The long non-coding RNA UGDH-AS1 encodes an NK-cell-inhibiting micropeptide in triple-negative breast cancers

Domain: neuroscience
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
NK cells are important for the anti-tumour immune response for their potential to kill MHC class I-deficient tumor cells, but they often become dysfunctional in the immune-hostile tumour microenvironment. Here, using single-cell RNA sequencing, we identify an NK cell subpopulation that is specific to triple-negative breast cancers (TNBC) subtype, characterised by the expression of the long non-coding RNA UGDH-AS1. In these NK cells, UGDH-AS1 encodes the micropeptide, NKSM, which renders these NK cells dysfunctional due to the loss of their activation program, which leads to cancer progression. Conditional NKSM knock-in into NK cells of mice results in NK cell deactivation and increased growth of transplanted tumours. Targeted NKSM therapy effectively reduces tumor growth in TNBC mouse models. We find that UGDH-AS1+ NK cells are shaped by the tumor microenvironment (TME). Following upregulation by the TGF-β signaling pathway, NKSM binds to Myc, inhibiting its ERK1/2-mediated phosphorylation at Serine 62 and thus reducing its stability. Decreased Myc activity results in deregulation of T-bet, a key protein involved in NK cell function, which leads to NK cell deactivation. Our study thus provides mechanistic insight into NK cell dysfunctionality in TNBC and lays down the proof of principle for an NK-cell-targeting TNBC immunotherapy.

Introduction
Breast cancer continues to be the most frequently diagnosed malignancy and the primary cause of cancer-related deaths among women worldwide1. Breast cancers are typically classified according to both molecular and histological subtypes2. Molecularly, tumors are divided into hormone receptor-positive (HR+), human epidermal growth factor receptor two-positive (HER2+), and triple-negative breast cancers (TNBC)3. HR+ breast cancers (luminal A and luminal B) are most common (60–70%), characterized by estrogen receptor (ER) and/or progesterone receptor (PR) expression. Luminal A tumors have the best prognosis among all subtypes, while patients with luminal B tumors generally experience shorter overall and disease-free survival compared to luminal A tumors4–6. HER2-enriched tumors are distinguished by their overexpression of the HER2 oncogene and typically show low-to-absent expression of ER/PR. HER2+ disease is associated with a poor prognosis; however, advancements in anti-HER2 therapies have significantly enhanced outcomes for women diagnosed with HER2+ breast cancer7,8. TNBC is a malignant subtype of breast cancer that accounts for approximately 10-24% of all breast cancer cases9. TNBC lacks expression of ER, PR, and HER2 and has low sensitivity to chemotherapy10; hence, traditional therapy and regular immunotherapy are ineffective in TNBC patients. Although several new antitumor immunotherapies, such as immune checkpoint inhibitors and chimeric antigen receptor (CAR)-T cells, have achieved great success, TNBC patients still have a higher rate of recurrence and a worse prognosis compared to patients with other forms of breast cancer11. Therefore, TNBC remains a significant challenge in clinical treatment, and novel therapeutic strategies or improvements to existing therapies urgently need to be developed.

Natural killer (NK) cells are innate cytolytic and cytokine-producing lymphocytes that play critical roles in antitumor and anti-viral responses. Unlike T lymphocytes, NK cells can efficiently kill MHC class I-deficient tumor cells, highlighting the potential of developing antitumor immunotherapy based on NK cells, such as CAR-NK cells and immune checkpoint inhibitors (e.g., PD-1 inhibitors). However, due to the complex tumor microenvironment, the tumor-infiltrating NK cells are often dysfunctional and deactivated in various solid tumors12. For example, TGFβ within the tumor microenvironment drives NK cell dysfunction, suppressing their antitumor activity, compromising the efficacy of NK-based immunotherapies, and contributing to poor clinical outcomes13–16. Tumor-associated fibroblasts in melanoma can modulate cell-to-cell interactions and release PGE2, which inhibits NK cell function17. In addition, HIF1-α and c-Myc control the activity of NK cells by regulating NK cell metabolism18,19. Therefore, restoring the function and increasing the activity of tumor-infiltrating NK cells could provide a novel immunotherapeutic strategy for TNBC treatment.

In the past decade, long noncoding RNAs (lncRNAs) have been reported to be involved in many biological processes, including cancer, the immune response, and inflammation20,21. Traditionally, lncRNAs are defined as RNA transcripts longer than 200 nt that lack protein-coding ability22,23. However, recent studies have reported that some RNAs, previously defined as lncRNAs, can encode functional protein products24,25, such as LINC00278, which has been demonstrated to encode the micropeptide YY1BM to promote apoptosis in esophageal squamous cell carcinoma (ESCC) cells26. In TNBC, the lncRNA-encoded polypeptide ASRPS can inhibit angiogenesis by blocking STAT3 phosphorylation, thereby suppressing tumor growth27. Although lncRNAs have been revealed to play critical roles in NK cells28, no previous study has investigated the function of lncRNA-encoded peptides in NK cells.

In this study, we identify a triple-negative breast cancer-specific NK cell subset expressing the lncRNA UGDH-AS1, which encodes the micropeptide NKSM that inhibits NK cell function. Using in vitro and in vivo models, we demonstrate that NKSM suppresses NK cell activity and promotes tumor growth. NKSM is upregulated by TGF-β signaling and modulates c-Myc stability by interfering with its phosphorylation. This study provides insight into the mechanism of NK cell dysfunction in TNBC and supports NKSM as a potential therapeutic target.

Results
ScRNA-seq analysis identifies a TNBC-specific UGDH-AS1+ NK cell subset in breast cancer
To construct a breast cancer single-cell transcriptome atlas for tumor-infiltrating NK cells, we analyzed single-cell RNA-sequencing (scRNA-seq) data derived from six TNBC patients, five HR+ patients, and three HER2+ patients. After quality control (QC) and data merging, a total of 1209 single cells were used to construct a TNBC microenvironment atlas (Supplementary Fig. S1A). These cells were classified into six main clusters, which contained epithelial cells, macrophages, fibroblasts, T cells, B cells, and NK cells (Supplementary Fig. S1B). SingleR and known cell markers included in the CellMarker database were used for cell type identification (Supplementary Fig. S1C–H). The top markers of the main clusters were visualized as a heatmap (Supplementary Fig. S1I and Supplementary Table S1). Interestingly, the lncRNA UGDH-AS1 was highly upregulated in TINK cells (Supplementary Fig. S1I). We next extracted NK cell data for further analysis. In addition, to compare TINK cells with infiltrating NK cells from breast cancer subtypes (HR+ and HER2+), we merged 200 TINK cells with 769 NK cells derived from HR+ and HER2+ breast cancer scRNA-seq datasets (Supplementary Fig. S2A–E and Supplementary Table S2). Through further analysis, we found that the integrated NK cells were further subdivided into five subsets: DNAJB1+ NK, GZMH+ NK, IL17R+ NK, ITGAE+ NK, UGDH-AS1+ NK (Fig. 1A, B and Supplementary Fig. S3A). Interestingly, compared to other breast cancer subtypes, UGDH-AS1+ NK cell clusters were predominantly found in TINK cells (Fig. 1C). As shown in Fig. 1D, UGDH-AS1+ NK cells showed a deactivated status, which was consistent with decreased expression of FCGR3A (CD16), KLRB1 (CD161), LAMP1 (CD107a) and CD244 (Supplementary Fig. S3B). Moreover, the expression of the transcription factors TBX21 (T-bet) and EOMES (Eomesodermin), as well as that of the genes encoding the cytolytic molecules granzyme B (GZMB), perforin (PRF1) and IFN-γ (IFNG) were also significantly downregulated in UGDH-AS1+ NK (Supplementary Fig. S3C). Subsequently, Kaplan-Meier survival curves show that patients with breast cancer in the TCGA dataset with high UGDH-AS1+ NK cells have shorter overall survival (Fig. 1E). These data indicate that UGDH-AS1+ NK cells are closely associated with the occurrence, progression, and poor prognosis of TNBC.

UGDH-AS1 suppressed NK cell activity by blocking IFN-γ expression
Considering that UGDH-AS1 is one of the top markers of deactivated UGDH-AS1+ NK cells, we hypothesized that UGDH-AS1 may suppress the activity of UGDH-AS1+ NK cells. Hence, we isolated human NK cells from peripheral blood mononuclear cells (PBMCs; PBNK cells) or breast cancer tissues from patients (Supplementary Fig. S4A, B). QPCR data showed that UGDH-AS1 expression was significantly upregulated in TINK cells compared with PBNK or non-TNBC infiltrating NK cells (Supplementary Fig. S4C, D).

The protein levels of GZMB, PRF1, IFN-γ, and CD107a were decreased in UGDH-AS1 overexpressed NK-92MI cells (Fig. 2A). We also showed that UGDH-AS1 inhibited NK-92MI cell activity without affecting their proliferation or survival (Supplementary Fig. S5A). In addition, the cytotoxicity of UGDH-AS1-overexpressing NK-92MI cells toward TNBC cells was lower compared to control NK-92MI cells (Fig. 2B and Supplementary Fig. S5B). These data indicate that UGDH-AS1 expression is associated with NK cell activity.

UGDH-AS1+ NK cells encode a micropeptide
We next biologically characterized UGDH-AS1. The UGDH-AS1 locus is located on chromosome 4 and spans from 39,528,019 to 39,594,707. A nuclear/cytoplasmic fractionation experiment indicated that UGDH-AS1 was mainly located in the cytoplasm (Supplementary Fig. S5C). Interestingly, using ribosome profiling (Ribo-seq) data obtained from the GWIPS-viz database, we found that UGDH-AS1 might contain two small open-reading frames (sORFs) interacting with ribosomes (Fig. 2C). Polysome profiling assays also suggested that UGDH-AS1 could bind to ribosomes (Supplementary Fig. S5D–F). These data indicated that UGDH-AS1 is an lncRNA with coding potential.

To validate the coding potential of UGDH-AS1, we subsequently cloned the above two sORFs with an in-frame FLAG epitope tag at the C terminus to construct expression vectors. After transfection into 293T cells for 48 h, we determined sORF expression using western blotting with an anti-FLAG antibody. As shown in Fig. 2D, western blotting confirmed that sORF1 of UGDH-AS1, which encodes a micropeptide of 76 amino acids, has coding ability. Furthermore, to further determine the coding ability of sORF1, we mutated the initiation codon (ATG to ATT) of sORF1 of UGDH-AS1 and the GFP-ORF to construct FLAG-tagged and GFP-fusion expression vectors (Fig. 2E–J). Then, we transfected wild-type (GFPwt and sORF1-FLAG) or mutant (GFPmut, sORF-mut-GFPmut, and sORFmut-FLAG) expression vectors into 293T cells for 48 h. Using western blotting and immunofluorescence, we showed that the sORF1-FLAG expression vector, but not the sORF1mut-FLAG expression vector, could express the FLAG-tagged micropeptide (Fig. 2F, G). As shown in Fig. 2I, J, expression of the GFP fusion protein was observed in the cells transfected with the GFPwt and sORF1-GFPmut expression vectors but not in the cells transfected with GFPmut and sORFmut-GFPmut.

