s2fieldsofstudy: Biology Medicine

text:
Immunomodulatory Cell Therapy Using αGalCer-Pulsed Dendritic Cells Ameliorates Heart Failure in a Murine Dilated Cardiomyopathy Model

Background: Dilated cardiomyopathy (DCM) is a life-threatening disease, resulting in refractory heart failure. An immune disorder underlies the pathophysiology associated with heart failure progression. Invariant natural killer T (iNKT) cell activation is a prospective therapeutic strategy for ischemic heart disease. However, its efficacy in nonischemic cardiomyopathy, such as DCM, remains to be elucidated, and the feasible modality for iNKT cell activation in humans is yet to be validated. Methods: Dendritic cells isolated from human volunteers were pulsed with α-galactosylceramide ex vivo, which were used as α-galactosylceramide-pulsed dendritic cells (αGCDCs). We treated DCM mice harboring mutated troponin TΔK210/ΔK210 with αGCDCs and evaluated the efficacy of iNKT cell activation on heart failure in DCM mice. Furthermore, we investigated the molecular basis underlying its therapeutic effects in these mice and analyzed primary cardiac cells under iNKT cell-secreted cytokines. Results: The number of iNKT cells in the spleens of DCM mice was reduced compared with that in wild-type mice, whereas αGCDC treatment activated iNKT cells, prolonged survival of DCM mice, and prevented decline in the left ventricular ejection fraction for 4 weeks, accompanied by suppressed interstitial fibrosis. Mechanistically, αGCDC treatment suppressed TGF (transforming growth factor)-β signaling and expression of fibrotic genes and restored vasculature that was impaired in DCM hearts by upregulating angiopoietin 1 (Angpt1) expression. Consistently, IFNγ (interferon gamma) suppressed TGF-β-induced Smad2/3 signaling and the expression of fibrotic genes in cardiac fibroblasts and upregulated Angpt1 expression in cardiomyocytes via Stat1. Conclusions: Immunomodulatory cell therapy with αGCDCs is a novel therapeutic strategy for heart failure in DCM.

D ilated cardiomyopathy (DCM) is characterized by left ventricular dilatation and contractile dysfunction. It is a heterogeneous syndrome caused by numerous etiologies, including genetic mutations and acquired myocardial damage. 1,2 Although its prognosis is based on an underlying etiology, the overall outcomes remain poor with high morbidity and mortality despite the advances in standard medical therapy with β-blockers and renin-angiotensin-aldosterone system inhibitors. 2 Hence, the development of new therapeutics is imperative to improve outcomes for patients with DCM-heart failure (HF). Circ Heart Fail. 2022;15:e009366. DOI: 10.1161/CIRCHEARTFAILURE.122. 009366 December 2022 The immune system is one of the most promising therapeutic targets in chronic HF. 3 In the early 1990s, it was reported that circulating TNF-α (tumor necrosis factor-α) is elevated in patients with severe chronic HF. 4 Furthermore, animal studies showed that overexpressing TNF-α or administering it aggravates cardiac remodeling and HF in mice, 5,6 demonstrating that immune system plays the key role in HF progression. 7,8 However, blocking TNF-α using infliximab (ATTACH Trial) or etanercept (RENEWAL) failed to demonstrate clinical benefits in patients with chronic HF. 9,10 In addition, a nonspecific immunomodulatory therapy using celecade was not effective for patients with severe HF, categorized as New York Heart Association functional class III-IV chronic HF (ACCLAIM study). 11 The efficacy of several therapeutic approaches targeting the immune system in HF was also evaluated. [12][13][14][15] However, these studies were small and inconclusive, and thus immunomodulatory therapy for HF remains unestablished.
Natural killer T (NKT) cells, characterized by the expression of a specific (invariant) antigen receptor (Vα14Jα18 in mice and Vα24Jα18 in humans) and discovered in the 1980s, 16,17 are CD1d-restricted innatelike immune cells that express both an natural killer cell marker and a unique antigen receptor, of which gene fragments are located in the T cell receptor (TCR) loci, but not used by conventional T cells. Although the endogenous physiological regulation of NKT cell activation remains unknown, α-galactosylceramide (αGalCer), identified as an exogenous NKT cell ligand, activates invariant NKT (iNKT) cells. 18 Since the first 4 amino acids of Jα18 of iNKT cell receptors, which are important for the recognition of αGalCer presented on human and mouse CD1d, are conserved between humans and mice, 19 both human and mouse iNKT cells can be activated by αGalCer-pulsed antigen-presenting cells (APCs) of either mouse or human origin, demonstrating their cross-species reactivity. 20 Notably, iNKT cells, a major subset of NKT cells in mammals, secrete multiple cytokines, such as IFNγ (interferon gamma), IL-4 (interleukin-4), and IL-10 (interleukin-10), activate various immune cells, and thereby induce anti-tumor responses. [21][22][23] Previously, we demonstrated that iNKT cell activation induced by αGalCer ameliorates cardiac remodeling, improves survival following myocardial infarction, and reduces ischemia/reperfusion injury in mice. 24,25 Moreover, we showed that anti-IL-10 receptor antibodies abolish the cardioprotection induced by iNKT cell activation in these models, suggesting that the anti-inflammatory effect of IL-10 is a key mediator of the cardioprotection through iNKT cell activation against myocardial ischemic insults. However, the post-myocardial infarction HF model, referred to as ischemic cardiomyopathy, is intimately associated with necrosis-driven inflammation of the heart, 26

WHAT IS NEW?
• Dilated cardiomyopathy is a fatal cardiomyopathy, resulting in refractory heart failure. The immune system has been recognized as a prospective therapeutic target for severe heart failure, and invariant natural killer T cell activation may be a potential strategy for treating dilated cardiomyopathy. • In this study, we showed that the α-galactosylceramide-pulsed dendritic cell is a potent modality for activating invariant natural killer T cells in the heart, and invariant natural killer T cell activation through alpha-galactosylceramidepulsed dendritic cell treatment prevents the decline of cardiac contractility, suppresses fibrosis, and improves survival in dilated cardiomyopathy mice. • Mechanistically, invariant natural killer T cell activation promotes vasculogenesis and suppresses TGF-β (transforming growth factor-beta)-Smad signaling, and IFNγ (interferon-gamma) plays a key role in these processes.

WHAT ARE THE CLINICAL IMPLICATIONS?
• Despite the advances in medical and device therapies, prognosis of patients with dilated cardiomyopathy remains poor, and the number of patients receiving heart transplantation or destination therapy with a left ventricular assist device is increasing. • Here, we demonstrate cell therapy with αgalactosylceramide-pulsed dendritic cells as a novel therapeutic modality through immune-modulation via iNKT cell activation. • Cell therapy with alpha-galactosylceramide-pulsed dendritic cell may be a new therapeutic strategy for patients with heart failure who have dilated cardiomyopathy, and clinical studies investigating its efficacy and safety are currently underway.
pertaining to HF in nonischemic cardiomyopathy, such as DCM, remains unknown. In addition, the long-term administration of αGalCer potentially causes lethal liver injuries and anergy, thereby limiting the possibility of directly administering αGalCer to humans. 23,27 To overcome these limitations, APCs expressing CD1d, such as dendritic cells (DCs), can be pulsed ex vivo with αGalCer. 23 The safety of using prepared αGalCer-pulsed DCs (αGCDCs) in humans has been confirmed. 28 Therefore, the use of αGCDCs might be considered a potential modality for clinically treating patients with HF. However, the efficacy of this approach needs to be fully elucidated in an animal HF model.
In this study, we investigated the efficacy of αGCDCs in activating iNKT cells and the mechanism underlying this process in a murine DCM model harboring mutated troponin T ΔK210/ΔK210 .

METHODS
A full description of the materials and methods can be found in the Supplemental Material. The authors declare that all supporting data are available within the article and its Supplemental Material.

Animal Study
All procedures involving animals and animal care protocols were approved by the Committee on Ethics of Animal Experiments of the Kyushu University Graduate School of Medical and Pharmaceutical Sciences (A29-348, A19-029, and A21-032) and were performed in accordance with the Guideline for Animal Experiments of Kyushu University and the Guideline for the Care and Use of Laboratory Animals published by the US National Institutes of Health (revised in 2011). BALB/c mice were purchased from Kyudo Co. Ltd. (Saga, Japan), and all mice, including DCM mice, 29,30 were housed in a temperature-and humidity-controlled room and fed a commercial diet (CRF-1LID; Oriental Yeast Co. Ltd, Tokyo, Japan) with free access to water. The study design is described in the Supplemental Material.

Preparation of αGCDCs From Human Peripheral Blood
All procedures involving the preparation of αGCDCs were approved by the Kyushu University Hospital Ethics Committee (Permission numbers are 29-62, 30-571, 2019-498, 2020-516, and 2021-381). The αGCDCs were prepared by performing apheresis on volunteers as described previously. 31 Detailed methods are described in the Supplemental Material.

Statistical Analysis
Statistical analyses were performed using JMP16 software (SAS Institute, Cary, NC) and GraphPad Prism version 9.3.1 (GraphPad Software, La Jolla, CA). Data are presented as mean±SD. Paired or unpaired Student t test, Dunnett test, and 1 way-ANOVA followed by Tukey's post hoc test were used. The ratiometric data were presented on a log-scale axis, and log-transformed data, obtained with log 2 (x+1) data transformation, were analyzed using these statistical tests. The log-rank test was used for comparison among the 3 groups (PBS, CTRL-DC, and αGCDC groups), followed by log-rank test for calculating the statistical significance, hazard ratio, and CI between 2 groups. Statistical significance of the results was set at P<0.05.

