB activation and p38 phosphorylation were also suppressed in K377E or R390G knockin HT-29 cells upon TNF stimulation (Supplementary Figure. 2g, h). Consequently, the transcription of TNF, IL1B, and IL8 was reduced in K377E or R390G knockin HT-29 cells compared to WT control (Supplementary Figure. 2i). The TNFR signaling complex forms upon TNF stimulation is crucial for NF-κB activation 19. Notably, both P2 HDFs and K377E or R390G knockin HT-29 cells showed reduced RIPK1 ubiquitination shifts within the TNFR signaling complex (Supplementary Figure. 2j, k). K377 is a ubiquitination site in the RIPK1 intermediate domain (ID domain) 18. Consistently, the K377E mutation, but not R390G, directly abolished the ubiquitination of the ID domain (Supplementary Figure. 2l). Since R390 is not expected to be a direct site for ubiquitination, we considered the possibility that R390G might affect the recruitment of E3 ubiquitination modifiers. We screened key reported key regulators of K63-linked ubiquitination within the TNFR signaling complex, including the E3 ubiquitin ligases TNF Receptor-Associated Factor 2 (TRAF2), cellular Inhibitor of Apoptosis 1 (cIAP-1), cellular Inhibitor of Apoptosis 2 (cIAP-2), Mind Bomb-2 (MIB2), Tumor Necrosis Factor Alpha-Induced Protein 3 (A20), the deubiquitinase cylindromatosis (CYLD), as well as the key adaptor protein TRADD (TNFR1-associated death domain protein). We found that the interaction between MIB2 and the RIPK1 R390G mutation was significantly reduced (Supplementary Figure. 2m, n). Furthermore, MIB2 has been reported to mediate the ubiquitination of RIPK1 at Lys377 and Lys 63420. The reduced interaction between MIB2 and R390G therefore could explain the impaired ubiquitination of the R390G RIPK1 mutant. Collectively, these data demonstrate that both K377E and R390G mutations promote RIPK1 activation-dependent cell death and impair pro-survival NF-κB signaling.

Activation of inflammatory signaling in myeloid cells of the patients

We next studied the inflammatory signaling pathways by performing single-cell RNA sequencing on peripheral blood mononuclear cells (PBMCs) from P1, P2, and unaffected controls. The myeloid compartment in patients was significantly expanded (Fig. 2a, Supplementary Figure. 3a). Similar to the previously reported RIPK1 D324V mutant4, transcriptional up-regulation of proinflammatory cytokines (IL1B and CXCL8) was detected in patient cells, especially in monocyte subsets (Fig. 2b). Likewise, differential gene expression analysis revealed the greatest up-regulation of genes governed by NF-κB and type-I IFN pathways in myeloid cells from the patients (Fig. 2c, d, Supplementary Figure. 3b, c). The up-regulated genes in the patients’ monocytes were also enriched for pathways of cytokine production, cytokine stimulation, and negative regulation of cell death in the gene ontology (GO) analysis (Fig. 2e, Supplementary Figure. 3d), suggesting that myeloid cells play a crucial role in increased cytokine production and proinflammatory signal transduction in the patients. Increased basal expression of IL-1β, IL-6 as well as IFNγ in monocytes from P1 was confirmed by flow cytometry (Fig. 2f). However, monocytes isolated from the patients exhibited reduced transcription of NF-κB regulated genes including IL1B, IL6, CXCL8 and TNF in response to TNF stimulation (Fig. 2g), in accordance with the partially suppressed NF-κB activation in SV40-HDFs as demonstrated earlier (Supplementary Figure. 2g). As RIPK1 overactivation can promote PCD-induced inflammation21, we hypothesized that excessive cell death in myeloid cells instead of impaired TNF-stimulated NF-κB and MAPK signaling, were the main cause of systemic inflammation.

Thus, we assessed the cell death response of monocytes to TNF stimulation. The monocytes from the patients were more sensitive to TS-induced apoptosis than that of unaffected controls, whereas they showed similar sensitivity to necroptosis induced by TSZ (Fig. 2h). In addition, no differences of apoptosis and necroptosis were observed between monocyte-derived macrophages (MDMs) from patients and that from unaffected controls (Fig. 2i, Supplementary Figure. 3e). On the other hand, although the expression of cell death suppressor genes, including TNFAIP3, BCL2A1, BCL6, BCL3, RNF144B, BIRC2 and BIRC3, was elevated across various patient immune cell types, their expression levels were markedly higher in myeloid lineage cells compared to lymphocyte subsets (Fig. 2j, k and Supplementary Figure. 3f). Therefore, the elevated inflammatory response of monocytes in patients may be regulated non-autonomously by other cell types that were more susceptible to cell death.

Myeloid cells and T cells play different roles in IECD patients

To identify the upstream factors driving the inflammatory cytokine production in myeloid cells, we performed a semi-quantitative protein microarray analysis on serum samples from unaffected controls and P2 (Fig. 3a, b). Samples from P2 were collected both before and after the first fever episode (disease onset). Differentially expressed cytokines compared to healthy donors were clustered into 3 groups based on their changing patterns relative to the fever stage. Group 1 comprised 26 cytokines (e.g., IL-1β) that peaked exclusively during the flare, indicating their specific association with the febrile stage (Fig. 3c, Supplementary Figure. 4a, b). Group 2 contained 24 cytokines (e.g., sCD95) that were persistently elevated, while Group 3 consisted of 32 cytokines that showed a continuous decrease (Fig. 3c, Supplementary Figure. 4c-f). The expression levels of cytokines in Groups 2 and 3 were consistently altered, suggesting they were likely directly influenced by the genetic mutations rather than being secondary to the fever episodes.

In order to further define the source of the cytokines in group 1 and 2, we compare the expression scores of the two groups between patients and unaffected controls by analyzing single-cell RNA-seq data. The cytokines in group 1 were most significantly up-regulated in patients’ monocytes, low-density granulocyte (LDG), plasmacytoid dendritic cell (pDC), and plasma cells (Fig. 3d), consistent with the strong inflammatory signature in myeloid cells during fever. Thus, amplification of inflammation by myeloid cells is an important step for fever episode. Notably, the genes encoding cytokines in group 2 were up-regulated most significantly in the patients’ lymphocyte subsets, especially in CD8+ T cells, implying a pathogenic role of K377E/R390G variants in T lymphocytes (Fig. 3e, Supplementary Figure. 4g). In addition, the expression levels of cell death suppressor genes in T lymphocytes were also lower compared with myeloid cells (Supplementary Figure. 3f), suggesting that T lymphocytes may be sensitized to undergo programmed cell death. These results supported a critical role of monocytes in promoting inflammation and implied that T lymphocyte subsets may also contribute to the pathophysiology of this autoinflammatory disease.

RIPK1 activation leads to increased PCD and CD4/CD8 ratio in T cells

To better understand the impact of RIPK1 mutations on the immune profile, we performed cytometry by time-of-flight (CyTOF) on PBMCs isolated from unaffected controls and patients. CyTOF result revealed that CD4+ T, B, and γδ T cell proportions in patients were elevated, whereas CD8+ T and NK cell proportions were reduced compared to unaffected controls (Supplementary Figure. 5a, b). Apoptosis, indicated by cleaved caspase-3, was increased in CD8+ TEMRA, γδT, NK, and B cell subsets of patients (Fig. 3f). Flow cytometric analysis confirmed that patients’ CD4+ and CD8+ T cells, not CD19+ B cells and CD14+ monocytes, displayed augmented activation of RIPK1 and cleaved caspase-3 (Fig. 3g-j and Supplementary Figure. 5c-5g). Therefore, T cells were the most sensitive compartment for RIPK1 activation and PCD in vivo.

The CyTOF results also revealed a global activation of patients’ immune cells as indicated by CD69 expression, while elevated CD38 expression appeared to be restricted to B cells and T cells (Supplementary Figure. 5h, i). Increased expression of CD69 in T cells was further validated by flow cytometry (Fig. 3k). Concomitantly, higher levels of T cell activation-related proinflammatory mediators, including sCD25, TNF, and IFNγ, were observed in the serum of P1 and P2 as well as (cleavage-resistant RIPK1-induced autoinflammatory) CRIA patients (Fig. 3l), indicating that patients' T cells were overactivated.

Activated lymphocytes undergo cell expansion and indeed we observed an elevation in the number of total lymphocytes, as well as that of CD3+ T cells, CD4+ T cells and B cells, except for that of cytotoxic CD8+ T cells and NKT cells in the immunophenotyping results (Fig. 4a, Supplementary Figure. 6a). As a consequence of the loss of CD8+ T cells, CD4/CD8 ratio was significantly increased in patients. Of note, CD4/CD8 T cell ratio in heterozygous parents remained normal, indicating that alteration of this ratio requires biallelic K377/R390 variants in RIPK1 (Supplementary Figure. 6b). In vitro expansion of T cells isolated from P1 upon anti-CD3/CD28 stimulation was abnormal, but the proliferation ability was undampened (Fig. 4b, c), reflecting the occurrence of T cell death. Specifically, CD8+ T cells went through a contraction phase after activation, presumably resulted from activation-induced cell death (AICD) 22. The augmented signal of CD95 (Fas) on the surface of patients’ T cells, accompanied by elevations of serum sCD95 and CD95L levels, also supported increased AICD (Fig. 4d).

To further characterize the abnormalities in the patient's T cells, we analyzed T cell subsets from in vitro cultured PBMCs. We observed that CD8 expression was decreased in approximate half of the CD8+ T cells after culturing PBMCs isolated from P2 in vitro for 24 hours, while the expression on CD8+ T cells of unaffected controls remained normal (Fig. 4e). Through gating on CD8hi (CD8+ T cells with normal CD8 expression) or CD8lo (CD8+ T cells with lower CD8 expression) T cells, we found that in contrast to CD8hi T cells, most of the CD8lo T cells exhibited augmented cleaved caspase-3, indicative of activated apoptosis. In addition, total RIPK1 level in CD8lo T cells decreased, which may be caused by caspase-3 cleavage (Fig. 4e).

The decreased CD8 expression in patients’ T cells and the increases in CD4/CD8 ratio were more prominent after prolonged in vitro culture or after TS or TSZ stimulation compared to that of control (Fig. 4f, Supplementary Figure. 6c-e). Therefore, we hypothesized that CD8+ T cells were more prone to undergo PCD than CD4+ T cells. Then we isolated CD4+ and CD8+ T cells from patients and heterozygous carriers and stimulated with TS or TSZ. We found that CD8+ T cells were more sensitive to PCD than CD4+ T cells, which explained the increase of the CD4/CD8 ratio under PCD stimulation (Supplementary Figure. 6f, g). Moreover, we also performed in situ knock-in of WT RIPK1 in patients’ T cells, which successfully reversed the CD4/CD8 ratio induced by TS or TSZ, further confirming the activated RIPK1 resulting in increased cell death and CD4/CD8 ratio in T cells (Fig. 4g, h and Supplementary Figure. 6h, i). In contrast, neither ferroptosis induced by RSL3 nor RIPK1-independent apoptosis induced by staurosporine (STS) can differentiate cell death of CD8+ T cells from the patients compared to that of unaffected controls (Supplementary Figure. 6j). In addition, the activation of patients’ T cells by anti-CD3 antibody stimulation increased CD4/CD8 ratio, which could be reversed by ponatinib or partially reversed by Nec-1s (Fig. 4i, j and Supplementary Figure. 6k, l). Since ponatinib inhibits the kinase activity of RIPK1 and RIPK3 23, these results suggest that they might play important roles in regulating AICD in the patient T cells bearing K377E/R390G compound RIPK1 mutations.

To investigate the effect of RIPK1 K377E and R390G mutations on the CD4/CD8 ratio of T cells in mice, we crossed Ripk1K376R/+ with Ripk1+/– mice to generate Ripk1K376R/− mice, which could be weaned at Mendelian ratio but develop severe systemic inflammation (Supplementary Figure. 6m) 15, 16. Interestingly, no difference of CD4/CD8 ratio in peripheral T cells was observed between Ripk1K376R/– and Ripk1+/– mice at 3 weeks old (Fig. 4k). In contrast, the CD4/CD8 ratio in peripheral T cells from 8-week-old Ripk1K376R/− mice was significantly higher than that in the other three genotypes (Fig. 4l, Supplementary Figure. 6n), which is consistent with that of K377E/R390G patients. Together, these results suggested that T cells, especially CD8+ T cells, were the most susceptible hematopoietic lineage to RIPK1 activation-induced cell death, as reflected by the elevated CD4/CD8 in patients with the RIPK1 K377E/R390G variants.

RIPK1 activation-induced T cell death drives inflammation in myeloid cells

Given the differential impact of the RIPK1 K377E/R390G variants on T cells and myeloid cells, we next investigated cell-cell communication between these two cell types using a transwell system (Fig. 5a). The results showed that patients’ T cells induced higher cytokine expression in monocytes derived macrophages (MDMs) comparing to that of T cells isolated from unaffected controls (Fig. 5b, c). However, MDMs from patients showed less response than that of unaffected controls upon culturing with the identical T cells. The impaired inflammatory response by patients’ MDMs may be explained by the suppressed NF-κB activation caused by RIPK1 K377E/R390G mutations. Interestingly, patients’ T cells could still trigger massive cytokine production in autologous MDMs (Supplementary Figure. 7a). Thus, we conclude that T cells from the patients can act to potentiate the inflammatory response of monocytes and macrophages with RIPK1 K377E/R390G mutations.

Previous studies have proposed TNF and IFNγ secreted by chimeric antigen receptor (CAR) T cells as key contributors to the activation of macrophages during cytokine release syndrome (CRS) 24. TNF combined with IFNγ was also reported to be a key inducer of inflammation in patients with severe COVID-19 25. IFNγ and TNF were specifically upregulated in patients’ T lymphocytes and NK cells, whereas receptors of these two cytokines were highly expressed in myeloid cells (Fig. 5d, Supplementary Figure. 7b, c), providing additional support for T cell-monocyte/macrophage communication through these two receptor-ligand pairs. In comparison to unaffected controls, T cells, especially CD8+ T cells from patients, exhibited greater production of IFNγ and TNF. The production of these cytokines was further enhanced upon TS or TSZ stimulation (Fig. 5e, Supplementary Figure. 7d). These results indicated that IFNγ and TNF produced by activated T cells may be the key factors to initiate the inflammatory response of MDMs. Consistently, increased production of TNF, IL1B, and IL6 in MDMs generated from unaffected controls stimulated by either patient T cells or anti-CD3/CD28-activated donor-matched T cells could be partially inhibited by neutralization of IFNγ or TNF alone. The cytokine production was completely blocked by combined blockade of IFNγ and TNF (Fig. 5f, Supplementary Figure. 7e). Collectively, these data demonstrated that RIPK1 overactivation in CD8+ T cells promote TNF and IFNγ secretion to trigger inflammatory cytokine production in MDMs.

In order to validate this model and assess whether the function of RIPK1 K377E and R390G mutations in CD8+ T cells was sufficient to induce inflammation, we exploited the Cre-loxp system combined with bone marrow transplantation to conditionally express WT or mutated human RIPK1 in specific hematopoietic compartments (Fig. 5g, Supplementary Figure. 8a). Mouse lineage negative bone marrow cells (Lin- BMs, including hematopoietic stem cells and marrow stromal cells) isolated from donor Cd8-creERT2 mice or Lyz2-creERT2 mice were transduced with lentivirus encoding EF1α-promoted loxp-GFP-stop-loxp-TagBFP2-P2A-human RIPK1 expression cassettes (LSL-hRIPK1). Afterwards, transduced Line- BMs were transplanted into lethally irradiated donor mice followed by tamoxifen induction. Transplantation efficiency and the specificities of Cd8 and Lyz2 promoters were validated (Supplementary Figure. 8b-h). GFP expression in Line- BMs reflected transduction efficiency, and TagBFP2 confirmed WT/mutated RIPK1 expression in specific cell types upon tamoxifen induction (Supplementary Figure. 9). We then evaluated the serum levels of inflammatory cytokines in the recipients. Under basal conditions, serum Tnf and Il-6 levels were higher in mice with CD8+ T cells expressing mutated RIPK1 (K377E, R390G or D324V) than WT RIPK1 and empty vector (EV) controls (Fig. 5h). No significant differences were observed between mice bearing myeloid cells overexpressing EV/WT RIPK1 controls and mutated RIPK1 (Fig. 5i). The result demonstrated the pathogenic function of K377E and R390G as well as the initiator role of T cells in the autoinflammation.

Combined therapy targeting TNF and IFNγ reverses systemic inflammation in the patients

Since TNF and IFNγ were considered to mediate the inflammatory cytokine production of myeloid cells from patients, we assumed that disruption of communication between T cells and myeloid cells by pharmacologic blockade of TNF and IFNγ could be a promising therapeutic strategy. Therefore, we compared the treatment effectiveness of TNF inhibitor or IL-6 inhibitor alone with combined blockade of TNF and IFNγ pathway (Fig. 6a, b). Both patients showed alleviation of fever frequencies and the inflammatory indexes. However, the P1 developed skin rashes during the treatment. Moreover, the increased serum levels of TNF in both patients could not be reversed by tocilizumab. The symptoms of colitis presented in P1 could not be relieved by tocilizumab (Fig. 6a). TNF-targeting therapy, adalimumab, was also administered, and both P1 and P2 responded with reduced severity of skin rashes and decreased frequency of fevers (Fig. 6a, b). Nevertheless, P2 still developed colitis (Fig. 6b). Moreover, adalimumab had limited effects on WBC and lymphocyte counts (Fig. 6c). At the same time, adalimumab alone was not enough to suppress serum levels of IL-6, in accordance with the partial inhibitory effect of adalimumab on IL-6 produced by MDMs in the co-culture assay (Fig. 6c and 5f). We next trailed the combined therapy blocking both TNF and IFNγ signaling in P1 and P2. Since the IFNγ-neutralizing antibody emapalumab was only approved for hemophagocytic lymphohistiocytosis locally, we used ruxolitinib, which selectively targets IFNγ downstream JAK1/JAK2 kinase as an alternative in combination with adalimumab (Fig. 6a-c). Strikingly, after treated with combined therapy, both P1 and P2 no longer exhibited symptoms of fever and colitis and remained clinically asymptomatic for over one year. Concurrently, all tested inflammatory profiles, including increased counts and frequencies of WBC and lymphocyte, elevated serum levels of IL-6 and TNF, were recovered after the treatment (Fig. 6c). These results support a synergistic effect of TNF and IFN signaling in promoting the autoinflammation caused by RIPK1 activation-induced T cell death.

T cell death mediates autoinflammation in IECD

RIPK1 mutations at D324 3, 4 and SHARPIN deficiency 11, which could activate RIPK1 activation-induced cell death, have been reported to cause SAPIDs. Therefore, we examined the CD4/CD8 ratio and RIPK1 activation to verify our model. Similar to patients with RIPK1 K377E/R390G variants, we observed elevated CD4/CD8 ratio paired with increased RIPK1 phosphorylation and cleaved caspase-3 in PBMCs from CRIA patients (Fig. 7a-d, Supplementary Figure. 10a-c), indicating that CD8+ T cell death might be a general phenomenon during the inflammation of CRIA. Notably, CD8lo T cells from CRIA-1 and SHARPIN deficiency (Sharpenia) patients were increased upon in vitro culture (Fig.7e, f). CD4 and CD8 expression levels in CRIA patients’ T cells were validated by CyTOF and CD8 expression decreased more in CD8+ TEMRA cells than in other subsets, reflecting higher levels of cell death in CD8+ TEMRA cells (Supplementary Figure. 10d-f).

Methods
Patients

Evaluation of the patients (P1 and P2; CRIA-1 and CRIA-2) was approved by Institutional Review Board and Ethical Committee at the Sir Run Run Shaw Hospital (2025-0440) and Children's Hospital (protocol 2021-IRB-172) of Zhejiang University School of Medicine. P1 and P2 were evaluated at Children’s Hospital affiliated to the Capital Institute of Pediatrics. SHARPIN deficiency patient were evaluated under the approval by the Institutional Review Board of the National Institutes of Health (94-HG-0105) as described30. All relevant ethical regulations were followed. All patients provided written informed consent.

Whole exome sequencing and genotyping with Sanger sequencing

Whole blood genomic DNA was extracted using Maxwell RSC Whole Blood DNA Kit (Promega, AS1520) and 1 μg DNA was used for whole-exome sequencing. WES and data analysis were performed as previously described31. For genotyping of other affected or unaffected family members, 437 bp genomic DNA fragment containing RIPK1 K377/R390 was amplified with forward primer 5’-TTGAGA TACAGATTCATGACGGCG-3’ and reverse primer 5’-CTGGTGCACTGCATTACCATGAC-3’ for Sanger sequencing. For genotyping of CRIA patients, 514 bp genomic DNA fragment containing RIPK1 D324 was amplified with forward primer 5’-AAATCAGGAAGTGTGAGTCCTACAACC-3’ and reverse primer 5’-TGTTCAGAACACTCTATGACTGGTGAG-3’ for Sanger sequencing.