To determine whether the micropeptide encoded by UGDH-AS1 is endogenously expressed in human cells, we generated a rabbit polyclonal antibody against the micropeptide. Using western blotting and immunofluorescence with the anti-micropeptide antibody, we showed that the micropeptide level detected by the anti-micropeptide antibody was significantly increased when UGDH-AS1 was introduced into 293T cells or NK cells (Fig. 2K, L, and Supplementary Fig. S5G, H). We also detected the micropeptide in 293T cells transfected with various expression vectors (GFPwt, sORF1-FLAG, GFPmut, sORF1-GFPmut, sORFmut-GFPmut, and sORFmut-FLAG) by western blotting with the anti-micropeptide antibody (Fig. 2F, I). Moreover, our data revealed that the micropeptide was expressed at significantly higher levels in TINKs compared with other breast cancer (HR+ and HER2+) infiltrating NK cells and PBNK cells from TNBC patients (Fig. 2M, N, and Supplementary Fig. S5I). These findings validated the specificity of the antibody and the endogenous expression of the UGDH-AS1-encoded micropeptide. Furthermore, NKSM expression was not detected in NK cells infiltrating other tumor types (Supplementary Fig. S5J).

UGDH-AS1-encoded micropeptide inhibited the antitumor activity of NK cells and promoted TNBC tumor growth
Because UGDH-AS1 can suppress NK cell activity and encode a micropeptide, it was necessary to elucidate how UGDH-AS1 suppresses NK cell activity, either through its RNA transcript or through its encoded micropeptide. Therefore, we next sought to determine how UGDH-AS1 functions as an NK cell suppressor. We overexpressed full-length UGDH-AS1 (UGDH-AS1-OE) and sORF1 of UGDH-AS1 (sORF1-OE) in NK-92MI cells and injected these cells into TNBC xenograft mice. As shown in Fig. 3A, compared with mice injected with control NK-92MI cells, the mice injected with UGDH-AS1-OE or sORF1-OE NK-92MI cells exhibited significantly increased tumor growth. However, the tumor growth inhibition effects of UGDH-AS1-OE and sORF-OE NK-92MI cells were not significantly different. We further knocked down UGDH-AS1 in UGDH-AS1-OE (UGDH-AS1-OE-KD) and sORF-OE (sORF-OE-KD) NK-92MI cells using UGDH-AS1-specific shRNAs and injected the cells into TNBC xenograft mice. Compared with the UGDH-AS1-OE NK-92MI cell-injected mice, the UGDH-AS1-OE-KD NK-92MI cell-injected mice showed decreased tumor growth (Fig. 3B). However, the sORF1-OE-KD and sORF1-OE NK-92MI cell-injected mice did not show a significant difference in tumor growth (Fig. 3C). These data suggest that UGDH-AS1 suppresses human NK cell activity through its encoded micropeptide but not its RNA transcript. Consequently, we designated the micropeptide encoded by UGDH-AS1 as the NK cell suppressor micropeptide (NKSM).

Decreasing NK cell activity resulted in faster tumor growth in NKSM+/+ mice
To investigate the function of NKSM in murine NK cells and TNBC tumors, we generated NKSM+/+ mice, which harbor a conditional knock-in of NKSM specifically in murine NK cells (Supplementary Fig. S6A, B). NKSM+/+ mice did not exhibit changes in the numbers of NK cells in different organs (Supplementary Fig. S6C, D) or the proliferation and survival of NK cells (Supplementary Fig. S6E). Our data showed that the production of IFN-γ, Granzyme B, and Perforin in NKSM+/+ mouse NK cells isolated from the spleen was significantly reduced (Fig. 3D). In addition, our results demonstrate that the production of IFN-γ, Granzyme B, and Perforin by CD8+ T cells remains unaffected in NKSM+/+ mice (Supplementary Fig. S6F).

Next, we examined the antitumor activity of NKSM+/+ NK cells in vivo. We subcutaneously transplanted 1×10^6 E0771 cells into wild-type and NKSM+/+ mice and found that tumor growth was significantly increased in NKSM+/+ mice (Fig. 3E). We also established lung metastasis mouse models by tail vein injection of 5×10^5 E0771 cells, and found an increase in tumor lung metastasis in NKSM+/+ mice (Fig. 3E). Furthermore, we conditionally knocked in NKSM in MMTV-PyMT mice (MMTV-PyMT; NKSM+/+), a spontaneous breast cancer mouse model. Consistent with the results from NKSM+/+ mice, we observed a similar tumor growth pattern in MMTV-PyMT; NKSM+/+ mice (Fig. 3F), with an increase in tumor lung metastasis in these mice (Fig. 3F). In addition, to further investigate the role of NKSM in murine NK cells, we utilized anti-NK1.1 (PK136) to deplete NK cells in mice. Tumor growth and lung metastasis analysis revealed no significant differences between MMTV-PyMT; NKSM+/+ mice and MMTV-PyMT; NKSM+/+ + PK136 mice (Fig. 3G). These data demonstrate that NKSM inhibits the antitumor activity of murine NK cells and promotes TNBC progression.

TGF-β signaling pathway promoted the formation of UGDH-AS1+ NK cells and the expression of NKSM
To explore the mechanism of NKSM upregulation in UGDH-AS1+ NK cells, we predicted the transcription factor binding sites in the UGDH-AS1 promoter. Surprisingly, we found that UGDH-AS1 has a potential TGF-β signaling pathway key protein (SMAD2/3/4) binding site in the −184 to −172 region of its promoter (Supplementary Fig. S7A, B). Because the TGF-β signaling pathway has been reported to play a critical role in NK cells29,30, we hypothesized that the TGF-β signaling pathway may upregulate the expression of NKSM in a manner involved in NK cell biological processes. Therefore, we analyzed SMAD2/3/4 chromatin immunoprecipitation (ChIP)-sequencing (ChIP-seq) data derived from different human cells. We showed that these transcription factors could bind to the UGDH-AS1 promoter (Fig. 4A). These findings supported our prediction and suggested that the TGF-β signaling pathway regulates the transcription of UGDH-AS1. Indeed, TGF-β stimulation significantly increased NKSM expression (Fig. 4B).

We further conducted ChIP experiments using anti-SMAD2/3/4 and confirmed that SMAD2/3/4 could precipitate the UGDH-AS1 promoter −200 to +300 region (Fig. 4C). Moreover, we constructed UGDH-AS1 promoter luciferase reporters with or without the SMAD2/3/4 binding site (Fig. 4D). Luciferase assays showed that UGDH-AS1 transcription was upregulated by SMAD4 overexpression and downregulated by SMAD4 knockdown in TGF-β-stimulated cells, while the absence of the SMAD2/3/4 binding site abolished the above changes (Fig. 4E). When we inhibited SMAD3/4 using the SMAD3 phosphorylation inhibitor SIS3 or SMAD4-specific small interfering RNAs (siRNAs), the TGF-β-induced upregulation of NKSM was significantly abolished (Fig. 4F, G). Taken together, these results demonstrate that the TGF-β signaling pathway is responsible for the upregulation of NKSM expression by promoting UGDH-AS1 transcription.

NKSM interacted with c-Myc
To explore how NKSM is involved in NK cell biological processes, we identified NKSM-interacting proteins using co-immunoprecipitation (co-IP) with an anti-FLAG antibody coupled with mass spectrometry (MS) analysis of NK-92MI and 293T cells transfected with an NKSM-FLAG expression vector (Fig. 5A, B). The subsequent co-IP and western blot results indicated that NKSM could interact with the proto-oncogene protein c-Myc (Fig. 5C). To further confirm this interaction, we co-transfected NKSM-FLAG and c-Myc-HA expression vectors into 293T and NK-92MI cells, performed co-IP assays using anti-FLAG and anti-HA antibodies, and found that the corresponding western blot results confirmed the interaction between NKSM and c-Myc (Fig. 5D). Moreover, we demonstrated that an anti-c-Myc antibody could precipitate endogenous c-Myc and NKSM in NK-92MI cells (Fig. 5E).

NKSM reduced the stability of the c-Myc protein by blocking Ser62 phosphorylation
We next sought to map the NKSM binding domain on the c-Myc protein. We constructed vectors expressing HA-tagged c-Myc truncation variants and cotransfected them with the NKSM-FLAG expression vector into 293T cells (Fig. 5F). Co-IP with an anti-FLAG antibody and western blotting using anti-FLAG and anti-HA antibodies indicated that NKSM binds to the 1–144 region of c-Myc (Fig. 5G). Because the 1–144 region of c-Myc contains the conserved phosphorylation site Ser6231, which increases the stability of the c-Myc protein32, and c-Myc is crucial for NK cell function19, we hypothesized that NKSM could negatively regulate c-Myc Ser62 phosphorylation. Interestingly, we found that both the protein level and Ser62 phosphorylation of c-Myc were decreased in NKSM-overexpressing cells (Fig. 5H). To exclude the influence of another conserved phosphorylation site, Thr58, in c-Myc, we also co-transfected NKSM-FLAG with c-Myc-WT or the c-Myc mutants S62A, T58A or S62A/T58A into 293T cells. The results showed that NKSM decreased only c-Myc Ser62 phosphorylation (Fig. 5I). Furthermore, to exclude the effect of protein stability on c-Myc Ser62 phosphorylation, we treated NKSM-transfected cells with the proteasome inhibitor MG132 to inhibit protein degradation. As shown in Fig. 5J, NKSM inhibited c-Myc Ser62 phosphorylation in both the absence and presence of MG132 but decreased the c-Myc protein level only in the absence of MG132, suggesting that NKSM decreased the c-Myc protein level by reducing its stability. We also performed cyclohexamide (CHX) assays to measure the c-Myc protein level. Our data indicated that NKSM could significantly reduce the half-life of the c-Myc protein (Fig. 5K and Supplementary Fig. S7C).

Furthermore, because c-Myc Ser62 phosphorylation is mediated by ERK1/232, we next used co-IP to determine whether the interaction between ERK1/2 and c-Myc could be altered by NKSM. As shown in Fig. 5L, the interaction between c-Myc and ERK1/2 was blocked in NKSM-overexpressing cells. We also demonstrated that NKSM could not regulate phosphorylation and protein level of c-Myc when cells were treated with SCH772984, a selective ERK1/2 inhibitor (Fig. 5M). These data suggest that NKSM regulates the c-Myc protein level in NK cells by blocking ERK1/2-mediated c-Myc Ser62 phosphorylation and reducing c-Myc protein stability.

NKSM regulated NK cell activity by downregulating c-Myc-mediated T-bet expression
Given that the transcription factor T-bet controls the expression of the key cytokines for NK cell function33, we next examined T-bet expression and IFN-γ, Granzyme B, and Perforin production in NKSM-overexpressing and control NK-92MI cells. As shown in Fig. 6A, B, the production of IFN-γ, Granzyme B, Perforin and the expression of T-bet were significantly reduced by NKSM overexpression. To investigate whether NKSM-regulated c-Myc controls the transcription of T-bet, we predicted a potential c-Myc binding site in the promoter of T-bet (Supplementary Fig. S7D, E). Then, we analyzed public c-Myc ChIP-seq datasets and visualized c-Myc occupancy on the T-bet promoter. The data suggested that c-Myc potentially bound to −149 to −160 regions of the T-bet promoter (Fig. 6C), which was subsequently confirmed by ChIP assays with an anti-c-Myc antibody (Fig. 6D and Supplementary Fig. S7F). Moreover, we constructed T-bet promoter-containing luciferase reporters with or without c-Myc binding sites and showed that T-bet transcription was inhibited in both NKSM-overexpressing cells and c-Myc-knockdown cells using luciferase assays (Fig. 6E and Supplementary Fig. S7G). Such changes were abolished by c-Myc binding site deletion (Fig. 6E). Furthermore, when c-Myc and T-bet were knocked down, the expression of IFN-γ, Granzyme B and Perforin in NKSM-overexpressing and control NK-92MI cells was not significantly different (Supplementary Fig. S7H, I). Our data suggest that NKSM regulates c-Myc to transcriptionally downregulate T-bet in NK cells, resulting in decreased production of IFN-γ, Granzyme B, and Perforin, thereby inhibiting NK cell activity.