RESULTS
αGCDC Treatment Induces iNKT Cell Activation and Subsequent Immune Responses The αGCDCs were produced by loading αGalCer on human APCs, including DCs, collected from the peripheral blood of volunteers via leukapheresis as previously described. 31 We examined the dose-dependent effects of αGCDCs in the murine myocardium in vivo and found that the gene expression of invariant TCR (Vα14Jα18) and iNKT cell-secreted cytokines such as interferon-γ (Ifng), interleukin-4 (Il4), and interleukin-10 (Il10) was upregulated ( Figure S1A). Consistent with the upregulation of these genes, the corresponding transcription factors, Stat1 and Stat6, were phosphorylated in a dose-dependent manner ( Figure S1B and S1C). We used a higher dose (3×10 6 αGCDCs per DCM mouse) to achieve maximum effects on the myocardium in this study. In comparison with αGCDCs, the effect of IFNγ on Stat1 phosphorylation peaked and plateaued at a relatively lower dose of αGalCer ( Figure S2A). Additionally, the phosphorylation of Stat1 with αGCDC treatment was stronger than that with αGalCer treatment, whereas the phosphorylation of Stat6 in αGCDCs was equivalent to that under higher dose of αGalCer ( Figure S2A). The transcriptional upregulation of invariant TCR (Vα14Jα18) and Il10 was not observed in mice treated with isolated APCs and control-DCs without αGalCer (CTRL-DCs), although the gene expression of Ifng and Il4 was slightly upregulated in mice treated with isolated APCs and CTRL-DCs, respectively ( Figure S2B).
To examine the sustained effect of a single treatment with αGCDCs (3×10 6 cells in 50 μL PBS per mouse) on iNKT cell activation in mice, we analyzed BALB/c mice treated with αGCDCs on days 1, 4, 7, 14, and 28. The expression of invariant TCR in the myocardium peaked on day 4 ( Figure 1A). Concomitantly, the number of iNKT cells in the spleen that were labeled with anti-CD3 antibodies as well as αGalCer-loaded CD1d tetramers also increased on day 4 following αGCDC administration ( Figure 1B and 1C). Consistent with the upregulation of invariant TCR expression in the myocardium, iNKT cellsecreted cytokines in the myocardium peaked on day 4 and reverted to nearly normal levels on day 14 (Figure 1D). However, a single administration of αGCDCs maintained the slight upregulation of Ifng, Il4, and Il10 (≈ 5-, 2.5-, and 2-fold, respectively) even on day 28 following treatment ( Figure 1D). Stat6 phosphorylation, representing the effect of IL-4, peaked on day 4, whereas Stat1 phosphorylation, representing the effect of IFNγ, peaked on day 7 and continued until day 14 following the treatment ( Figure S3A and S3B). In contrast, plasma IFNγ and IL-10 levels peaked on day 1 after the treatment, whereas no significant response was observed for plasma IL-4 ( Figure 1E).

The Number of iNKT Cells Is Reduced in DCM Mice
Echocardiograms showed severely impaired contractility and left ventricular dilatation in 5-week-old DCM   Figure 3A). In surviving DCM mice, the mice treated with PBS or CTRL-DCs showed a decline in the left ventricular ejection fraction (LVEF) at 4 weeks after each treatment, whereas αGCDC treatment significantly prevented this decline (%Change in LVEF: −7%, −10%, and 0% in the PBS, CTRL-DC, and αGCDC groups, respectively; Figure 3B and 3C), although there was no significant difference in the average of the echocardiographic parameters in the surviving mice among all groups (Table S2). The histological analysis showed that interstitial fibrosis in DCM mice was significantly suppressed in the αGCDC group compared with that in the PBS and CTRL-DC groups (Figure 3D and 3E). αGCDC Treatment Improves Contractile Function in DCM Mice 4 Days After Treatment To further investigate the mechanistic basis underlying the effect of αGCDC treatment on DCM mice, we analyzed DCM mice treated with αGCDCs on day 4 and found that the expression of invariant TCR, Ifng, Il4, and Il10 was markedly upregulated ( Figure S4A). The phosphorylation of Stat1 and Stat6 in the myocardium of DCM mice was also enhanced by αGCDC treatment ( Figure 4A). Notably, the LVEF of DCM mice treated with αGCDCs was significantly higher than that of mice treated with PBS on day 4 following αGCDC treatment ( Figure 4B and 4C). A detailed analysis showed that the LVEF of DCM mice significantly improved in response to αGCDC treatment, whereas that of mice in the PBS group was slightly reduced on day 4 after PBS treatment (%Change in LVEF: −6% and 8% in the PBS and αGCDC groups, respectively; Figure 4D and 4E), although there was no significant difference in the average of the echocardiographic parameters, excluding the LVEF and FS, between mice in these groups (Figure 4C‚ Table S3). Furthermore, whereas Nppb expression was upregulated in the PBS-treated DCM mice, it was attenuated in the αGCDC-treated DCM mice (Figure 4F). On day 4 after αGCDC treatment, no difference in interstitial fibrosis was observed between the PBStreated and αGCDC-treated DCM mice ( Figure S4B and S4C). In contrast, the upregulation of iNKT cell-secreted cytokines and the cardioprotective phenotypes of LVEF and Nppb gene expression were not observed in CTRL-DC-treated mice, although Ifng gene expression was slightly upregulated in CTRL-DC-treated mice ( Figure  S5A through S5E‚ Table S4).
αGCDC Treatment Suppresses TGF-β Signaling and Improves the Vasculature Density in the Myocardium of DCM Mice To comprehensively investigate the effects of αGCDCs on cardiac function and fibrosis in DCM mice, we performed microarray analysis on the myocardium on day 4 following treatment. We selected genes according to an algorithm ( Figure S6A) and identified 277 genes (defined as cluster 2) whose expression was upregulated in DCM+PBS mice but suppressed in DCM+αGCDC mice. in the myocardium of wild-type (WT) and DCM mice on day 4 after αGCDC treatment, measured using reverse transcription-quantitative polymerase chain reaction (n=4-7). Data are presented as mean±SD. Statistical significance was determined using Student t test for C and E, paired Student t test for D, and 1-way ANOVA with a post hoc test (Tukey) for F. Moreover, we identified 117 genes (defined as cluster 9) whose expression was downregulated in DCM+PBS mice but restored in DCM+αGCDC mice ( Figure S6B). Gene ontology (GO) analysis of cluster 2 and 9 revealed that αGCDCs promoted vasculogenesis and suppressed the fibrotic response, particularly TGF-β (transforming growth factor-beta) signaling, in DCM mice ( Figure  S6C). Reverse transcription-quantitative polymerase chain reaction analysis revealed that αGCDC treatment suppressed the upregulation of fibrotic genes, such as connective tissue growth factor (Ctgf), Col1a, and Col3a, in the myocardium of DCM mice ( Figure 5A). Although αGCDC treatment did not suppress the upregulation of Tgfb1 expression ( Figure 5A), the phosphorylation of Smad2 and Smad3 was suppressed in the myocardium of αGCDC-treated DCM mice ( Figure 5B and 5C). Taken together, these findings indicate that αGCDC treatment suppresses fibrosis by inhibiting Smad2 and Smad3 phosphorylation but not TGF-β expression. Further detailed GO analysis revealed that αGCDC treatment restored the downregulation of angiopoietin 1 (Angpt1) in the myocardium of DCM mice ( Figure 5D) but did not alter vascular endothelial growth factor A (Vegfa) expression. Consistent with the restoration of Angpt1 expression, αGCDC treatment also restored the myocardial capillary density, which was impaired in the hearts of DCM mice ( Figure 5E). In contrast, these cardioprotective responses, such as fibrotic gene downregulation and Angpt1 upregulation, were not observed in CTRL-DC-treated DCM mice ( Figure S7A and S7B).

IFNγ Suppresses TGF-β-Smad2/3 Signaling in Primary Fibroblasts
To further investigate the mechanisms underlying the suppression of the TGF-β-Smad signaling axis mediated by αGCDC treatment, we treated primary fibroblasts with major cytokines, specifically IFNγ, IL-4, and IL-10, which are secreted from iNKT cells. IFNγ and IL-4 induced Stat1 and Stat6 phosphorylation, respectively; however, IL-10 did not alter Stat3 phosphorylation ( Figure S8A and S8B). IFNγ suppressed the expression of fibrotic genes, such as Ctgf, Col1a1, and Col3a1, whereas IL-4 and IL-10 did not, suggesting that IFNγ is the candidate suppressor of TGF-β signaling ( Figure S8C). Consistent with the findings in vivo, the phosphorylation of Smad2 and Smad3 was significantly attenuated by IFNγ pretreatment ( Figure 6A and 6B). Furthermore, the upregulation of fibrotic genes by TGF-β was suppressed by IFNγ pretreatment in a dose-dependent manner ( Figure 6C).