Single-cell RNA sequencing

PBMCs of patient 1 and patient 2 were freshly isolated and 10000 single-cell were captured by 10X Genomics Chromium for cDNA library preparation according to the manufacturer’s instruction. Sequencing was carried out on Illumina NovaSeq PE150 platform by Novogene Company (Beijing, China). The resulting count matrices followed the standard pipeline with default parameters. Cell type annotation is based on reported annotation methods for PBMCs32, 33, 34, 35 and web-based databases like CellMarker36 and PanglaoDB37. Further data analysis was performed as previously described 31. NF-kB expression score and IFN response score of cell subtypes were calculated and the total cell number of each subtype from three unaffected controls or two patients, was employed for p-value calculations. Unaffected controls: CD14+ Mono, n=2915 cells; CD16+ Mono, n=398 cells; LDG, n=341 cells; pDC, n=108 cells; NK, n=1747 cells; CD4+ T, n=15028 cells; GZMK+ CD8+ T, n=2629 cells; GZMB+ CD8+ T, n=5272 cells; γδT, n=10 cells; Naive T, n=3678 cells; B, n=4535 cells; Plasma, n=87 cells. Patients: CD14+ Mono, n=4782 cells; CD16+ Mono, n=789 cells; LDG, n=11084 cells; pDC, n=139 cells; NK, n=1970 cells; CD4+ T, n=16463 cells; GZMK+ CD8+ T, n=2943 cells; GZMB+ CD8+ T, n=6124 cells; γδT, n=513 cells; Naive T, n=3779 cells; B, n=5586 cells; Plasma, n=159 cells.

Cell lines, cell isolation, culture and stimulation

The HEK293T and HT-29 cell lines were from the American Type Culture Collection (ATCC) and were authenticated by STR profile report. Human PBMC samples were generated from peripheral blood samples using gradient centrifuging with LSM™ lymphocyte separation medium (MP Bio, Cat.50494X). Then primary T lymphocytes, primary CD8+/CD4+ T cells and monocytes were isolated from PBMCs by negative selection using Pan T Cell Isolation Kit (Miltenyi Biotec, 130-096-535), CD8+ T Cell Isolation Kit (Miltenyi Biotec, 130-096-495)/ CD4+ T Cell Isolation Kit (Miltenyi Biotec, 130-096-533) and Pan Monocyte Isolation Kit (Miltenyi Biotec, 130-096-537) according to manufacturer’s instructions. SV40-HDFs were derived from skin biopsies of patient or control donors as described and transformed by lentiviral transduction of SV40-large T antigen. The HEK293T cells, SV40-HDFs were cultured in Dulbecco's Modified Eagle Medium (DMEM, Gibco) supplemented with 10% FBS. Human PBMCs, primary T cells and monocytes were cultured in RPMI 1640 (Gibco) supplemented with 10% FBS. HT-29 cells were cultured in McCoy's 5A medium (Gibco) supplemented with 10% FBS.

Recombinant human TNF (Peprotech, 300-01A) was used to stimulate HT-29 cells (20 ng ml-1), SV40-HDFs (20 ng ml-1) and human primary monocytes (50 ng ml-1) for the indicated times. z-VAD-fmk (Selleck, S7023, 25 μM), SM-164 (Selleck, S7089, 250 nM) and Nec-1s (Selleck, S8641, 10 μM) were used to treat HT-29 cells, human primary T cells and monocytes. Staurosporine (MCE, HY-R15141, 100 nM) and RSL3 (Selleck, S8155, 1 μM) were used to induce apoptosis or ferroptosis in T cells.

Flow cytometry

Cells were washed with FACS buffer (0.5% BSA in PBS) and blocked with human Fc Block (BD Biosciences, 564219) or mouse Fc Block (BD Biosciences, 553141) followed by staining with antibodies against surface molecules in dark for 30 min at 4 °C. Then cells were washed twice and stained with Fixable Viability Stain 780 (BD Biosciences, 565388) to distinguish live versus dead cell. For intracellular staining, cells were fixed and permeabilized with BD Phosflow Fix Buffer and Perm Buffer (BD Biosciences: 557870, 558052) and then washed twice and stained with antibodies against intracellular antigens for 30 min at 4 °C. For pRIPK1 (Ser166) and cleaved caspase-3 staining, cells were incubated with Alexa Fluor 647 labeled goat anti-rabbit IgG (Thermo, A-21244, 1:500) for 30 min at 4°C after primary antibody staining. Cells were acquired on CytoFlex (Beckman) and data were analyzed using FlowJo software. The following antibody clones were used for staining in flow cytometry analysis: CD3 (SK7, BD Biosciences, 1: 100), CD4 (RPA-T4, BD Biosciences, 1: 100), CD8 (RPA-T8, BD Biosciences, 1: 100), CD14 (M5E2, BD Biosciences, 1: 100), CD16 (3G8, BD Biosciences, 1: 100), CD19 (SJ25C1, BD Biosciences, 1: 100), CD56 (NCAM16.2, BD Biosciences, 1: 100), IL-1β (JK1B-1, BioLegend, 1: 100), TNF (MAb11, BD Biosciences, 1: 100), IL-6 (MQ2-13A5, BD Biosciences, 1: 100), IFNγ (4S.B3, BD Biosciences, 1: 100), Cleaved Caspase-3 (Asp175) (CST, #966S1, 1: 200), pRIPK1(Ser166, 1: 200) (D8I3A, CST), RIPK1 (D94C12, CST, 1: 800).

RNA isolation and real-time RT-PCR

Total RNA from cells was extracted using a RNeasy Mini Kit (Qiagen, 74104) according to the manufacturer’s instructions. 1 μg RNA was reverse transcribed into cDNA with ReverTra Ace® qPCR RT Master Mix (TOYOTBO, FSQ-301). cDNA was then diluted and used for real-time PCR using 2x Universal SYBR Green Fast qPCR Mix (Abclonal, RK21203) with primers targeting IL1B (F, 5’-TGCTCTGGGATTCTCTTCAGC-3’; R, 5’-AAGTCATCCTCATTGCCACTGT-3’), IL6 (F, 5’-CATCCTCGACGGCATCTCAG-3’; R, 5’-ACCAGGCAAGTCTCCTCATTG-3’), IL8 (F 5’-CCAAACCTTTCCACCCCAAA-3’; R, 5’-GAATTCTCAGCCCTCTTCAAAAAC-3’) TNF (F, 5’-CTCTTCTGCCTGCTGCACTTTG-3’; R, 5’-ATGGGCTACAGGCTTGTCACTC-3’), and GAPDH (F, 5’-TCGACAGTCAGCCGCATCTTC-3’; R, 5’-GCGCCCAATACGACCAAATCC-3’). Relative transcription levels were calculated by normalization to GAPDH mRNA.

Cell viability assay and cell death assay

Primary T cells (15000 cells/well), SV40-HDFs (2000 cells/well) or HT-29 (5000 cells/well) were plated in 384-well plate and were stimulated with indicated conditions. General cell viability was measured by using CellTiter-Glo Luminescent Cell Viability Assay (Promega, G7572) according to the manufacturer’s instructions. The percentage of viability was normalized to untreated controls. Cell death detection was performed by direct staining with 0.5 μg/ml Propidium Iodide (PI) and 0.5 μg/ml DAPI for 10 min and imaging with fluorescence microscope. PI positive dead cell was counted and normalized to DAPI counts. For continuous cell death measurement, cells were simulated with indicated conditions and stained with 200 nM Hoechst 33342 (Beyotime, C1022) and 200 nM SYTOX Orange (Thermo, S34861) at the same time. The percentage of dead cell was assayed every 1 hour by imaging with BioTek Cytation 3 for 18 hours with 5% CO and 37 °C condition. Results were exported as counts per well to be processed and graphed using Prism 9.

Protein array

Semiquantitative analysis of 174 cytokine levels of P2's serum samples before (normal stage) or after (onset, non-flare) the first onset of fever was performed with human Cytokine Array G2000 (RayBiotech) by Novogene Company (Beijing, China). Generally, the cytokine array glass slides were firstly blocked for 1 hour at room temperature and then incubated with 100 μl serum samples at 4 °C overnight. On the second day, the slides were washed with wash buffer and incubated with Biotin-Conjugated Anti-Cytokines and HiLyte Plus 555 Streptavidin-Fluor according to the manufacturer’s instructions. The results were scanned with InnoScan 300 Microarray Scanner (Innopsys) at 532 nm with 10 μm resolution. The mean fluorescence intensity (MFI) was calculated and exported for further analysis. According to the pattern of serum cytokine changes corresponding to the fever stage, the differentially expressed cytokines compared with healthy donors were clustered into 3 groups as listed below. Group 1: GRO, MIF, uPAR, IL-12 p40, HGF, I-TAC, sTNF-RI, IL-11, ICAM-3, Eotaxin-2, IL-1 R4/ST2, IL-8, GCP-2, M-CSF, TRAIL R4, Osteoprotegerin, VEGF, MCP-3, IL-1β, FGF-4, MIP-3α, ENA-78, MIP-1α, TRAIL R3, MIP-1β, Lymphotactin. Group 2: MCP-2, ErbB3, IL-3, NT-4, Flt-3 Ligand, LAP, Dtk, b-NGF, IGFBP-1, MIP-3β, Fas/TNFRSF6, BLC, sTNF RII, Angiopoietin-2, IGFBP-3, IL-12 p70, IL-1α, IL-16, bFGF, TNF-α, MCP-4, LIF, MIG, IP-10. Group 3: HCC-4, Eotaxin, IGFBP-6, MPIF-1, DR6 (TNFRSF21), VEGF R3, IL-18 Rβ, Thrombopoietin, MMP-1, VE-Cadherin, PIGF, IL-2 Rγ, IL-1 R II, Leptin R, IL-18 BPα, IL-2 Rα, IL-2 Rβ, IL-4, PDGF Rβ, BMP-5, IL-13 Rα2, NGF R, Prolactin, Cardiotrophin-1, BMP-7, IL-10 Rβ, SDF-1β, IL-21R, IL-5 Rα, Siglec-5, TIMP-4, SCF.

Human and mouse cytokines measurement

Human or mouse serum cytokines were measured by enzyme-linked immunosorbent assay (ELISA) (Multi Sciences: 70-EK1280, 70-EK194, 70-EK182HS, 70-EK106HS, 70-EK191, 70-EK180HS, 70-EK1F01, 70-EK1F02, EK206HS, EK282HS/2) according to the manufacturer’s instructions. T cell culture supernatant cytokines were determined by Cytometric Bead Arrays (BD Biosciences) according to the manufacturer’s instructions. Human serum adenosine deaminase (ADA) activity was measured with Boyle colorimetric method by using ADA Activity Assay Kit (Yuanye Bio, R22207) according to the manufacturer’s instructions. Student’s t-test was performed for the statistical analysis.

In vitro cell proliferation assay

Fresh isolated CD3+ T cells were labeled with CFSE (Thermo, 65-0850-84, 5 μM) and then stimulated with anti-CD3/CD28 beads (T&L Biotechnology) at 1:1 ratio supplemented with hrIL-7 (PrimeGene) and hrIL-15 (PrimeGene) in 48-well plates for 96 hours. Then all cells were co-stained with CD4-BB515 and CD8-BV510 antibodies and analyzed using CytoFlex (Beckman).

Gene editing of patient T cells

Primary T cells isolated from patients were stimulated with anti-CD3/CD28 beads for 3 days. For electroporation, 180 pmol sgRNA and 25 μg Cas9 protein were mixed and incubated for 15 min at room temperature for ribonucleoprotein complexes (RNP) formation. Then 3e6 T cells resuspended in opti-MEM were mixed with RNP and 6 μg dsDNA template and electroporated using Gemini X2 (BTX) under the #905 program for human primary T cells. After electroporation, 1 ml warm X-VIVOTM 15 Serum-free Hematopoietic Cell Medium (Lonza) supplemented with 10% FBS was immediately added to the cuvette and the mixture was then carefully transferred to 24-well plates for further culture and analysis.

T cell-MDM co-culture assay

Freshly isolated monocytes were seeded in 24-well plate and differentiated into MDMs with 20 ng/ml GM-CSF treatment for 10 days. Then, 0.4 µm pore polycarbonate membrane inserts containing T cells isolated from PBMCs of patient or healthy donors were placed into the 24-well plate for the T cell-MDM co-culture at the ratio of 5:1 for 8 hours. Finally, T cells were removed and bottom MDMs were collected for mRNA extraction.

Mice

All mouse studies complied with relevant ethical regulations and approved by Laboratory Animal Welfare and Ethics Committee of Zhejiang University (Registration No.: ZJU20230495). The Cd8-creERT2 and Lyz2-creERT2 strains on C57BL/6J background were purchased from GemPharmatech. The Rosa26-mTmG strain on C57BL/6J background was purchased from Cyagen. The Ripk1 knockout and Ripk1 +/k376r mouse strains were kind gifts from Professor Haibing Zhang (Shanghai Institute of Nutrition and Health, , Chinese Academy of Sciences). All mice were maintained under specific pathogen-free condition with free access to food and water at Laboratory Animal Center of Zhejiang University (Temperature: 20–26℃, humidity: 40–70%, light intensity: 15–20 lx, noise: <60 dB). In mouse studies, male Rosa26-mTmG mice in lineage negative bone marrow cell transplantation assay were used. For other mouse strains, mice were randomized assigned to the experiments with no regard for sex.

Lineage negative bone marrow cells isolation, transduction, transplantation, induction and LPS challenge

Mice were euthanized by CO₂ inhalation. Mouse lineage negative bone marrow cells (Lin– BMs, including hematopoietic stem cells and marrow stromal cells) from bone marrow were isolated from 6- to 8-week-old donor mice using Lineage cell depletion kit (Miltenyi Biotec, 130-090-858) according to manufacturer’s instruction. Lin– BMs were plated in 12-well plate and were transduced with lentivirus carrying human RIPK1 inducible expression system (MOI=50). Transduction efficiencies were measured with flow cytometry after 24 hours culturing and 200,000 of transduced Lin– BMs cells were transplanted into lethally irradiated (4 Gy, 2 times) male recipient mice (8 to 12 weeks old). Transplantation efficiencies were validated by flow cytometry analysis after 2 weeks. Successfully transplanted mice were injected i.p with 20 mg/ml tamoxifen (MCE, HY-13757A) dissolved in corn oil (MCE, HY-Y1888) every two days for 5 times to induce the expression of WT, K377E, R390G or D324V mutated RIPK1. Then, these mice were injected i.p with 0.5 mg/kg lipopolysaccharide (LPS) (Sigma, L6529) and serum were collected before injection or injected for 2 hours. Serum Il6 and Tnf were measured by ELISA as described.

Immunoprecipitation and western blot

For TNFR signaling complex (TNF-RSC) enrichment, 5e6 SV40-HDF cells or HT-29 cells were seeded and treated with flag tagged TNF (ENZO, ALX-522-008) for indicated times. The samples were immediately washed with cold PBS and lysed in cold lysis buffer (25 mM Tris-HCl, pH 7.4, 150 mM NaCl, 0.5% NP-40, 10% glycerol) with freshly added inhibitors blocking protease and phosphatase (Merck: 4906837001, 4693159001) for 30 min. Then samples were cleared through centrifugation at 20000 xg for 10 min and protein concentrations were determined by Pierce™ BCA Protein Assay Kits (Thermo, 23225). Then the TNF-RSC was immunoprecipitated with FLAG M2 magnetic beads (Sigma, M8823) at 4 °C for 4 hours and the protein complex was directly eluted by boiling in 1×SDS loading buffer. For TNF downstream signaling analysis, 4e5 cells were seeded and treated as indicated and then cells were harvested and lysed directly with sample loading buffer. These samples were separated through routine SDS-PAGE and transferred to 0.45 μm nitrocellulose membrane (Cytiva, 10600002) and blotted with indicated antibodies (For Western blotting, antibodies were applied at a 1:1000 dilution).

CyTOF

PBMCs were freshly isolated from whole bloods of four patients (P1 and P2; CRIA-1 and CRIA-2) and four unaffected controls, then blocked and labeled with conjugated antibodies as described38. Fixed cell samples were resuspend analyzed on a mass cytometer (Helios, Fluidigm). The CyTOF data exported as FCS files were gated in FlowJo (v10.0.7) to remove doublets and CD45+ single cells identified by DNA (191Ir and 193Ir)、194Pt and event length. For downstream analysis, 10,000 cells were randomly selected from each sample. The raw values of FCS files were transformed using the arcsinh function with a cofactor of 5. Phenograph clustering in cytofkit R package was performed on all subsampled sample events and the transformed values of all events were subjected to t-SNE dimension reduction using R-tsne package39, 40.

Antibodies

The following antibodies were purchased from Cell Signaling Technology: pRIPK1 (S166) (65746), RIPK1 (3493), pMLKL (S358) (91689), MLKL (14993), pIKKα/β (S176/180) (2697), IKKβ (2370), Sharpin (12541), pp65 (S65) (3033), p65 (8242), pIκBα (S32) (2859), IκBα (4814), pERK1/2 (T202/Υ204) (4370), ERK1/2 (4695), pJNK (T183/Y185) (9251), JNK (9252), HA (3724), cleaved caspase-3 (D175) (9661), caspase-3 (9662). NEMO (sc-8330) was purchased from Santa Cruz Biotechnology. GAPDH (SA30-01) was purchased from HUABIO.