NKSM is a potential target in TNBC immunotherapy
Based on our findings, we hypothesized that blocking NKSM expression to prevent NK cells from being deactivated by the TGF-β signaling pathway in the TNBC tumor microenvironment may provide a novel strategy for TNBC NK cell-based antitumor immunotherapy. Therefore, we generated an NKSM knockout (NKSM-KO) NK-92MI cell line (Supplementary Fig. S8A–C). Our data indicated that NKSM-KO NK-92MI cells exhibited higher cytotoxicity against K562 cells both in culture medium supplemented with TGF-β (Fig. 6F and Supplementary Fig. S8D) and in conditioned medium from TNBC cells (Supplementary Fig. S8E). We next injected WT or NKSM-KO NK-92MI cells into TNBC xenograft mice to determine their antitumor effect in vivo. We found that the injection of NKSM-KO NK-92MI cells significantly reduced tumor growth and improved mouse survival (Fig. 6G, H and Supplementary Fig. S8F, G). In addition, to further investigate and validate the findings for potential therapeutic applications, we constructed NKSM knockout cells in human primary NK cells (Supplementary Fig. S8H). Through in vivo experiments in mice, we found that NKSM inhibits the activity of NK cells (Fig. 6I). Therefore, pre-blocking NKSM expression has the potential to improve the efficacy of NK-cell based antitumor immunotherapy.

Methods
Human tissue specimens
Breast cancer tissues (TNBC, n=57; HR+, n=29; HER2+, n=33) were obtained from patients who underwent tylectomies, and non-breast cancer tissues, including esophageal squamous-cell carcinoma (ESCC), gastric cancer, endometrial carcinoma, and lung cancer, were obtained from patients undergoing surgical resection at the Affiliate Hospitals of Soochow University. None of these participants received anticancer treatment, including chemotherapy or radiotherapy, before surgery. All participants provided written informed consent prior to their inclusion in the study. The clinical characteristics of the participants are shown in Supplementary Table 3.

Animals and cell cultures
NOD/ShiLtJGpt-Prkdcem26Il2rgem26/Gpt (NCG, Strain No. T001475) and C57BL/6JGpt (Strain NO. N000013) female mice (4–8 weeks of age) were purchased from GemPharmatech Co., Ltd (Nanjing, China). MMTV-PyMT mice were obtained from Zhongjun Dong’s laboratory at the Institute for Immunology and School of Medicine, Tsinghua University. All mouse were raised at the Laboratory Animal Center of Soochow University (SPF level). All mice were housed in a pathogen-free facility under controlled conditions (26°C, 40–70% humidity) with a 12h light/dark cycle. Sterile food and water were provided throughout the housing period. The experimental and control mice were co-housed. At the endpoint of the experiments, all animals were euthanized by CO2 inhalation in strict accordance with the institutional animal care and use guidelines to minimize suffering.

For xenograft experiments, a total of 0.1 ml of cell suspension (1×10^6/ml) was injected subcutaneously into the hind flank of mice. For NK cell cytotoxicity experiments, different NK-92MI cell lines were injected via the mouse tail vein (twice a week for 4 weeks) when the xenograft tumor volume reached ~100 mm3. Tumor volume was calculated according to the following formula: volume = length × width^2 × 0.5.

All cell lines were purchased from Procell Life Science & Technology Co., Ltd (Wuhan). These cell lines were all characterized by DNA fingerprint analysis and passaged less than 6 months in this study. DMEM and fetal bovine serum (FBS) were purchased from Invitrogen. MDA-MB-231 and 293T cells were grown in DMEM supplemented with 10% FBS; Hs578T cells were grown in DMEM supplemented with 10% FBS, 0.01 mg/mL bovine insulin; NK-92MI cells were grown in MEM-alpha supplemented with 12.5% FBS, 12.5% Horse serum, 0.2 mM inositol, 0.02 mM folic acid, 0.1 mM beta-mercaptoethanol. Primary human NK cells were cultured in a dedicated NK cell medium (catalog no. iCell-i006-002h, iCell). All cell lines were grown in penicillin/streptomycin-containing medium at 37°C in a humidified atmosphere with 5% CO2.

NK cell isolation and expansion
For human NK cell isolation, PBMCs were isolated by centrifugation over a Ficoll 400 cell separation solution (density 1.077 g/mL; Biocoll, VWR). NK cells were isolated by magnetic cell sorting with the NK Cell Isolation Kit (Miltenyi, 130-092-657) from PBMCs. Fresh tumor tissues were harvested, cut into small pieces, and digested into single-cell suspensions with the Tumor Dissociation Kit (Miltenyi, 130-095-929) using gentle MACS (Miltenyi). NK cells (CD3-CD56+) were sorted using a BD FACSAria III. For mouse NK cell isolation, NK cells were isolated from mouse spleens by negative selection with the NK Cell Isolation Kit (Miltenyi, 130-115-818). NK-cell expansion was performed as previously described54,55.

RNA extraction and qRT-PCR
Total RNA was isolated from cells and tissues using TRIzol Reagent (Invitrogen). First-strand cDNA was synthesized with the Superscript II reverse transcriptase kit (Invitrogen, Carlsbad). All qRT-PCR primers are listed in Supplementary Table S4.

Cell transfection
Small interfering RNA (siRNA) and non-targeting siRNA control (Supplementary Table S5) were purchased from GenePharma. Transfections with siRNA were performed with Lipofectamine 3000 (Life Technologies), following the manufacturer’s protocol.

For overexpression or RNA interference of target genes in NK cells, 10 micrograms of plasmids or siRNA (100 nM) was mixed with NK-92MI cells and incubated on ice for 20 min in sterile electroporation cuvettes (0.2 cm). After electroporation, the NK-92MI cells were immediately cultured in complete culture medium.

ScRNA-seq data analysis
The scRNA-seq data of six TNBC samples were obtained from the GEO database (GSE118389). The quality control process was performed by Seurat. Briefly, single cells that expressed less than 200 genes were considered low-quality cells and discarded. Then, the filtered data were merged using the SCTransform and IntegrateData functions in Seurat. The main cell clusters were identified using the FindClusters function (resolution=0.2) of Seurat and visualized using t-distributed stochastic neighbor embedding (t-SNE), and the results were plotted by the DimPlot function of Seurat. Subsequently, the markers of each main cell cluster were identified by the FindAllMarkers function. The top 10 cluster markers are listed in Supplementary Table S1. Cell type was recognized by SingleR and combined with markers obtained from the CellMarker database. NK cells were extracted from TNBC cells using the subset function of Seurat for further analysis.

The scRNA-seq data of non-TNBC breast cancer tissues (HR+ and HER2+) were obtained from the GEO database (GSE167036). Single cells with less than 100 unique feature counts (nFeature_RNA), with more than 20% mitochondrial UMI or with more than 50% ribosomal UMI counts were considered low-quality cells and discarded. Delete the double cells annotated by the DoubletFinder package (version: 2.0.3). We exclude genes detected in fewer than three cells, as well as mitochondrial and ribosomal genes. The Seurat package (version: 4.3.0) was used for cell normalization and regression to obtain the scaled data. We selected the top 3000 largest variable genes as highly variable genes (HVGs). We then used the “RunPCA” function to perform the principal component analysis (PCA). The number of significant principal components is determined by the “Elbow” function, and PC is selected as 25. The main cell clusters were identified using the FindClusters function (resolution=1.0) and visualized using t-SNE, with the results plotted by the DimPlot function. Subsequently, the markers of each main cell cluster were identified by the FindAllMarkers function. The top 10 cluster markers are listed in Supplementary Table S2. Based on these marker genes, we manually annotated cell clusters, identifying 10 cell populations, including T cells, Plasma B cells, Naive B cells, NK/T cells, Myeloid cells, Epithelial cells, Endothelial cells, Fibroblasts, myoepithelial cells, and cycling cells. For NK/T cells, we used a similar dimensionality reduction clustering method. Unlike before, the Harmony package is used to remove batch effects on patient_id. The selected PC dimension is 30, and the resolution is 1.5. Based on cell cluster-specific marker genes, 769 NK cells were obtained by manual annotation. NK cells were extracted using the subset function of Seurat and merged with NK cells from TNBC by the SCTransform and IntegrateData functions in Seurat. The merged NK cells were clustered by the FindClusters function (resolution=0.2) and visualized using t-SNE. A hexbin plot of gene expression in single cells was generated with the schex package (version: 1.14.0).

ChIP-seq data analysis
ChIP-seq reads were preprocessed by Fastp software for filtering low-quality reads and aligned to GRCh38 by Bowtie2 with default parameters; the mapped reads of ChIP-seq were preprocessed by SAMtools and then submitted to MACS2 for peak calling with default parameters. The peaks were visualized by the Integrative Genomics Viewer (IGV) software.

Transcription factor binding site prediction
For transcription factor binding site prediction for the promoters of UGDH-AS1 and TBX21, promoter sequences from −2000 to +300 were obtained from Ensembl. Then, the SMAD2/3/4 or MYC binding sites were identified by the R package TFBSTools56. SMAD (MA0513.1) and MYC (MA0147.3) motifs obtained from JASPAR were used for the analyses.

Cell cytotoxicity assay
TNBC cells were seeded at a density of 1×10^4 cells per well in a 96-well plate. After 48 h of culture, the supernatants were collected and centrifuged for further use. Then, 1×10^4 K562 cells were cultured in the collected TNBC cell medium, and different NK-92MI cell lines were added at various ratios (1:1, 1:5, and 1:15; target cells: effector cells). After 4 h of coculture, the 96-well plate was centrifuged at 250×g for 3 min, and the supernatants were removed to a new flat-bottom 96-well plate for detection using the CyQUANT™ LDH Cytotoxicity Assay (Thermo Fisher Scientific, C20301) according to the manufacturer’s instructions. NK cell cytotoxicity was defined as:
% Cytotoxicity = (Experimental value - Effector Cells Spontaneous Control - Target Cells Spontaneous Control) / (Target Cell Maximum Control - Target Cells Spontaneous Control) * 100

Immunofluorescence staining
Cells were plated on glass coverslips, fixed with 4% paraformaldehyde (PFA) at room temperature (RT) for 15 min, rinsed three times with PBST (0.3% Triton), incubated with primary antibodies overnight at 4°C, washed four times with TBSTx and incubated with FITC-conjugated secondary IgG antibodies (Beyotime) at RT for 1 h in the dark. Cell nuclei were stained with DAPI.

Western blot
Cells were collected and lysed in RIPA buffer. Proteins were separated on an SDS-polyacrylamide gel and transferred to a nitrocellulose membrane. Immunoblotting of membranes was performed using the following primary antibodies: anti-FLAG (sc-807, Santa Cruz), anti-HA (ab9110, Abcam), anti-GFP (2956, CST), anti-phospho-SMAD2 (3108, CST), anti-SMAD2 (5339, CST), anti-phospho-SMAD3 (9520, CST), anti-SMAD3 (9523, CST), anti-SMAD4 (38454, CST), anti-HSPA1A (4873, CST), anti-GCN1 (ab86139, Abcam), anti-c-Myc (ab32072, Abcam), anti-phospho-c-Myc (ab185656, Abcam), anti-DIS3 (ab68570, Abcam), anti-RACK1 (ab129084, Abcam), anti-p-ERK1/2 (ab76299, Abcam), anti-T-bet (5214, CST), and anti-β-actin (A5441, Sigma-Aldrich). Signals were revealed after incubation with the recommended secondary antibody coupled to peroxidase using enhanced chemiluminescence. The antibodies used in the study were listed in the Supplementary Table 6.