IFNγ Upregulates Angpt1 in Primary Cultured Cardiomyocytes
To identify the mechanisms by which αGCDC treatment upregulated Angpt1 expression, we treated cultured cardiomyocytes with IFNγ, IL-4, and IL-10 and found that treatment with IFNγ and IL-4 led to the phosphorylation of Stat1 and Stat6, respectively, whereas treatment with IL-10 did not alter Stat3 phosphorylation ( Figure S9A and S9B). Notably, treatment with IFNγ, but not IL-4 or IL-10, upregulated Angpt1 expression ( Figure 7A), and the silencing of Stat1 abolished this upregulation (Figure 7B through 7D), indicating that αGCDC treatment promotes vasculogenesis via the IFNγ-Stat1-Angpt1 axis. Based on the present findings, we have summarized the mechanistic basis of αGCDC treatment for chronic HF in DCM mice in Figure 8.

DISCUSSION
Our study on αGCDC treatment of DCM mice harboring mutated troponin T ΔK210/ΔK210 revealed 3 major findings as follows: (1) the immune system, particularly iNKT cells, is impaired in DCM mice; (2) αGCDCs activate iNKT cells, ameliorate contractile dysfunction and interstitial fibrosis in the heart, and prolong the survival of DCM mice; and (3) IFNγ plays a key role in cardioprotection induced by αGCDCs by suppressing the TGF-β-Smad2/3 signaling pathway and upregulating Angpt1 expression. Previously, we demonstrated that iNKT cell activation with αGalCer ameliorates cardiac remodeling and improves survival following myocardial infarction. 24 However, little is known regarding alterations to the immune system in severe HF observed in DCM, 26 and the efficacy of immunomodulatory therapeutics has not been established. Here, we found that the iNKT cell number decreased in the spleens of DCM mice ( Figure 2D and 2E). Given that αGCDC treatment activated iNKT cells and prolonged the survival of DCM mice, impairment of the immune system in terms of iNKT cells might be responsible for the progression of cardiomyopathy and HF in DCM. Interestingly, Stat1 and Stat6 phosphorylation in the heart in response to αGCDC treatment correlated with the gene expression of invariant TCR in the myocardium and not with the increase in cytokine levels in the plasma ( Figure 1‚ Figure S3), indicating that Stat1 and Stat6 phosphorylation in the myocardium depends on iNKT cells localized in the heart.
To identify the potential mechanisms by which αGCDC treatment ameliorates HF in DCM mice, we performed GO analysis using microarray data and investigated the 2 phenotypes produced following treatment, namely antifibrosis and vasculogenesis. Fibrosis is a detrimental feature observed in failing myocardia, and thus, anti-fibrotic mechanisms are the mainstay of cardioprotective effects mediated by αGCDC treatment. Based on microarray analysis, we found that αGCDC treatment suppressed Smad2/3 phosphorylation and the expression of fibrotic genes, such as Ctgf, Col1a, and Col3a, which were upregulated in DCM mice (Figure 5). Several studies have shown that the TGF-β-Smad axis plays a key role in the progression of HF via fibrosis of the heart. Kuwahara et al 32 demonstrated that the inhibition of TGF-β using a neutralizing antibody prevents myocardial fibrosis in pressure overloadinduced cardiac hypertrophy. In addition, Sakata et al 33 demonstrated that a TGF-β antagonist suppresses myocardial fibrosis in mice with the cardiac-restricted overexpression of TNF-α. Recently, Khalil et al 34 reported that TGF-β-Smad3 signaling in fibroblasts is a major regulator of myocardial fibrosis. Collectively, these findings indicate that the anti-fibrotic effects of αGCDC treatment result from the suppression of TGFβ-Smad2/3 signaling.
Furthermore, we showed that IFNγ suppressed the TGF-β-Smad2/3 axis. IFNγ has an established role in inducing anti-fibrotic effects, with particular reference to the TGF-β-Smad2/3 axis. Ulloa et al 35 first reported that IFNγ/Stat inhibits TGF-β-Smad signaling by upregulating Smad7, an inhibitory Smad. In addition, Kimura et al 36 demonstrated that αGalCer attenuates the development of bleomycin-induced pulmonary fibrosis and improves survival, with IFNγ playing a pivotal role in the antifibrotic effects induced by αGalCer.
In the present study, we demonstrated that fibrosis was suppressed in DCM mice treated with αGCDCs and that IFNγ suppressed the phosphorylation of Smad2/3 and fibrotic genes induced by TGF-β stimulation in primary cardiac fibroblasts. Nevertheless, Smad7 was not upregulated in response to αGCDC treatment in vivo and IFNγ in primary cardiac fibroblasts ( Figure S10A and S10B). These results suggest that an inhibitory interaction between TGF-β and IFNγ, which is not mediated via Smad7, plays a key role in the anti-fibrotic effects of αGCDCs or IFNγ in the heart. Thus, further investigations are needed to clarify these interactions.
Vasculogenesis and angiogenesis are dysregulated in the hypertrophied myocardium, 37,38 and the insufficient vasculature of coronary artery circulation is a major cause of cardiac dysfunction. 39,40 In this study, Figure 6. Role of IFNγ (interferon gamma) in TGF (transforming growth factor)-β/Smad signaling during α-galactosylceramide (αGalCer)-pulsed dendritic cell (αGCDC) treatment. A, Phosphorylation of Smad2 and Smad3 with TGF-β (10 ng/mL) and IFNγ pretreatment (1, 10, and 50 ng/mL). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as a loading control. B, Quantification of Western blotting results shown in B (n=4-8). C, Expression of fibrotic genes such as Ctgf, Col1a, and Col3a in response to TGF-β (10 ng/mL) with IFNγ pretreatment (1, 10, and 50 ng/mL) in primary cultured cardiac fibroblasts (n=6). Data are presented as mean±SD. Statistical significance was determined using the 1-way ANOVA with a post hoc test (Tukey). transcriptome analysis indicated that αGCDC treatment ameliorated impaired vasculogenesis in DCM mice. Indeed, αGCDC treatment restored the capillary density in the hearts of DCM mice, which was less dense than that in WT mice ( Figure 5). GO analysis of genes involved in vasculogenesis revealed that Angpt1 expression, which was markedly downregulated in DCM mice, was completely restored by αGCDC treatment ( Figure 5). Angpt1 is essential for vasculogenesis in the developing heart 41 as it promotes vascular maturity and activates the endothelial-specific tyrosine kinase Tie2, strengthening reciprocal interactions between the endothelium and the surrounding matrix as well as the mesenchyme. The overexpression of Angpt1 using viral vectors increases capillary density and improves cardiac function in a murine model of HF, 39,42 suggesting that Angpt1 is essential not only for heart development but also for the preservation of cardiac function. Given that αGCDCs restored Angpt1 expression and vasculature density in the DCM heart, upregulated Angpt1 expression, at least in part, contributes to the improvement in the survival of DCM mice with preserved left ventricular contractility. Furthermore, other mechanisms might contribute to the improvement in LVEF on day 4 after αGCDC treatment. As the improvement in LVEF following αGCDC treatment was not evident in wild-type BALB/c mice ( Figure S11), we deduced that iNKT cell activation would revert some impairments associated with cardiac contractility observed in severe HF. Indeed, a microarray analysis also revealed other potential mechanisms as shown in Figure S6. However, further investigation might be needed to fully confirm the mechanism by which αGCDC treatment improves contractility in the HF model.
We demonstrated that IFNγ, but not IL-4 and IL-10, mediated upregulated Angpt1 expression induced by αGCDC treatment via Stat1 activation (Figure 7). Some studies have demonstrated that IFNγ exerts a proangiogenetic effect, 43 whereas others have suggested that it exhibits an inhibitory effect on angiogenesis, especially in malignant tumors. 44 These observations suggest that the role of IFNγ in angiogenesis or vasculogenesis depends on the cell type, organ, and specific conditions.
In this study, we demonstrated the cardioprotective effects of IFNγ during αGCDC treatment on contractile function and fibrosis. Although IFNγ is widely recognized as a pro-inflammatory cytokine, previous studies have also shown its significance in cardioprotection considering that its deletion aggravates HF in an aldosteroneinduced hypertrophy model and a pressure overload model. 45,46 This evidence indicates that IFNγ is necessary for maintaining cardiac function during HF and that it plays a key role in cardioprotection induced by iNKT cell activation. Nevertheless, we deduced that multiple effectors, including IFNγ induced by iNKT cell activation, contribute to cardioprotection in the HF model. Indeed, we and others have demonstrated that IL-10, an anti-inflammatory cytokine, plays a pivotal role in cardioprotection mediated by αGalCer in post-myocardial infarction HF and cardiac hypertrophy induced by angiotensin II. 24,47 This study has 2 major limitations. First, we transplanted human αGCDCs into DCM mice (thereby producing a xenograft model), mainly because this study was designed to develop and validate a cell product for humans. Human APCs can present αGalCer to murine iNKT cells and activate them, 20 and iNKT cells in mice were evidently activated in response to human αGCDCs in our study; however, xenografting, which can induce immune rejection, could be considered a limitation. Second, we utilized a DCM model that harbors a troponin T mutant. Although this mutation is also observed in human DCM, DCM is a heterogeneous cardiomyopathy induced by numerous etiologies, and therefore, the present findings cannot be broadly generalized to all clinical DCMs. Thus, detailed investigations are needed to validate the application of this concept to clinical DCMs.
In conclusion, our findings revealed the novel and beneficial effects of αGCDC treatment on chronic HF in a DCM model. This supports the clinical application of αGCDCs as a therapeutic modality for DCM patients with chronic HF. A clinical trial investigating the efficacy and safety of αGCDC treatment for chronic HF is currently underway (Japan Registry of Clinical Trials; jRCT2073210116).