Data availability

The raw sequence data of single-cell RNA sequencing generated in this study have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human) under accession code: HRA016329 [https://ngdc.cncb.ac.cn/gsa-human/browse/HRA016329]. The raw whole-exome sequencing data are protected and are not publicly available due to data privacy laws and ethnics policy. Due to patient privacy concerns, the data generated from human samples can be obtained from the corresponding author upon reasonable request. All other study data are available within the article or its supplementary information files with no restriction or from the corresponding author. Source data are provided with this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors make a compelling case for primary CD8 T-cells as sensitised for (RIPK1 induced) programmed cell death as instigators for triggering an elevated inflammatory response of autologous monocytes in patients with inherited inborn errors of cell death. Patient data (of 2 siblings) and mouse model data piece together a story that includes genetic reconstitution data, additional CRIA patient family data and is confirmed by anti-TNF/IFN treatment data of the patients. This reviewer is overall satisfied by the wealth of data presented. However, could it be that other cells, non-immune cells such as smooth muscle cells, endothelial cells and fibroblasts that can produce TNF, contribute to the auto inflammatory disease? It might not only CD8+ T-cells that instigate the cascade; perhaps TNF produced by tissue cells (non-immune cells) triggers the auto inflammatory cascade that otherwise works via increased sensitisation to apoptosis. please comment. Otherwise, only small points, a graphic summary would be a helpful addition to the manuscript (although the journal editors should decide if this fits). Two typographical errors seen in line 293 (generated) and 357 (specially).", "reviewer_2": "Dai et al have identified two related autoinflammatory disease patients with heterozygous variants in RIPK1 K377E/R390G. They associate these heterozygous variants with increased CD8+ T cell death and elevated inflammatory cytokine secretion from patient monocytes and macrophages. Disruption of crosstalk between CD8+ T cells and myeloid cells by pharmacological inhibition of TNF and JAK1/2 resulted in disease control. This is a noteworthy study which would be of interest to the rare disease community. The characterisation of individual cell types (T cells, monocytes, etc), their interactions and the proposed mechanism of how this drives disease is extensive and well executed, particularly use of the transwell culture system and HSCs-transplantation in Figure 5. However, the mechanistic insight into how the R390G variant contributes to disease is lacking and a cause for concern. Because this is a study on a single kindred to convincingly show that this is a monogenic disorder the authors should correct the patient mutations in patient cells (eg: fibroblasts) using homology directed repair or prime editing. Then determine NFkB signalling and cell death responses to TNF stimulation. These isogenic cells would be ideal controls in all follow up experiments. Disruption of the ubiquitination site: K377 through a missense substitution to an E is understandably pathogenic, RIPK1 ubiquitination at K377 is required for stable TNFR induced NFkB activation rather than TNFR triggered cell death (PMID: 16603398, PMID: 16543241, PMID: 32999468). Over expression of K377E RIPK1 in HT-29 cells shows a convincing increased phosphorylation of RIPK1 and MLKL and decrease in NFkB signalling, ubiquitination of and elevated cell death, in response to TNF stimulation (Ex Fig 2), although these should be quantified and the appropriate number of experimental replications (ie: at least three) should be performed. These data, coupled with the induction of elevated NFkB driven cytokines in serum of mice transplanted with K377E RIPK1 T cells (Figure 5) provide a solid argument for K377E RIPK1 driving disease. In contrast, R390G RIPK1 expression alone had a limited effect on RIPK1 and MLKL phosphorylation and NFKB signalling in response to TNF stimulation (Ex data 2i and g). This data is particularly unconvincing since it is representative of only two replicates. At a minimum these experiments should be performed three times and densitometry changes for relevant bands should be presented (eg: (p)p65 compared to p65, IkBa bands relative to zero time point of each cell line, etc). Similarly, the change in TNFa and IL-6 in mouse serum in R390G RIPK1 expressing CD8+ linage cells is modest compared to WT RIPK1 expressing cells and it appears that this experiment has only been performed once. R390G RIPK1 overexpression does associate with increased TNF induced cell death in HT-29s (Ex Fig 2e-f) and R390G RIPK1 ubiquitination does appear delayed in Ex Fig 2l). But the data presented to validate the author’s proposed mechanism of R390G driving dysfunction of RIPK1 via interrupting interaction with MIB2 (Ex Fig 2l-n) is not convincing. Ubiquitination of WT and R390G RIPK1 looks comparable to me in Ex Fig 2m and the change in MIB2 binding in minimal in Ex Fig 2n and could be explained by the slightly smaller pulled down FLAG band (again, quantification using densitometry would help with data interpretation). The authors also do not provide reasoning on how a change to a glycine at 390 would interrupt binding to MIB2. Overall, I am not convinced that R390G RIPK1is a disease driving variant in itself, it may instead be a disease modifying variant that is only pathogenic in the context of a driving variant such as K377E. eg: R390G RIPK1 could have decreased stability and is not/lowly expressed in patient cells (this may not be seen in an over expression system), therefore disease pathogenesis is driven almost entirely driven by K377E. I note that while the CADD score is high, alpha missense rates this amino acid change for R390G as “ambiguous”. I encourage the authors to better determine the effect of R390G to publish the most robust findings which clinicians may take advice from. Here are a few points the authors should address to determine the role of R390G RIPK1: 1. Stability of R390G RIPK1: Use the established HT-29 over expression system to perform a cycloheximide chase to compare stability of R390G RIPK1 with other RIPK1 variants. 2. Interaction with MIB2: Try titrating levels of RIPK1 variants and/or MIB2 in the over expression IP to clearly show a decreased interaction between MIB2 and R390G over a range of concentrations. Alternatively, try BioID or TurboID. 3. Determine if Ubiquitination of RIPK1 at 377 is altered: Given that in the patient cells RIPK1 Ub is altered post TNF stimulation but the R390G mutant looks comparable to WT (over expression) it may be that (if stable) R390G inhibits K377 ubiquitination but does not affect other Ub sites on RIPK1. Thus, the change in Ub could be masked in the over expression system. The authors should specifically determine if K377 Ub is altered, eg: using plasmid mutagenesis to isolate specific Ub sites on RIPK1 and compare WT to R390G or perform Ub site mapping by Mass Spec. 4. Could R390G disrupt RIPK1 interaction with proteins other than MIB2 which are important for recruitment to the TNFR1 cytoplasmic domain or ubiquitination eg: TRADD (PMID: 8612133), cIAPs (PMID: 25192652) this could be tested in the same over expression system as Ex Data Fig 2. 5. The authors should also determine if R390 effects: RIPK1 trafficking to the TNFR1 complex, confirmational changes that allow RIPK1 to interact with other proteins or alters dimerization. I recommend the authors also try testing the above with coexpression of R390G with WT or K377E RIPK1 as this may contribute to the phenotype. Discussion Points that should be included: The authors should contextualise their findings on the T cell/Mono Mac driven disease course in autoinflammatory disorders driven by RIPK1 variants with autoinflammatory diseases driven by OTULIN deficiency/dysfuncton(driven almost entirely by myeloid cells, PMID: 30804083) and LUBAC deficient patients (more involvement of lymphocytes and stromal cell types, eg: PMID: 32687504, PMID: 31462647, PMID: 35534698, etc) The equivocation of monoclonal blockade of IFNg in Figure 5f and treatment of patients with ruxolitinib (Fig 6) is incorrect, JAK1/2 blockade inhibits inflammatory signalling from a variety of cytokines including IFNab and IL-6 which are elevated in the RIPK1 patients. I appreciate that the authors call this treatment regime TNF/IFN but the distinction should be highlighted in the discussion. Particularly because RIPK1 has been shown to drive IFNab signalling under conditions of OTULIN dysfuncton(PMID: 29950720) and JAK1/2 blockade had therapeutic benefit in these patients (PMC11022884 ). Do the authors think that IFNg is specifically is important for cell death in these patients as per PMID: 35139355? -No increase in TSZ induced cell death compared to TS and TZ, in fact TSZ looked less effective than TS in T cells (Figure 1h) -comment? Minor Points: The manuscript presents an impressive amount of experimental data; but many key experiments have been performed only once or twice (from my reading of the legends) and generally only show a single representative repeat. As I highlighted in the context of western blotting of the NFkB signalling cascade this is a real problem when trying to establish the robustness of the patient phenotype. This is particularly important to the field of rare disease since these findings may be used for differential diagnosis of other patients, reporting that there is a strong effect when it is actually variable will impede our understanding of other novel RIPK1 variants. The authors need to repeat key experiments at least three times to strengthen the validity of their conclusions and provide summary data from independent biological replicates (ie: pooling results across experiments). -Ex Figure 2A is missing panels and does not appear to show protection from cell death by Nec-1 in patient fibroblasts. Please quantify to clearly show this result. -Ext Fig 3e, panels are missing -Extended 2d,e should have empty vector control -No isotype controls shown for Fig 2f -Please list for cytokine groups in Figure 3. “which was reported to induce more severer cell death in Ripk1K376R/K376R cells” Should say: “which was reported to induce more sever cell death …\"", "reviewer_3": "Biallelic loss-of-functon(LOF) variants in RIPK1 cause RIPK1 deficiency, whereas monoallelic cleavage-resistant gain-of-functon(GOF) variants result in an autoinflammatory syndrome called cleavage-resistant RIPK1 induced autoinflammatory (CRIA) syndrome. In this manuscript, Jialin Dai, Qing Zhou, and colleagues describe two patients from a single family harboring compound heterozygous missense variants in RIPK1 (K377E and R390G), who presented with an autoinflammatory syndrome characterized by recurrent fever, lymphadenopathy, rash, and enteropathy. At the molecular level, the RIPK1-K377E variant was shown to be ubiquitination-deficient and constitutively active. The R390G variant is also considered a GOF mutation, although concerns remain regarding its pathogenicity and molecular mechanism, as discussed in major comment no. 4. Functional studies demonstrated augmented apoptosis and necroptosis following TNF stimulation in the patients’ dermal fibroblasts and primary T cells, consistent with RIPK1 GOF activity. scRNA-seq of patients’ PBMCs identified an aberrant monocyte/macrophage population with elevated proinflammatory cytokine expression. CyTOF-based PBMC subset analysis revealed reduced frequencies of CD8+ T cells in the patients. Upon anti-CD3/CD28 stimulation in in vitro, a further decrease in CD8-hi T cells and an emergence of CD8-lo T cells were noted. Cytokine production was enhanced in both CD8+ and CD4+ T cells. Co-culture experiments with patient-derived T cells and healthy donor monocyte-derived macrophages demonstrated that the patient T cells exacerbated proinflammatory cytokine production by allogeneic monocyte-derived macrophages, suggesting a central role of T cells in driving autoinflammation in these patients. Therapeutically, the patients’ autoinflammatory manifestations were successfully managed with the anti-TNFα monoclonal antibody adalimumab and the JAK1/2 inhibitor ruxolitinib. Notably, a reduced proportion of CD8+ T cells among total T cells was also consistently observed in other immune dysregulation conditions, including CRIA and SHARPIN deficiency. In summary, the experiments in this study are thoughtfully designed, and the data generated are largely robust and support the central conclusions. However, several experiments require revision or clarification, as detailed in the major and minor comments below. This study demonstrate decreased CD8+ T cells as one of common immunological abnormalities in other inborn errors of cell death (IECD) such as CRIA and SHARPIN deficiency, indicating the CD8+ T cell-driven autoinflammation may play a role in other IECDs. While this study will likely be of interest to researchers in the field of inborn errors of immunity, the findings may limit its appeal to the broader readership of Nature Communications. I believe the manuscript may be more appropriately suited for a clinically oriented journal that emphasizes translational research and patient-centered findings. Major comments: 1. One of the study’s principal observations is the pronounced depletion of CD8+ T cells in patients carrying RIPK1‑K377E/R390G variants and, similarly, in those with CRIA. The authors hypothesize that augmented programmed cell death of CD8+ T cells results in increased cytokine release from those cells, in turn, potentiate monocytes and macrophages to secrete proinflammatory cytokines. To support this model, direct comparisons of CD8+ versus CD4+ T cell responses are indispensable. Several critical comparisons, however, are currently missing: - Viability following TS or TSZ stimulation (as shown in Fig 1h) for CD8+ versus CD4+ T cells - Phosphorylation of MLKL (as shown in Extended Data Fig 2c) in CD8+ versus CD4+ T cells - Proliferation following anti-CD3/CD28 stimulation (as shown in Fig 4b) for CD8+ versus CD4+ T cells 2. In Fig. 3g‑j, both RIPK1 phosphorylation and caspase‑3 cleavage appear more pronounced in CD4+ than in CD8+ T cells. This seems at odds with the model that positions CD8+ T cells as the primary drivers of the pathogenesis. Could the authors clarify this discrepancy? 3. In Fig 4e and 4f, Patient 2 already shows an expanded CD4/8 double‑negative T cell (DNT) population at baseline (day 0), and this subset appears to increase further over time. - Were elevated DNT frequencies observed consistently in all patients’ (P1 and P2) PBMC samples? - Are these DNTs αβ T cells or γδ T cells? If they are αβ DNTs, it is important to assess whether they originate from CD8+ T cells. Clarifying the identity and origin of the DNT compartment is critical, as it could significantly impact the interpretation of CD8+ T-cell reduction in these patients. To directly address this, CD8+ T cells should be isolated and stimulated under the same conditions used in Fig. 4f to determine whether they transition into the DNT phenotype. 4. Pathogenicity and underlying molecular mechanism of the RIPK1‑R390G variant are not yet convincingly demonstrated. Given that the monoallelic CRIA‑associated RIPK1 variants act as GOF alleles, the pathogenic potential of biallelic GOF variants must be interpreted with caution. Below are the reasons of my concern: - MLKL phosphorylation in R390G‑transduced HT‑29 cells is markedly lower than in K377E (Extended Data Fig 2g). - pIKKα/β ,pp65, pIκBα, pERK, and pJNK in R390G-transduced HT-29 cells following TNF stimulation seem almost comparable to WT (Extended Data Fig 2i). - Despite a modestly reduced MIB2 interaction (Extended Data Fig 2n), total ubiquitination of R390G RIPK1 appears intact (Extended Data Figs 2m) Extended Data Fig 2d also may indicate ubiquitination of RIPK1-R390G is normal (there was no apparent size difference between WT and R390G RIPK1, whereas the K377E variant exhibited a reduced molecular weight, which may reflect impaired ubiquitination). The statement in Line 167 (“The reduced interaction between MIB2 and R390G could partly explained the impaired ubiquitination of R390G RIPK1.”) therefore seems inaccurate. Taken together, the evidence still allows for the possibility of RIPK1-K377E being pathogenic GOF variant with incomplete penetrance, rather than the R390G being bona fide GOF variant. I would recommend following experiments to address this point. - Immunophenotyping of mono‑allelic K377E and R390G carriers to determine whether either (or both) variant(s) consistently segregates with immunological abnormalities such as increased CD4/8 ratio. - Assessment of RIPK1 ubiquitination in patient‑derived cells or in K377E/R390G double knock‑in cell lines. 5. Interpretation of scRNAseq analysis requires revision. The manuscript does not describe the methodology used to assign hematopoietic cell subset identities to the identified clusters. Please describe the tool/reference used for cell type assignment in the Methods section. The UMAP illustrates cell types that are either rare or absent in peripheral blood such as an oversized NKT cluster and a megakaryocyte cluster. With such data, it is inaccurate to discuss the quantitative assessment of the specific population (Line 176: The monocyte compartment in patients was significant expanded (Fig. 2a, Extended Data Fig. 3a). The CyTOF data do not show an expanded monocyte compartment. Did the patients’ complete blood counts (CBCs) reveal monocytosis? Moreover, the expanded cluster in the scRNA‑seq analysis is labelled “LDG,” not CD14+ or CD16+ monocytes. A more rational model would be that the cluster unique to patients, currently labelled “LDG”, represents a pathogenic, cytokine‑high monocyte subset. Intracellular staining of IL-1β and IL-6 show a bimodal distribution within the monocyte (Fig. 2f), indicating two functionally distinct subsets. 6. The current title is too broad. Consider rephrasing it to reflect the key discoveries. Minor comments: 1. Line 98: Please clarify whether the perianal abscess was of infectious origin or a manifestation of autoinflammation. If it was infectious, the phrasing in lines 103–104 may need to be revised accordingly. 2. Line 106: Do the authors have any insights into the cause of elevated ADA levels observed in the patients? 3. Line 135 / Fig 1h: Although the manuscript states that T cells from heterozygous carriers and controls did not exhibit sensitivity to TS/TSZ stimulation, both the father and mother appear to show an intermediate phenotype. This should be confirmed by repeating the experiment in the parental samples and expanding the number of healthy controls. 4. Line 131 / Extended Data Fig 2a: Some data in Extended Data Fig 2a are lacking and difficult to interpret. However, the available data ( TS vs TS + Nec-1s in P2) do not clearly support the manuscript’s claim that Nec-1s rescued both apoptosis and necroptosis in patient-derived HDFs. Please clarify or revise this point. 5. Line 163: The list of E3 ubiquitin ligases that were screened should be stated, even if the data are not shown. 6. Line 167: Reference 20 does not provide evidence of MIB2-mediated RIPK1 ubiquitination. Please ensure that citations accurately reflect the claims being made. 7. Line 164 and 166: These lines contain redundant information and should be consolidated to improve clarity and conciseness. 8. Line 198 and Extended Data Fig 3e: data appear to be missing and should be provided. 9. Line 216-218: Although the group 2/3 cytokine definitions are included in the figure legend, a brief description in the main text would greatly improve reader comprehension. 10. Line 228: While statistically significant, the differences in group 2 cytokine expression appear modest. For biological relevance, individual expression data for key cytokines implicated in disease pathogenesis should also be presented. 11. Fig 5e: The data show a clear bimodal peak in cytokine expression. This raises the question of whether a distinct CD8+ T cell subset (e.g., CD8-lo) is more prone to RIPK1-induced cytokine production. Additional clarification would be valuable. 12. Line 350 / Fig 5h: Although described as \"high\", many comparisons yielded p-values > 0.05. Either replicate numbers should be increased or the description amended to avoid overstating the significance. 13. Treatment section and relevant discussion / abstract sections: While it is clear that adalimumab and ruxolitinib were clinically effective, the current text may give the impression that IFN-γ blockade specifically contributed to the therapeutic effect of ruxolitinib. Given that JAK inhibition affects a broad range of cytokine pathways, this conclusion is premature. Please revise the relevant discussion to reflect this appropriately. 14. Line 444: Is there direct evidence that CD8+ T cells secrete high levels of TNF and IFN-γ during the contraction phase induced by programmed cell death? If not, this mechanistic link should be presented more cautiously. 15. The following issues in the Methods section should be addressed for clarity and accuracy: - Line 594: The SHARPIN-deficient patient is mentioned in the Results section but not included in the Methods. - Line 750: Lineage-depleted bone marrow cells should not be referred to as hematopoietic stem cells, as this population includes a mixture of progenitor subsets. Please use a more accurate definition. - Line 765: Please clearly define what cells were used. 16. To improve clarity and ensure consistency across figures, the following revisions are recommended: - Fig 1c: The unit of measurement is missing - Fig 1e and Extended Data Fig 1a-c: Marking the time points corresponding to febrile episodes would enhance the interpretability. - Fig 1f: The error bars seem to represent SEM, although the figure legend defines them as SD. Please ensure the figure legend accurately reflects the error bar definition. - Fig 2a: The current color scheme uses similar colors for adjacent populations, making them difficult to distinguish. Please apply a revised color palette with greater contrast between clusters. - Fig 3: Marker sizes vary within the same figure panel and should be standardized. - Fig 4f, Extended Data Fig 6b: Axis labels are missing and should be added for clarity. - Extended Data Fig 2f: Marker symbols are too small to distinguish clearly. Please enlarge the points for better visibility. - Extended Data Fig 2f and 2j: The K377R variant data are not discussed in the main text and may be omitted. - Extended Data Fig 5a: One of the two major CD16+ monocyte populations and the CD14+ monocyte population shown in Fig. 5a do not appear in any of the replicates presented in Fig. 5b. For consistency, both subsets should be represented, or the discrepancy should be explained. - Extended Data Fig 6b: The DNT fraction in P1/DMSO condition appears to be incorrectly labeled and should be re- examined. 