Anti-NKSM antibody preparation
Peptide synthesis and anti-NKSM antibody generation were performed as previously described with some modifications57. Briefly, BSA and the OVA-coupled peptide WHEHVSLQPQPPRLK were synthesized, and a polyclonal antibody against NKSM was obtained from inoculated rabbits. The antibody was purified using affinity chromatography on columns containing the corresponding peptide.

Polysome profiling
Polysome profiling was performed to measure the translation of UGDH-AS1, with monitoring by qRT-PCR. We performed polysome profiling following a procedure described in ref. 58. The primers for qRT-PCR are listed in Supplementary Table S4.

Cell fractionation of RNA and proteins
To determine the cellular localization of RNAs and proteins, cytosolic and nuclear fractions were collected from cells according to the manufacturer’s instructions for the Nuclear/Cytoplasmic Isolation Kit (BioVision).

Co-IP assay
Co-IP assays were performed using the Pierce™ Co-Immunoprecipitation Kit (Thermo Scientific) according to the manufacturer’s instructions. Briefly, lysates were applied to columns containing 10 μg of immobilized antibodies covalently linked to an amine-active resin and incubated overnight at 4°C. Then, the coimmunoprecipitate and controls were eluted and analyzed by SDS-PAGE or mass spectrometry. Co-IP assays were performed using the following antibodies: anti-FLAG (sc-807, Santa Cruz), anti-HA (ab9110, Abcam), and anti-c-Myc (ab32072, Abcam).

Mass spectrometry analysis
Samples were analyzed on a Thermo Fisher LTQ Orbitrap ETD mass spectrometer. Briefly, samples were loaded onto an HPLC chromatography system (Thermo Fisher Easy-nLC 1000) equipped with a C18 column (1.8 mm, 0.15×1.00 mm). Solvent A contained 0.1% formic acid, and solvent B contained 100% acetonitrile. The elution gradient was from 4% to 18% in solvent A for 182 min and 18–90% in solvent B for 13 min at a flow rate of 300 nL/min. Mass spectrometry analysis was carried out at AIMS Scientific Co., Ltd. (Shanghai, China) in the positive-ion mode with automated data-dependent MS/MS analysis with full scans (350–1600 m/z) acquired using Fourier transform mass spectrometry at a mass resolution of 30,000, and the ten most intense precursor ions were selected for MS/MS. MS/MS results were acquired using higher-energy collision dissociation at 35% collision energy and a mass resolution of 15,000.

ChIP
ChIP assays were performed with an EZ-ChIP kit (Millipore, Bedford, MA) according to the manufacturer’s instructions. Chromatin from cells was immunoprecipitated with antibodies against SMAD4 (38454, CST) and c-Myc (ab32072, Abcam).

Dual-luciferase reporter assay
Cells were seeded in 24-well plates (1×10^5 cells per well) and cultured to 60–70% confluence before transfection. Then, the cells were transfected with 800 ng of the reporter plasmids described above using Lipofectamine 3000 (Invitrogen). After transfection for 24 h, the cells were collected using 100 μl of passive buffer, and Renilla luciferase activity was detected using the Dual-Luciferase Reporter Assay System (Promega) and a TD-20/20 illuminometer (Turner Biosystems).

Production of UGDH-AS1-overexpressing and NKSM-overexpressing cells
To produce cells with overexpression, full-length human UGDH-AS1 or NKSM cDNA was synthesized by GeneWiz (Beijing, China) and cloned into the lentiviral expression vector pLVX-IRES-neo (Clontech Laboratories Inc.). To produce lentiviruses containing a full-length UGDH-AS1 or NKSM targeting sequence, 293T cells were cotransfected with the vector described above and a lentiviral vector packaging system using Lipofectamine 3000. Infectious lentiviruses were collected at 48 h and 72 h after transfection and filtered through 0.45 μm filters. These lentiviruses were designated UGDH-AS1-overexpressing and NKSM-overexpressing recombinant lentiviruses, respectively. We used the empty plenty-pLVX-IRES-neo vector to generate negative control lentiviruses. Recombinant lentiviruses were concentrated by centrifugation. The virus-containing pellet was resuspended in DMEM, and aliquots were stored at −80°C until use. Cells were infected with concentrated virus in the presence of polybrene (Sigma-Aldrich). The supernatant was replaced with complete culture medium after 24 h, followed by selection with 800 µg/ml G418, and the expression of UGDH-AS1 and NKSM in infected cells was verified by qRT-PCR.

CRISPR/Cas9 mediated gene knockout
NK-92MI cells were infected with lentivirus to stably knockout NKSM (hU6-sgRNA-EF1a-SpCas9-NLS-FLAG-P2A-Puro). The gRNA sequence designed specifically for the NKSM initiation codon was 5’-CACTCACTGGTCCAGTCATGAGG-3’. Cells seeded in 96-well plates were spin transduced at 1000×g for 120 min at 37°C with 5 μg/mL polybrene supplemented. The transfected cells were continuously screened with interval puromycin (2.5 μg/mL) screening, taking 7 days as a screening cycle. The expanded cells that survived from puromycin screening were used for western blot analysis, sequencing, and further experiments.

Generation of NKSM+/+ and MMTV-PyMT; NKSM+/+ mice
We generated an NKSM-overexpressing mouse model using a gene-targeting construct. A single copy of NKSM cDNA was inserted downstream of the STOP cassette in the ROSA26 (R26) locus through homologous recombination, so exogenous NKSM cDNA was transcribed under the control of the R26 promoter, allowing reproducible and stable overexpression of NKSM. This gene-targeting construct was electroporated into embryonic stem (ES) cells, and microinjection of the recombinant ES clones led to the generation of chimeric animals. To analyze the in vivo consequences of NKSM overexpression, conditional knock-in animals were crossed with the Ncr1-Cre strain to obtain NKSM+/- heterozygous mice. The Ncr1-Cre mice were purchased from Biocytogen (China). NKSM+/+ homozygous animals were generated by intercrossing heterozygous mice. Finally, NKSM+/+ mice were crossed with C57BL/6 MMTV-PyMT mice to generate MMTV-PyMT; NKSM+/+ mice on the C57BL/6 background.

Flow cytometry analysis
Non-specific binding of mouse and human cells were blocked by FcR blocking reagent for mice (Cat#130-092-575, Miltenyi) and human (Cat#130-059-901, Miltenyi), respectively. Dead cells were excluded using the Zombie Aqua Fixable Viability Kit (BioLegend), and intracellular cytokine staining was performed with the FIX&PERM Kit (FcMACS). Cells were stained for 20 min at 4°C with conjugated antibodies. To measurement IFN-γ, Perforin, and Granzyme B production, cells were stimulated with appropriate reagents for 12 h, then with Brefeldin A at a final concentration of 10 μg/mL for 4 h. For CD107a staining, cells were incubated with anti-CD107a for 4 h at 37°C in a 5% CO2 incubator, followed by staining for extracellular markers. Analyses of flow cytometry data were performed using FlowJo software (version 10.8.1). FACS sequential gating/sorting strategies are shown in the Supplementary Figs. 4 and 9.

Statistical analysis
All experiments were repeated at least 3 times unless stated in the figure legend. Data analysis was performed using SPSS 19.0 software for Windows. The significance of differences between datasets is expressed as p-values, and p<0.05 was considered statistically significant (NS, no significance; *p<0.05; **p<0.01; ***p<0.001). Survival curves were obtained using the Kaplan-Meier method and compared using the log-rank test. Paired or unpaired Student’s t tests were used for comparisons of various types of data.