The genome sequence of the wall brown, Lasiommata megera (Linnaeus, 1767) [version 1; peer review: awaiting peer review]

We present a genome assembly from an individual female Lasiommata megera (the wall brown; Arthropoda; Insecta; Lepidoptera; Nymphalidae). The genome sequence is 488 megabases in span. The majority of the assembly (99.97%) is scaffolded into 30 chromosomal pseudomolecules with the W and Z sex chromosomes assembled. The complete mitochondrial genome was also assembled and is 15.3 kilobases in length.

Background
The wall brown, Lasiommata megera (Linnaeus 1767), is a widely distributed butterfly found across the Palearctic. This species inhabits open sunny places such as grasslands and sand dunes, and is known for basking on bare surfaces such as walls and rocks. Larvae feed on various grasses including false broom (B. sylvaticum), tor-grass (Brachypodium pinnatum) and bents (Agrostis spp.). Forewings possess a single large eyespot, and hindwings contain four smaller eyespots, set against orange and brown markings. This species is generally bivoltine; adults can be found on the wing from May to October.
In the British Isles, this butterfly is widespread but scarce, with a higher density towards the coast. Since the 1970s, the wall brown has experienced a major decline in both abundance and occurrence in the British Isles, with an 87% decrease in abundance in southern Britain due to loss of colonies (Fox et al., 2015). The Wall has also experienced a significant decline in abundance across Europe based on the European Grassland Butterfly Indicator (van Swaay et al., 2013). One potential explanation for this decline is that warmer conditions due to climate change, may be triggering a third generation, resulting in a high mortality rate in autumn (Van Dyck et al., 2015). Other changes in the environment, such as nitrogen deposition, have also been implicated in their decline (Klop et al., 2015). The Wall has an estimated genome size of 381 Mb based on flow cytometry (Mackintosh et al., 2019).

Genome sequence report
The genome was sequenced from a single female L. megera from Aberlady Bay, Scotland, UK (Figure 1). A total of 45-fold coverage in Pacific Biosciences single-molecule HiFi long reads and 77-fold coverage in 10X Genomics read clouds were generated. Primary assembly contigs were scaffolded with chromosome conformation Hi-C data. Manual assembly curation corrected 17 missing/misjoins and removed 1 haplotypic duplication, reducing the assembly size by 0.33% and the scaffold number by 24.56%, and increasing the scaffold N50 by 0.002%.
The final assembly has a total length of 488 Mb in 43 sequence scaffolds with a scaffold N50 of 17.8 Mb (Table 1). The majority, 99.97%, of the assembly sequence was assigned to 30 chromosomal-level scaffolds, representing 28 autosomes (numbered by sequence length) and the W and Z sex chromosomes (Figure 2- Figure 5; Table 2). The assembly has a BUSCO v5.3.2 (Manni et al., 2021) completeness of 98.6% (single 98.2%, duplicated 0.4%) using the lepidoptera_odb10 reference set (n=5,286). While not fully phased, the assembly deposited is of one haplotype. Contigs corresponding to the second haplotype have also been deposited.

Project accession data
Assembly identifier ilLasMege1.1

Raw data accessions
PacificBiosciences SEQUEL II ERR7224284

Genome assembly
Assembly accession GCA_928268935.  was removed from a 200-ng aliquot of extracted DNA using 0.8X AMpure XP purification kit prior to 10X Chromium sequencing; a minimum of 50 ng DNA was submitted for 10X sequencing. HMW DNA was sheared into an average fragment size between 12-20 kb in a Megaruptor 3 system with speed setting 30. Sheared DNA was purified by solid-phase reversible immobilisation using AMPure PB beads with a 1.8X ratio of beads to sample to remove the shorter fragments and concentrate the DNA sample. The concentration of the sheared and purified DNA was assessed using a Nanodrop spectrophotometer and Qubit Fluorometer and Qubit dsDNA High Sensitivity Assay kit. Fragment size distribution was evaluated by running the sample on the FemtoPulse system.

Sequencing
Pacific Biosciences HiFi circular consensus and 10X Genomics Chromium read cloud sequencing libraries were constructed according to the manufacturers' instructions. Sequencing was performed by the Scientific Operations core at the Wellcome Sanger Institute on Pacific Biosciences SEQUEL II (HiFi) and Illumina HiSeq (10X) instruments. Hi-C data were generated in the Tree of Life laboratory from whole organism tissue of ilLasMege3 using the Arima v2 kit and sequenced on a NovaSeq 6000 instrument.

Genome assembly
Assembly was carried out with Hifiasm (Cheng et al., 2021); haplotypic duplication was identified and removed with purge_dups (Guan et al., 2020). One round of polishing was performed by aligning 10X Genomics read data to the assembly with longranger align, calling variants with freebayes (Garrison & Marth, 2012). The assembly was then scaffolded with Hi-C data (Rao et al., 2014) using SALSA2 (Ghurye et al., 2019. The assembly was checked for contamination and corrected using the gEVAL system (Chow et al., 2016) as described previously (Howe et al., 2021). Manual curation (Howe et al., 2021) was performed using gEVAL, HiGlass (Kerpedjiev et al., 2018) and Pretext. The mitochondrial genome was    assembled using MitoHiFi (Uliano- Silva et al., 2021), which performs annotation using MitoFinder (Allio et al., 2020). The genome was analysed and BUSCO scores generated within the BlobToolKit environment (Challis et al., 2020). Table 3 contains a list of all software tool versions used, where appropriate.
The genome sequence is released openly for reuse. The L. megera genome sequencing initiative is part of the Darwin Tree of Life (DToL) project. All raw sequence data and the assembly have been deposited in INSDC databases. The genome will be annotated and presented through the Ensembl pipeline at the European Bioinformatics Institute. Raw data and assembly accession identifiers are reported in Table 1.


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Infiltration of Apoptotic M2 Macrophage Subpopulation Is Negatively Correlated with the Immunotherapy Response in Colorectal Cancer

The polarization of tumor-associated macrophages (TAMs) plays a key role in tumor development and immunotherapy in colorectal cancer (CRC) patients. However, the impact of apoptosis on TAM polarization and immunotherapy efficacy in patients with different mismatch repair statuses (MMR) remains unclear. Here, we constructed an atlas of macrophage and found a higher rate of infiltration of M2-like TAM subpopulation in pMMR CRC tumor tissues compared with that in dMMR CRC tumor tissues. Importantly, a lower infiltration rate of M2c-like TAMs was associated with immunotherapy response. The M2 polarization trajectory revealed the apoptosis of M2c-like TAMs in dMMR while the differentiation of M2c-like TAMs in pMMR, implying a higher polarization level of M2 in pMMR. Furthermore, we found that a high expression of S100A6 induces the apoptosis of M2c-like TAMs in dMMR. In conclusion, we identified apoptotic TAM subpopulations in the M2 polarization trajectory and found that apoptosis caused by the high expression of S100A6 reduces their infiltration in tumors as well as the level of M2 polarization and contributes to a favorable immunotherapy response. These findings provide new insights into the potential role of apoptosis in suppressing tumors and enhancing immunotherapeutic efficacy.

Introduction
Colorectal cancer (CRC) shows a large dynamic range of immune responsiveness, with a striking difference between two genetically distinct subtypes: patients with deficient mismatch repair (dMMR) have a high neoantigen burden and a high survival rate and respond to immunotherapy, associated with the decreased infiltration of tumor-promoting macrophages [1,2]. In contrast, patients with proficient mismatch repair (pMMR) have the opposite characteristics [1]. However, how the infiltration of macrophages leads to distinct outcomes in CRC patients with different MMR statuses needs to be elucidated.
Tumor-associated macrophages (TAMs) are the essential components of the innate immune system, and they play a crucial role in tumor development, invasion, and metastasis [3]. The polarization of TAMs profoundly affects tumor development [4]. The "classically activated" M1 and the "alternatively activated" M2 macrophage polarization system have been used to describe the in vitro activation state of TAMs [5]. However, due to the complexity of the tumor immune microenvironment (TME), TAMs exhibit more phenotypes, i.e., a "spectrum" model, in vivo [6]. Numerous studies have shown that although the M2 polarization of TAMs promotes tumor growth and metastasis [7], the apoptosis of TAMs can inhibit tumor growth and improve patients' anti-tumor immunity [8]. However, the current studies on the development trajectory of TAM polarization toward the M2 phenotype and the effect of apoptosis on M2 polarization are unclear, and it is necessary to 2 of 15 construct a well-defined trajectory of M2 polarization and identify intermediate-state cell types in combination with the phenotypic diversity of TAMs.
TAMs are a key component of the immunosuppressive pathways targeted by immune checkpoint inhibitors and, therefore, may provide tools to predict or increase the activity of such treatments [4]. For example, TAMs can express the PD-1 ligands PD-L1, as well as the CTLA-4 ligands B7-1 (CD80) and B7-2 (CD86) [9], which inhibit the phagocytosis of TAMs [10]. How the expression of PDL1 (and/or PDL2) and CTLA-4 ligands on TAMs has not been fully elucidated [4]. Therefore, the expression of these triggers of inhibitory immune checkpoints by TAMs needs to be carefully assessed, and the factors influencing the efficacy of immunotherapy by TAMs must be investigated.
In this study, we analyzed the diversity of macrophages in the tumor tissues of dMMR and pMMR patients at the single-cell resolution level and constructed the M2 polarization trajectory of TAMs. The trajectory revealed that S100A6-induced apoptosis decreases the level of TAM polarization. Finally, we explored the impact of the infiltration of immunosuppressive TAMs on immunotherapy efficacy.