17. The following wording issues were noted: - Line 110: Capitalize “Sanger”. - Line 351: Consider specifying the subset as “CD8+” (or “CD8α+”). - Line 414: The term “gain-of-function” appears prior. Please introduce the abbreviation the first time it is used. - Line 422: Define “MEF”. - Line 733: “MDM” has already been defined earlier; no need to redefine it here. - Line 744: Use “Ripk1” instead of “RIPK1”. - Throughout the manuscript, “patients” is occasionally used when data from only P1 or P2 are shown. Replace with “P1” or “P2” as appropriate to ensure accuracy."}, "author_response": "Reply to the Referee #1\nReviewer #1 (Remarks to the Author):\nThe authors make a compelling case for primary CD8 T-cells as sensitised for (RIPK1 induced)\nprogrammed cell death as instigators for triggering an elevated inflammatory response of\nautologous monocytes in patients with inherited inborn errors of cell death. Patient data (of 2\nsiblings) and mouse model data piece together a story that includes genetic reconstitution data,\nadditional CRIA patient family data and is confirmed by anti-TNF/IFN treatment data of the\npatients.\nThis reviewer is overall satisfied by the wealth of data presented. However, could it be that other\ncells, non-immune cells such as smooth muscle cells, endothelial cells and fibroblasts that can\nproduce TNF, contribute to the auto inflammatory disease? It might not only CD8+ T-cells that\ninstigate the cascade; perhaps TNF produced by tissue cells (non-immune cells) triggers the auto\ninflammatory cascade that otherwise works via increased sensitisation to apoptosis. please\ncomment.\nReply: We sincerely appreciate your constructive comments. In response to your question, we\nemployed the SV40-immortalized human dermal fibroblasts (SV40-HDFs) derived from\nunaffected controls and the patient 2, which is the only available patient-derived non-immune cells.\nThe SV40-HDFs were subjected to programmed cell death induction via TS/TSZ stimulation for\n6 hours and then cultured in fresh DMEM for 24 hours. The secreted TNF in the medium was\nmeasured, and we found that TS/TSZ-stimulated SV40-HDFs from P2 produced more TNF\ncompared to those from unaffected controls (Fig. R1). This suggests that the patient’s fibroblasts\ncould be a potential source of inflammation, which might contribute to the skin rash phenotype.\nHowever, since the fever episodes showed significantly stronger periodicity than the skin\nmanifestations, this study focused primarily on alterations in peripheral immune cells.\nNevertheless, this finding provides evidence for future investigations into the skin phenotype\ncaused by RIPK1 overactivation.\nFig. R1 SV40-immortalized human dermal fibroblasts (SV40-HDFs) derived from eight\nunaffected controls (C, n=6) and P2 (n=6) were stimulated with TS or TSZ for 6 h. After\nstimulation, cells were washed twice with PBS and further cultured in complete DMEM medium\nfor 24 h. The Supernatants were collected and TNF production were determined by cytometry bead\narray. T denotes 20 ng ml-1 TNF; S denotes 250 nM SM-164; Z denotes 25 μM z-VAD-fmk. The\ngraphs showed mean±SD. Statistical significance was determined by two-tailed unpaired t-test,\n***P < 0.001,****P < 0.0001.\nOtherwise, only small points, a graphic summary would be a helpful addition to the manuscript\n(although the journal editors should decide if this fits). Two typographical errors seen in line 293\n(generated) and 357 (specially).\nReply: We sincerely appreciate your insightful suggestions. As recommended, we provided a\ngraphical summary (Fig. 7g) to illustrate the key findings.\n[editorial note: figure redacted]\nFig. 7g RIPK1 activation-dependent CD8+ T cell death-triggered inflammatory response of\nmyeloid cells causes systemic autoinflammatory diseases.\nReply to the Referee #2\nReviewer #2 (Remarks to the Author):\nDai et al have identified two related autoinflammatory disease patients with heterozygous variants\nin RIPK1 K377E/R390G. They associate these heterozygous variants with increased CD8+ T cell\ndeath and elevated inflammatory cytokine secretion from patient monocytes and macrophages.\nDisruption of crosstalk between CD8+ T cells and myeloid cells by pharmacological inhibition of\nTNF and JAK1/2 resulted in disease control. This is a noteworthy study which would be of interest\nto the rare disease community. The characterisation of individual cell types (T cells, monocytes,\netc), their interactions and the proposed mechanism of how this drives disease is extensive and\nwell executed, particularly use of the transwell culture system and HSCs-transplantation in Figure\n5. However, the mechanistic insight into how the R390G variant contributes to disease is lacking\nand a cause for concern.\nReply: We are grateful for your supportive assessment and constructive comments. The mechanism\nby which the RIPK1 R390G variant contributes to disease has been carefully elucidated in the\nfollowing sections.\nBecause this is a study on a single kindred to convincingly show that this is a monogenic disorder\nthe authors should correct the patient mutations in patient cells (eg: fibroblasts) using homology\ndirected repair or prime editing. Then determine NFkB signalling and cell death responses to TNF\nstimulation. These isogenic cells would be ideal controls in all follow up experiments.\nReply: We appreciate your suggestion and agree that the correction of K377E/R390G mutations\nin patient cells is a direct and compelling approach to validate the pathogenic role of the variants.\nHowever, during our attempts, the patient 2 (P2) fibroblasts exhibited abnormal morphological\nchanges, likely due to stress from viral transduction (Fig. R2). Despite multiple efforts, we were\nunable to obtain viable monoclonal lines from the transduced P2 fibroblasts, preventing successful\ncompletion of this experiment.\nFig. R2 Images showing the morphology of SV40-immortalized human dermal fibroblasts\n(SV40-HDFs) from patient 2 (P2) before and after adeno-associated virus transduction.\nAlthough the correction of RIPK1 K377E/R390G mutation in fibroblasts was not achieved,\nwe have performed in situ knockin of wild type (WT) RIPK1-P2A-TurboGFP in P2’ T cells using\nCRISPR/Cas9 (Extended Data Fig. 6 h). Flow cytometry analysis showed a knockin efficiency of\n9.92%, as indicated by TurboGFP positivity (Extended Data Fig. 6 i). Functional assessment\ndemonstrated that this genetic correction successfully rescued the aberrant T cell phenotype, as\nmeasured by the reversal of the increased CD4/CD8 ratio following TS/TSZ stimulation (Fig. 4 g,\nh). These results provide direct evidences that K377E/R390G mutations in RIPK1 are responsible\nfor the T cell hypersensitivity to cell death.\nExtended Data Fig. 6h, Schematic of CRISPR/Cas9-mediated in situ knockin of Wild type (WT)\nRIPK1 into P2’s RIPK1 locus. i, Representative contour plots of Turbo-GFP+ T cells percentage\nindicating WT RIPK1 knockin efficiency.\nFig. 4g, h,Representative contour plots showing percentages of CD4+ T, CD8+ T in CD3+ T cells\nof P2 with (TurboGFP+) or without (TurboGFP-) restoration using CRISPR/Cas9-mediated in situ\nknockin of WT RIPK1-P2A-TurboGFP under stimulation of T with or without S, Z for 6 hours (g).\nStatistical analysis of fold change of CD4 /CD8 ratio in CD3+ T cells from unaffected controls\n(n=7) and P2 (n=3) (h).\nAdditionally, we employed CRISPR-Cas9 genome editing to generated isogenic\nRIPK1K377E/K377E or RIPK1R390G/R390G knockin HT-29 cells (Fig. R3). Both mutant cell lines\nexhibited comparable RIPK1 expression levels, thereby resolving the problem of inconsistent\nRIPK1 expression observed in virus-mediated RIPK1 complementation system (Fig. R4 c, d). We\nthen systematically reevaluated the function of K377E and R390G mutations, demonstrating that\nboth mutations sensitize cells to programmed cell death, suppress NF-κB signaling and the\ndownstream cytokine productions (Fig. R4 a-e). Consistent with results from the complementation\nsystem, K377E consistently exhibited stronger pathogenicity than R390G. All experiments were\nperformed in three independent biological replicates to ensure the reproducibility and reliability.\nFig. R3 Sanger sequencing chromatograms of RIPK1 in genomic DNA from RIPK1 WT, K377E\nor R390G knockin cell lines.\nFig. R4 a, b, RIPK1 WT or K377E/R390G knockin cell lines under stimulation of TZ with or\nwithout N as indicated. Cell viability measured by CellTiter-Glo luminescent cell viability assay.\nThe graphs showed mean±SD. Data from three independent biological experiments are shown.\nFig. R4 c-e, c,RIPK1 WT or K377E/R390G knockin cell lines under stimulation of TZ with or\nwithout N as indicated. Whole cell lysates were immunoblotted with indicated antibodies. d, NF-\nκB and p38 activation of RIPK1 WT or K377E/R390G knockin cell lines treated with TNF for 5,\n15 immunoblotted with indicated antibodies. e,mRNA levels of TNF, IL-8, IL-1B in RIPK1 WT\nor K377E/R390G knockin cell lines treated with TNF for 6 hours and determined with qRT-PCR.\nThe graphs showed mean±SD. Data from three independent biological experiments are shown.\nStatistical significance was determined by two-tailed unpaired t-test, *P < 0.05, **P < 0.01, ***P\n< 0.001, ****P < 0.0001.\nTo ensure experimental rigor, we replaced all previous results from the complementation\nassays with data obtained from the isogenic knockin cell models (Extended Data Fig. 2 d, e, f, h,\ni. k).\nDisruption of the ubiquitination site: K377 through a missense substitution to an E is\nunderstandably pathogenic, RIPK1 ubiquitination at K377 is required for stable TNFR induced\nNFkB activation rather than TNFR triggered cell death (PMID: 16603398, PMID: 16543241,\nPMID: 32999468). Over expression of K377E RIPK1 in HT-29 cells shows a convincing increased\nphosphorylation of RIPK1 and MLKL and decrease in NFkB signalling, ubiquitination of and\nelevated cell death, in response to TNF stimulation (Ex Fig 2), although these should be quantified\nand the appropriate number of experimental replications (ie: at least three) should be performed.\nThese data, coupled with the induction of elevated NFkB driven cytokines in serum of mice\ntransplanted with K377E RIPK1 T cells (Figure 5) provide a solid argument for K377E RIPK1\ndriving disease.\nIn contrast, R390G RIPK1 expression alone had a limited effect on RIPK1 and MLKL\nphosphorylation and NFKB signalling in response to TNF stimulation (Ex data 2i and g). This data\nis particularly unconvincing since it is representative of only two replicates. At a minimum these\nexperiments should be performed three times and densitometry changes for relevant bands should\nbe presented (eg: (p)p65 compared to p65, IkBa bands relative to zero time point of each cell line,\netc) . Similarly, the change in TNFa and IL-6 in mouse serum in R390G RIPK1 expressing CD8+\nlinage cells ismodest compared to WT RIPK1 expressing cells and it appears that this experiment\nhas only been performed once. R390G RIPK1 overexpression does associate with increased TNF\ninduced cell death in HT-29s (Ex Fig 2e-f) and R390G RIPK1 ubiquitination does appear delayed\nin Ex Fig 2l). But the data presented to validate the author’s proposed mechanism of R390G\ndriving dysfunction of RIPK1 via interrupting interaction with MIB2 (Ex Fig 2l-n) is not\nconvincing. Ubiquitination of WT and R390G RIPK1 looks comparable to me in Ex Fig 2m and\nthe change in MIB2 binding in minimal in Ex Fig 2n and could be explained by the slightly smaller\npulled down FLAG band (again, quantification using densitometry would help with data\ninterpretation). The authors also do not provide reasoning on how a change to a glycine at 390\nwould interrupt binding to MIB2.\nOverall, I am not convinced that R390G RIPK1is a disease driving variant in itself, it may instead\nbe a disease modifying variant that is only pathogenic in the context of a driving variant such as\nK377E. eg: R390G RIPK1 could have decreased stability and is not/lowly expressed in patient\ncells (this may not be seen in an over expression system), therefore disease pathogenesis is driven\nalmost entirely driven by K377E. I note that while the CADD score is high, alpha missense rates\nthis amino acid change for R390G as “ambiguous”. I encourage the authors to better determine the\neffect of R390G to publish the most robust findings which clinicians may take advice from.\nReply: We are grateful for your important questions regarding the functional characterization and\nunderlying mechanisms of the R390G mutation. Following your suggestion, we quantified all the\nwestern blot results relative to NF-κB signaling pathways, as presented in the revised manuscript\n(Extended Data Fig. 2 b, c, f, g, h). These quantitative analyses support our conclusions that both\nK377E and R390G mutations suppressed the NF-κB activation. We agree that K377E is a strong\ndriver of the inflammatory response and R390G is a contributing driver of the inflammatory\nresponse.\nTo address your concerns about the role of R390G in cell death,NF-κB signaling activation\nas well as autoinflammation pathogenesis, we have conducted the following additional\nexperiments, the results of which provide substantial further support for our original conclusions:\n1. We generated K377E or R390G knockin HT-29 cell lines using CRISPR-Cas9 (Fig. R3). This\napproach ensures that the mutations were studied in the native genomic and promoter context,\nwhich we believe is crucial for directly reflecting the bona fide cellular phenotype. In this\noptimized model, we repeated the key functional assays in triplicate. The data consistently\ndemonstrate that both K377E and R390G mutations lead to impaired NF-κB activation and\nenhanced RIPK1 activation-induced cell death, albeit the effect of R390G mutation is slightly\nweaker than that of K377E (Fig. R4 a-e).\nFig. R3 Sanger sequencing chromatograms of RIPK1 in genomic DNA from RIPK1 WT or\nK377E/R390G knockin cell lines.\nFig. R4 a, b, RIPK1 WT or K377E/R390G knockin cell lines under stimulation of TZ with or\nwithout N as indicated. Cell viability measured by CellTiter-Glo luminescent cell viability assay.\nc, Whole cell lysates were immunoblotted with indicated antibodies. d, NF-κB and p38 activation\nof RIPK1 WT or K377E/R390G knockin cell lines treated with TNF for 5, 15 immunoblotted with\nindicated antibodies. The graphs showed mean±SD. Data from three independent biological\nexperiments are shown.\nFig. R4 e,mRNA levels of TNF, IL-8, IL-1B in RIPK1 WT or K377E/R390G knockin cell lines\ntreated with TNF for 6 hours and determined with qRT-PCR.The graphs showed mean±SD. Data\nfrom three independent biological experiments are shown. Statistical significance was\ndetermined by two-tailed unpaired t-test, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.\n2. To further confirm the pathogenic function of K377E and R390G in vivo, we conducted\nadditional bone marrow transplantation experiments. The results from two independent\nexperiments support the autoinflammation-causing functional consequences of K377E /R390G\nmutation-expressing cd8+ Τ cells (Fig. R5 a, b; new data shown in panel b). Importantly, this\npathogenic effect was observed in R390G-expressing Cd8+ Τ cells in the absence of the K377E\nmutation, indicating that R390G is also a contributing driver of disease pathogenesis. The basal\ngroup from the new experimental data has been incorporated into the main figure to support our\nconclusions (new Fig. 5 h, i).\nFig. R5a,b ELISA measurement of serum Tnf and Il-6 levels in mice specifically overexpressing\nWT or mutated hRIPK1 in Cd8+ T cells or myeloid cells with or without 0.5 mg/kg LPS challenge\nfor 2 hours. The graphs showed mean±SD. Statistical significance was determined by two-tailed\nunpaired t-test. Data from two independent biological experiments are shown.\n3. To confirm that the pathogenic effects of the K377E and R390G mutations require a biallelic\nform, we conducted a series of comprehensive experiments using peripheral blood samples\nobtained from the patients and their unaffected parents. The results showed that neither K377E nor\nR390G heterozygous carriers exhibited overactivation of RIPK1-dependent cell death.\n3.1 We analyzed CD4/8 ratio in CD3+ T cells from flow cytometry data and found that patients showed\na significantly elevated CD4/8 ratio compared to their parents and unaffected controls (Fig. R6).\nFig. R6 Summary histogram of CD4/8 ratio in CD3+ T cells of the patients (P, n=10), their parents\n(n=7) and unaffected controls (C, n=20) were shown. Statistical significance was determined by\ntwo-tailed unpaired t-test, ***P < 0.001, ns, P >0.05.\n3.2 We repeated the cell viability assay and expanded the number of unaffected controls. Consistent\nwith the initial findings, T cells from heterozygous carriers showed comparable susceptibility of\nTS or TSZ-induced cell death to those from unaffected controls (new Fig. 1 h).\nnew Fig. 1 h CD3+ T cells were isolated from PBMCs of patients, their parents and eight\nunaffected controls (C) followed by stimulated with 20 ng ml-1 TNF (T), 250 nM SM-164 (S)\nwith or without 25 μM z-VAD-fmk (Z) for 3 h, 6 h and 9 h as indicated.Cell viability was measured\nby CellTiter-Glo luminescent cell viability assay.\nMoreover, we further isolated CD4+ T cells and CD8+ T cells from the patients, their parents\nand unaffected controls and determined cell death sensitivity by viability assay. CD8+ T cells and\nCD4+ T cells were stimulated with TS/TSZ to induce programmed cell death, and cell viability\nwas analyzed using the CellTiter-Glo Assay. The results showed that CD8+ T cells and CD4+ T\ncells from patients were more sensitive to TS/TSZ-induced cell death than those from their\nheterozygous parents as well as unaffected controls (new Extended Data Fig 6 f).\nnew Extended Data Fig 6 f, CD8+ T cells and CD4+ T cells were isolated from PBMCs of patients,\ntheir parents and five unaffected controls (C) followed by stimulated with 20 ng ml-1 TNF (T),\n250 nM SM-164 (S) with or without 25 μM z-VAD-fmk (Z) for 3 h, 6h as indicated.Cell viability\nwas measured by CellTiter-Glo luminescent cell viability assay. The graphs showed mean±SD.\nWe also employed SYTOX Orange staining to quantify plasma membrane rupture, a hallmark\nterminal event in necroptosis downstream of MLKL activation. We stimulated CD8+ T cells and\nCD4+ T cells with TS or TSZ followed by SYTOX Orange staining. Consistent with the results of\ncell CellTiter-Glo Assay, SYTOX Orange staining showed that TS/TSZ could induce higher levels\nof cell death in CD8+ T cells than in CD4+ T cells (new Extended Data Fig 6 g). Given that cell\ndeath levels were comparable in unaffected controls and the patients' parents, we demonstrate that\na single heterozygous mutation (K377E or R390G) is insufficient to drive RIPK1-dependent cell\ndeath.\nnew Extended Data Fig 6 g, CD8+ T cells and CD4+ T cells were isolated from PBMCs of patients\n(n=5 for P1 and P2), their parents (n=5 for mother and father) and unaffected controls (C, n=6)\nfollowed by stimulated with 20 ng ml-1 TNF (T), 250 nM SM-164 (S) with or without 25 μM z-\nVAD-fmk (Z) for 6h.Cell death levels were measured by SYTOX Orange staining.Representative\nhistograms of SYTOX Orange (left) and summary histograms (right) were shown. The graphs\nshowed mean±SD. Statistical significance was determined by two-tailed unpaired t-test, ***P <\n0.001.\nHere are a few points the authors should address to determine the role of R390G RIPK1:\n1. Stability of R390G RIPK1: Use the established HT-29 over expression system to perform a\ncycloheximide chase to compare stability of R390G RIPK1 with other RIPK1 variants.\nReply: Thank you for the insightful comment, which has provided guidance for our investigation\ninto the mechanism of R390G mutation. We have performed the following experiments:\nFirst, we performed a cycloheximide (CHX) chase assay to compare the protein stability of\nthe R390G mutant with WT RIPK1. However, in our initial experiments using virus-mediated\nreconstitution in RIPK1−/− HT-29 cells, which involved overexpressing RIPK1 WT and mutant\nconstructs, we encountered a technical challenge that cells expressing high levels of the pathogenic\nRIPK1 mutants (K377E or R390G) exhibited poor viability and were difficult to expand, likely\ndue to the enhanced cell death propensity induced by these mutations. Consequently, in the stable\npolyclonal cell lines we eventually established, the expression levels of the mutant RIPK1 proteins\nwere comparatively lower. Therefore, as an alternative approach, we evaluated the stability of\nR390G RIPK1 under overexpression conditions in HEK293T cells. We transfected HEK293T cells\nwith carefully titrated amounts of WT and R390G RIPK1 constructs to ensure comparable\nexpression levels for direct comparison. We then treated these cells with CHX to inhibit new\nprotein synthesis and monitored protein degradation at multiple time points. Quantitative analysis\nof western blots demonstrated that WT and R390G RIPK1 exhibit comparable degradation kinetics,\nindicating that the R390G mutation does not affect protein stability (Fig. R7).\nFig. R7 HEK293T cells transfected with either HA-tagged WT or R390G mutant RIPK1 were\ntreated with cycloheximide (CHX, 100 μg/ml). Whole-cell lysates were collected at the indicated\ntime points post-treatment. Protein levels were analyzed by Western blot, data from three\nindependent biological experiments are shown (a). Quantitative analysis results are presented as\nmean ± SD (b).\nSecond, we assessed the endogenous RIPK1 protein levels in isogenic RIPK1 mutant knockin\nHT-29 cells. The results showed that neither the K377E nor the R390G mutation led to a decrease\nin RIPK1 protein levels (Fig. R8 a). Consistently, the expression of RIPK1 in T cells from\nheterozygous carriers was also comparable to that in healthy donors (Fig. R8 b). These findings\nfrom endogenous models robustly support the conclusion that these variants do not intrinsically\naffect RIPK1 protein stability.\nFig. R8 a, RIPK1 WT or K377E/R390G knockin cell lines treated with TNF for 5, 15 were\nimmunoblotted with RIPK1 antibody. b, CD3+ T cells were isolated from PBMCs of patients’\nparents and four unaffected controls (C). Protein levels of RIPK1 were analyzed by Western blot.\n2. Interaction with MIB2: Try titrating levels of RIPK1 variants and/or MIB2 in the over\nexpression IP to clearly show a decreased interaction between MIB2 and R390G over a range of\nconcentrations. Alternatively, try BioID or TurboID.\nReply: Following your constructive suggestion, we performed coimmunoprecipitation assays in\nHEK293T cells by titrating the expression of either full length or the intermediate domain (ID) of\nWT or R390G RIPK1. Co-immunoprecipitation was performed using antibodies against Flag-\nMIB2 or the Flag-RIPK1 ID domain, followed by western blot analysis. Both experiments\nconsistently demonstrated that the R390G mutation disrupts the interaction between RIPK1 and\nMIB2 (Fig. R9). The representative data have been incorporated into the new Extended Data Fig.\n2m, n.\nFig. R9 a, b, HEK293T cells were transfected with plasmids encoding full length/intermediate\ndomain (ID) of RIPK1 together with MIB2 as indicated. MIB2 (a) or ID domain of RIPK1 (b)\nwere immunoprecipitated with anti-Flag beads and immunoblotted with indicated antibodies.Data\nfrom two independent biological experiments are shown.