Ethics declarations and consent to participate
This study was approved by the Ethics Committee of Soochow University (SUDA20210301H10) and was conducted in accordance with the provisions of the Ethics Committee of Soochow University as well as the principles outlined in the Declaration of Helsinki. All participants provided written informed consent prior to their inclusion in the study. All animal experiments were approved by the Soochow University Institutional Animal Care and Use Committee (202012A674) and carried out in strict accordance with the relevant guidelines and regulations.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Recommendation: Accept with minor revisions\nSummary:\nTumor-infiltrating NK cells in TNBC express the lncRNA UGDH-AS1 which encodes the newly-identified (by this paper) micropeptide NKSM. NKSM suppresses NK cell function. UGDH-AS1 expression is induced by TGFb in the TME (UGDH-AS1 has a SMAD2/3 binding site in its promoter and treatment with TGFb increases NKSM expression). Mechanistically, NKSM suppresses NK function by binding to c-myc at the phosphorylation site Ser62. This prevents phosphorylation of Ser62 by ERK1/2, thereby reducing the stability of c-myc, and preventing it from binding to T-bet (a known mediator of NK cytokine production). This in turn prevents T-bet from mediating transcription of effector transcripts (e.g. IFNG, GZMB), thereby suppressing NK function. The authors present convincing evidence of this novel pathway using a combination of biochemical techniques and in vivo studies in NSG mice implanted with patient-derived organoid breast tumors and NK cell specific NKSM knock-in mice created for this study.\nMajor Comments:\n1. The authors present convincing evidence that UGDH-AS1 encodes the novel micropeptide NKSM, and that NKSM has an inhibitory effect on NK cell functon(e.g. overexpression of UGDH-AS1 in NK-92 cells resulting in acceleration tumor growth, NKSM knock-in mice exhibiting reduced NK cell function, NKSM expression in primary TINK cells, mutational studies on UGDH-AS1 that abrogate NKSM expression, etc.).\n2. Similarly, the extensive co-IP assays offer strong support for the signaling cascade proposed to be initiated by NKSM (i.e. interference with c-myc stability via reduced ERK1/2 binding). Furthermore, the authors validate the in vivo effect of NKSM deletion as being beneficial to tumor surveillance.\n3. A more robust NK cell signature should be used to annotate tumor-infiltrating NK (TINK) cells in the scRNAseq data queried. Currently, it appears that KLRD1 is the only well-recognized NK marker enriched in the cluster annotated as NK cells (Fig. S1C). Donna Farber, Zemin Zhang, and others have published atlases of tissue-resident and tumor-infiltrating NK cells w/ signatures that would be of use. Additionally, NCR1 and KLRF1 are useful NK markers to consider.\n4. Additionally, the T/B cluster should be parsed into separate clusters of T and B cells to increase annotation confidence, particularly as NK cell clusters are typically proximal to CD8 T clusters in t-sne/UMAP projections. This may be important given the low number of cells from breast tumors sequenced (1,209 cells from 14 patients which includes only 213 NK cells from triple-negative tumors), and the lack of canonical NK cell markers present before sub-clustering. Finally, the strong enrichment of UGDH-AS1+ NK cells in TNBC along with the lack of canonical NK marker expression compared to NK cells from other tumor types underscores the importance of having high confidence that the authors are actually looking at NK cells.\n5. The author’s work could be strengthened by testing whether addition of NKSM peptide directly to healthy donor peripheral blood NK cells and NK-92 cells inhibits their function directly.\n6. It would be interesting to see how generalizable NKSM expression is in NK cells from other solid tumors. In part because even within Breast Cancer subtypes UGDH-AS1 is almost exclusively confined to NK cells from TNBC tumors and not really HR+ or HER2+ tumors.\n7. Similarly, what do the authors propose as the reason for over-representation of UGSH-AS1+ NK cells in TNBC vs. non-TNBC Breast Cancer?\n8. Would deletion of UGDH-AS1 in NK cells still improve tumor surveillance in an immunocompetent animal model? That is, in the presence of other immune cells infiltrating the tumor. While the NSG/PDX model the authors use is appropriate, it would be useful to determine what occurs in a humanized mouse, or in a mouse model with at least one additional suppressive immune cell type such as macrophages or MDSC etc.\nMinor Comments:\n1. In paper title change to “inhibiting NK cell activity”,\n2. Update Reference #13 as there has been additional work showing how TME suppresses NK cell fxn since 2013.\n3. The figures in the combined PDF need to be numbered as it is confusing to quickly determine where to look when reading the text and Fig. legends.\n4. Line 104 “cancer” misspelled.\n5. Fig. panels should be ordered left to right rather than top to bottom in some cases (e.g. Fig. S1A+B).\n6. Tables should be numbered according to when they are introduced in the text. Currently, the first Table referenced is Table S4.\n7. The nomenclature for NK cells is confusing (e.g. TINK for triple negative NK cells, HRINK for NK cells from HR+ tumors, and HER2INK from HER2+ tumors, etc.).\n8. Please specify more clearly that Cluster 4 is the UGDH-AS1 NK cluster in the panels of Fig. 1.\n9. Line 123-124: KLRB1 is not NK1.1, it is CD161. NK1.1 is only expressed by murine NK cells.\n10. Fig. 1E: It is unclear whether the authors are showing UGDH-AS1+ NK cells in the Kaplan-Meir plot or just expression of UGDH-AS1 in TCGA data.\n11. Line 135: Unclear if NK cells from PBMC are from patients or healthy donors. Would be best\n12. Fig. 5C: Please specify if this blot was performed in NK-92 or 293T cells.\n13. Fig. 5D: FLAG is misspelled as “FALG” on top panel.\n14. Fig. S4A+S4B: FACS plots should be changed to pseudocolor plots, should show outlier cells, and should show original axis titles.\n15. Line 317: Subtitle should be changed to “NKSM is”.\n16. Line 882: NCG should be NSG. There are several other instances of this elsewhere so please check.", "reviewer_2": "The study by Zhang et al. investigates the identification and characterization of UGDH-AS1+ NK cells in triple-negative breast cancer (TNBC) through single-cell RNA seq. These NK cells express a micropeptide named NKSM, which is encoded by UGDH-AS1 and suppresses NK cell activity, thus facilitating cancer progression. The research demonstrates that NKSM is upregulated in tumor-infiltrating NK cells (TINK) within TNBC, leading to a decrease in anti-tumor activity. Conditional expression of NKSM in mouse NK cells results in NK cell deactivation and enhanced tumor growth. NKSM, regulated by the TGF-β signaling pathway, impairs NK cell function by destabilizing c-Myc, which reduces the expression of the key transcription factor T-bet and subsequently lowers the production of cytotoxic molecules such as GZMB, PRF1, and IFN-γ. The study highlights NKSM as a critical factor driving NK cell deactivation in the TME and contributing to TNBC progression. The authors propose targeting NKSM as a novel therapeutic strategy to improve NK cell-based immunotherapies for TNBC, aiming to counteract the suppressive effects of TGF-β on NK cells.\nTargeting NK cell deactivation as a therapeutic strategy is a familiar concept in immuno-oncology, although the specific targets and mechanisms in this study appear new. Overall, while some foundational concepts regarding the tumor microenvironment, TGF-β signaling, and NK cell suppression are already known, the identification of UGDH-AS1+ NK cells, the NKSM micropeptide, and their role in TNBC are likely novel contributions.\nMajor concerns:\n1. The functional assay of degranulation, marked by CD107a expression should be included in the panels of Figures 2A, 3D, and 6A. This addition will provide a comprehensive assessment of NK cell functionality. Granzyme B secretion is usually in vehicles, and CD107a reflects its trafficking to the membrane, which is important for target cell lysis.\n2. The NK-92MI cell line, a derivative of the parental NK-92, lacks several key receptors typically expressed by primary human NK cells, including the absence of CD16 and several KIRs, potentially impacting downstream signaling and transcriptional regulation, raising concerns about the relevance of NK-92MI cells as a model for primary NK cells. Thus, specifying NK-92MI characteristics used in this study is crucial for assessing its relevance to primary human NK cells.\n3. While the RNA sequencing dataset is robust, including a Principal Component Analysis (PCA) would provide a quantitative and visual representation of transcriptional differences, enhancing the interpretation of the data.\n4. The term \"NK cells\" is used redundantly throughout the text. Clearly distinguishing between murine and human NK cells and specifying where each is used would improve clarity and comprehension. Additionally, it would be beneficial to indicate whether cell lines or patient tumor samples were employed to develop the respective mouse models in the study.\n5. To strengthen the study's translational potential, it is recommended to replicate one of the in vivo models, preferably a TNBC xenograft model (NSG/NRG), using primary human NK cells engineered to overexpress UGDH-AS1+ or knocking-out (KO) the NKSM micropeptide. This will help validate the relevance of the findings for potential therapeutic applications. Additionally, performing functional assays on the primary NK cells obtained from these tumors will add significant impact to the study.\n6. For all in vivo experiments reported in the manuscript, please include statistical power analyses to determine if the sample size of mice (n=5) is adequate to draw statistically and scientifically valid conclusions.\n7. Although well-executed, the gating strategy presented in Supplementary Figures 4A and 4B would be more informative if switched to a dot plot format with enumeration. This adjustment would improve understanding of the sample size used to generate the data.\nMinor concerns:\n1. The manuscript contains numerous grammatical errors. These should be corrected to enhance readability and professionalism.\n2. The sizes of each protein band in the western blot data should be included throughout the manuscript to provide clarity on the molecular weights of the detected proteins.", "reviewer_3": "Zhou and colleagues present a significant data set supporting a new mechanism by which triple negative breast cancer can potentially evade anti-tumor immunity provided by NK cells. They provide support for a pathway in which tgfb signaling induces transcription of a long lncRNA, UGDH-AS1, in a subset of NK cells, which inhibits NK activity (IFNg and cytolytic molecules) via a \"micropeptide\" encoded by an ORF in UGDH-AS1. This micropeptide inhibits phosporylation of a serine residue in Myc which results in decreased Myc stability and association with erk1/2, which in turn prevents Myc/Erk from serving as a co-transcriptional activator with Tbet to transcribe IFNg and genes encoding the cytolytic molecules (granzymes, perforin) that NK cells use to kill tumor cells. They provide evidence for each of these steps and use various human and murine conditional NK-specific micropeptide expression systems to support each of these steps. Overall, while it is still an open question as to the role of this particular pathway in tumor immunity writ large, the body of work they present for this novel mechanism deserves publication and will likely stimulate important follow-up work in the field. With any body of work delineating a multistep pathway of this sort, there are many places one could request more confirmation and validation, but I will list the ones that I think would be important for them to address:\n1. They have a very small number of single cell profiled cells. Delineation of cell populations in sup fig 1 is poor and should be beefed up. For example, T and B cells not separated in the UMAP. There are a surprisingly high number of NK cells relative to T + B cells. This is somewhat incongruent with TCGA bulk RNAseq profiling.\n2. IFNg FACS – normally, one cannot see IFNg signal on FACS unless one uses a brefeldin A (Golgi-Plug) on stimulated cells because the IFNg is secreted. I did not see this protocol for ICS in the methods so not sure how they get such good signal.\n3. With so few cells, surprising to have enough for such clean western blots with anti-NKSM antisera on various TINK populations. They have <1000 UCDH-AS1+ TINK on single cell and normally it takes >10e6 lymphocytes on a good day to get a good signal with a polyclonal antiserum on a Western.\n4. Fig 3 – Important to show that T cell production of IFNg, GZMB, PFN1 NOT affected in NKSM+/+ mice.\n5. MMTV-PyMT tumor growth experiments would be more convincing if they showed loss of effect with NKSM+/+ mice after NK depletion with anti-NK1.1 (PK136)."}, "author_response": "Response: We would like to thank the reviewer for their high evaluation of our work.\nMajor Comments:\n1. The authors present convincing evidence that UGDH-AS1 encodes the novel micropeptide NKSM, and that NKSM has an inhibitory effect on NK cell functon(e.g. overexpression of UGDH-AS1 in NK-92 cells resulting in acceleration tumor growth, NKSM knock-in mice exhibiting reduced NK cell function, NKSM expression in primary TINK cells, mutational studies on UGDH-AS1 that abrogate NKSM expression, etc.).\nResponse: We would like to thank the reviewer for recognizing our work.\n2. Similarly, the extensive co-IP assays offer strong support for the signaling cascade proposed to be initiated by NKSM (i.e. interference with c-myc stability via reduced ERK1/2 binding). Furthermore, the authors validate the in vivo effect of NKSM deletion as being beneficial to tumor surveillance.\nResponse: We sincerely thank the reviewer for their recognition on our work.