Higher Infiltration of the M2-like TAM Subpopulation in pMMR CRC Tumor Tissues Compared with That in dMMR CRC Tumor Tissues
To characterize macrophage heterogeneity within the tumor immune microenvironment (TIME) and investigate the various cell types, gene expression patterns, and functions of TAMs, Uniform Manifold Approximation and Projection (UMAP) was performed on macrophages, and 13,704 macrophages were divided into 8 clusters by initial clustering based on representative markers ( Figure 1A and Table S1). Specifically, SPP1+ TAMs overexpressed the classical markers SPP1 and MARCO ( Figure 1B,D) [11]; MKI67+ Macros and STMN1+ Macros were proliferation-associated macrophages with a high expression of the MKI67, TOP2A, and STMN1 ( Figure 1B,D) [12,13]. M1-like Macros and M2-like TAMs were identified based on marker genes (CXCL10, IL15RA, F13A1, and FOLR2), respectively [14][15][16]. In addition, we identified GNLY+ Monolike Macros as a unique cluster of macrophages that had cytotoxicity-related gene expression similar to that of CD3E+ monocytes from a previous study [17].
TAMs that typically had the M2 phenotype exhibited higher heterogeneity and were associated with multiple functions that promote tumor progression [18,19]. Therefore, we extracted M2-like TAMs for further subgrouping and obtained four distinct subgroups ( Figures 1C and S1B). C1QC+ TAMs expressed the complement components C1QC, C1QB, and C1QA, confirming the findings of prior research [11]. M2a-like TAMs, M2b-like TAMs, and M2c-like TAMs highly expressed unique marker genes, respectively (Figure S1C) [20,21]. Overall, different cell subtypes were widely distributed in dMMR and pMMR tumor tissues with distinct gene expression patterns ( Figure 1D and Figure S1A), and the diversity of macrophage infiltration and function needs additional investigation.
To assess the functional activity of different cell types, we collected macrophage signature gene sets from published studies and performed Gene Set Variation Analysis (GSVA) ( Table S2). M2-like TAMs displayed unique tumor progression or immunomodulationrelated capabilities. C1QC+ TAMs exhibited increased immune activity and decreased tumor progression-related functions, indicating that they do not promote tumors (Figure 2A). C1QC+ TAMs showed the strongest antigen processing and presenting capacity, and this function was significantly upregulated in dMMR. However, the activity of antigen processing and presentation function was higher in M2c-like TAMs in pMMR than in dMMR, implying significant differences in the partial immune function activity of TAMs in different tissues. M2b-like TAMs promoted angiogenesis and positively regulated epithelial-mesenchymal transition (EMT), indicating their pro-tumor potential (Figure 2A). Similarly, M2c-like TAMs and M2a-like TAMs also functioned as a "gas pedal" for tumor development. Moreover, M2-like TAMs had different levels of metabolic signaling intensity and inflammatory regulation ability, and most of the functions related to tumor progression and immune activity were upregulated in dMMR ( Figure 2A). In addition, SPP1+ TAMs had the strongest pro-angiogenesis function, while STMN1+ Macros and FTL+ Macros exhibited low overall function activity, suggesting that they might be resting macrophages ( Figure S1D). TAMs that typically had the M2 phenotype exhibited higher heterogeneity and were associated with multiple functions that promote tumor progression [18,19]. Therefore, we extracted M2-like TAMs for further subgrouping and obtained four distinct subgroups ( Figures 1C and S1B). C1QC+ TAMs expressed the complement components C1QC, C1QB, and C1QA, confirming the findings of prior research [11]. M2a-like TAMs, M2b-like TAMs, and M2c-like TAMs highly expressed unique marker genes, respectively ( Figure  S1C) [20,21]. Overall, different cell subtypes were widely distributed in dMMR and pMMR tumor tissues with distinct gene expression patterns ( Figures 1D and S1A), and the diversity of macrophage infiltration and function needs additional investigation.
To assess the functional activity of different cell types, we collected macrophage signature gene sets from published studies and performed Gene Set Variation Analysis (GSVA) ( Table S2). M2-like TAMs displayed unique tumor progression or immunomodulation-related capabilities. C1QC+ TAMs exhibited increased immune activity and decreased tumor progression-related functions, indicating that they do not promote tumors ( Figure 2A). C1QC+ TAMs showed the strongest antigen processing and presenting capacity, and this function was significantly upregulated in dMMR. However, the activity of antigen processing and presentation function was higher in M2c-like TAMs in pMMR than in dMMR, implying significant differences in the partial immune function activity of TAMs in different tissues. M2b-like TAMs promoted angiogenesis and positively regulated epithelial-mesenchymal transition (EMT), indicating their pro-tumor potential (Fig- It has been demonstrated that the MMR status of patients affects macrophage infiltration [2,22]. Meanwhile, the enrichment of TAMs promotes tumor progression and metastasis in the TME [23]. We compared the abundance of all macrophages in dMMR and pMMR and found that the cell proportion of most subpopulations was considerably different. Except for SPP1+TAMs, all TAMs associated with tumor progression were enriched in dMMR, while GNLY+ Monolike Macros with the anti-tumor immune function were abundant in pMMR ( Figure 2B). The infiltration of macrophages was also associated with the tumor and lymph node stage in patients. In this study, we focused on M2-like TAMs. The proportion of M2 b-like TAMs in pMMR increased with tumor progression whereas the proportion of immune-activated C1QC+ TAMs decreased ( Figure 2C). Similar trends of changing cell ratio were not found in dMMR, indicating greater fluctuations in cell proportion with tumor progression. Surprisingly, during cancer progression, M2a-like TAMs were only present in pMMR patients but not in dMMR patients ( Figure 2C), and the underlying causes for this phenomenon require further investigation.
for tumor development. Moreover, M2-like TAMs had different levels of metabolic signaling intensity and inflammatory regulation ability, and most of the functions related to tumor progression and immune activity were upregulated in dMMR (Figure 2A). In addition, SPP1+ TAMs had the strongest pro-angiogenesis function, while STMN1+ Macros and FTL+ Macros exhibited low overall function activity, suggesting that they might be resting macrophages ( Figure S1D). indicates that the activity of this function or pathway is upregulated in TAMs, and the purple color indicates that the activity of this function or pathway is downregulated in TAMs. The asterisk indicates that one function is significantly upregulated in dMMR or pMMR; (B) the lollipop plot shows the infiltration of 10 clusters of macrophages in dMMR and pMMR. Solid circles indicate that this cluster of macrophages is significantly enriched in dMMR or pMMR (chi-square test, p < 0.05), and hollow circles indicate no significant difference in cell ratios between dMMR and pMMR (p > 0.05); (C) changes in the cell ratios of M2-like TAMs in dMMR and pMMR at different tumor stages (T1, T2, T3, and T4) and lymph node stages (N0, N1, and N2), *** p < 0.001, and * p < 0.05.
It has been demonstrated that the MMR status of patients affects macrophage infiltration [2,22]. Meanwhile, the enrichment of TAMs promotes tumor progression and metastasis in the TME [23]. We compared the abundance of all macrophages in dMMR and pMMR and found that the cell proportion of most subpopulations was considerably different. Except for SPP1+TAMs, all TAMs associated with tumor progression were enriched in dMMR, while GNLY+ Monolike Macros with the anti-tumor immune function were abundant in pMMR ( Figure 2B). The infiltration of macrophages was also associated with the tumor and lymph node stage in patients. In this study, we focused on M2-like TAMs. The proportion of M2 b-like TAMs in pMMR increased with tumor progression whereas the proportion of immune-activated C1QC+ TAMs decreased ( Figure 2C). Similar trends of changing cell ratio were not found in dMMR, indicating greater fluctuations in cell proportion with tumor progression. Surprisingly, during cancer progression, M2alike TAMs were only present in pMMR patients but not in dMMR patients ( Figure 2C), and the underlying causes for this phenomenon require further investigation.

Apoptosis of M2c-like TAMs in dMMR While Differentiation of M2c-like TAMs in pMMR
To better portray the dynamic changes in the cell types and expression characteristics of M2-like TAMs, we inferred the polarization trajectory of four subpopulations of M2- The green color indicates that the activity of this function or pathway is upregulated in TAMs, and the purple color indicates that the activity of this function or pathway is downregulated in TAMs. The asterisk indicates that one function is significantly upregulated in dMMR or pMMR; (B) the lollipop plot shows the infiltration of 10 clusters of macrophages in dMMR and pMMR. Solid circles indicate that this cluster of macrophages is significantly enriched in dMMR or pMMR (chi-square test, p < 0.05), and hollow circles indicate no significant difference in cell ratios between dMMR and pMMR (p > 0.05); (C) changes in the cell ratios of M2-like TAMs in dMMR and pMMR at different tumor stages (T1, T2, T3, and T4) and lymph node stages (N0, N1, and N2), *** p < 0.001, and * p < 0.05.