\n3. Determine if Ubiquitination of RIPK1 at 377 is altered: Given that in the patient cells RIPK1\nUb is altered post TNF stimulation but the R390G mutant looks comparable to WT (over\nexpression) it may be that (if stable) R390G inhibits K377 ubiquitination but does not affect other\nUb sites on RIPK1. Thus, the change in Ub could be masked in the over expression system. The\nauthors should specifically determine if K377 Ub is altered, eg: using plasmid mutagenesis to\nisolate specific Ub sites on RIPK1 and compare WT to R390G or perform Ub site mapping by\nMass Spec.\nReply: We sincerely appreciate your constructive suggestion. Our new data showed that the RIPK1\nR390G mutation suppressed the interaction between RIPK1 and MIB2 (Fig. R9). As MIB2 has\nbeen reported to mediate ubiquitination at K377, K316, K571, K604 and K634 of RIPK1, the\nR390G mutation would likely affect ubiquitination at these sites1. We have performed Mass\nSpectrometry to verify this finding in an overexpression system. To identify the ubiquitination sites\non RIPK1 affected by the R390G mutation via MIB2, HEK293T cells were co-transfected with\nplasmids encoding 3×Flag-tagged WT or R390G RIPK1, MIB2, and ubiquitin. Then RIPK1 was\nimmunoprecipitated with anti-Flag beads and the ubiquitination modifications were analyzed by\nMass Spectrometry. The analysis results showed that nine ubiquitination sites were enriched by\nRIPK1 WT versus R390G (K13, K105, K115, K163, K306, K316, K571, K627, K642), and two\nof these sites (K316, K571) has been identified as ubiquitination site mediated by MIB2 (Fig. R10)\n1. However, consistent with the previous report, ubiquitination at K377 could not be detected,\npossibly due to overly long peptide fragments generated by tryptic digestion1. Of note, R390G\nRIPK1 showed significant inhibited ubiquitination at K316 compared to WT, supporting that\nR390G RIPK1 could suppress RIPK1 ubiquitination through MIB2 (Fig. R10b).\nFig. R10 a, Schematic representation of the ubiquitylation sites of RIPK1 that are mediated by\nMIB2. Fold Ub enrichment of RIPK1 sites in MIB2WT compared with\nMIB2F > A is shown (Fig. 6 panel D from R. Feltham, et al. Cell Rep 2018 1). b, Schematic of\nubiquitylation sites of RIPK1 identified by mass spectrum. Red cycle represents site with reduced\nubiquitination affected by R390G mutation; orange cycle represents site with reduced\nubiquitination caused by both R390 and MIB2 F>A mutation; yellow cycle represents sites with\nreduced ubiquitination level caused by MIB2 F>A mutation.\nHowever, RIPK1 overexpression can lead to RIPK1 dimerization and oligomerization via its\nRHIM and death domain (DD), which non-specifically recruits numerous E3 ligases. Consequently,\nin accordance with your inference, the ubiquitination regulation of RIPK1 often exhibits\nnonspecific characteristics in overexpression systems. Therefore, the ubiquitin-binding assay in\nHEK293 overexpression system (Extended Fig. 2l) was designed to identify K377E, but not\nR390G as a direct ubiquitination site. This result also implied that an endogenous setting might\nprovide more reliable analysis of ubiquitination sites. For this reason, we also attempted to analyze\nthe ubiquitination sites under endogenous conditions. At first, we evaluated the RIPK1\nubiquitination in WT, K377E and R390G knockin HT-29 cells and demonstrated that both K377E\nand R390G mutation impaired RIPK1 ubiquitination upon TNF stimulation. Consistent with\nfindings from other assays, R390G mutation exhibited weaker effect than K377E (Fig. R11). The\nrepresentative result has been included in new Extended Fig. 2k.\nTo determine which ubiquitination site is affected by the R390G mutation, we then utilized\nWT and R390G knockin cells to enrich Complex I or RIPK1 for the identification of RIPK1\nubiquitination sites by mass spectrometry. We employed two complementary immunoprecipitation\nstrategies: one targeting Complex I enriched via Flag-TNF, and the other targeting total RIPK1.\nUbiquitination sites were subsequently analyzed using antibody-based immunoprecipitation\ncoupled with mass spectrometry. However, RIPK1 ubiquitination was undetectable by mass\nspectrometry, likely owing to the limited complex enrichment efficiency. Despite extensive efforts,\nincluding scaling up the cell culture, we failed to reach the detection limit of mass spectrometry.\nBased on the evidence of mass spec analysis on in vitro overexpression samples and the complex\nI analysis, we revealed that R390G suppressed MIB2 mediated RIPK1 ubiquitination.\nFig. R11 The TNFR signaling complex (TNF-RSC) of RIPK1 WT or K377E/R390G knockin cell\nlines was immunoprecipitated with anti-Flag beads upon Flag-TNF stimulation for 5 or 15 min\nand immunoblotted with indicated antibodies.Data from two independent biological experiments\nare shown.\n4. Could R390G disrupt RIPK1 interaction with proteins other than MIB2 which are important for\nrecruitment to the TNFR1 cytoplasmic domain or ubiquitination eg: TRADD (PMID: 8612133),\ncIAPs (PMID: 25192652) this could be tested in the same over expression system as Ex Data Fig\n2.\nReply: Thank you for the constructive comments. In fact, we have screened known K63\nubiquitin modifiers, including E3 ubiquitin ligases such as cIAP1/2, TRAF2, A20 and MIB2, as well as\ndeubiquitinases like A20 or CYLD, and the adaptor protein TRADD. We overexpressed WT or\nmutant RIPK1, along with these proteins in HEK293 cells, to determine the interactions between\nRIPK1 and each protein (Fig. R12). The results demonstrated that neither the R390G nor the\nK377E mutation altered the binding of RIPK1 to TRADD,cIAP1/2, TRAF2, A20 or CYLD. All\nexperiments were performed in two independent biological replicates.\nFig. R12HEK293T cells were transfected with plasmids encoding RIPK1 together with TRAF2,\nTRADD, CIAP1, CIAP2, A20 or CYLD as indicated. RIPK1 were immunoprecipitated with\nanti-Flag dynabeads and immunoblotted with indicated antibodies. Data from two independent\nbiological experiments are shown.\n5. The authors should also determine if R390 effects: RIPK1 trafficking to the TNFR1 complex,\nconfirmational changes that allow RIPK1 to interact with other proteins or alters dimerization.\nReply: To assess the impact of the R390G mutation on the recruitment of RIPK1 to the TNFR\nsignaling complex, we employed the proximity ligation assay (PLA). This technique detects close\nmolecular interactions by generating a fluorescent signal. Stimulation of WT and R390G knockin\nHT-29 cells with Flag-TNF revealed that the R390G mutation reduced the fluorescence signals\nrepresenting Flag-TNF/RIPK1 proximity, indicating partially impaired recruitment of RIPK1 to\nthe TNFR complex (Fig. R13).\nFig. R13 a, Representative images of Flagg-TNF/RIPK1 complexes analyzed by proximity\nligation assay (PLA) in WT (n=20 cells) or R390G knockin (n=20 cells) HT-29 cells. b, Statistical\nanalysis is shown. Scale bar, 5 μm. The graphs showed mean±SD. Statistical significance was\ndetermined by two-tailed unpaired t-test, *P < 0.05.\nWe agree that elucidating the structural impact of the R390G mutation is crucial for a\ncomplete mechanistic understanding, which aligns with our initial hypothesis for the R390G\nmechanism. However, the lack of structures for the ID domain and full length RIPK1 presents a\nsignificant challenge in the field, that prevents a direct conformational analysis of the R390G\nmutant. Thus, we could only make a proposal based on the functional consequence of R390G. Our\ndata demonstrated that R390G has a disruptive effect on the RIPK1-MIB2 interaction, which\nmediated by the ID domain of RIPK1 1.To gain further insight, we used AlphaFold to predict the\nstructure of the RIPK1 full length and ID domain. However, a large number of random coiled\nregions was observed between R390 and the RHIM domain, which precluded any assessment of\nthe impact of the R390G mutation on RIPK1 oligomerization and the conformation of the ID\ndomain.\nAlternatively, we calculated the free energy changes induced by the R390 mutation in the\npredicted RIPK1-MIB2 complex. The thermodynamic hypothesis has become a default physical\ndescription of protein folding 2, 3. The free energy of unfolding (ΔG), which represents the energy\nchange from unfolded state to folded state, can be used to measure protein stability 4. ΔG is\ncalculated by a sum of energy terms, including van der Waals contributions, the difference in\nsolvation energy for apolar and polar groups, the electrostatic contribution from charged groups\ninteractions 5. We calculate the difference in ΔG (ΔΔG) between the WT and mutated protein by\nusing FOLDX to estimate the effect of R390 mutation on the interaction between RIPK1 and MIB2\n6. As shown in Fig. R14, the R390 mutation resulted in a ΔΔG of approximately 3 kcal/mol, which\nled to destabilization of the RIPK1-MIB2 complex. As a negative control, we evaluated the effect\nof the D324 mutation in the RIPK1-FADD complex7. The results showed that D324 did not affect\nthe death domain-mediated RIPK1-FADD interaction. Therefore, we hypothesize that the R390G\nmutation may affect the conformation of the intermediate domain, thereby impairing the RIPK1-\nMIB2 interaction.\nFig. R14 Predicted structural impact of RIPK1 variants computed from Alphafold3 with FoldX on\nRIPK1 complexes with MIB2 and FADD. Each box contains five data points corresponding to the\nfive predicted structures. Box, 25th and 75th percentiles; middle line, median.\nIn response to your question regarding the effect of R390G mutation on RIPK1 dimerization,\nwe overexpressed GFP- and Flag-tagged constructs (WT, K377E, or R390G RIPK1) in HEK 293\ncells. The results demonstrated that neither the K377E nor the R390G mutation affected RIPK1\ndimerization (Fig. R15).\nFig. R15 HEK293T cells were transfected with plasmids encoding GFP- and Flag-tagged\nconstructs (WT, K377E, or R390G RIPK1) as indicated. RIPK1 were immunoprecipitated with\nanti-Flag dynabeads and immunoblotted with indicated antibodies. Data from two independent\nbiological experiments are shown.\nI recommend the authors also try testing the above with coexpression of R390G with WT or K377E\nRIPK1 as this may contribute to the phenotype.\nReply:Based on the results from reconstituted RIPK1−/− HT-29 cells, the differing pathogenicity\nof the K377E and R390G variants leads to inconsistent expression levels of the two proteins in the\noverexpression system. Consequently, it is challenging to ensure consistent expression levels\nbetween WT and mutants during co-overexpression. There is a high probability that after screening,\ncells with higher expression of the WT protein and lower expression of the mutant protein are\nretained, thereby undermining the experimental intent. Thus, we consider the co-expression system\nmay not suitable for accurate assessment of RIPK1 function. For the same reason, we have\nexcluded all previous results from the overexpression complementation experiments and re-\nevaluate the relevant assays using isogenic knockin cells (Extended Data Fig. 2 d, e, f, h, i, k).\nDiscussion Points that should be included:\nThe authors should contextualise their findings on the T cell/Mono Mac driven disease course in\nautoinflammatory disorders driven by RIPK1 variants with autoinflammatory diseases driven by\nOTULIN deficiency/dysfuncton(driven almost entirely by myeloid cells, PMID: 30804083) and\nLUBAC deficient patients (more involvement of lymphocytes and stromal cell types, eg:\nPMID: 32687504, PMID: 31462647, PMID: 35534698, etc)\nReply: We thank you for the insightful suggestions. Human myeloid cell lines (such as THP-1 and\nU937), as well as murine myeloid cells, are highly susceptible to cell death. In contrast, our data\ndemonstrated that primary T cells rather than monocytes/macrophages is the most sensitive\ncompartment in RIPK1 K377E/R390G patients.\nSubstantial differences in cell death susceptibility between human and mouse systems have\nbeen widely reported. For instance, genetic perturbations in key regulators (such as Ripk1 k376\nmutation) 8, Hoip deficiency 9, Hoil deficiency 10, Otulin deficiency 11 of cell death pathway result\nin embryonic lethality due to excessive endothelial cell death, whereas human patients bearing\nmutations in these genes only exhibit immune dysregulation12, 13, 14. This phenomenon indicates\nthat dysregulated cell death exerts distinct effects on human and mouse cell. Thus, findings from\nmouse models or cell line-based system may not accurately reflect the physiological state of\nprimary human cells, the cell types identified as drivers of OTULIN/LUBAC-related deficiencies\nin mouse or cell line-based models require further validation using patient-derived samples 15.\nHowever, research on human primary myeloid cells and comparison between different peripheral\ncell compartments remain extremely limited.\nIn this study, we uncovered pathogenic drivers of inborn errors of cell death using patient-\nderived samples. Despite limited sample availability, analysis of existing patient samples\n(including cases such as CRIA, and SHARPIN-related mutations), consistently reveals increased\nT cell death and RIPK1 activation, underscoring T cell death as a key factor in autoinflammatory\ndiseases (Fig. 7). We have incorporated some of the above points into the discussion section (Line\n437-441).\nThe equivocation of monoclonal blockade of IFNg in Figure 5f and treatment of patients with\nruxolitinib (Fig 6) is incorrect, JAK1/2 blockade inhibits inflammatory signalling from a variety\nof cytokines including IFNab and IL-6 which are elevated in the RIPK1 patients. I appreciate that\nthe authors call this treatment regime TNF/IFN but the distinction should be highlighted in the\ndiscussion. Particularly because RIPK1 has been shown to drive IFNab signalling under conditions\nof OTULIN dysfuncton(PMID: 29950720) and JAK1/2 blockade had therapeutic benefit in these\npatients (PMC11022884 ).\nReply: We appreciate the reviewer for the valuable comments. Our original intention was to\nevaluate the efficacy of emapalumab in combination with adalimumab. However, as emapalumab\nis only currently approved for the treatment of hemophagocytic lymphohistiocytosis (HLH) in\nChina, we selected ruxolitinib as an alternative. Ruxolitinib targets JAK1 and JAK2, which are\ndownstream of the interferon-gamma receptor (IFN-γR). As noted, ruxolitinib also inhibits JAK1\n(downstream of type I-IFN) and may affect IL-6 signaling. However, several symptoms in the\npatients could not be relieved by tocilizumab treatment (such as colitis and skin rashes), indicating\nan inadequate response of the patients to anti-IL-6 therapy. Therefore, we believe that IL-6\npathway blockade contributes minimally to the efficacy of the ruxolitinib/adalimumab\ncombination. We also trialed infliximab, adalimumab, canakinumab, tofacitinib alone,\nadalimumab/tofacitinib combination, and adalimumab/ruxolitinib combination in an extremely\nrefractory CRIA patient. To date, the CRIA patient only showed a therapeutic response to the\nadalimumab/ruxolitinib combination instead of tofacitinib. Tofacitinib is an FDA-approved\ninhibitor of JAK1 and JAK3, with comparatively weaker activity against JAK216, 17. Given that\ntofacitinib demonstrated significantly lower efficacy than ruxolitinib, we think that IFN-γ blockade\nmay play the primary role in the therapeutic effect of the ruxolitinib/adalimumab combination in\nboth the CRIA patient and the patient with RIPK1 K377E/R390G mutations.We have replaced the\n“TNF/IFN inhibitor” with “TNF/JAK inhibitor” in the revised manuscript for accurate description\nand added some of the above points into the main text and discussion section (Line 388-392; Line\n476-479).\nDo the authors think that IFNg is specifically is important for cell death in these patients as per\nPMID: 35139355?\nReply: We do not consider IFN-γ to be essential for the increased cell death observed in these\nautoinflammatory patients, as JAK inhibitor monotherapy demonstrates limited efficacy in most\ncases. However, we support the concept that IFN-γ primes macrophages to enhance inflammatory\nresponses, which is consistent with its established role in diseases like hemophagocytic\nlymphohistiocytosis and autoimmune polyendocrine syndrome type 118, 19. Our single-cell analysis\nconsistently showed a more pronounced increase in IFN-γ expression and secretion in patient, and\nT cell death was found to further promote IFN-γ secretion. Therefore, we propose that IFN-γ-\nmediated signaling represents a critical pathway linking T cell death to macrophage activation.\nAdditionally, while patients with NEMO deficiency typically present with primary\nimmunodeficiency and exhibit significantly reduced IFN-γ expression. Our data demonstrate that\nNEMO-deficient CD8+ T cells remains markedly increased cell death reflected by increased cl.\nCasp-3, pRIPK1(S166) and CD4/CD8 ratio in two Ectodermal Dysplasia and Immunodeficiency\n1 (EDAID1) patients (Fig. R16). This finding suggests that IFN-γ was not a key inducer for RIPK1\nactivation and cell death in these IECD patients.\nFig. R16 a, Pedigrees of two families with Ectodermal Dysplasia and Immunodeficiency 1\n(EDAID1) patients (EDAID1-1 and EDAID1-2). Patients were represented with filled symbols. b,\nRepresentative contour plots of cl. Casp-3 (b, left panel) and histograms of pRIPK1(S166) (b, right\npanel) protein levels in CD8+ T cells in freshly isolated PBMCs from patients. c, Summary\nhistograms of CD4/CD8 ratio in CD3+ T cells of unaffected controls (n=12), EDAID1-1 (n=3)\nand EDAID1-2 (n=5) were shown.\n-No increase in TSZ induced cell death compared to TS and TZ, in fact TSZ looked less effective\nthan TS in T cells (Figure 1h) -comment?\nReply: We appreciate your valuable comments. The mechanisms of TS and TSZ models vary, and\nthe associated regulatory processes are distinct. TS treatment primarily triggers apoptosis, which\nis dependent on FADD and caspase signaling, whereas TSZ induces necroptosis, mediated by\nRIPK3 and MLKL activation.Consequently, the sensitivity of different cell types to these stimuli\nmay vary considerably. The reduced effectiveness of TSZ compared to TS in inducing T cell death\nindicates that T cell is more susceptible to apoptosis than to necroptosis.\nAlthough regulated by distinct downstream factors, both TS- and TSZ-induced cell death can\nbe promoted by RIPK1 activation. As shown in Fig. 1g, patient-derived cells exhibited\nsignificantly higher levels of cell death compared to unaffected controls after 6–9 hours of TSZ\nstimulation. We further present the image data captured at different time points following TSZ\nstimulation, which visually confirmed that patient fibroblasts are more susceptible to TSZ-induced\ncell death, supporting conclusions consistent with those from TS and TZ treatments (Fig. R17).\nFig. R17 SV40-HDFs isolated from three unaffected controls (C1-C3) and P2 were treated with T\nwith or without S, Z and N as indicated. Cell death was monitored by imaging of SYTOX Orange\nstaining after stimulation for 1 h, 3 h, 6 h and 9 h. The images of the same view field were presented.\nMinor Points:\nThe manuscript presents an impressive amount of experimental data; but many key experiments\nhave been performed only once or twice (from my reading of the legends) and generally only show\na single representative repeat. As I highlighted in the context of western blotting of the NFkB\nsignalling cascade this is a real problem when trying to establish the robustness of the patient\nphenotype. This is particularly important to the field of rare disease since these findings may be\nused for differential diagnosis of other patients, reporting that there is a strong effect when it is\nactually variable will impede our understanding of other novel RIPK1 variants. The authors need\nto repeat key experiments at least three times to strengthen the validity of their conclusions and\nprovide summary data from independent biological replicates (ie: pooling results across\nexperiments).\nReply: We appreciate your valuable feedback. Following the suggestions, we have repeated the\nkey experiments in triplicate to strengthen the conclusions and provide a comprehensive data\nsummary (Fig. R4).\nFig. R4 a, b, RIPK1 WT or K377E/R390G knockin cell lines under stimulation of TZ with or\nwithout N as indicated. Cell viability measured by CellTiter-Glo luminescent cell viability assay.\nc, Whole cell lysates were immunoblotted with indicated antibodies. d, NF-κB and p38 activation\nof RIPK1 WT or K377E/R390G knockin cell lines treated with TNF for 5, 15 immunoblotted with\nindicated antibodies. The graphs showed mean±SD. Data from three independent biological\nexperiments are shown.\nFig. R4 e, mRNA levels of TNF, IL8, IL1B in RIPK1 WT or K377E/R390G knockin cell lines\ntreated with TNF for 6 hours and determined with qRT-PCR.The graphs showed mean±SD. Data\nfrom three independent biological experiments are shown. Statistical significance was determined\nby two-tailed unpaired t-test, *P < 0.05, **P < 0.01, ***P < 0.001, ***P < 0.0001.\n-Ex Figure 2A is missing panels and does not appear to show protection from cell death by Nec-1\nin patient fibroblasts. Please quantify to clearly show this result.\nReply: We thank the reviewer for the thorough review. First, we carefully checked the figures and\napologize for having inadvertently swapped the images for P2 TS and TSN stimulation (marked\nin red in the figure) (new Extended Data Fig. 2a). We have rechecked and corrected in the\nmanuscript. Meanwhile, it is evident that Nec-1s does not provide complete protection against TS-\ninduced cell death. We reanalyzed the cell death results and found that at the early stage (8 h) of\nTS-induced apoptosis, Nec-1s reduced the dead cell proportion from 60% to 30%. However, after\n24 hours of stimulation, Nec-1s only suppressed TS-induced cell death from 90% to 65% (Fig.\nR18). This indicates that Nec-1s only partially inhibits TS-induced RIPK1-dependent apoptosis in\nfibroblasts. In fact, this observation is consistent with previously reported findings that RIPK1-\nmediated apoptosis in human cells is only partially dependent on RIPK1 kinase activity 20. We also\nexamined the inhibitory effect of Nec-1s under TNF (50 ng/ml) and 5z7 (200 nM, TAK1inhibitor),\na commonly used combination for inducing RIPK1 mediated apoptosis, and obtained similar\nresults (Fig. R19). Therefore, we will revise the corresponding description in the original text\naccordingly.\nnew Extended Data Fig. 2 a, SV40-HDFs isolated from three unaffected controls (C1-C3) and\nP2 were treated with TNF with or without S, Z and N as indicated. Cell death was monitored by\nimaging of SYTOX Orange staining. The red boxes indicated the mixed-up images.\nFig. R18 SV40-HDFs isolated from three unaffected controls (C1-C3) and P2 were treated with\n50 ng/ml TNF and 250 nM SM164 with or without 10 μM Nec-1s as indicated. Cell death was\nmonitored by imaging of SYTOX Orange staining after stimulation for 8 h or 24 h. The percentage\nof cell death was determined by calculating the ratio of SYTOX Orange-positive cells to the total\ncell count. The graphs showed mean±SD, n=4. Statistical significance was determined by two-\ntailed unpaired t-test, *P < 0.05, ***P < 0.001.