\n3. A more robust NK cell signature should be used to annotate tumor-infiltrating NK (TINK) cells in the scRNAseq data queried. Currently, it appears that KLRD1 is the only well-recognized NK marker enriched in the cluster annotated as NK cells (Fig. S1C). Donna Farber, Zemin Zhang, and others have published atlases of tissue-resident and tumor-infiltrating NK cells w/ signatures that would be of use. Additionally, NCR1 and KLRF1 are useful NK markers to consider.\nResponse: Thank for the comments and helpful suggestion. In the complex tumor microenvironment, immunosuppression, cell heterogeneity, and metabolic dysregulation are observed1, 2, 3, 4, 5. In the tumor microenvironment, factors such as immune suppressive cytokines (e.g., TGF-β, IL-10), hypoxic and acidic metabolic stress, and specific cytokines (e.g., IL-15) lead to NK cell dysfunction and changes in NK cell subpopulations' marker expression, causing infiltrating NK cells to transition into atypical subpopulations. In response to the reviewer's comments, we re-annotated the single-cell RNA sequencing (scRNA-seq) data at a higher resolution to identify cell subpopulations in our study. We used markers such as KLRD1, SLAM76, 7, 8, CD3D, and CD3E for annotating NK cells (Fig.S1F). Furthermore, to further validate the accuracy of NK cell subpopulations, we treated peripheral blood NK cells (PBNK) with culture medium from TNBC cells to simulate the tumor microenvironment for 24h, followed by qPCR to measure the expression of KLRD1, SLAM7, and UGDH-AS1. The results of the experiments are shown in the figure below.\nFigure Legend: The expression levels of UGDH-AS1, KLRD1, and SLAM7 were quantified by quantitative PCR (qPCR) analysis(n=3，PBNK(TNBC): PBNK+ culture media from TNBC cell). (mean ± SD, p <0.01, **; p > 0.05, NS)\n4. Additionally, the T/B cluster should be parsed into separate clusters of T and B cells to increase annotation confidence, particularly as NK cell clusters are typically proximal to CD8 T clusters in t-sne/UMAP projections. This may be important given the low number of cells from breast tumors sequenced (1,209 cells from 14 patients which includes only 213 NK cells from triple-negative tumors), and the lack of canonical NK cell markers present before sub-clustering. Finally, the strong enrichment of UGDH-AS1+ NK cells in TNBC along with the lack of canonical NK marker expression compared to NK cells from other tumor types underscores the importance of having high confidence that the authors are actually looking at NK cells.\nResponse: We are grateful to the reviewer for helpful suggestion and we apologize to the reviewer for the confusion. In our study, the scRNA-seq dataset comprises tumor samples from six TNBC patients. In response to the reviewer's comments, we re-annotated the scRNA-seq data at a higher resolution to identify cell subpopulations in the revision, and 200 NK cells were precisely annotated (Fig.S1). In addition, due to the complexity and heterogeneity of the tumor microenvironment, infiltrating immune cells may lose their markers and transition into atypical subpopulations. Therefore, we used multiple markers (KLRD1, SLAM7, CD3E, CD3D) to precisely annotate NK subpopulations. We sincerely appreciate the reviewer's comments once again.\n5. The author’s work could be strengthened by testing whether addition of NKSM peptide directly to healthy donor peripheral blood NK cells and NK-92 cells inhibits their function directly.\nResponse: We would like to thank the reviewer very much for the valuable comments. In response to the reviewer's comments, we performed the relevant experiments. The cytotoxicity assay results indicated that the exogenous addition of NKSM (300ug/mL) did not alter the cytotoxic activity of peripheral blood NK cells (PBNK) or NK-92MI cells. Based on these results, we hypothesize that the intrinsic properties of NK cells may have prevented NKSM from entering the cells effectively. To validate this hypothesis, we performed western blot analysis on NK cells pre-treated with the micropeptide (NKSM), and the results showed that NKSM was not detectable in the NK cells. The experimental results are presented in the figure below.\nFigure Legend: (A) Cytotoxic activity of wild-type (NK-92MI or PBNK) and exo-NKSM (NKSM+NK-92MI or NKSM+PBNK) cells towards K562 cells (n=3; mean ± SD, p > 0.05, NS). (B) The levels of NKSM were determined by western blot.\n6. It would be interesting to see how generalizable NKSM expression is in NK cells from other solid tumors. In part because even within Breast Cancer subtypes UGDH-AS1 is almost exclusively confined to NK cells from TNBC tumors and not really HR+ or HER2+ tumors.\nResponse: Thanks for the comment and helpful suggestion. According to the reviewer's comment, we isolated NK cells from various tumor tissue samples (esophageal squamous-cell carcinoma, gastric cancer, endometrial carcinoma, and lung cancer) and performed western blot validation of NKSM in these NK cells. The experimental results indicated that NKSM is not universally present in other solid tumors. The experimental results are presented in the figure below.\nFigure Legend: Western blot analysis was conducted to evaluate the expression levels of NKSM protein across multiple tumor types, including esophageal squamous-cell carcinoma (ESCC), gastric cancer, endometrial carcinoma, and lung cancer.\n7. Similarly, what do the authors propose as the reason for over-representation of UGSH-AS1+ NK cells in TNBC vs. non-TNBC Breast Cancer?\nResponse: We thank the reviewer very much for the valuable comments. In our study, we found that UGDH-AS1 is regulated by the TGF-β signaling pathway. In response to the reviewer's comments, we believe that the activation of the TGF-β signaling pathway in NK cells in TNBC breast cancer is a key factor in the over-representation of UGDH-AS1⁺ NK cells. To explore this hypothesis, we analyzed gene expression in TNBC and non-TNBC breast cancer cells using the public TCGA database. We found that TGF-β2 is highly expressed in the tumor microenvironment of TNBC. This result suggests that TGF-β2 is an important factor leading to the accumulation of UGDH-AS1⁺ NK cells in TNBC. The result is presented in the figure below.\nFigure Legend: The violin plot illustrates the differential expression of TGF-β2 between non-TNBC and TNBC in the TCGA database. (mean ± SD,**p<0.01)\n8. Would deletion of UGDH-AS1 in NK cells still improve tumor surveillance in an immunocompetent animal model? That is, in the presence of other immune cells infiltrating the tumor. While the NSG/PDX model the authors use is appropriate, it would be useful to determine what occurs in a humanized mouse, or in a mouse model with at least one additional suppressive immune cell type such as macrophages or MDSC etc.\nResponse: We sincerely thank the reviewer for the suggestion. Through a BLAST comparison with public databases, we found that UGDH-AS1 has no homology in mice. As mentioned in our study, UGDH-AS1 inhibits NK cells by encoding NKSM, which is why we constructed the NKSM+/+ transgenic mice (Fig.S6A). In Fig.3E and F, we performed subcutaneous tumor and lung metastasis model experiments using NKSM+/+ mice with an intact immune system. The experimental results indicated that, under conditions of an intact immune system, NKSM promotes tumor growth and lung metastasis.\nMinor Comments:\n1. In paper title change to “inhibiting NK cell activity”,\nResponse: We greatly appreciate the reviewer for this valuable suggestion and have revised accordingly (See line 1).\n2. Update Reference #13 as there has been additional work showing how TME suppresses NK cell fxn since 2013.\nResponse: We thank the reviewer for pointing this out. In the revised manuscript, we have made changes to the relevant references (See line 70).\n3. The figures in the combined PDF need to be numbered as it is confusing to quickly determine where to look when reading the text and Fig. legends.\nResponse: We sincerely thank the reviewer for this thoughtful suggestion. In accordance with the advice, we have added figure numbers to the combined PDF in the revised version to improve readability and clarity.\n4. Line 104 “cancer” misspelled.\nResponse: We thank the reviewer for pointing this out. We have corrected this error in the revised version (See line 105).\n5. Fig. panels should be ordered left to right rather than top to bottom in some cases (e.g. Fig. S1A+B).\nResponse: We thank the reviewer for pointing this out. In the revised manuscript, we have corrected this error (See Fig.S1A+B).\n6. Tables should be numbered according to when they are introduced in the text. Currently, the first Table referenced is Table S4.\nResponse: We sincerely thank the reviewer for pointing this out. We have also noticed this error in the manuscript and corrected it in the revised manuscript (See line 113).\n7. The nomenclature for NK cells is confusing (e.g. TINK for triple negative NK cells, HRINK for NK cells from HR+ tumors, and HER2INK from HER2+ tumors, etc.).\nResponse: We sincerely thank the reviewer for highlighting this issue. We apologize for the confusion. In response to the suggestion, we have adjusted the naming convention accordingly and made the necessary revisions in the revised manuscript.\n8. Please specify more clearly that Cluster 4 is the UGDH-AS1 NK cluster in the panels of Fig. 1.\nResponse: Thanks for the comment and helpful suggestion. In accordance with the advice, we have added the relevant details (See Fig.1).\n9. Line 123-124: KLRB1 is not NK1.1, it is CD161. NK1.1 is only expressed by murine NK cells.\nResponse: We thank the reviewer for pointing this out. In the revised manuscript, we have corrected this error (See line 124).\n10. Fig. 1E: It is unclear whether the authors are showing UGDH-AS1+ NK cells in the Kaplan-Meir plot or just expression of UGDH-AS1 in TCGA data.\nResponse: We sincerely thank the reviewer for this comment. Kaplan-Meier survival curves of patients with different abundances of UGDH-AS1+ NK cells. This analysis was guided by insights from single-cell transcriptomic data. To define the molecular signatures of the five NK cell subpopulations, we employed the FindAllMarkers function to compare gene expression within each subpopulation against the remaining NK subpopulations. The top 50 differentially expressed genes were selected as characteristic markers for each subpopulation. Next, we calculated the characteristic scores of the five NK cell subtypes in TCGA-BRCA patients using single-sample gene set enrichment analysis (ssGSEA). Based on the median score, patients were stratified into high- and low-score groups. To assess the prognostic significance, Kaplan-Meier survival analysis was conducted using the survival package in R, and the survival curves were visualized with the ggsurvplot function from the survminer package.\n11. Line 135: Unclear if NK cells from PBMC are from patients or healthy donors. Would be best.\nResponse: We apologize for the confusion pointed out by the reviewer. The NK cells from PBMCs are derived from patients, and we have added and corrected the relevant details.\n12. Fig. 5C: Please specify if this blot was performed in NK-92 or 293T cells.\nResponse: We greatly appreciate the reviewer for this valuable suggestion and have revised accordingly. In brief, co-IP assay was performed in the cell lysis of NK-92MI cells.\n13. Fig. 5D: FLAG is misspelled as “FALG” on top panel.\nResponse: We thank the reviewer for pointing this out. We have corrected this error in the revised version (See Fig.5D).\n14. Fig. S4A+S4B: FACS plots should be changed to pseudocolor plots, should show outlier cells, and should show original axis titles.\nResponse: We sincerely thank the reviewer for this comment and have revised accordingly (See Fig.S4A+B).\n15. Line 317: Subtitle should be changed to “NKSM is”.\nResponse: We thank the reviewer for this helpful suggestion and have revised accordingly (See line 326).\n16. Line 882: NCG should be NSG. There are several other instances of this elsewhere so please check.\nResponse: We sincerely thank the reviewer for this comment and we apologize to the reviewer for the confusion. First of all, this is not a writing error. The NSG mice were sourced from the Jackson laboratory, while the NCG mice were obtained from the Nanjing Biomedical Research Institute of Nanjing University / GemPharmatech. NCG is a severe immunodeficient strain obtained by knocking out the Prkdc (Protein kinase, DNA activated, catalytic polypeptide) and Il2rg (Common gamma chain receptor) genes in NOD/ShiltJGpt mice using gene editing techniques.\nReviewer #2 (Remarks to the Author):\nThe study by Zhang et al. investigates the identification and characterization of UGDH-AS1+ NK cells in triple-negative breast cancer (TNBC) through single-cell RNA seq. These NK cells express a micropeptide named NKSM, which is encoded by UGDH-AS1 and suppresses NK cell activity, thus facilitating cancer progression. The research demonstrates that NKSM is upregulated in tumor-infiltrating NK cells (TINK) within TNBC, leading to a decrease in anti-tumor activity. Conditional expression of NKSM in mouse NK cells results in NK cell deactivation and enhanced tumor growth. NKSM, regulated by the TGF-β signaling pathway, impairs NK cell function by destabilizing c-Myc, which reduces the expression of the key transcription factor T-bet and subsequently lowers the production of cytotoxic molecules such as GZMB, PRF1, and IFN-γ. The study highlights NKSM as a critical factor driving NK cell deactivation in the TME and contributing to TNBC progression. The authors propose targeting NKSM as a novel therapeutic strategy to improve NK cell-based immunotherapies for TNBC, aiming to counteract the suppressive effects of TGF-β on NK cells.\nTargeting NK cell deactivation as a therapeutic strategy is a familiar concept in immuno-oncology, although the specific targets and mechanisms in this study appear new. Overall, while some foundational concepts regarding the tumor microenvironment, TGF-β signaling, and NK cell suppression are already known, the identification of UGDH-AS1+ NK cells, the NKSM micropeptide, and their role in TNBC are likely novel contributions.