Apoptosis of M2c-like TAMs in dMMR While Differentiation of M2c-like TAMs in pMMR
To better portray the dynamic changes in the cell types and expression characteristics of M2-like TAMs, we inferred the polarization trajectory of four subpopulations of M2-like TAMs. The C1QC+ TAMs were the origin of the M2 polarization trajectory, which went through the intermediate states of M2b-like TAMs and M2c-like TAMs before eventually differentiating into M2a-like TAMs ( Figure 3A,B). IGF1 and MSR1 were M2 polarization markers, and their progressively higher expression with polarization indicated that the cells were increasingly characterized by M2 polarization ( Figure 3C).
The M2 polarization trajectory terminated at M2a-like TAMs, demonstrating that M2c-like TAMs in dMMR could not successfully differentiate into M2a-like TAMs. It was found that apoptosis inhibited the normal differentiation of M2c-like TAMs. CASP3 and CASP7 were significantly overexpressed in M2c-like TAMs in dMMR ( Figure 3D), implying a higher apoptotic effect in this cluster of cells. According to the GSVA analysis of hallmark gene sets, M2c-like TAMs exhibited different pathway activities in dMMR and pMMR ( Figure 3E). The apoptosis pathway was significantly enriched in dMMR. Meanwhile, the P53 signaling pathway and the hypoxia pathway, both pro-apoptosis pathways, had upregulated activities in dMMR as well ( Figure 3E). MYC signaling pathways were enriched in pMMR and associated with the continuing differentiation of M2c-like TAMs in pMMR ( Figure 3E).
Furthermore, the SCENIC results showed that HIF1A, with the largest variance, and RUNX1 as candidate transcription factors (TFs) underlie the differences in gene expression in M2c-like TAMs between dMMR and pMMR ( Figure 3F). HIF1A is a hypoxia-inducible factor that induces apoptosis via the P53 pathway, and RUNX1 is also an apoptosis-promoting TF. Interestingly, HIF1A is linked to P53 expression in tumors [24], and other genes involved in apoptosis also show putative HIF1A/RUNX1 binding sites [25]. These suggest that the upregulation of their activities in dMMR is responsible for the apoptosis of M2c-like TAMs. In contrast, the upregulation of FOXO1 and FOXO3 are associated with proliferation, which might explain the continuing differentiation of M2c-like TAMs in pMMR [26].  Figure 3A,B). IGF1 and MSR1 were M2 polarization markers, and their progressively higher expression with polarization indicated that the cells were increasingly characterized by M2 polarization ( Figure 3C).  The Wilcox test was used to compare expression differences in dMMR and pMMR. p < 0.05 is statistically significant; (E) differences in hallmark pathway activity scores of M2c-like TAMs between dMMR and pMMR (n = 754 and 829, respectively). Proliferation-related pathways are marked in red, and apoptosis-related pathways are marked in blue. T values are from limma; (F) the heatmap shows the transcription factor regulatory activity (AUC z-score) obtained from the Single-Cell Regulatory Network Inference and Clustering (SCENIC) analysis. The top five TFs with upregulated transcriptional activity were selected for visualization in dMMR and pMMR, respectively. Limma was used to compare the differences in AUCs between dMMR and pMMR; (G) communication among macrophages via the GAS signaling pathway, which promotes cell proliferation; (H) communication among macrophages via the TNF signaling pathway, which promotes apoptosis.
Finally, we explored whether cell-to-cell communication across all macrophages affects the differentiation endpoints of particular cells. GAS6 promotes cell proliferation and inhibits apoptosis by binding to its receptors MERTK and AXL [27]. We found more types of cells involved in the GAS signaling pathway network in pMMR, along with a higher communication probability and upregulation of LR pair expression in pMMR ( Figures 3G and S2A,B,F). Specifically, M2a-like TAMs transmitted the strongest proliferation signal to M2c-like TAMs in pMMR. TNF involves extrinsic apoptosis by specifically binding with its receptor to form a death domain [28], which might "accelerate" the cell death of M2c-like TAMs in dMMR. The TNF signaling pathway network was enhanced in dMMR with higher expression of LR pairs ( Figure S2E). As the sender of the TNF signaling pathway, M2 b-like TAMs conveyed a potent apoptosis signal to M2c-like TAMs ( Figures 3H and S2C,D).In conclusion, we suggested that the increased apoptotic effect of M2c-like TAMs in dMMR might contribute to their different cell fates.

High Expression of S100A6 Induced Apoptosis of M2c-like TAMs in dMMR
To explore the genes leading to the apoptosis of M2c-like TAMs in dMMR, we performed differential analysis and identified a total of 284 differentially expressed genes (DEGs) ( Table S3). Of these, 154 DEGs were significantly upregulated in dMMR, and 130 DEGs were significantly upregulated in pMMR ( Figure 4A). They were found to be enriched in some pathways related to apoptosis or proliferation, such as the apoptotic process, the positive regulation of I-kappaB kinase/NF-kappaB signaling, and the positive regulation of ERK1 and ERK2 cascade ( Figure 4B). Next, we used the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to further screen DEGs related to apoptosis ( Figure S4A). A total of 25 genes were associated with the apoptotic effect of M2c-like TAMs in both dMMR and pMMR ( Figure 4C). These genes were used for further analysis. Subsequently, we selected 16 apoptosis-related genes with known functions (8 pro-apoptosis and 8 anti-apoptosis) and analyzed correlations among genes to identify the potential functions of DEGs. The few DEGs with unclear apoptotic effects were removed, and a total of 21 genes were found to show a higher correlation with apoptosisrelated genes, which we referred to as apoptosis-related DEGs ( Figure 4D). The expression of S100A6 and TSPO was positively correlated with most of the pro-apoptosis genes and negatively correlated with most of the anti-apoptosis genes, indicating that these genes may have pro-apoptosis effects ( Figure 4D). The remaining DEGs displayed opposite gene expression relationship patterns, implying their crucial role in apoptosis inhibition. Among them, CXCL8 is mainly secreted by macrophages and contributes to the immunosuppressive microenvironment [29]. CXCL3 is also a chemokine secreted by macrophages and associated with the invasion and metastasis of various malignancies [30]. Rel is critical for the NF-kappa B-dependent inhibition of apoptosis [31]. MCL1 is an essential member of the BCL2 family that can promote macrophage survival [32]. Their upregulated expression might be a potential factor in suppressing the apoptosis of M2c-like TAMs in pMMR. expression relationship patterns, implying their crucial role in apoptosis inhibition. Among them, CXCL8 is mainly secreted by macrophages and contributes to the immunosuppressive microenvironment [29]. CXCL3 is also a chemokine secreted by macrophages and associated with the invasion and metastasis of various malignancies [30]. Rel is critical for the NF-kappa B-dependent inhibition of apoptosis [31]. MCL1 is an essential member of the BCL2 family that can promote macrophage survival [32]. Their upregulated expression might be a potential factor in suppressing the apoptosis of M2c-like TAMs in pMMR. (D) the heatmap shows the Pearson correlation between DEGs and apoptosis-related genes. The significance level of the correlation is indicated by asterisks: *** p < 0.001, ** p < 0.01, and * p < 0.05; (E) expression of S100A6 and P53 in dMMR and pMMR. Differences in expression are checked using the Wilcox test; (F) survival analysis of S100A6 and P53 in TCGA-CRC data. Log-rank test.
We proposed that the pro-apoptosis-related DEGs upregulated in dMMR and the anti-apoptosis-related DEGs upregulated in pMMR are the hub genes responsible for the differential apoptotic effects of M2c-like TAMs. Therefore, seven hub genes were finally identified. Among these, S100A6 and TSPO were upregulated in dMMR ( Figures 4E and  S3B), and REL, ARL4C, AKAP9, GPR183, and MCL1 were upregulated in pMMR ( Figure  S3B), which might have the largest impact on apoptosis in M2c-like TAMs. The gene with the largest fold change was S100A6, and it could upregulate the expression of the apoptosis-inducing gene P53 to promote apoptosis [33]. In our study, the expression of P53 was also found to be upregulated in dMMR ( Figure 4E). In addition, P53, not S100A6, was associated with survival in CRC patients ( Figure 4F). An examination of the bulk RNA-seq data showed that the expression of S100A6 and P53 was higher in dMMR patients than in pMMR patients ( Figure S3C). These results suggest that overexpression of S100A6 might upregulate the expression of P53 and promote apoptosis of M2c-like TAMs in dMMR.