\nFig. R19 SV40-HDFs isolated from three unaffected controls (C1-C3) and P2 were treated with\n50 ng/ml TNF with or without 200 nM 5z7(5Z-7-Oxozeaenol, TAK1inhibitor), 25 μM z-VAD\nand 10 μM Nec-1s as indicated. Cell death was monitored by imaging of SYTOX Orange staining.\n-Ext Fig 3e, panels are missing\nReply: In response to this issue, we have rechecked Extended Data Fig. 3e. As monocyte-derived\nmacrophages (MDMs) from unaffected controls and patients were not sensitive to TS-induced cell\ndeath, the PI-positive dead cells were much less than that in TSZ-induced cell death, resulting in\nminimal detectable signaling in the corresponding images. Representative images for unaffected\ncontrol (C), P1 and P2 are shown in Extended Data Fig. 3e, and the statistical results from both P1\nand P2 are pooled in Fig. 2i as patient group (P).\nExtended Data Fig. 3 e, Representative images of dead M1 macrophages stimulated with TS or\nTSZ for 24 hours by imaging of propidium iodide (PI). M1 macrophages were differentiated from\nprimary monocytes of unaffected controls (n=6) and two patients (n=4 from P1 and P2). Pooled\nstatistics of dead M1 macrophages were shown in Fig.2i.\n-Extended 2d, e should have empty vector control\nReply: Due to the difficulty in ensuring comparable expression levels between the WT and various\nRIPK1 mutants, as well as among the mutants themselves in reconstitution experiments, we\nconsidered the K377E or R390G knockin system rather than the overexpression complementation\nsystem more reliable for comparatively assessing the pathogenicity of the mutations. Therefore,\nwe removed all results from the reconstitution overexpression assays conducted in RIPK1–/– HT29\ncells from the manuscript and replaced with data from K377E or R390G RIPK1 knockin HT-29\ncell lines. The data of the empty vector control have also been omitted accordingly.\n-No isotype controls shown for Fig 2f\nReply: Following the reviewer’s suggestion, we have added isotype controls to new Fig. 2 f.\nnew Fig. 2 f, Intracellular cytokine analysis of IL-1β, IL-6 and IFNγ in monocytes isolated from\nthree unaffected (C1-C3) controls and P1.\n-Please list for cytokine groups in Figure 3.\nReply: Three cytokine groups were listed below:\nGroup 1: GRO, MIF, uPAR, IL-12p40, HGF, I-TAC, sTNF-RI, IL-11, ICAM-3, Eotaxin-2, IL-1\nR4/ST2, IL-8, GCP-2, M-CSF, TRAIL R4, Osteoprotegerin, VEGF, MCP-3, IL-1β, FGF-4, MIP-\n3α, ENA-78, MIP-1α, TRAIL R3, MIP-1β, Lymphotactin\nGroup 2: MCP-2, ErbB3, IL-3, NT-4, Flt-3 Ligand, LAP, Dtk, b-NGF, IGFBP-1, MIP-3β,\nFas/TNFRSF6, BLC, sTNF RII, Angiopoietin-2, IGFBP-3, IL-12 p70, IL-1α, IL-16, bFGF, TNF-\nα, MCP-4, LIF, MIG, IP-10\nGroup 3: HCC-4, Eotaxin, IGFBP-6, MPIF-1, DR6 (TNFRSF21), VEGF R3, IL-18 Rβ,\nThrombopoietin, MMP-1, VE-Cadherin, PIGF, IL-2 Rγ, IL-1 RII, Leptin R, IL-18 BPα, IL-2 Rα,\nIL-2 Rβ, IL-4, PDGF Rβ, BMP-5, IL-13 Rα2, NGF R, Prolactin, Cardiotrophin-1, BMP-7, IL-10\nRβ, SDF-1β, IL-21R, IL-5 Rα, Siglec-5, TIMP-4, SCF\nWe have incorporated the requested details into the Methods section.\n“which was reported to induce more severer cell death in Ripk1K376R/K376R cells”\nShould say: “which was reported to induce more sever cell death …”\nReply: We are grateful for your thorough review of our manuscript and have revised the text\naccordingly.\nReferences\n1. Feltham R, et al. Mind Bomb Regulates Cell Death during TNF Signaling by Suppressing\nRIPK1's Cytotoxic Potential. Cell Rep23, 470-484 (2018).\n2. Haber E, Anfinsen CB. Side-chain interactions governing the pairing of half-cystine residues\nin ribonuclease. J Biol Chem237, 1839-1844 (1962).\n3. Anfinsen CB. Principles that govern the folding of protein chains. Science181, 223-230 (1973).\n4. Braselmann E, Chaney JL, Clark PL. Folding the proteome. Trends Biochem Sci38, 337-344\n(2013).\n5. Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L. The FoldX web server: an\nonline force field. Nucleic Acids Res33, W382-388 (2005).\n6. Delgado J, Radusky LG, Cianferoni D, Serrano L. FoldX 5.0: working with RNA, small molecules\nand a new graphical interface. Bioinformatics35, 4168-4169 (2019).\n7. Tao P, et al. A dominant autoinflammatory disease caused by non-cleavable variants of RIPK1.\nNature577, 109-114 (2020).\n8. Zhang X, et al. Ubiquitination of RIPK1 suppresses programmed cell death by regulating\nRIPK1 kinase activation during embryogenesis. Nat Commun10, 4158 (2019).\n9. Peltzer N, et al. HOIP deficiency causes embryonic lethality by aberrant TNFR1-mediated\nendothelial cell death. Cell Rep9, 153-165 (2014).\n10. Peltzer N, et al. LUBAC is essential for embryogenesis by preventing cell death and enabling\nhaematopoiesis. Nature557, 112-117 (2018).\n11. Heger K, et al. OTULIN limits cell death and inflammation by deubiquitinating LUBAC. Nature\n559, 120-124 (2018).\n12. Boisson B, et al. Human HOIP and LUBAC deficiency underlies autoinflammation,\nimmunodeficiency, amylopectinosis, and lymphangiectasia. J Exp Med212, 939-951 (2015).\n13. Boisson B, et al. Immunodeficiency, autoinflammation and amylopectinosis in humans with\ninherited HOIL-1 and LUBAC deficiency. Nat Immunol13, 1178-1186 (2012).\n14. Zhou Q, et al. Biallelic hypomorphic mutations in a linear deubiquitinase define otulipenia,\nan early-onset autoinflammatory disease. Proc Natl Acad Sci U S A113, 10127-10132 (2016).\n15. Damgaard RB, et al. OTULIN deficiency in ORAS causes cell type-specific LUBAC degradation,\ndysregulated TNF signalling and cell death. EMBO Mol Med11, (2019).\n16. Singh V, Joshi K, Chatterjee S, Qureshi S, Siddh S, Nunia V. Evaluation of the Efficacy of\nTofacitinib, a JAK Inhibitor, in Alleviating Sepsis-Induced Multiple Organ Dysfunction\nSyndrome. Drug Res (Stuttg)74, 394-404 (2024).\n17. Palmroth M, et al. Tofacitinib Suppresses Several JAK-STAT Pathways in Rheumatoid Arthritis\nIn Vivo and Baseline Signaling Profile Associates With Treatment Response. Front Immunol\n12, 738481 (2021).\n18. Vallurupalli M, Berliner N. Emapalumab for the treatment of relapsed/refractory\nhemophagocytic lymphohistiocytosis. Blood134, 1783-1786 (2019).\n19. Oikonomou V, et al. The Role of Interferon-gamma in Autoimmune Polyendocrine Syndrome\nType 1. N Engl J Med390, 1873-1884 (2024).\n20. Mannion J, et al. A RIPK1-specific PROTAC degrader achieves potent antitumor activity by\nenhancing immunogenic cell death. Immunity57, 1514-1532 e1515 (2024).\nReply to the Referee #3\nReviewer #3 (Remarks to the Author):\nBiallelic loss-of-functon(LOF) variants in RIPK1 cause RIPK1 deficiency, whereas\nmonoallelic cleavage-resistant gain-of-functon(GOF) variants result in an\nautoinflammatory syndrome called cleavage-resistant RIPK1 induced\nautoinflammatory (CRIA) syndrome. In this manuscript, Jialin Dai, Qing Zhou, and\ncolleagues describe two patients from a single family harboring compound\nheterozygous missense variants in RIPK1 (K377E and R390G), who presented with an\nautoinflammatory syndrome characterized by recurrent fever, lymphadenopathy, rash,\nand enteropathy.\nAt the molecular level, the RIPK1-K377E variant was shown to be ubiquitination-\ndeficient and constitutively active. The R390G variant is also considered a GOF\nmutation, although concerns remain regarding its pathogenicity and molecular\nmechanism, as discussed in major comment no. 4. Functional studies demonstrated\naugmented apoptosis and necroptosis following TNF stimulation in the patients’ dermal\nfibroblasts and primary T cells, consistent with RIPK1 GOF activity.\nscRNA-seq of patients’ PBMCs identified an aberrant monocyte/macrophage\npopulation with elevated proinflammatory cytokine expression. CyTOF-based PBMC\nsubset analysis revealed reduced frequencies of CD8+ T cells in the patients. Upon anti-\nCD3/CD28 stimulation in in vitro, a further decrease in CD8-hi T cells and an\nemergence of CD8-lo T cells were noted. Cytokine production was enhanced in both\nCD8+ and CD4+ T cells. Co-culture experiments with patient-derived T cells and\nhealthy donor monocyte-derived macrophages demonstrated that the patient T cells\nexacerbated proinflammatory cytokine production by allogeneic monocyte-derived\nmacrophages, suggesting a central role of T cells in driving autoinflammation in these\npatients.\nTherapeutically, the patients’ autoinflammatory manifestations were successfully\nmanaged with the anti-TNFα monoclonal antibody adalimumab and the JAK1/2\ninhibitor ruxolitinib. Notably, a reduced proportion of CD8+ T cells among total T cells\nwas also consistently observed in other immune dysregulation conditions, including\nCRIA and SHARPIN deficiency.\nIn summary, the experiments in this study are thoughtfully designed, and the data\ngenerated are largely robust and support the central conclusions. However, several\nexperiments require revision or clarification, as detailed in the major and minor\ncomments below. This study demonstrate decreased CD8+ T cells as one of common\nimmunological abnormalities in other inborn errors of cell death (IECD) such as CRIA\nand SHARPIN deficiency, indicating the CD8+ T cell-driven autoinflammation may\nplay a role in other IECDs.\nWhile this study will likely be of interest to researchers in the field of inborn errors of\nimmunity, the findings may limit its appeal to the broader readership of Nature\nCommunications. I believe the manuscript may be more appropriately suited for a\nclinically oriented journal that emphasizes translational research and patient-centered\nfindings.\nReply: We are grateful for your supportive assessment and constructive comments,\nwhich have enabled us to improve our revised manuscript.\nMajor comments:\n1. One of the study’s principal observations is the pronounced depletion of CD8+ T\ncells in patients carrying RIPK1 K377E/R390G variants and, similarly, in those with\nCRIA. The authors hypothesize that augmented programmed cell death of CD8+ T cells\nresults in increased cytokine release from those cells, in turn, potentiate monocytes and\nmacrophages to secrete proinflammatory cytokines. To support this model, direct\ncomparisons of CD8+ versus CD4+ T cell responses are indispensable. Several critical\ncomparisons, however, are currently missing:\n- Viability following TS or TSZ stimulation (as shown in Fig 1h) for CD8+ versus\nCD4+ T cells\nReply: We sincerely appreciate your suggestion to strengthen the study with\ncomparisons of CD8+ T cells versus CD4+ T cells. Following the advice, CD8+ T\ncells and CD4+ T cells were stimulated with TS/TSZ to induce programmed cell death,\nand cell viability was analyzed using the CellTiter-Glo Assay. The results showed that\nCD8+ T cells and CD4+ T cells from patients were more sensitive to TS/TSZ-induced\ncell death than those from their heterozygous parents as well as unaffected controls\n(new Extended Data Fig 6f, left). Moreover, CD8+ T cells from the patients exhibited\nlower viability upon TS/TSZ stimulation compared to CD4+ T cells (new Extended\nData Fig 6f, right). These results demonstrate that patients' CD8+ T cells showed higher\nsensitivity to TS/TSZ induced cell death than CD4+ T cells.\nnew Extended Data Fig 6 f, CD8+ T cells and CD4+ T cells were isolated from PBMCs\nof patients, their parents and five unaffected controls (C) followed by stimulated with\n20 ng ml-1 TNF (T), 250 nM SM-164 (S) with or without 25 μM z-VAD-fmk (Z) for 3\nh, 6h as indicated. Cell viability was measured by CellTiter-Glo luminescent cell\nviability assay. The graphs showed mean±SD. Statistical significance was determined\nby two-tailed unpaired t-test, ***P < 0.001.\n- Phosphorylation of MLKL (as shown in Extended Data Fig 2c) in CD8+ versus\nCD4+ T cells\nReply: We are grateful for your asuggestion to assess MLKL phosphorylation in CD8+\nversus CD4+ T cells to compare their sensitivity to necroptosis. However, we were\nonly able to isolate almost 1-2×10^6 CD8+ or CD4+ T cells from the patients, which are\ninsufficient for reliable western blot detection of phospho-MLKL. To address this issue,\nwe considered flow cytometry as an alternative approach. Given that phosphorylated\nMLKL antibody for flow cytometry detection is not commercially available, we\nemployed SYTOX Orange staining to quantify plasma membrane rupture, a hallmark\nterminal event in necroptosis downstream of MLKL activation. We stimulated CD8+ T\ncells and CD4+ T cells with TS or TSZ followed by SYTOX Orange staining. Consistent\nwith the results of CellTiter-Glo Assay, SYTOX Orange staining showed that TS/TSZ\ncould induce higher levels of cell death in CD8+ T cells than in CD4+ T cells (new\nExtended Data Fig 6g). Given that cell death levels were comparable in unaffected\ncontrols and the patients' parents, we demonstrate that a single heterozygous mutation\n(K377E or R390G) is insufficient to drive RIPK1-dependent cell death.\nnew Extended Data Fig 6 f, CD8+ T cells and CD4+ T cells were isolated from PBMCs\nof patients (n=5 for P1 and P2), their parents (n=5 for mother and father) and unaffected\ncontrols (C, n=6) followed by stimulated with 20 ng ml-1 TNF (T), 250 nM SM-164\n(S) with or without 25 μM z-VAD-fmk (Z) for 6h.Cell death levels were measured by\nSYTOX Orange staining. Representative histograms of SYTOX Orange (left) and\nsummary histograms (right) were shown. The graphs showed mean±SD. Statistical\nsignificance was determined by two-tailed unpaired t-test, ***P < 0.001.\n- Proliferation following anti-CD3/CD28 stimulation (as shown in Fig 4b) for CD8+\nversus CD4+ T cells\nReply: Following your advice, we stimulated CD8+ T cells and CD4+ T cells with anti-\nCD3/CD28 dynabeads and quantified the absolute cell numbers at indicated time points\npost-stimulation. Our analysis showed that the expansion of both CD8+ T cells and\nCD4+ T cells from patients was abnormal compared to cells from their parents or\nunaffected controls (Fig. R20).\nFig. R20 In vitro expansion of primary CD8+ T cells and CD4+ T cells isolated from\npatients (n=3 for P1 and P2), their parents and unaffected controls (C, n=6) and as\nindicated. Cell numbers were counted every two days post anti-CD3/CD28 dynabeads\nstimulation. The graphs showed mean±SD.\n2. In Fig. 3g j, both RIPK1 phosphorylation and caspase 3 cleavage appear more\npronounced in CD4+ than in CD8+ T cells. This seems at odds with the model that\npositions CD8+ T cells as the primary drivers of the pathogenesis. Could the authors\nclarify this discrepancy?\nReply: We thank you for raising this important observation and providing suggestions\nto compare cell death between CD8+ and CD4+ T cells, which significantly improved\nour understanding of this phenomenon. The cell death and ATP assay performed under\nyour guidance showed that CD8+ T cells have a greater propensity to undergo cell\ndeath compared to CD4+ T cells.\nHowever, dead cells show different morphology and membrane permeability from\nnormal cells. Necroptotic cells and late-stage apoptotic cells would most likely be ruled\nout by density gradient centrifugation during peripheral blood mononuclear cell\n(PBMC) isolation. As CD8+ T cells were more prone to cell death, most of them were\nexcluded during PBMC isolation prior to flow or CyTOF analysis. CD4+ T cells were\nless sensitive to cell death, probably leading to their accumulation in the early stage of\napoptosis or necroptosis. Consequently, although exhibited enhanced caspase-3\ncleavage and RIPK1 activation, these surviving CD4+ T cells maintained normal\nmembrane permeability and might be kept after density gradient centrifugation. This\nhypothesis presumably contributed to the more pronounced signals of RIPK1\nphosphorylation and caspase-3 cleavage observed in the CD4⁺ T cell population.\n3. In Fig 4e and 4f, Patient 2 already shows an expanded CD4/8 double negative T cell\n(DNT) population at baseline (day 0), and this subset appears to increase further over\ntime.\n- Were elevated DNT frequencies observed consistently in all patients’ (P1 and P2)\nPBMC samples?\nReply: We reanalyzed CD4/8 double negative T cell (DNT) frequencies from previous flow\ncytometry data. Both P1 and P2 exhibited increased DNT population at baseline (Fig. R21).\nFig. R21Representative contour plots (a) andsummary histogram (b) of CD4/8 double\nnegative T cell (DNT) proportions in CD3+ T cells of unaffected controls (C, n=7),\nP1\n(n=7) and P2 (n=5) were shown. The graphs showed mean±SD. Statistical significance\nwas determined by two-tailed unpaired t-test, ***P < 0.001.\n- Are these DNTs αβ T cells or γδ T cells? If they are αβ DNTs, it is important to assess\nwhether they originate from CD8+ T cells. Clarifying the identity and origin of the DNT\ncompartment is critical, as it could significantly impact the interpretation of CD8+ T-\ncell reduction in these patients. To directly address this, CD8+ T cells should be isolated\nand stimulated under the same conditions used in Fig. 4f to determine whether they\ntransition into the DNT phenotype.\nReply: In order to figure out which types of DNTs contribute to the increase of CD3+ DNT\nfrequencies in the patients, we stained PBMCs from the patients (P), their parents and\nunaffected controls (C) with anti-TCRγδ and anti-TCRαβ antibodies and analyzed by flow\ncytometry. We found that TCRγδ+ DNT cells were highly increased in both patients\ncompared to those in their parents and unaffected controls (Fig. R22a). To investigate\nwhether TCRαβ+ DNTs play roles in CD3+ DNT frequency increase, DNT frequencies in\nCD3+TCRαβ+ T cells were analyzed. The results showed no difference in CD3+TCRαβ+\nDNT frequencies among the patients (P), their parents and unaffected controls (C) (Fig.\nR22b). In addition, the absolute counts and frequency of TCRγδ+ T cells in peripheral blood\nmeasured by immunophenotyping (Extended Data Fig. 6a) also confirmed the increase of\nTCRγδ+ T cells in patients. Taken together, abnormal frequencies of CD3+ DNT in the\npatients were attributed by the increase of TCRγδ+ T cells.\nFig. R22 Representative contour plots and summary histogram of TCRγδ+ DNT\nproportions in CD3+ T cells (a) or DNT proportions in CD3+ TCRαβ+ T cells (b) from\npatients (P, n=5), their parents (n=5) and unaffected controls (C, n=9) were shown. The\ngraphs showed mean±SD. Statistical significance was determined by two-tailed\nunpaired t-test, ***P < 0.001, ns, P >0.05.\nExtended Data Fig 6 a, Percentages and absolute counts of γδ T cells from whole\nblood of P1 (red dots) and P2 (blue dots) measured by immunophenotyping. The\ngraphs showed mean±SD.\n4. Pathogenicity and underlying molecular mechanism of the RIPK1 R390G variant are\nnot yet convincingly demonstrated. Given that the monoallelic CRIA associated RIPK1\nvariants act as GOF alleles, the pathogenic potential of biallelic GOF variants must be\ninterpreted with caution. Below are the reasons of my concern:\nReply:We sincerely appreciate your insightful comments. In the family reported in this\nmanuscript, the K377E and R390G variants were inherited separately from the father\nand mother (Fig. 1a). We carefully evaluated the clinical manifestations of the\nheterozygous carriers, and all of them were normal with no inflammatory signatures\n(new Fig. 1f). Only the compound heterozygous patients showed similar\nautoinflammatory phenotypes. This provides strong genetic evidence. We also isolated\nT cells from the parents and patients. This experiment was repeated and the number of\nunaffected controls were expanded at your guidance. The results showed that the\nparents’ T cells showed similar sensitivity to cell death as unaffected controls (new Fig.\n1h), which further demonstrated that heterozygous K377E or R390G is nondeleterious.\nFig. 1 a, Pedigree of a family with two patients (filled symbols) carrying mutations at\nRIPK1K377 and R390.\nnew Fig. 1 f, C-reactive protein (CRP), serum ADA enzymatic activity and IL-6, TNF\nlevels of unaffected controls (C, n=13), P1 during prednisone treatment (4 samples\ntaken during flare and 5 samples taken during non-flare), P2 (4 samples taken during\nflare and 6 samples taken during non-flare), and unaffected parents carrying\nheterozygous K377E mutation (K377E/WT, 1 sample from I-2 and 2 samples from II-\n2) or R390G mutation (R390G/WT, 3 samples from II-3). The graphs showed\nmean±SD.\nnew Fig. 1 h, CD3+ T cells were isolated from PBMCs of patients, their parents and\nfive unaffected controls (C) followed by stimulated with 20 ng ml-1 TNF (T), 250 nM\nSM-164 (S) with or without 25 μM z-VAD-fmk (Z) for 3 h, 6 h and 9 h as indicated.\nCell viability was measured by CellTiter-Glo luminescent cell viability assay. The\ngraphs showed mean±SD.\nRIPK1 activation is complex and can be negatively regulated by phosphorylation,\nubiquitination, and proteolytic cleavage. Although K377E and R390G can activate\nRIPK1-dependent cell death, their further activation remains subject to multiple\nregulatory constraints. Thus, different mutations vary in their capacity to activate\nRIPK1. The D324 mutation in CRIA syndrome is a dominant mutation, and monoallelic\nmutation at D324 is sufficient to induce autoinflammation. D324-mutated RIPK1\ncannot be cleaved by caspase-8, leading to not only RIPK1 activation but also\naccumulation of full length RIPK1. In mouse models, Ripk1D325A/D325A (homologous site\nof D324 in human) mice died at embryonic day 10.5. Both Ripk1D325A/+ and\nRipk1D325A/D325A mouse embryonic fibroblasts (MEFs) were sensitive to TS or TSZ\ninduced cell death, which could not be inhibited by RIPK1 kinase inhibition1. In\ncontrast, Ripk1K376R/K376R(homologous site of K377 in human) mice died at embryonic\nday 12.5. Ripk1K376R/K376RMEFs were sensitive to TSZ induced cell death, which could\nbe fully rescued by RIPK1 kinase inhibition2, 3. Therefore, the D324 mutation is more\ndeleterious than the K376 mutation. The embryonic lethality of Ripk1D325A/+ mice and\nthe viability of Ripk1K376R/+ mice parallel the clinical patterns observed in CRIA patients\nand the patients in this manuscript, supporting the pathogenic potential of biallelic\nK377E/R390G variants.\n- MLKL phosphorylation in R390G transduced HT 29 cells is markedly lower than in\nK377E (Extended Data Fig 2g).\nReply: We appreciate your insightful comment. To directly compare the pathogenic\neffect of the R390G and K377E mutations, we have generated isogenic knockin HT-29\ncell lines for both the K377E and R390G mutations. This model allows for a direct and\nrigorous comparison of their pathogenic strengths under endogenous expression (Fig.\nR23).\nIn this optimized model, we repeated the key functional assays. The cell viability\nassay demonstrated that both mutations sensitized cells to TNF (T) and zVAD-fmk (Z)-\ninduced cell death which could be rescued by the RIPK1 inhibitor, necrostatin-1s (N)\n(Fig. R24a, b). Assessment of necroptosis markers upon TZ treatment demonstrated\nthat both K377E and R390G mutations significantly enhanced RIPK1 and MLKL\nphosphorylation over wild type (WT) levels (Fig. R24c). Despite the stronger\npathogenicity of K377E, the R390G mutation still induced a marked increase in cell\ndeath levels as well as the phosphorylation of both RIPK1 and MLKL compared to the\nWT (Fig. R24). All experiments were performed in three independent biological\nreplicates to ensure the reproducibility and reliability. The representative data have been\nincorporated into the new Extended Fig. 2d-f.