\nResponse: We would like to thank the reviewer for their high evaluation of our work.\nMajor concerns:\n1. The functional assay of degranulation, marked by CD107a expression should be included in the panels of Figures 2A, 3D, and 6A. This addition will provide a comprehensive assessment of NK cell functionality. Granzyme B secretion is usually in vehicles, and CD107a reflects its trafficking to the membrane, which is important for target cell lysis.\nResponse: We would like to thank the reviewer for making this critical point and have revised the manuscript in line with their suggestion (See Fig2A, 3D, 6A).\n2. The NK-92MI cell line, a derivative of the parental NK-92, lacks several key receptors typically expressed by primary human NK cells, including the absence of CD16 and several KIRs, potentially impacting downstream signaling and transcriptional regulation, raising concerns about the relevance of NK-92MI cells as a model for primary NK cells. Thus, specifying NK-92MI characteristics used in this study is crucial for assessing its relevance to primary human NK cells.\nResponse: We would like to thank the reviewer for this excellent suggestion. As the reviewer mentioned, the NK-92MI cell line, a derivative of the parental NK-92, lacks several key receptors typically expressed by primary human NK cells, including the absence of CD16 and several KIRs. While we acknowledge that NK-92MI cells do not fully replicate the receptor profile of primary NK cells, they still serve as a valuable model for certain aspects of NK cell biology, especially in controlled in vitro studies. Also, the NK92MI cell line has significant advantages over primary NK cells, primarily in terms of its expandability and stability. As an immortalized cell line, NK92MI cells can be expanded on a large scale in vitro and maintain stable characteristics over time, which is highly beneficial for reproducible experiments and large-scale studies. In addition, the standardized nature and controllability of NK92MI cells make them an ideal model for studying specific genes or pathways, and they are relatively easy to manipulate and genetically edit. In contrast, primary NK cells are limited in number and may undergo functional and phenotypic changes over time as their culture progresses.\nIn our study, we found that the micropeptide encoded by UGDH-AS1 inhibits the cellular activity of NK cells. Upregulated by the TGF-β signaling pathway, NKSM could bind to proto-oncogene c-Myc, inhibiting ERK1/2-mediated Ser62 phosphorylation and reducing its stability, thereby modulating the transcription of T-bet, a key protein involved in NK cell function, and leading to NK cell deactivation. In our study, after conditionally knocking out NKSM in NK92MI cells, both in vitro and in vivo experiments indicated that NKSM inhibits NK cell activity through the c-myc/T-bet pathway. Additionally, we conducted experiments to establish a primary NK cell model with NKSM knockout. Experimental results showed that the primary NK cell model and the NK92-MI cell model exhibited consistent findings (See Fig.6I). Furthermore, in response to the reviewer’s comments, we have elaborated on this issue in the discussion section.\n3. While the RNA sequencing dataset is robust, including a Principal Component Analysis (PCA) would provide a quantitative and visual representation of transcriptional differences, enhancing the interpretation of the data.\nResponse: We greatly appreciate the reviewer’s helpful suggestion. As shown in Fig. S3A (right) in the revised version, the scatter plot presents the principal component analysis (PCA) results of five NK cell subpopulations based on gene expression profiles.\n4. The term \"NK cells\" is used redundantly throughout the text. Clearly distinguishing between murine and human NK cells and specifying where each is used would improve clarity and comprehension. Additionally, it would be beneficial to indicate whether cell lines or patient tumor samples were employed to develop the respective mouse models in the study.\nResponse: We would like to thank the reviewer for pointing this out, and we apologize for not clearer information in the manuscript. In the revised manuscript, we have made revisions in line with their suggestion.\n5. To strengthen the study's translational potential, it is recommended to replicate one of the in vivo models, preferably a TNBC xenograft model (NSG/NRG), using primary human NK cells engineered to overexpress UGDH-AS1+ or knocking-out (KO) the NKSM micropeptide. This will help validate the relevance of the findings for potential therapeutic applications. Additionally, performing functional assays on the primary NK cells obtained from these tumors will add significant impact to the study.\nResponse: In the revised version, in accordance with the reviewer’s suggestion, we have supplemented the relevant experiments (See Fig.6H and Fig.S8G). We would like to thank the reviewer again for their valuable feedback.\n6. For all in vivo experiments reported in the manuscript, please include statistical power analyses to determine if the sample size of mice (n=5) is adequate to draw statistically and scientifically valid conclusions.\nResponse: Thank you for the reviewer’s suggestion. We have performed statistical power analysis for all in vivo experiments to assess whether the chosen mouse sample size (n=5) is sufficient to support statistically and scientifically valid conclusions. Based on our experimental design, significance level (α=0.05), data variability (standard deviation), and effect size based on observed or literature-reported values, we conducted the power calculation. The analysis results indicate that with n=5, our experiment achieves 80% statistical power, which is adequate to detect biologically meaningful effects within a reasonable range. Additionally, we carefully considered animal ethics and experimental feasibility during the design phase, adopting strategies to minimize animal use while ensuring the scientific integrity and robustness of the data.\n7. Although well-executed, the gating strategy presented in Supplementary Figures 4A and 4B would be more informative if switched to a dot plot format with enumeration. This adjustment would improve understanding of the sample size used to generate the data.\nResponse: We thank the reviewer for this helpful suggestion and have revised accordingly. In the revised manuscript, we have changed FACS plots to pseudocolor plots.\nMinor concerns:\n1. The manuscript contains numerous grammatical errors. These should be corrected to enhance readability and professionalism.\nResponse: We appreciate the reviewer’s feedback and apologize for the grammatical errors in the manuscript. We have carefully reviewed and corrected the manuscript to enhance readability and professionalism. All necessary revisions have been made to address these issues.\n2. The sizes of each protein band in the western blot data should be included throughout the manuscript to provide clarity on the molecular weights of the detected proteins.\nResponse: Thank you for the reviewer’s valuable suggestion. We have now included the molecular weights of the detected proteins by adding the sizes of each protein band in both the revised manuscript and the original data, as requested.\nReviewer #3 (Remarks to the Author):\nZhou and colleagues present a significant data set supporting a new mechanism by which triple negative breast cancer can potentially evade anti-tumor immunity provided by NK cells. They provide support for a pathway in which tgfb signaling induces transcription of a long lncRNA, UGDH-AS1, in a subset of NK cells, which inhibits NK activity (IFNg and cytolytic molecules) via a \"micropeptide\" encoded by an ORF in UGDH-AS1. This micropeptide inhibits phosporylation of a serine residue in Myc which results in decreased Myc stability and association with erk1/2, which in turn prevents Myc/Erk from serving as a co-transcriptional activator with Tbet to transcribe IFNg and genes encoding the cytolytic molecules (granzymes, perforin) that NK cells use to kill tumor cells. They provide evidence for each of these steps and use various human and murine conditional NK-specific micropeptide expression systems to support each of these steps. Overall, while it is still an open question as to the role of this particular pathway in tumor immunity writ large, the body of work they present for this novel mechanism deserves publication and will likely stimulate important follow-up work in the field. With any body of work delineating a multistep pathway of this sort, there are many places one could request more confirmation and validation, but I will list the ones that I think would be important for them to address:\nResponse: We would like to thank the reviewer for their high evaluation of our work.\n1. They have a very small number of single cell profiled cells. Delineation of cell populations in sup fig 1 is poor and should be beefed up. For example, T and B cells not separated in the UMAP. There are a surprisingly high number of NK cells relative to T + B cells. This is somewhat incongruent with TCGA bulk RNA-seq profiling.\nResponse: Thank the reviewer for pointing this out and we apologize to the reviewer for the confusion. In our study, the scRNA-seq dataset comprises tumor samples from six TNBC patients9. In response to the reviewer's comments, we re-annotated the scRNA-seq data at a higher resolution to identify cell subpopulations in our study (Fig.S1). Compared to bulk RNA-seq, scRNA-seq technology is used to analyze gene expression heterogeneity at the single-cell level, enabling the identification of distinct cell types, states, and their dynamic changes in both physiological and pathological processes. Due to individual differences among patients, and the tumor heterogeneity present in samples from different patients, urther validation of NK cells in larger sample sizes is needed. Additionally, because of the limited sample size in our study, there may be certain limitations to our findings. We would like to thank the reviewer again for their comments.\n2. IFNg FACS – normally, one cannot see IFNg signal on FACS unless one uses a brefeldin A (Golgi-Plug) on stimulated cells because the IFNg is secreted. I did not see this protocol for ICS in the methods so not sure how they get such good signal.\nResponse: We would like to thank the reviewer for pointing this out, and we apologize to the reviewer for the confusion. In the revised manuscript, we have added the relevant experimental details (See line 670).\n3. With so few cells, surprising to have enough for such clean western blots with anti-NKSM antisera on various TINK populations. They have <1000 UCDH-AS1+ TINK on single cell and normally it takes >10e6 lymphocytes on a good day to get a good signal with a polyclonal antiserum on a Western.\nResponse: We would like to thank the reviewer for pointing this out, and we apologize to the reviewer for the confusion. Peptide synthesis and anti-NKSM antibody generation were performed as previously described with some modifications10. Briefly, BSA and the OVA-coupled peptide WHEHVSLQPQPPRLK were synthesized, and a polyclonal antibody against NKSM was obtained from inoculated rabbits. The antibody was purified using affinity chromatography on columns containing the corresponding peptide. As shown in Fig.2, western blotting and immunofluorescence validated the specificity of the antibody. Here, we sincerely thank the reviewer again for their valuable comments.\n4. Fig 3 – Important to show that T cell production of IFNg, GZMB, PFN1 NOT affected in NKSM+/+ mice.\nResponse: We would like to thank the reviewer for this constructive feedback. Based on the reviewer's comments, we have added relevant experiments in the revised manuscript (See Fig.S6F).\n5. MMTV-PyMT tumor growth experiments would be more convincing if they showed loss of effect with NKSM+/+ mice after NK depletion with anti-NK1.1 (PK136).\nResponse: We thank the reviewer for this helpful suggestion. Based on the reviewer's comments, we have added relevant MMTV-PyMT tumor experiments in the revised manuscript (See Fig.3G). We would like to thank the reviewer once again for their help and constructive comments.\nReferences\n1. Zheng H, Siddharth S, Parida S, Wu X, Sharma D. Tumor Microenvironment: Key Players in Triple Negative Breast Cancer Immunomodulation. Cancers 13, (2021).\n2. Azizi E, et al. Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. Cell 174, (2018).\n3. Jia Q, Wang A, Yuan Y, Zhu B, Long H. Heterogeneity of the tumor immune microenvironment and its clinical relevance. Exp Hematol Oncol 11, 24 (2022).\n4. Guillerey C. NK Cells in the Tumor Microenvironment. Adv Exp Med Biol 1273, 69-90 (2020).\n5. Jin H-R, et al. Lipid metabolic reprogramming in tumor microenvironment: from mechanisms to therapeutics. Journal of Hematology & Oncology 16, 103 (2023).\n6. Boles KS, Mathew PA. Molecular cloning of CS1, a novel human natural killer cell receptor belonging to the CD2 subset of the immunoglobulin superfamily. Immunogenetics 52, 302-307 (2001).\n7. Hsi ED, et al. CS1, a potential new therapeutic antibody target for the treatment of multiple myeloma. Clinical Cancer Research : an Official Journal of the American Association For Cancer Research 14, 2775-2784 (2008).\n8. Buller CW, Mathew PA, Mathew SO. Roles of NK Cell Receptors 2B4 (CD244), CS1 (CD319), and LLT1 (CLEC2D) in Cancer. Cancers 12, (2021).\n9. Karaayvaz M, et al. Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq. Nat Commun 9, 3588 (2018).\n10. Yu X, et al. Prokaryotic Expression of alpha-13 Giardin Gene and Its Intracellular Localization in Giardia lamblia. Biomed Res Int 2017, 1603264 (2017)."