Lower Infiltration of M2c-like TAMs Was Associated with Immunotherapy Response
The impact of M2c-like TAMs, a cluster of cells with tumor-promoting potential, on immunotherapy response required further evaluation. We examined the expression of reported immune checkpoint molecules on the surface of macrophages and found that M2c-like TAMs highly expressed CTLA ligands CD80 and CD86, as well as the PD1 ligand PDCD1LG2, indicating a higher immunosuppressive profile ( Figure 5A). PDCD1LG2 was also found to be highly expressed in M2c-like TAMs in dMMR, which represented the discrepancy in the immunosuppressive characteristics of TAMs in different patients ( Figure S4A).To assess the level of infiltration, the top30 marker genes of M2c-like TAMs were used as signature gene sets. We found that the infiltration of M2c-like TAMs was negatively correlated with the expression of S100A6, which might be related to the decrease in cell number due to S100A6-induced apoptosis ( Figure 5B). Patients with lower infiltration of M2c-like TAMs had longer survival times ( Figure 5C).
To further investigate the potential relationship between immunosuppressive macrophages and immunotherapy response, we assessed the likelihood of each patient in the TCGA-CRC data to benefit from immune checkpoint blockade (ICB) treatment using EaSIeR. A patient with a higher relative score was more likely to respond to immunotherapy. Due to the great heterogeneity of immunotherapy response in pMMR patients, on the basis of the response scores using the K-means algorithm, they were categorized into three groups: pMMR high, pMMR median, and pMMR low ( Figure S4B). dMMR patients were found to have higher response scores, indicating that they were more likely to benefit from immunotherapy ( Figure 5D). There were statistically significant differences in response scores across the three groups of pMMR patients ( Figure 5D). The infiltration rate of M2clike TAMs was significantly negatively correlated with immunotherapy response in all four groups of CRC patients, suggesting that patients with lower infiltration rates of M2c-like TAMs might respond to immunotherapy ( Figure 5E). We concluded that M2c-like TAMs are a subpopulation of macrophages with highly immunosuppressive profiles and highly expressed immune checkpoint ligands, and a lower infiltration rate of M2c-like TAMs is critical for patients to benefit from immunotherapy. To further investigate the potential relationship between immunosuppressive macrophages and immunotherapy response, we assessed the likelihood of each patient in the TCGA-CRC data to benefit from immune checkpoint blockade (ICB) treatment using EaS-IeR. A patient with a higher relative score was more likely to respond to immunotherapy. Due to the great heterogeneity of immunotherapy response in pMMR patients, on the basis of the response scores using the K-means algorithm, they were categorized into three groups: pMMR high, pMMR median, and pMMR low ( Figure S4B). dMMR patients were found to have higher response scores, indicating that they were more likely to benefit from immunotherapy ( Figure 5D). There were statistically significant differences in response scores across the three groups of pMMR patients ( Figure 5D). The infiltration rate of M2clike TAMs was significantly negatively correlated with immunotherapy response in all four groups of CRC patients, suggesting that patients with lower infiltration rates of M2clike TAMs might respond to immunotherapy ( Figure 5E). We concluded that M2c-like TAMs are a subpopulation of macrophages with highly immunosuppressive profiles and

Discussion
To elucidate the mechanisms of tumor development, a sufficient understanding of macrophages in the TME of CRC patients with different MMR statuses is essential. In this study, we integrated the scRNA-seq data from dMMR patients and pMMR patients to identify a total of 10 populations of macrophages with distinct gene expression and functional characteristics. Of these, five distinct TAM populations played a critical role in tumor promotion and immunomodulation. The remaining TAMs, with the exception of C1QC+ TAMs, exhibited characteristics associated with tumor development. These findings also confirmed that infiltrating TAMs in tumor tissue tend to promote tumor development [23].
The infiltration of macrophages is an essential feature of the TME and is influenced by the patient's MMR status [2]. Our results indicated differences in the abundance of some clusters of macrophages between dMMR and pMMR patients. More infiltration of immunoreactive macrophages was demonstrated in dMMR, whereas TAMs were more infiltrated in pMMR, which might explain the poorer clinical performance of pMMR patients [34]. Although TAMs were more enriched in pMMR, many of their pro-tumor functions were upregulated in dMMR, implying that the increased infiltration of TAMs had a greater impact on tumor development than the upregulation of pro-tumor function. Changes in the proportions of macrophages among different tumor grades have revealed their close relationship with tumor progression [35,36]. We speculated that increasing the infiltration of macrophages with tumor-promoting function and decreasing the infiltration of immunoreactive macrophages may drive cancer progression. In conclusion, future studies could focus more on the adverse effects of TAM infiltration on tumor development.
The "spectrum" model of macrophage polarization has received much recognition [37]. TAMs in tumors are commonly believed to polarize toward the M2 phenotype. This study identified the cell types involved in M2 polarization and constructed a well-defined M2 polarization trajectory. We found the M2 polarization of TAMs to be a tumor-promoting differentiation trajectory characterized by a steadily increasing M2 phenotype involving the upregulation of colony-stimulating factors, hypoxia, and angiogenesis factors and the downregulation of the immune function [38]. More infiltration of polarization terminal TAMs in advanced patients suggested that TAMs are likely to polarize toward the terminal along with tumor progression. M2a-like TAMs specific to pMMR revealed a different cell fate from their predecessor cell state M2c-like TAMs, suggesting a higher degree of M2 polarization of TAMs in pMMR, which may promote tumor progression and metastasis in such patients [39,40].
The increased apoptosis of M2c-like TAMs in dMMR was an essential reason for its failure to differentiate normally into M2a-like TAMs. This was confirmed by the upregulation of pro-apoptosis genes, pro-apoptosis pathways, and the regulatory activity of TFs in dMMR. Based on the differential expression of M2c-like TAMs in dMMR and pMMR, we finally identified seven hub genes. According to the difference in ranking, we found that S100A6 was a marker of apoptosis in M2c-like TAMs, and its expression and that of P53 were upregulated in dMMR. In addition, P53 signaling pathway activity was upregulated in M2c like TAMs in dMMR. S100A6 is an important calcium signal-regulating gene, and the calcium-binding protein it encodes is a member of the S100 family, which is associated with DNA damage and a variety of cancers [41]. S100A6 has previously been shown to be a favorable prognostic marker for urothelial cancer. Previous research has demonstrated that when S100A6 binds to RAGE, it causes apoptosis in epithelial cells by activating Jun N-terminal kinase and plays a crucial role in osteosarcoma metastasis [42][43][44]. S100A6 can interact with P53 in a calcium-dependent manner and upregulate its transcriptional activity, which may induce the high expression of P53-regulated downstream pro-apoptosis genes, such as CASP3 and CASP7 [33]. P53 is a crucial tumor suppressor gene capable of inducing apoptosis [45]. P53-induced apoptosis in TAMs is an important mechanism to inhibit tumor growth. The mechanism of S100A6 identified in these studies combined with the gene expression patterns in ours suggested that S100A6 as a hub gene has the potential to trigger the apoptosis of M2c-like TAMs and, thus, inhibit their differentiation and reduce the degree of M2 polarization of TAMs in dMMR [33,45].
Exploring the impact of TAMs' characteristics on the efficacy of immunotherapy in patients has become an essential focus of tumor immunology research [46]. TAMs can express a variety of immune checkpoint molecules that inhibit the immune response and promote tumor escape [4]. M2c-like TAMs in this study specifically expressed PD1 and CTLA4 ligands, and their highly immunosuppressive nature suggested that they might act as detrimental factors in anti-tumor immunity. We found a negative correlation between the level of M2c-like TAM infiltration and the predicted response of patients with different MMR statuses to immunotherapy, suggesting that immunotherapy may be ineffective in patients with a high level of infiltration of M2c-like TAMs. In addition, such patients had significantly lower survival rates. Moreover, the infiltration of M2c-like TAMs was likely to be reduced by the high expression of S100A6. These findings suggested that the infiltration of M2c-like TAMs has an important clinical value. In conclusion, S100A6-mediated apoptosis may reduce the infiltration of M2c-like TAMs, which will improve patient survival as well as immunotherapy efficacy.
However, this study has several limitations. While we identified a specific M2c-like TAM subtype, further validation is needed for the population of TAMs via flow cytometry. Further knockdown experiments could confirm the exact mechanism of how S100A6 triggers apoptosis in M2c-like TAMs and affects their infiltration, which may provide more evidence. Whether M2c-like TAMs can be used as immunotherapeutic targets needs to be further investigated.
In short, we constructed a more comprehensive macrophage atlas and M2 polarization trajectory, elucidating the extensive heterogeneity and diversity of macrophages in the TME of dMMR and pMMR patients. The excessive apoptosis of the M2c-like TAM subpopulation in dMMR patients may reduce the level of infiltration and inhibit tumor development. The apoptosis-associated hub DEG S100A6 provides new clues to potential immunotherapeutic targets.

Data Processing
The single-cell RNA sequencing (scRNA-seq) data used in this paper were obtained from the GEO database, and the access number was GSE178341 [1]. We extracted the expression matrix of 18,976 tumor tissue macrophages from 28 pMMR and 34 dMMR patients for screening. Genes and cells that meet the following screening criteria were retained: (1) A gene was expressed in at least 10 cells, and a cell expressed at least 200 genes; (2) the sum of the counts per cell was less than 25,000, and the number of the expressed genes was less than 5000; and (3) the mitochondrial gene content was less than 15%, and the ribosomal gene content was less than 20%. Finally, a macrophage expression matrix containing 13,704 cells with 14,551 genes was obtained, and this expression matrix was used in the next analysis. In addition, the bulk RNA sequencing data from TCGA-COAD and TCGA-READ were downloaded from the UCSC Xena database (http://xena.ucsc.edu/ (accessed on 20 February 2022)) and integrated as TCGA-CRC. For subsequent analyses, the format Log2 (FPKM+1) was used.

Cell Clustering and Atlas Construction
To cluster all macrophages, the R package Seurat was used (version 4.0.5) [47]. In the initial clustering, we selected 2500 high variable genes for subsequent analysis, and the default parameters were used for the PCA. Since the data were obtained from multiple patient samples, Harmony (version 0.1.0) was used to integrate the data to remove batch effects [48]. Subsequently, the first 14 PCs were selected for dimensionality reduction, and the resolution parameter in FindClusters was set to 0.1. The macrophages were eventually classified into 7 subpopulations. Next, we extracted M2-like TAMs for the second clustering. In all, 2000 high variable genes were selected for subsequent analysis, and the default parameters were used for the PCA. The first 12 PCs were picked for dimensionality reduction, and the resolution parameter in FindClusters was set to 0.2. The visualization of the macrophage atlas was presented using UMAP.