\nFig. R23 Sanger sequencing chromatograms of RIPK1 in genomic DNA from RIPK1\nWT or K377E/R390G knock-in cell lines.\nFig. R24 a,b,RIPK1 WT or K377E/R390G knockin cell lines under stimulation of TZ\nwith or without N as indicated. Cell viability measured by CellTiter-Glo luminescent\ncell viability assay. c, Whole cell lysates were immunoblotted with indicated antibodies.\nThe graphs showed mean±SD. Data from three independent experiments are shown.\n- pIKKα/β ,pp65, pIκBα, pERK, and pJNK in R390G-transduced HT-29 cells following\nTNF stimulation seem almost comparable to WT (Extended Data Fig 2i).\nReply: We analyzed the MAPK and NF-κB activation using K377E and R390G knock-\nin HT-29 cells and found that both mutations could impair NF-κB and MAPK activation\n(Fig. R25a). Consistently, although the inhibitory effect of R390G was weaker than\nK377E, it still significantly suppressed MAPK and NF-κB activation. Furthermore, we\nalso evaluated the cytokine levels upon TNF stimulation. qPCR analysis revealed that\ntranscription of the NF-κB downstream genes TNF, IL1B, and IL8 was significantly\ndownregulated in K377E or R390G knockin HT-29 cells, confirming the impairment\nof NF-κB signaling (Fig. R25b). All experiments were performed in three independent\nbiological replicates to ensure the reproducibility and reliability. The representative data\nhave been incorporated into the new Extended Fig. 2h, i.\nFig. R25 a, NF-κB and p38 activation of RIPK1 WT or K377E/R390G knockin cell\nlines treated with TNF for 5, 15 immunoblotted with indicated antibodies.\nFig. R25 b, mRNA levels of TNF, IL8, IL1B in RIPK1 WT or K377E/R390G knockin\ncell lines treated with TNF for 6 hours and determined with qRT-PCR. The graphs\nshowed mean±SD. Data from three independent biological experiments are shown.\nStatistical significance was determined by two-tailed unpaired t-test, *P < 0.05, **P <\n0.01, ***P < 0.001, ****P < 0.0001.\nTo ensure experimental rigor, we replaced all previous results from the\ncomplementation assays with data obtained from the isogenic knockin cell models\n(Extended Data Fig. 2 d, e, f, h, i).\n- Despite a modestly reduced MIB2 interaction (Extended Data Fig 2n), total\nubiquitination of R390G RIPK1 appears intact (Extended Data Figs 2m) Extended Data\nFig 2d also may indicate ubiquitination of RIPK1-R390G is normal (there was no\napparent size difference between WT and R390G RIPK1, whereas the K377E variant\nexhibited a reduced molecular weight, which may reflect impaired ubiquitination). The\nstatement in Line 167 (“The reduced interaction between MIB2 and R390G could partly\nexplained the impaired ubiquitination of R390G RIPK1.”) therefore seems inaccurate.\nReply: We sincerely appreciate your comments on mechanistic insights of R390G\nmutation. In response, we reanalyzed the interaction between MIB2 and RIPK1 and\ntittered the plasmids to avoid dose effect on the result (Fig. R26a). Being aware of the\nhigh levels of endogenous WT RIPK1, which may interact with overexpressed RIPK1\nthrough the death domain and interfere with detection, we also expressed the RIPK1\nintermediate domain with MIB2 (Fig. R26b). Both forms of RIPK1 bearing R390G\nmutation showed reduced interaction with the MIB2 protein, which support our initial\nfindings. These panels have been updated as new Extended Data Fig. 2m, 2n.\nFig. R26 Figure legend: HEK293T cells were transfected with plasmids encoding full\nlength/intermediate domain of RIPK1 together with MIB2 as indicated. MIB2 (a) or ID\ndomain of RIPK1 (b) were immunoprecipitated with anti-Flag dynabeads and\nimmunoblotted with indicated antibodies. Data from two independent biological\nexperiments are shown.\nRIPK1 overexpression can lead to RIPK1 dimerization and oligomerization via its\nRHIM and death domain (DD), which non-specifically recruits numerous E3 ligases.\nConsequently, the ubiquitination regulation of RIPK1 often exhibits nonspecific\ncharacteristics in overexpression systems. Therefore, the ubiquitin-binding assay in\nHEK 293 overexpression system (Extended Data Fig. 2l) was designed to identify\nK377E, but not R390G as a direct ubiquitination site. This result also implied that an\nendogenous setting might provide more reliable analysis of ubiquitination.\nFor this reason, we evaluated the RIPK1 ubiquitination in WT, K377E and R390G\nknockin HT-29 cells and demonstrated that both K377E and R390G mutation impaired\nRIPK1 ubiquitination upon TNF stimulation. Furthermore, both K377E and R390G\nmutations exhibited the same molecular weight as WT RIPK1 (input RIPK1 band),\nwhich reflected the real endogenous condition (Fig. R26). The representative result has\nbeen included in new Extended Fig. 2k.\nFig. R26 The TNFR signaling complex (TNF-RSC) of RIPK1 WT or K377E/R390G\nknockin cell lines was immunoprecipitated with anti-Flag beads upon Flag-TNF\nstimulation for 5 or 15 min and immunoblotted with indicated antibodies. Data from\ntwo independent biological experiments are shown.\nTaken together, the evidence still allows for the possibility of RIPK1-K377E being\npathogenic GOF variant with incomplete penetrance, rather than the R390G being bona\nfide GOF variant. I would recommend following experiments to address this point.\n- Immunophenotyping of mono allelic K377E and R390G carriers to determine whether\neither (or both) variant(s) consistently segregates with immunological abnormalities\nsuch as increased CD4/8 ratio.\nReply: We appreciate your constructive suggestion and analyzed CD4/8 ratio in CD3+\nT cells from flow cytometry data. The results revealed that patients showed a significantly\nelevated CD4/8 ratio compared to their parents and unaffected controls (Fig. R27).\nFig. R27Summary histogram of CD4/8 ratio in CD3+ T cells of the patients (P, n=10),\ntheir parents (n=7) and unaffected controls (C, n=20) were shown. Statistical\nsignificance was determined by two-tailed unpaired t-test, ***P < 0.001, ns, P >0.05.\n- Assessment of RIPK1 ubiquitination in patient derived cells or in K377E/R390G\ndouble knock in cell lines.\nReply: We appreciate your insightful suggestion. Given that RIPK1 ubiquitination\nanalysis requires large amounts of cells, we performed this assay in SV40-immortalized\nhuman dermal fibroblasts (SV40-HDFs) derived from unaffected controls and the\npatient 2 (Extended Data Fig. 2j).\nExtended Data Fig. 2j The TNFR signaling complex (TNF-RSC) of SV40-HDFs\nwas immunoprecipitated with anti-Flag beads upon Flag-TNF stimulation fo 5 or 15\nmin and immunoblotted with indicated antibodies.\nAdditionally, we have successfully generated single knockin cell lines for each\nmutation (K377E and R390G). These new cell models allowed us to systematically\ninvestigate the functional consequences and the underlying mechanisms of these\nmutations in endogenous expression conditions. However, we failed to establish\nK377E/R390G double knockin cell lines due to technical challenges. Subsequent\nanalysis of RIPK1 ubiquitination in the single knockin cells revealed that both the\nK377E and R390G mutations led to a reduction in ubiquitination compared to the WT.\nThe experiments were performed in two independent biological replicates (Fig. R26).\nThe representative result has been included in new Extended Fig. 2k.\nFig. R26 The TNFR signaling complex (TNF-RSC) of RIPK1 WT or K377E/R390G\nknockin cell lines was immunoprecipitated with anti-Flag beads upon Flag-TNF\nstimulation for 5 or 15 min and immunoblotted with indicated antibodies. Data from\ntwo independent biological experiments are shown.\n5. Interpretation of scRNAseq analysis requires revision. The manuscript does not\ndescribe the methodology used to assign hematopoietic cell subset identities to the\nidentified clusters. Please describe the tool/reference used for cell type assignment in\nthe Methods section. The UMAP illustrates cell types that are either rare or absent in\nperipheral blood such as an oversized NKT cluster and a megakaryocyte cluster. With\nsuch data, it is inaccurate to discuss the quantitative assessment of the specific\npopulation (Line 176: The monocyte compartment in patients was significant expanded\n(Fig. 2a, Extended Data Fig. 3a). The CyTOF data do not show an expanded monocyte\ncompartment. Did the patients’ complete blood counts (CBCs) reveal monocytosis?\nReply: We sincerely appreciate your constructive comments. Our cell type annotation\nis based on reported annotation methods for PBMCs4, 5, 6, 7 and web-based databases\nlike CellMarker 8 and PanglaoDB 9. We have added this explanation to method section\nin the revised manuscript. The annotation strategy of our previous submission is shown\nas Fig. R28. Based on your subsequent suggestion, our final annotation strategy is\nshown as Fig. R31.\nFig. R28 Gene markers used for cell type annotation in old Fig. 2a.\nAs for the second question, our current cell annotation strategy referred a previous\nresearch based on large-scale single cell data with almost 68K human PBMCs 6. This\nstudy recommends ZNF683, CD8A, and CD8B as gene marker for NKT annotation. As\nshown in the Fig. R28, current NKT cell was characterized by specific expression of\nZNF683, together with co-expression of CD8A and CD8B. However, as you mentioned,\nthis strategy indeed not matched the reported proportion of circulating NKT in\nperipheral blood, which implicates that there are some limitations in this annotation\nmethod.\nIn previous researches, a subset of CD8+ T cells were identified as NKT-like cells\nthat simultaneously express the features of NK and T cells4, 7, 10. This population\ncurrently annotated as NKT also expressed CD8+ T cell features. Therefore, we\nproposed that cell populations that currently annotated as NKT is a CD8+ T subset.\nHereby, we reviewed the expression pattern of differential expressed genes in the\npopulation currently annotated as NKT and CD8+ T. As shown in Fig. R29, we observed\nthat these two populations specifically expressed GZMB and GZMK, respectively.\nFig. R29 Top differential expressed genes in populations currently annotated as CD8 T\nand NKT.\nGZMB+ CD8+ T and GZMK+ CD8+ T cell are two distinct subsets of CD8+ T cells.\nGZMB+ CD8+ T cells are cytotoxic that depend primarily on granzyme B and perforin,\nwhile GZMK+ CD8+ T cells exhibit less cytotoxic activity with reduced expression of\nGNLY, GZMB, GZMH, and PRF1 11, 12, but enhanced cytokine production 11, 12, 13, 14, 15,\n16. Referring to strategy from multiple sources, we figured that current NKT population\nexpressed identical features of GZMB+ CD8+ T cells, including GZMB, FGFBP2,\nCX3CR1, GZMH, and PRF1 in the research of Cantoni et al and Terekhova et al 4, 7, 11,\n12 (Fig. R30). Besides, it also expressed CD8+ T cell marker, including GZMH, KLRG1,\nNKG7, GZMA, CCL5 based on the Azimuth5 (Fig. R30). On the other hand, we also\nreviewed the marker expression of the population currently annotated as CD8+ T cell.\nAs shown in Fig. R30, this population expressed identical feature of GZMK+ CD8+ T\ncells, including GZMK and lower expression of GZMH, GNLY, and PRF14, 7, 11, 12, while\nthis population also expressed CD8+ T cell marker, including NKG7, KLRG1, CCL5,\nAND GZMA 5 (Fig. R30). Based on above results, we agree with your suggestion and\nreannotate current NKT and CD8+ T population to GZMB+ CD8+ T and GZMK CD8+\nT, respectively.\nFig. R30 Expression of gene markers of GZMK+/GZMB+ CD8+ T cells in current cell\nannotation.\nOur current annotation strategy to megakaryocytes is also referring to the research\nof Debajyoti Sinha et al 6. We also reviewed this annotation. Given the low ratio of\ncirculating megakaryocytes17, and the specifically expression of platelet features in\ncurrent MK, including PPBP and PF4 in the large-scaled single cell database on\nAzimuth5, we reannotated this cluster as platelet.\nIn conclusion, we refined our annotation strategy and reannotated cell populations\ncurrently annotated as NKT, CD8+ T, MK to GZMB+ CD8+ T, GZMK+ CD8+ T cell,\nand Platelet, respectively new Fig. 2a. Gene markers used for current annotation is\nshown as Fig. R31\nnew Fig. 2 a, UMAP of reannotated single cell sequencing data from the patient.\nFig. R31 Gene markers used for cell type annotation in revised Fig. 2a.\nOur results are based on the total T cells, this change therefore has no effect on our\ncurrent conclusion. We have substituted related panels and marked them in our revised\nmanuscript (new Fig. 2a, 2c, 3d, 3e, 5d; new Extended Data Fig. 3a, 3b, 3f, 7b; new\nExtended Data Fig.4g).\nAs the patient samples were acquired during flare, and the WBC and myeloid\nlineage neutrophils and monocytes were all significantly expanded according to\npatients’ complete blood counts (CBCs) (Fig. R32). Likewise, low-density granulocytes\n(LDGs) presenting with highly proinflammatory properties were also massively\nproduced 18. This LDG population was co-isolated with PBMC after density gradient\ncentrifugation and led to a huge LDG population in patient samples 19. We agreed with\nyour advice that this huge population would cross with CD14+ CD16+ monocytes and\nmake it hard to evaluate the quantitative assessment of specific populations. As the CBC\nresults indeed indicated monocytosis and neutrophilic granulocytosis, which were\nconsistent with the scRNA-seq results, we revised the monocyte compartment to be the\nmyeloid compartment.\nFig. R32 Percentages and absolute counts of monocytes from whole blood of the\npatients (red dots show samples taken during flare, blank dots show samples taken\nduring non-flare) measured by immunophenotyping.\nMoreover, the expanded cluster in the scRNA seq analysis is labelled “LDG,” not\nCD14+ or CD16+ monocytes. A more rational model would be that the cluster unique\nto patients, currently labelled “LDG”, represents a pathogenic, cytokine high monocyte\nsubset. Intracellular staining of IL-1β and IL-6 show a bimodal distribution within the\nmonocyte (Fig. 2f), indicating two functionally distinct subsets.\nReply: We are grateful for your valuable comment. LDGs present with highly\nproinflammatory properties. They were massively produced under severe inflammatory\nconditions18. The increased proportion of them could also be detected in other\nautoinflammatory diseases reported previously 20. Therefore, LDGs could be regarded\nas a sign of elevated inflammation.\nAccording to your suggestion, we reviewed our annotation and found that current\n“LDG”-labelled cell type expresses similar neutrophil markers like previously reported\nLDGs including FCGR3B and CMTM2 21. Though this current “LDG”-labelled\npopulation also expresses monocytic markers like S100A8, S100A9, and LYZ, the\ntranscriptional profile is different from monocytes, but still a subset of myeloid lineage.\nGiven its expression of neutrophil markers and myeloid derivative, we believe that the\nannotation of this cluster is not supposed to interfere our results in the discussion about\nmyeloid cells and T cells in the patient. We advocate for retaining the annotation \"LDG\"\n(Fig. R28).\nWhile in Fig. 2f, monocytes were isolated using the Pan Monocyte Isolation Kit\n(Miltenyi Biotec, 130-096-537), which allows the simultaneous enrichment of classical\n(CD14++CD16–), non-classical (CD14+CD16++), and intermediate (CD14++CD16+)\nmonocytes. Therefore, we propose that the two functionally distinct subsets implied by\ntheir differential cytokine expression, represent classical and non-classical monocytes.\nFig. R28 Gene markers used for cell type annotation in old Fig. 2a.\n6. The current title is too broad. Consider rephrasing it to reflect the key discoveries.\nReply: We sincerely appreciate your valuable feedback regarding the manuscript's title\nwhich we have given careful consideration. Our inclination is to retain the current title.\nThis is based on our desire to highlight the study's most critical finding and its direct\nclinical translation.\nAs illustrated in our data (Fig. 7g), a central discovery is the heightened propensity\nof CD8+ T cells to undergo RIPK1-mediated cell death. We have validated this\ndiscovery in models of SHARPIN deficiency (Sharpenia) and Cleavage-resistant\nRIPK1-induced autoinflammation (CRIA). Our mechanism could be indicated by the\nelevated CD4/CD8 ratio and cell death markers (pRIPK1 and cleaved caspase-3) in\nthese cases. Most importantly, the mechanistic insight directly enlightened a combined\ntherapy of TNF inhibition (TNFi) and ruxolitinib. This combined therapy has been used\nin a refractory CRIA patient and other reported inborn errors of cell death (IECD) cases\nwith the best effectiveness observed to date, the full details of which will be reported\nin another story.\nTherefore, we believe the current title most effectively communicates the core\ntranslational message of our work, which we hope will have a broad impact on the\nIECD field by highlighting a novel pathogenic mechanism and an effective treatment\nstrategy.\n[editorial note: figure redacted]\nFig. 7g RIPK1 activation-dependent CD8+ T cell death-triggered inflammatory\nMinor comments:\n1. Line 98: Please clarify whether the perianal abscess was of infectious origin or a\nmanifestation of autoinflammation. If it was infectious, the phrasing in lines 103–104\nmay need to be revised accordingly.\nReply: We thank the reviewer for their insightful comments. While an infectious origin\ncannot be entirely ruled out, we consider the perianal abscess not to be indicative of an\nunderlying immunodeficiency regarding the phenotype observed in Patient 1. The\nperianal abscess occurred only once following severe diarrhea and colitis, and these\nphenotypes were all relieved after adalimumab and ruxolitinib combined treatment.\nThis supports intestinal inflammation as the primary cause of both the diarrhea and\nperianal abscess. Considering that severe diarrhea can lead to perianal abscesses even\nin immunocompetent individuals, we believe a single perianal abscess is not evidence\nof immunodeficiency. Besides, both Patient 1 and Patient 2 did not show susceptibility\nto pneumonia or other infectious diseases. Therefore, we conclude that neither patients\nexhibit evidence of immunodeficiency. In response to the reviewer’ suggestion, we\nhave removed \"infection\" in line 104 to ensure precise description.\n2. Line 106: Do the authors have any insights into the cause of elevated ADA levels\nobserved in the patients?\nReply: Adenosine deaminases (ADAs) comprise ADA1 and ADA2 in humans, and both\ncatalyze the conversion of adenosine to inosine. Increased ADA activity has been\nshown to be associated with rheumatoid arthritis, tuberculosis, systemic lupus\nerythematosus, and adult-onset Still's disease 22, 23, 24, 25. Thus, serum ADA activity\nserves as a clinical marker indicating abnormal immune regulation. ADA2 is a secreted\nprotein expressed mainly by myeloid cells 26. The significant expansion of myeloid cells\nin both patients could increase the serum level of ADA2. ADA1 is a ubiquitously\nexpressed intracellular protein and exhibits stronger enzymatic activity than ADA2 27.\nThe mechanism of ADA1 secretion from cells into serum remains unclear 28.\nConsidering that RIPK1 mutation promotes cell death in T cells or other cells, ADA1\nreleased from dying cells could also enhance serum ADA activity.\n3. Line 135 / Fig 1h: Although the manuscript states that T cells from heterozygous\ncarriers and controls did not exhibit sensitivity to TS/TSZ stimulation, both the father\nand mother appear to show an intermediate phenotype. This should be confirmed by\nrepeating the experiment in the parental samples and expanding the number of healthy\ncontrols.\nReply: We appreciate your suggestion. In response, we repeated the cell viability assay\nand expanded the number of unaffected controls as recommended. The new data\nrevealed that T cells from different individuals showed varying sensitivity to cell death.\nConsistent with the initial findings, T cells from heterozygous carriers showed\ncomparable susceptibility of TS or TSZ-induced cell death to those from unaffected\ndonors (new Figure 1h). These findings were further supported by viability assay as\nwell as SYTOX Orange staining from isolated CD4+ T cells and CD8+ T cells (new\nextended Data Figure 6f, g). In conclusion, T cells from patients, especially CD8+ T\ncells, were highly prone to TS/TSZ-induced cell death, indicating that the compound\nheterozygous mutation of RIPK1, rather than the heterozygous mutation alone, was\npathogenic.\nnew Fig. 1 h, CD3+ T cells were isolated from PBMCs of patients, their parents and\nfive unaffected controls (C) followed by stimulated with 20 ng ml-1 TNF (T), 250 nM\nSM-164 (S) with or without 25 μM z-VAD-fmk (Z) for 3 h, 6 h and 9 h as indicated.\nCell viability was measured by CellTiter-Glo luminescent cell viability assay.\n4. Line 131 / Extended Data Fig 2a: Some data in Extended Data Fig 2a are lacking and\ndifficult to interpret. However, the available data (TS vs TS + Nec-1s in P2) do not\nclearly support the manuscript’s claim that Nec-1s rescued both apoptosis and\nnecroptosis in patient-derived HDFs. Please clarify or revise this point.\nReply: We thank you for the thorough review. First, we have carefully reexamined the\nfigures and identified an inadvertent swap of the images corresponding to P2 TS and\nTSN stimulation (marked in red in the figure) (new extended Data Fig. 2a). We have\ncorrected this error in the updated manuscriptand sincerely apologize for the oversight.\nRegarding the efficacy of Nec-1s, we agree that Nec-1s does not provide complete\nprotection against TS-induced cell death. We reanalyzed the cell death results and found\nthat at the early stage (8 h) of TS-induced apoptosis, Nec-1s reduced the dead cell\nproportion from 60% to 30%. However, after 24 hours of stimulation, Nec-1s only\nsuppressed TS-induced cell death from 90% to 65% (Fig. R33). This indicates that Nec-\n1s only partially inhibits TS-induced RIPK1-dependent apoptosis in fibroblasts. In fact,\nthis observation is consistent with previously reported findings that RIPK1-mediated\napoptosis in human cells is only partially dependent on RIPK1 kinase activity 29. We\nalso examined the inhibitory effect of Nec-1s under TNF (50 ng/ml) and 5z7 (200 nM,\nTAK1inhibitor), a commonly used combination for inducing RIPK1 mediated\napoptosis, and obtained similar results (Fig. R34). Therefore, we will revise the\ncorresponding description in the original text accordingly.\nnew Extended Data Fig. 2 a, SV40-HDFs isolated from three unaffected controls (C1-\nC3) and P2 were treated with TNF with or without S, Z and N as indicated. Cell death\nwas monitored by imaging of SYTOX™ Orange staining. The red boxes indicated the\nmixed-up images.\nFig. R33 SV40-HDFs isolated from three unaffected controls (C1-C3) and P2 were\ntreated with 50 ng/ml TNF and 250 nM SM164 with or without 10 μM Nec-1s as\nindicated. Cell death was monitored by imaging of SYTOX™ Orange staining after\nstimulation for 8 h or 24 h. The percentage of cell death was determined by calculating\nthe ratio of SYTOX™ Orange-positive cells to the total cell count. The graphs showed\nmean±SD, n=4. Statistical significance was determined by two-tailed unpaired t-test,\n*P < 0.05, ***P < 0.001.