}, {"round": 2, "reviewer_comments": {"reviewer_2": "In this study, the authors identify TNBC-specific UGDH-AS1+ NK cells as producers of the micropeptide NKSM, which promotes TNBC progression by suppressing NK cell activity. Their findings reveal that NKSM is upregulated by TGF-β signaling, leading to the inhibition of c-Myc-mediated T-bet expression and a consequent reduction in NK cell cytotoxicity. The study further demonstrates that targeting NKSM presents a promising therapeutic strategy to enhance NK cell-based immunotherapies for TNBC by counteracting the suppressive effects of TGF-β on NK cells. The authors have addressed all the major comments in this revision, integrating them into the manuscript to enhance the study’s rigor and impact.", "reviewer_4": "The authors have adequately addressed the concerns, but it is critical to include selected results and to discuss these. Specifically, points on generalizability on their results (reviewer#1, point 6), over-representation of UGSH-AS1+ NK cells in TNBC vs. non-TNBC (reviewer#1, point 7), Western Blot technicalities (Reviewer#3, point 3).", "reviewer_5": "This is an interesting study describing a potentially new mechanism for suppression of NK cells in the TME. Reviewing the paper at this stage, I have focused on previously raised concerns and the response to these.\n1) As suggested in the previous review, the manuscript would benefit from a more robust annotation of the single-cell RNA sequencing data. Using tools like CellTypist to identify NK cells, including CD56bright (CD16neg) and CD56dim (CD16+) subsets, could clarify the specific state of the UGDH-AS1+ NK cells observed in triple-negative breast cancer. To increase the number of NK cells, there are numerous online scRNAseq breast cancer datasets that can be queried for presence of suppressed UGDH-AS1+ NK cells. Additionally, scoring these cells for tissue residency would help contextualize their role, as the CD56dim compartment often contains peripheral blood contamination, while CD56bright cells are the dominant tumor-infiltrating fraction.\n2) It is surprising that the TGF-beta-mediated suppressive effect on NK cells is observed only in triple-negative breast cancer, especially given the prominence of TGF-beta signaling in many tumors. The manuscript should discuss why this response profile is unique to TNBC and not seen in other cancers with similar TGF-beta activity.\n3) Regardless of how rare the observed NK cell state might be, the correction of this state to enhance NK cell resilience in the tumor microenvironment remains valuable. The effect in human NK cells seems very small though (Figure 6F and Supplementary Figure 8D). It would strengthen the manuscript if the engineered NK cells were specifically challenged with TGF-beta to demonstrate how the suppressive signaling is mitigated by the proposed edits."}, "author_response": "Dear Editors and Reviewers:\nThank you very much for your letter and for the valuable and constructive comments regarding our manuscript, \" TNBC-Specific UGDH-AS1+ NK Cells Encode NSKM, Inhibiting NK Cell Activity\".\nWe greatly appreciate the insightful feedback, which has been instrumental in improving the quality of our manuscript. We have carefully considered each suggestion and made thorough revisions accordingly. The corresponding changes have been highlighted in the revised manuscript.\nWe believe these revisions adequately address all concerns raised and have enhanced the clarity and rigor of our work. We sincerely appreciate the reviewers' helpful comments and hope that the manuscript is now suitable for publication.\nThank you again for your time and consideration.\nWith best wishes.\nYours sincerely,\nYifeng Zhou, MD, PhD\nREVIEWER COMMENTS\nReviewer #2 (Remarks to the Author):\nIn this study, the authors identify TNBC-specific UGDH-AS1+ NK cells as producers of the micropeptide NKSM, which promotes TNBC progression by suppressing NK cell activity. Their findings reveal that NKSM is upregulated by TGF-β signaling, leading to the inhibition of c-Myc-mediated T-bet expression and a consequent reduction in NK cell cytotoxicity. The study further demonstrates that targeting NKSM presents a promising therapeutic strategy to enhance NK cell-based immunotherapies for TNBC by counteracting the suppressive effects of TGF-β on NK cells. The authors have addressed all the major comments in this revision, integrating them into the manuscript to enhance the study’s rigor and impact.\nResponse: We sincerely thank the reviewer for the positive and encouraging comments. We are pleased that the reviewer recognizes the significance of our findings and appreciates the revisions made to enhance the rigor and impact of the manuscript. We greatly appreciate the constructive feedback provided during the review process, which helped us improve the quality of our work. Once again, we sincerely thank the reviewer for their thoughtful evaluation and continued support.\nReviewer #4 (Mediator to replace R#1, Remarks to the Author):\nThe authors have adequately addressed the concerns, but it is critical to include selected results and to discuss these. Specifically, points on generalizability on their results (reviewer#1, point 6), over-representation of UGSH-AS1+ NK cells in TNBC vs. non-TNBC (reviewer#1, point 7), Western Blot technicalities (Reviewer#3, point 3).\nResponse: We thank the reviewer for this helpful suggestion. As suggested, we have now explicitly included and discussed the selected results mentioned:\n(1). In response to the concern regarding the generalizability of our findings (Reviewer #1, Point 6), we have included the relevant experimental data (See Supplementary Fig. 5J) in the Results section and expanded the Discussion section accordingly (Lines 409-423). Thank you again for the valuable suggestions, which we have also discussed in the revised manuscript, and these additions and clarifications will significantly enhance the scientific value of our study.\n(2). In response to the reviewer's comments (Reviewer #1, Point 7), we have now included representative data in Supplementary Fig. 8D and described the results in the Results section (Lines 334). We sincerely appreciate the reviewer’s insightful and constructive comments, which have significantly strengthened the rigor and quality of our study and greatly contributed to its improvement.\n(3). We would like to thank the reviewer (Reviewer#3, point 3) for pointing this out, and we apologize to the reviewer for the confusion.\nFirst, anti-NKSM antibody preparation.\nIn our study, owing to the unavailability of commercially sourced anti-NKSM antibodies, we generated custom anti-NKSM antibodies following previously published protocols with minor modifications1(See Materials and Methods, Lines 591-596). Briefly, BSA and the OVA-coupled peptide WHEHVSLQPQPPRLK were synthesized, and a polyclonal antibody against NKSM was obtained from inoculated rabbits. The antibody was purified using affinity chromatography on columns containing the corresponding peptide. As shown in Fig.2, western blotting and immunofluorescence validated the specificity of the antibody.\nSecond , Western blot.\nIn our study, we performed Western blot analysis using Tris-Tricine SDS-PAGE kit (Solarbio; Cat. No.P1321). Conventional Tris-SDS-PAGE electrophoresis is suitable for resolving high molecular weight proteins but has very low resolution for proteins with relatively small molecular weights, especially those below 10 kDa. In contrast, Tricine-SDS-PAGE can effectively separate proteins and peptides in the 1-10 kDa range.\nThird, NK and PBNK Cell collection.\nBriefly, for human NK cell isolation, PBMCs were isolated by centrifugation over a Ficoll400 cell separation solution (density 1.077 g/mL; Biocoll, VWR). NK cells were isolated by magnetic cell sorting with the NK Cell Isolation Kit (Miltenyi, 130-092-657) from PBMCs. Fresh tumor tissues were harvested, cut into small pieces, and digested into single-cell suspensions with the Tumor Dissociation Kit (Miltenyi, 130-095-929) using gentleMACS (Miltenyi). NK cells were sorted using a BD FACSAria III (See Materials and Methods，Lines 482-491). The detailed gating strategy is presented in Supplementary Fig 4. The quantity of sorted target NK cells obtained was adequate to support reliable Western blot analysis.\nOnce again, we appreciate the reviewer’s suggestions, which have helped improve the clarity and completeness of the manuscript.\nReviewer #5 (Mediator to replace R#3 Remarks to the Author):\nThis is an interesting study describing a potentially new mechanism for suppression of NK cells in the TME. Reviewing the paper at this stage, I have focused on previously raised concerns and the response to these.\n1). As suggested in the previous review, the manuscript would benefit from a more robust annotation of the single-cell RNA sequencing data. Using tools like CellTypist to identify NK cells, including CD56bright (CD16neg) and CD56dim (CD16+) subsets, could clarify the specific state of the UGDH-AS1+ NK cells observed in triple-negative breast cancer. To increase the number of NK cells, there are numerous online scRNAseq breast cancer datasets that can be queried for presence of suppressed UGDH-AS1+ NK cells. Additionally, scoring these cells for tissue residency would help contextualize their role, as the CD56dim compartment often contains peripheral blood contamination, while CD56bright cells are the dominant tumor-infiltrating fraction.\nResponse: Thank you to the reviewer for the professional and highly targeted valuable comments, which have been very helpful in improving our study.\n(1). We thank the reviewer for the insightful suggestion. As recommended, we employed CellTypist2 to annotate NK cell subsets in our single-cell RNA-seq dataset, allowing us to distinguish between NK cells, Cycling NK cells, CD16- and CD16+ NK cells. Our analysis revealed that UGDH-AS1+ NK cells are predominantly enriched within the Cycling NK cells subset. The result is presented in the figure below.\nFigure Legend: (A) The t-SNE plot illustrates the heterogeneity of NK cell subsets annotated using the CellTypist tool. (B) Bar plot depicting the distribution of the NK cells assigned to specific cell clusters.\n(2). We appreciate the reviewer’s constructive suggestion. In response, we examined several publicly available scRNA-seq breast cancer datasets, including GSE1760783, GSE1486734, and GSE1615295. However, we were unable to detect a clear population of UGDH-AS1⁺ NK cells in these datasets. To further assess the prevalence of this subset, we performed deconvolution analysis using the TCGA breast cancer cohort, which revealed that this NK cell subpopulation is not consistently present across all patients. We believe this variability may stem from significant inter-sample heterogeneity commonly observed in clinical single-cell datasets. It is noteworthy that, in our study, we conducted a comprehensive analysis of UGDH-AS1 expression in TINK cells across an extensive cohort of samples (See Supplementary Fig. 4C)\n(3). We thank the reviewer for this insightful suggestion. Following your advice, we performed tissue residency scoring on UGDH-AS1⁺ NK cells using the gene signature described in the article \"Genetic distinction between functional tissue-resident and conventional natural killer cells.\"6 Our analysis showed that UGDH-AS1⁺ NK cells express conventional NK (cNK) cell-associated gene signatures at low levels, indicating that they do not originate from peripheral blood contamination. The result is presented in the figure below.\nFigure Legend: The violin plot illustrates the differential expression of cNK genes between PBMC NK and UGDH-AS1+ NK cells. (mean ± SD,****p<0.0001)\n2). It is surprising that the TGF-beta-mediated suppressive effect on NK cells is observed only in triple-negative breast cancer, especially given the prominence of TGF-beta signaling in many tumors. The manuscript should discuss why this response profile is unique to TNBC and not seen in other cancers with similar TGF-beta activity.\nResponse: We sincerely thank the reviewer for this thoughtful comment. In response to the reviewer’s comment, we have included a detailed discussion of this point in the revised discussion section (Lines 409–423). Once again, we appreciate the reviewer’s suggestions, which have helped enhance the logical flow and scientific rigor of the manuscript.\n3). Regardless of how rare the observed NK cell state might be, the correction of this state to enhance NK cell resilience in the tumor microenvironment remains valuable. The effect in human NK cells seems very small though (Figure 6F and Supplementary Figure 8D). It would strengthen the manuscript if the engineered NK cells were specifically challenged with TGF-beta to demonstrate how the suppressive signaling is mitigated by the proposed edits.\nResponse: We thank the reviewer for the insightful suggestion. In response, we conducted additional functional assays in which engineered human NK cells were co-treated with recombinant TGF-β to directly evaluate their cytotoxic activity. The corresponding results are now presented in the revised Figure 6C. Once again, we sincerely appreciate the reviewer’s suggestion, which has helped enhance the rigor and completeness of our study.\nReferences\n1. Yu X, et al. Prokaryotic Expression of alpha-13 Giardin Gene and Its Intracellular Localization in Giardia lamblia. Biomed Res Int 2017, 1603264 (2017).\n2. Domínguez Conde C, et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science (New York, NY) 376, eabl5197 (2022).\n3. Wu SZ, et al. A single-cell and spatially resolved atlas of human breast cancers. Nat Genet 53, 1334-1347 (2021).\n4. Gao R, et al. Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nature Biotechnology 39, 599-608 (2021).\n5. Pal B, et al. A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast. EMBO J 40, e107333 (2021).\n6. Hu L, et al. Genetic distinction between functional tissue-resident and conventional natural killer cells. IScience 26, 107187 (2023)."}, {"round": 3, "reviewer_comments": {"reviewer_4": "All concerns have been adequately addressed in the revised version of the manuscript.", "reviewer_5": "All concerns addressed"}, "author_response": ""}]

Decision letter