Identification of Differentially Expressed Genes and Function Analysis
To identify differentially expressed genes for each subpopulation, we used the Find-AllMarkers function in Seurat, and log FC > 0.25 and adj p < 0.05 as screening thresholds. To identify the differentially expressed genes of M2c-like TAMs in dMMR and pMMR, an analysis was performed using FindMarkers with the same thresholds as before. To characterize the functional differences among the subpopulations, macrophage-function-related gene sets were collected from previous studies for GSVA (version 1.42.0) [49]. Hallmark gene sets were downloaded from the MSigDB database, and the R package limma (version 3.50.1) was used to analyze the differences in pathway activity scores between dMMR and pMMR [50]. When assessing the immune infiltration scores, the kcdf parameter was set to "ssGSEA" for analysis. The GO pathway enrichment analysis of apoptosis-related genes was performed using the enrichGO function in the R package ClusterProfiler (version 4.2.2) with adj p < 0.05 [51].

Cell Abundance in Different Tissues
We counted a total of 10 clusters of macrophages in dMMR and pMMR, using chisq.test (R package stats, version 4.1.1) to determine whether there are differences in cell abundance in different tissues. The patients were then divided into different groups according to their tumor stage and node stage to observe the changes in cell abundance among different groups.

Inference of the Cell Differentiation Trajectory
To infer the differentiation polarization of M2-like TAMs, the "slingshot" method in the R package dyno (version 0.1.2) was used [52]. We used all the DEGs of the four subpopulations of M2-like TAMs as input for trajectory inference. After obtaining the cell differentiation trajectory, we finally determined the direction of the differentiation trajectory on the basis of the changes in the expression of some signature genes of macrophage differentiation.

Inference of Transcription Factors
The R package SCENIC (version 1.2.4) was used to identify candidate TFs from all the genes expressed by M2c-like TAMs and to analyze their transcriptional activities [53]. Subsequently, the top5 TFs with upregulated transcriptional activity in dMMR and pMMR were selected for visualization, respectively.

Analysis of Cell-to-Cell Communication
Cell-to-cell interactions were implemented by the R package CellChat (version 1.1.3) [54]. We imported all the human ligand-receptor interaction data in CellChatDB for analysis and then selected the LR pairs related to apoptosis or proliferation for further visualization.

Identification of Apoptosis-Related Hub Genes
We performed lasso regression to select the genes correlated with the apoptosis of M2c-like TAMs in dMMR. The apoptosis scores obtained from the apoptosis gene sets in hallmark were used as dependent variables, and all the DEGs were used as independent variables. The glmnet function in the R package glmnet was used for lasso regression, and the parameter lambda was selected by using 3-fold cross-validation. According to the above criteria, the data in dMMR and pMMR were regressed separately, and the intersection of the obtained genes was taken. The correlations between the intersecting genes and the apoptosis genes were calculated, and the genes that met the following criteria were removed: (1) positively correlated with more than half of the pro-apoptosis and anti-apoptosis genes at the same time, and (2) negatively correlated with more than half of the pro-apoptosis and anti-apoptosis genes at the same time. These genes were considered to have unclear apoptotic effects.

Immune Response Prediction
To predict the likelihood of patients responding to immune checkpoint inhibitors (ICI) treatment, the R package EaSIeR (version 1.0.0) was applied to calculate the immunotherapy response score for each patient [55]. This method integrated the quantitative descriptive information about the patient's tumor mutation burden (TMB) and the immune microenvironment to predict the immune response. EaSIeR provided the likelihood of each patient benefitting from the ICI treatment, with a higher relative score indicating a higher likelihood of a patient to respond to immunotherapy.


extfieldsofstudy: Medicine Biology

text:
Mapping functional regions of essential bacterial proteins with dominant-negative protein fragments

Significance Peptide fragments derived from protein sequences can inhibit interactions of their parental proteins, providing tools to study protein function in vivo. Here, we employed a massively parallel assay to measure inhibition of Escherichia coli growth by fragments tiling the sequences of 10 of its essential proteins. We leveraged these data to decipher principles of fragment-based inhibition, demonstrating that parental protein concentration drives activity and characterizing how fragment length interplays with activity and specificity. We employed statistical analysis to parse the roles of biophysical properties in fragment-to-fragment variation, finding that the specific characteristics of each fragment largely drive its inhibitory activity. These results advance our understanding of protein interactions in vivo and have implications for the rational design of peptide inhibitors.

Supplementary Figures
: A random protein fragment assay of DHFR shows that inhibition is reading framedependent. (A) Histogram of enrichment (E) by in-frame (red) and out-of-frame (grey) fragments of ≤30 residues derived from the random fragmentation of E. coli DHFR. (B) Average enrichment per site due to in-frame fragments covering each residue, across the DHFR sequence (see Materials and Methods). Inhibitory peaks are indicated. Inset: overlay of per-site enrichment of in-frame and out-of-frame fragments. (C) DHFR crystallographic structure (PDB ID 7dfr) with N-and C-termini indicated, overlaid by the known refolding inhibitory fragment (left), alongside the C-terminal inhibitory peak (middle) and the N-terminal inhibitory peak (right) from the random fragment assay.

Figure S2: A protein fragment from interaction site 1 inhibits FtsZ polymerization in vitro. Left:
Results from measurements of FtsZ filament polymerization by 90° light scattering (Materials and Methods) in the presence of either a protein fragment from interaction site 1 (FtsZ residues 166-195) or a neutral control fragment (FtsZ residues 34-63). In each reaction, FtsZ was present at 5.06 µM and the peptide was present at 18.4 µM. Error bars: s.e.m. from replicate measurements (N = 4 for the interaction site 1 fragment, N = 3 for the neutral control fragment). The asterisk indicates p < 0.05. Right: Inhibitory fragment map of FtsZ, plotted as in Figure 2, overlaid with the interaction site 1 and neutral control fragments.   50S ribosomal subunit protein L7/L12 (RplL) is shown with a grey fill color; linear fits to the nonribosomal protein data are shown for the 30-residue and 50-residue data as dashed lines in corresponding colors. Note that the slopes are very similar. The faded grey dashed line near the 20residue fragment data is a shifted copy of the trendline for the 30-residue data, suggesting that a similar slope may also be reasonable for 20-residue fragments; however, among the proteins for which 20residue fragment libraries were generated, a leveling off is evident as the mean E enters the neutral regime (E ≈ 0), as expected, and there is insufficient sampling of mean E values below zero to fit these data directly.    Figure S1) attributable to each property is plotted, based on a nested ANOVA. Properties include protein fragment length and in-frame secondary structure features of the fragment-covered sequence region of the full-length gene; "fragment ID" designates fragment-specific effects not attributable to variation in any of the properties considered.

SI Text
Fragment mapping reveals promising target sites for inhibitor development: additional discussion DHFR is the target of the antibiotic trimethoprim, which binds at the enzymatic active site and inhibits catalysis (51). Our results suggest that inhibition of folding by compounds that bind to the N-terminal beta strand may be another approach to target this important enzyme. Another route would be welcome given the serious issue of trimethoprim resistance (52); due to the distinct mechanisms, a putative folding inhibitor would likely not be subject to the same resistance mutations.
A number of compounds inhibiting bacterial DNA gyrase, including the fluoroquinolones currently employed clinically, function by trapping the enzyme in a DNA cleavage complex (53). Other compounds, such as the aminocoumarins, interfere with ATPase activity (53). Our finding of an inhibitory fragment peak localized to the ends of the C-gate arms of GyrA, which form a homomeric head-to-head complex to close the gate, suggests that compounds targeting these Cgate closure sites would provide another means to inhibit gyrase. Such inhibitors would rely on a distinct binding mode from the aforementioned compounds, which bind at sites at or near the DNA gate or the ATPase site (53,54). In line with our findings, a monoclonal antibody against Mycobacterium smegmatis and Mycobacterium tuberculosis GyrA appears to bind at the same location as our fragments and prevent C-gate dimerization. Furthermore, the antibody epitope corresponds to E. coli GyrA residues 340-402 (55), fully overlapping with the inhibitory fragment peak centered around residue 386.
Single-stranded DNA binding protein (Ssb) is a desirable antibiotic target because inhibition should interfere with DNA replication and repair. Screening has therefore been performed for Ssb inhibitors (56)(57)(58), leading to the identification of numerous compounds; some of these were shown to interfere with critical interactions of eight conserved C-terminal residues with genome maintenance proteins (57,58). Unlike the case for FtsZ, we did not observe an inhibitory fragment peak mapping near this interaction-mediating C-terminal tail. Instead, we observed a strong peak mapping to the single alpha-helical element of this protein, suggesting a novel approach for inhibitor development: mimicking the interactions formed by the alpha helix in Ssb dimers to disrupt dimer and tetramer formation.
A further note regarding the transferability of inhibitory sites identified in E. coli to other bacteria: The range of bacteria potentially targetable based on these fragment scan results is extensive, given that protein-peptide interactions are based heavily on structural properties. Therefore, more distantly related orthologs retaining the relevant structural features are likely susceptible to the same types of inhibitors, especially those based directly on peptide fragments.