\nFig. R34 SV40-HDFs isolated from three unaffected controls (C1-C3) and P2 were\ntreated with 50 ng/ml TNF with or without 200 nM 5z7 (5Z-7-Oxozeaenol,\nTAK1inhibitor), 25 μM z-VAD and 10 μM Nec-1s as indicated. Cell death was\nmonitored by imaging of SYTOX™ Orange staining.\n5. Line 163: The list of E3 ubiquitin ligases that were screened should be stated, even\nif the data are not shown.\nReply: Thank you for the constructive comments. We have screened known K63\nubiquitin modifiers, including E3 ubiquitin ligases such as cIAP1/2, TRAF2, A20 and\nMIB2, as well as deubiquitinases like A20 or CYLD, and the adaptor protein TRADD.\nWe overexpressed WT or mutant RIPK1, along with these proteins in HEK293 cells, to\ndetermine the interactions between RIPK1 and each protein (Fig. R35). The results\ndemonstrated that neither the R390G nor the K377E mutation altered the binding of\nRIPK1 to TRADD,cIAP1/2, TRAF2, A20 or CYLD. All experiments were performed\nin two independent biological replicates.\nFig. R35 HEK293T cells were transfected with plasmids encoding full length of RIPK1\ntogether with TRAF2, TRADD, CIAP1, CIAP2, A20 or CYLD as indicated. RIPK1\nwere immunoprecipitated with anti-Flag dynabeads and immunoblotted with indicated\nantibodies.\n6. Line 167: Reference 20 does not provide evidence of MIB2-mediated RIPK1\nubiquitination. Please ensure that citations accurately reflect the claims being made.\nReply: We thank you for pointing out this error. We have corrected the reference as\nbelow. Furthermore, we have carefully re-examined the entire reference list to prevent\nany similar inaccuracies.\nFeltham R, et al. Mind Bomb Regulates Cell Death during TNF Signaling by\nSuppressing RIPK1’s Cytotoxic Potential. Cell Reports 23, 470-484 (2018).\n7. Line 164 and 166: These lines contain redundant information and should be\nconsolidated to improve clarity and conciseness.\nReply: Thank you for your suggestion, the text has been carefully revised.\n8. Line 198 and Extended Data Fig 3e: data appear to be missing and should be provided.\nReply: In response to this issue, we have rechecked Extended Data Fig. 3e. As\nmonocyte-derived macrophages (MDMs) from unaffected controls and patients were\nnot sensitive to TS-induced cell death, the PI-positive dead cells were much less than\nthat in TSZ-induced cell death, resulting in minimal detectable signaling in the\ncorresponding images. Representative images for unaffected control (C), P1 and P2 are\nshown in Extended Data Fig. 3e, and the statistical results from both P1 and P2 are\npooled in Fig. 2i as patient group (P).\nExtended Data Fig 3 e, Representative images of dead M1 macrophages stimulated\nwith TS or TSZ for 24 hours by imaging of propidium iodide (PI). M1 macrophages\nwere differentiated from primary monocytes of unaffected controls (n=6) and two\npatients (n=4 from P1 and P2). Pooled statistics of dead M1 macrophages were shown\nin Fig.2i.\n9. Line 216-218: Although the group 2/3 cytokine definitions are included in the figure\nlegend, a brief description in the main text would greatly improve reader\ncomprehension.\nReply: In response to your suggestion, we have added a brief description to clarify the\nsignificance of the two groups: To identify the upstream factors driving the\ninflammatory cytokine production in myeloid cells, we performed a semi-quantitative\nprotein microarray analysis on serum samples from unaffected controls and P2 (Fig. 3a,\nb). Samples from P2 were collected both before and after the first fever episode (disease\nonset). Differentially expressed cytokines compared to healthy donors were clustered\ninto 3 groups based on their changing patterns relative to the fever stage. Group 1\ncomprised 26 cytokines (e.g., IL-1β) that peaked exclusively during the flare, indicating\ntheir specific association with the febrile stage (Fig. 3c, Extended Data Fig. 4a, b).\nGroup 2 contained 24 cytokines (e.g., sCD95) that were persistently elevated, while\nGroup 3 consisted of 32 cytokines that showed a continuous decrease (Fig. 3c, Extended\nData Fig. 4c-f). The expression levels of cytokines in Groups 2 and 3 were consistently\naltered, suggesting they were likely directly influenced by the genetic mutations rather\nthan being secondary to the fever episodes.\n10. Line 228: While statistically significant, the differences in group 2 cytokine\nexpression appear modest. For biological relevance, individual expression data for key\ncytokines implicated in disease pathogenesis should also be presented.\nReply: We appreciate your suggestions. The group 2 cytokines included MCP-2, ErbB3,\nIL-3, NT-4, Flt-3 Ligand, LAP, Dtk, b-NGF, IGFBP-1, MIP-3β, Fas/TNFRSF6, BLC,\nsTNF RII, Angiopoietin-2, IGFBP-3, IL-12 p70, IL-1α, IL-16, bFGF, TNF-α, MCP-4,\nLIF, MIG, IP-10. Here we showed the transcription of FAS, FLT3LG, IL12A, TNF and\nTNFRSF1B in different cell populations and we observed an increase in patients’ T cells\n(new Extended Data Fig 4g). As the microarray analysis was not sensitive enough to\ndetect serum IFN-γ, we only identified TNF enrichment and did not identify IFN-γ\nenrichment among group 2 cytokines in the microarray analysis. But scRNA-seq still\nrevealed a specific increase in CD8+ T cells instead of myeloid cells and B cells (Fig.\n5d). Therefore, both the microarray data and scRNA-seq data indicated a special role\nof group 2 cytokines and CD8+ T cells in RIPK1 activation-induced autoinflammation.\nThe data have been incorporated into the new Extended Data Fig. 4g.\nnew Extended Data Fig 4 g, Dot plot of FAS, FLT3LG, IL12A, TNF and TNFRSF1B\nin cell subtypes found in scRNA-seq of PBMCs from three unaffected controls and two\npatients.\n11. Fig 5e: The data show a clear bimodal peak in cytokine expression. This raises the\nquestion of whether a distinct CD8+ T cell subset (e.g., CD8-lo) is more prone to\nRIPK1-induced cytokine production. Additional clarification would be valuable.\nReply: We are grateful for your insightful question regarding which CD8+ T cell subset\nexhibits greater susceptibility to RIPK1-induced cytokine production. Through analysis\nof the flow cytometry data, we revealed that the CD8lo T cell subset exhibited more\nelevated cytokine production compared to CD8hi T cells (Fig. R36). This finding\nhighlights the pathogenic role of CD8lo T cells in autoinflammation.\nFig. R36 a, b, Representative histograms and statistical analysis of TNF and IFNγ\nexpression in CD8hi or CD8lo T cells in PBMCs isolated from patients (n=3 for P1 and\nP2) treated with 10 ng ml-1 TNF (T) with or without 250 nM SM-164 (S), 25 μM z-\nVAD-fmk (Z) for 24 hours.\n12. Line 350 / Fig 5h: Although described as \"high\", many comparisons yielded p-\nvalues > 0.05. Either replicate numbers should be increased or the description amended\nto avoid overstating the significance.\nReply: We thank you for the advice. As this experiment used an overexpression system\ntransduced into primary bone marrow cells and transduced into donor mice with HSTC,\nthe complicated process restricted the transduction efficiency. Only about 10%–15% of\nmyeloid cells and 19%–28% of CD8+ T cells could successfully express RIPK1 proteins\n(Extended Data Fig. 9b, d). At the same time, mutant RIPK1 could lead to more cell\ndeath, and the proportion of cells expressing mutant RIPK1 was lower than that of cells\nexpressing wild-type RIPK1 (Extended Data Fig. 9b, d). As a consequence, the cytokine\nchanges under basal conditions were weak, and some comparisons yielded p-\nvalues > 0.05. Even so, we could still observe a tendency that CD8+ T cells alone\ncarrying mutant RIPK1 could lead to spontaneous inflammation.\nTo confirm the pathogenic function of K377E and R390G in vivo, we conducted\nadditional bone marrow transplantation experiments. The results from two independent\nexperiments revealed that K377E /R390G mutation-expressing CD8+ Τ cells could\ninduce autoinflammation (Fig. R37 a, b; new data shown in panel b). Importantly, this\npathogenic effect was observed in R390G-expressing CD8+ Τ cells in the absence of\nthe K377E mutation, indicating that R390G is also a driver of disease pathogenesis.\nThe basal group from the new experimental data has been incorporated into the main\nfigure to provide robust support for our conclusions (new Fig. 5 h, i).\nFig. R37 a, b ELISA measurement of serum Tnf and Il-6 levels in mice specifically\noverexpressing WT or mutated hRIPK1 in Cd8α+ T cells or myeloid cells with or\nwithout 0.5 mg/kg LPS challenge for 2 hours. The graphs showed mean±SD. Statistical\nsignificance was determined by two-tailed unpaired t-test. Data from two independent\nbiological experiments are shown.\n13. Treatment section and relevant discussion / abstract sections: While it is clear that\nadalimumab and ruxolitinib were clinically effective, the current text may give the\nimpression that IFN-γ blockade specifically contributed to the therapeutic effect of\nruxolitinib. Given that JAK inhibition affects a broad range of cytokine pathways, this\nconclusion is premature. Please revise the relevant discussion to reflect this\nappropriately.\nReply: We appreciate your suggestion. Our initial plan was to evaluate the efficacy of\nemapalumab/adalimumab combinations. However, emapalumab was only approved for\nHLH treatment in China. Thus, we selected ruxolitinib, which targets JAK1 and JAK2\n(also downstream of IFN-γR), as an alternative. We have replaced the “TNF/IFN\ninhibitor” with “TNF/JAK inhibitor” in the revised manuscript for accurate description.\n14. Line 444: Is there direct evidence that CD8+ T cells secrete high levels of TNF and\nIFN-γ during the contraction phase induced by programmed cell death? If not, this\nmechanistic link should be presented more cautiously.\nReply: We appreciate and agree with your suggestion. We did not find direct evidence\nthat CD8+ T cells secrete high levels of TNF and IFN-γ during the contraction phase\ninduced by programmed cell death. To ensure accurate description, we have revised the\ndiscussion section accordingly.\n15. The following issues in the Methods section should be addressed for clarity and\naccuracy:\n- Line 594: The SHARPIN-deficient patient is mentioned in the Results section but not\nincluded in the Methods.\nReply: We are grateful for your reminder. The patient description in the Methods section\nhas been revised accordingly.\n- Line 750: Lineage-depleted bone marrow cells should not be referred to as\nhematopoietic stem cells, as this population includes a mixture of progenitor subsets.\nPlease use a more accurate definition.\nReply: Thank you for pointing this out. As you mentioned, the Lineage Cell Depletion\nKit (Miltenyi Biotec, 130-090-858) used in our experiments can enrich mouse lineage-\nnegative cells including hematopoietic stem cells and marrow stromal cells. Therefore,\nwe have replaced \" hematopoietic stem cells (HSC) \" with \" lineage-negative bone\nmarrow cells (Lin- BM)\" in the text.\n- Line 765: Please clearly define what cells were used.\nReply: We appreciate your careful reading and valuable suggestions. We have revised\nthe text as advised and rechecked the manuscript to ensure a more accurate and clear\ndescription.\n16. To improve clarity and ensure consistency across figures, the following revisions\nare recommended:\n- Fig 1c: The unit of measurement is missing\nReply: Thank you for your careful review. We have added the units to Fig. 1c.\n- Fig 1e and Extended Data Fig 1a-c: Marking the time points corresponding to febrile\nepisodes would enhance the interpretability.\nReply: Thank you for your valuable suggestion. However, prior to initiating prednisone\ntherapy for the patient, they had sought the cause of their condition across multiple\nmedical institutions. We collected all the patient's historical routine blood test reports.\nHowever, due to insufficient detail in the actual patient records, we were unable to\nprecisely determine the specific condition of the patient at the time each test was\nconducted. Considering the patient's overall medical history, these tests were typically\nperformed during episodes of persistent high fever. Therefore, in Fig. 1e the elevated\nvalues can reasonably be attributed to febrile episodes. Some normal values might result\nfrom short-term symptomatic treatments. Since an accurate distinction was unfeasible,\nwe opted not to mark febrile episodes in the figure to avoid potential misinterpretation.\nThe inflammatory status values that we could confidently associate with confirmed\nfebrile episodes, as opposed to normal conditions, are presented separately in Fig. 1f.\n- Fig 1f: The error bars seem to represent SEM, although the figure legend defines them\nas SD. Please ensure the figure legend accurately reflects the error bar definition.\nReply: Thank you for pointing this out. We apologize for the error in mislabeling the\nerror bars in the figure as SEM. Each data point represents an individual patient sample.\nThe figure has been corrected to use the standard deviation (SD) to represent the\nvariability (new fig. 1f).\nnew Fig. 1 f, C-reactive protein (CRP), serum ADA enzymatic activity and IL-6, TNF\nlevels of unaffected controls (C, n=13), P1 during prednisone treatment (4 samples\ntaken during flare and 5 samples taken during non-flare), P2 (4 samples taken during\nflare and 6 samples taken during non-flare), and unaffected parents carrying\nheterozygous K377E mutation (K377E/WT, 1 sample from I-2 and 2 samples from II-\n2) or R390G mutation (R390G/WT, 3 samples from II-3). The graphs showed\nmean±SD.\n- Fig 2a: The current color scheme uses similar colors for adjacent populations, making\nthem difficult to distinguish. Please apply a revised color palette with greater contrast\nbetween clusters.\nReply: Thank you for your suggestion. We have adjusted the color scheme to ensure\nclear visual distinction between adjacent cell populations (new Fig. 2a).\nnew Fig. 2 a, UMAP of reannotated single cell sequencing data from the patient.\n- Fig 3: Marker sizes vary within the same figure panel and should be standardized.\nReply: Thank you for the reminder. We apologize for the error in Figure g-j, where the\nhorizontal axis was inadvertently shifted during the editing process. We have now\ncarefully reviewed and corrected it.\n- Fig 4f, Extended Data Fig 6b: Axis labels are missing and should be added for clarity.\nReply: We are grateful for your reminder. We have proofread and added the axis\nlabels.\n- Extended Data Fig 2f: Marker symbols are too small to distinguish clearly. Please\nenlarge the points for better visibility.\nReply: Thank you for the reminder. Since the reconstitution of RIPK1 in HT-29 RIPK1\nKO cells could not guarantee consistent expression levels, which would compromise\nthe reliability of the results, we have removed the relevant data from this experimental\nsystem, including Extended Data Fig. 2f. The results have been updated with new data\nobtained from RIPK1 mutant knock-in HT-29 cells (new Extended Data Fig. 2i).\n- Extended Data Fig 2f and 2j: The K377R variant data are not discussed in the main\ntext and may be omitted.\nReply: Thank you for your valuable suggestion. K377R is a commonly used mutation\nthat abolishes ubiquitination at this site. However, for the same reason mentioned above,\nwe would not include a discussion of the K377R mutation as the results from K377R-\nreconstituted HT-29 cells have been removed.\n- Extended Data Fig 5a: One of the two major CD16+ monocyte populations and the\nCD14+ monocyte population shown in Fig. 5a do not appear in any of the replicates\npresented in Fig. 5b. For consistency, both subsets should be represented, or the\ndiscrepancy should be explained.\nReply: Thank you for the valuable comments. Extended Data Fig. 5a initially presented\npooled data, which included samples from four healthy donors, three samples from\npatients, and two samples from CRIA patients. The figure reveals certain deviations in\nthe distribution of various cell populations, such as CD16⁺ monocytes, CD14⁺\nmonocytes, and CD4⁺ central memory (CM) T cells. A gradient loss of CD8⁺ T TEMRA\ncells was observed, along with a localization shift of CD16⁺ monocytes, CD14⁺\nmonocytes, and CD4⁺ CM T cells across the P1, P2-1, P2-2, and CRIA1 samples (Fig.\nR38). These variations are likely associated with the inflammatory state of the patients,\nwhich may influence the expression levels of relevant markers. To ensure consistency,\nwe have replaced the original data with a new version of Extended Data Fig. 5a that\ncontains only the merged data from controls and patients in this manuscript.\nAccordingly, Fig. R38a has also been updated as the new Extended Data Fig. 5b.\nFig. R38 a, b, Integrated t-distributed stochastic neighbor embedding (tSNE)\nvisualization of CyTOF immunophenotyping of PBMCs from C1, C2, C3 P1 and P2\n(A) and CRIA patient (B) with color-indicated cell types\nnew Extended Data Fig. 5a Integrated t-distributed stochastic neighbor embedding\nvisualization of CyTOF immunophenotyping of PBMCs from four unaffected\ncontrols and two patients (n=3). Major 16 cell lineages from all samples were identified\nthrough unsupervised clustering with FlowSOM. tSNE plots showing 10000 cells of\nrepresentative C1, C2, C3 and three patients’ samples with color-indicated cell\ntypes.😠\n- Extended Data Fig 6b: The DNT fraction in P1/DMSO condition appears to be\nincorrectly labeled and should be re-examined.\nReply: Thank you for pointing out this issue. The DNT cell percentage in Extended\nData Fig. 6b has been updated to the correct value of 9.39%, as it was mistakenly copied\nfrom the CD8+ T cell data. In response to your comment, we have performed a full audit\nof all flow cytometry data presented in the study to ensure no other errors are present.\nWe sincerely appreciate your contribution to improving our work.\n17. The following wording issues were noted:\n- Line 110: Capitalize “Sanger”.\n- Line 351: Consider specifying the subset as “CD8+” (or “CD8α+”).\n- Line 414: The term “gain-of-function” appears prior. Please introduce the abbreviation\nthe first time it is used.\n- Line 422: Define “MEF”.\n- Line 733: “MDM” has already been defined earlier; no need to redefine it here.\n- Line 744: Use “Ripk1” instead of “RIPK1”.\n- Throughout the manuscript, “patients” is occasionally used when data from only P1\nor P2 are shown. Replace with “P1” or “P2” as appropriate to ensure accuracy.\nReply: We appreciate your careful reading and valuable suggestions for improvement.\nWe have corrected the text as advised and rechecked the manuscript.\nReferences\n1. Lalaoui N, et al. Mutations that prevent caspase cleavage of RIPK1 cause\nautoinflammatory disease. Nature577, 103-108 (2020).\n2. Tang Y, et al. K63-linked ubiquitination regulates RIPK1 kinase activity to prevent\ncell death during embryogenesis and inflammation. Nat Commun10, 4157 (2019).\n3. Zhang X, et al. Ubiquitination of RIPK1 suppresses programmed cell death by\nregulating RIPK1 kinase activation during embryogenesis. 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Front Immunol13, 903461 (2022).\n28. Cordero OJ, et al. Cytokines regulate membrane adenosine deaminase on human\nactivated lymphocytes. J Leukoc Biol70, 920-930 (2001).\n29. Mannion J, et al. A RIPK1-specific PROTAC degrader achieves potent antitumor\nactivity by enhancing immunogenic cell death. Immunity57, 1514-1532 e1515 (2024).\nReply to the Referee #2\nReviewer #2 (Remarks to the Author):\nThe authors have sufficiently addressed my comments.\nThe endogenous knock in data and other additions has notably improved the manuscript and I\ncommend the authors on their hard work.\nReply: We are grateful for your positive assessment.\nReply to the Referee #3\nReviewer #3 (Remarks to the Author):\nIn the revised manuscript, the authors have made substantial improvements by thoroughly\naddressing all of this reviewer’s comments. I appreciate the authors’ sincere and careful responses.\nI have only one minor comment as follows.\nReply: We are grateful for your supportive and constructive comments, which have greatly helped\nus enhance the quality of the study.\nLines 290–293 and Extended Data Fig. 6f, g: Although patients’ CD8+ T cells demonstrate\nincreased sensitivity to programmed cell death upon TS and TSZ stimulation compared to CD4+\nT cells, the same trend is also observed in cells from healthy controls and heterozygous parents.\nTherefore, from presented data, it is difficult to conclude that the augmented programmed cell\ndeath in CD8+ T cells is specifically attributable to the RIPK1 GOF variants.\nReply: We appreciate your comment and agree that the enhanced programmed cell death in CD8+\nT cells is not solely attributable to RIPK1 GOF variants. Given that CD8+ T cells are also\nintrinsically more susceptible to cell death than CD4+ T cells, even in unaffected controls, we\ncannot exclude the possibility that intrinsic factors in CD8+ T cells contribute to their higher cell\ndeath propensity compared to CD4+ T cells. Data in Extended Data Fig. 6f, g demonstrates that\nRIPK1 GOF variants could further enhance cell death in CD8+ T cells, which supports our\nconclusion that CD8+ T cells bearing RIPK1 GOF variants can trigger autoinflammation."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have sufficiently addressed my comments. The endogenous knock in data and other additions has notably improved the manuscript and I commend the authors on their hard work.", "reviewer_3": "In the revised manuscript, the authors have made substantial improvements by thoroughly addressing all of this reviewer’s comments. I appreciate the authors’ sincere and careful responses. I have only one minor comment as follows. Lines 290–293 and Extended Data Fig. 6f, g: Although patients’ CD8+ T cells demonstrate increased sensitivity to programmed cell death upon TS and TSZ stimulation compared to CD4+ T cells, the same trend is also observed in cells from healthy controls and heterozygous parents. Therefore, from presented data, it is difficult to conclude that the augmented programmed cell death in CD8+ T cells is specifically attributable to the RIPK1 GOF variants."}, "author_response": "Reply: We are grateful for your supportive and constructive comments, which have greatly helped us enhance the quality of the study. Reply: We appreciate your comment and agree that the enhanced programmed cell death in CD8+ T cells is not solely attributable to RIPK1 GOF variants. Given that CD8+ T cells are also intrinsically more susceptible to cell death than CD4+ T cells, even in unaffected controls, we cannot exclude the possibility that intrinsic factors in CD8+ T cells contribute to their higher cell death propensity compared to CD4+ T cells. Data in Extended Data Fig. 6f, g demonstrates that RIPK1 GOF variants could further enhance cell death in CD8+ T cells, which supports our conclusion that CD8+ T cells bearing RIPK1 GOF variants can trigger autoinflammation."}]

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