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# Metabolic reprogramming promotes apoptosis resistance in acute lymphoblastic leukemia through CASP3 lactylation

Zhi Lin<sup>1,2†</sup>, Fei Long<sup>3,4†</sup>, Jiao Liu<sup>5</sup>, Rui Kang<sup>6</sup>, Daniel J. Klionsky<sup>7</sup>, Guido Kroemer<sup>8,9,10</sup>, Daolin Tang<sup>6*</sup> and Minghua Yang<sup>1,2*</sup>

## Abstract

Acute lymphoblastic leukemia (ALL) is characterized by metabolic adaptations that support rapid cell proliferation and resistance to apoptosis. Our study identifies elevated sphingomyelin (SM) as a key metabolic alteration in ALL, contributing to apoptosis resistance via CASP3 (caspase 3) lactylation. Using comprehensive lipidomic analyses of plasma samples from pediatric ALL patients, we observed significantly increased SM concentrations in patients with manifest ALL compared to patients after remission. Mechanistic investigations revealed that elevated SM enhances SLC2A1-dependent glucose uptake and glycolysis, leading to increased lactate production and subsequent CASP3 lactylation on lysine residue 14, which inhibits CASP3 activation and apoptosis. Reduction of intracellular SM levels through SGMS1 knockout or SMPD3 overexpression reduced glycolytic flux and lactate levels, restored CASP3 activity and induced apoptosis in ALL cells. In vivo, SM depletion significantly suppressed ALL progression and prolonged survival in mouse models, highlighting the potential of targeting SM metabolism as a therapeutic strategy. In conclusion, our findings uncover a metabolic pathway linking lipid and glucose metabolism to apoptosis resistance in ALL.

**Keywords** Acute lymphoblastic leukemia, Apoptosis, Lactylation, Metabolic reprogramming

<footnote>†Zhi Lin and Fei Long have contributed equally to this work.</footnote>

<footnote>*Correspondence:
Minghua Yang
yangminghua@csu.edu.cn
Daolin Tang
daolin.tang@utsouthwestern.edu
<sup>1</sup>Department of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha 410013, China</footnote>

<footnote n="2"><sup>2</sup>Hunan Clinical Research Center of Pediatric Cancer, Changsha 410013, China</footnote>

<footnote n="3"><sup>3</sup>Department of Gastrointestinal Surgery, The Third Xiangya Hospital, Central South University, Changsha 410013, China</footnote>

<footnote n="4"><sup>4</sup>Postdoctoral Research Station of Basic Medicine, The Third Xiangya Hospital, Central South University, Changsha 410013, China</footnote>

<footnote n="5"><sup>5</sup>DAMP Laboratory, Department of Critical Care Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, Guangdong, China</footnote>

<footnote n="6"><sup>6</sup>Department of Surgery, UT Southwestern Medical Center, Dallas, TX 75390, USA</footnote>

<footnote n="7"><sup>7</sup>Life Sciences Institute, Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI, USA</footnote>

<footnote n="8"><sup>8</sup>Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université de Paris Cité, Sorbonne Université, INSERM U1138, Institut Universitaire de France, Paris, France</footnote>

<footnote n="9"><sup>9</sup>Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Campus, Villejuif 94800, France</footnote>

<footnote n="10"><sup>10</sup>Department of Biology, Institut du Cancer Paris CARPEM, Hôpital Européen Georges Pompidou, AP-HP, Paris 75015, France</footnote>
## Introduction

Acute lymphoblastic leukemia (ALL) is a hematological malignancy primarily affecting the bone marrow and blood, characterized by the uncontrolled proliferation of immature lymphoid progenitors, or lymphoblasts [1]. Although ALL can manifest across all age groups, it is predominantly diagnosed in children, representing the most common pediatric cancer [2]. The standard treatment regimen for ALL involves several phases. Induction therapy, which aims to achieve remission by eradicating detectable leukemia cells from the bone marrow, typically lasts 6 to 7 weeks [3]. This is followed by the consolidation phase, lasting 4 to 8 months, designed to eliminate residual leukemia cells. Maintenance therapy, lasting 2 to 3 years, is then employed to sustain remission and prevent relapse. Despite advancements in therapeutic approaches that have significantly improved survival rates, both acute and long-term adverse effects remain a significant concern [2] highlighting the urgent need for novel strategies that enhance efficacy while minimizing toxicity.

Cancers, including ALL, undergo extensive metabolic rewiring to support rapid growth, metastasis, and resistance to cell death [4]. A well-known example of this is the Warburg effect, in which cancer cells, characterized by increased glucose uptake, preferentially utilize glycolysis followed by lactate fermentation for energy production, even when sufficient oxygen is available [5]. In addition, cancer cells frequently exhibit lipid anabolism to further support their growth and proliferation. These changes include enhanced lipid synthesis (lipogenesis), increased uptake of lipids from the surrounding environment, and modifications in lipid breakdown (lipolysis) [6]. ALL, which is characterized by rapid proliferation, also exhibits a pronounced activation of anabolic pathways that support the biosynthesis of nucleic acids and proteins [7]. Some current treatment strategies target these metabolic pathways. For example, methotrexate disrupts nucleotide synthesis by inhibiting the conversion of folic acid to tetrahydrofolate [8] and L-asparaginase induces apoptosis by depleting asparagine, a crucial amino acid for leukemic cells [9]. Despite these strategies, resistance to treatment remains a substantial challenge, suggesting that a deeper understanding of metabolic dependencies in ALL could reveal new therapeutic targets.

One of the hallmarks of cancer is disabled apoptosis, the programmed cell death pathway that leads to the elimination of damaged and abnormal cells [10, 11]. In leukemia, oncogenic mutations and dysregulated metabolic pathways can result in reduced apoptosis, allowing malignant cells to survive and proliferate [12–14]. Caspases, a family of cysteine-aspartic proteases, play

a central role in the initiation and execution of apoptosis but are frequently inactivated in leukemia cells [15–17]. The activity of caspases is regulated by pro-apoptotic and anti-apoptotic members of the BCL2 family [18]. In particular, inhibitors targeting anti-apoptotic BCL2 family members are being evaluated in clinical trials for the treatment of hematological and solid cancer [19, 20]. Venetoclax, a specific inhibitor of BCL2, is clinically approved for the treatment of acute myeloid leukemia (AML), chronic lymphocytic leukemia, and small lymphocytic lymphoma [21–23]. Although metabolic rewiring is known to contribute to apoptosis resistance [24], the molecular mechanisms linking these processes remain poorly understood.

Here, we conducted a comprehensive lipidomic analysis of pooled plasma samples from 22 pediatric ALL patients. Our findings revealed elevated levels of sphingomyelin (SM) in the leukemic state compared to remission. We identified a novel role for SM in promoting glucose uptake and demonstrated that depleting SM can effectively impede ALL progression. Mechanistically, we showed that abundant SM levels facilitate glycolysis and lactate production, leading to CASP3 (caspase 3) inactivation through lactylation. Experimental depletion of SM or inhibition of CASP3 lactylation inhibited ALL growth both in vitro and in vivo.

## Results

### SM is elevated in pediatric ALL patients

To delineate the lipidomic landscape of pediatric ALL, we performed untargeted lipidomics on plasma samples, assessing all classes and subclasses of lipids, including fatty acids, triglycerides (TGs), phospholipids, sphingolipids, and sterols. The primary objective of induction therapy in ALL is to achieve remission and restore normal hematopoiesis. Our study cohort consisted of 22 pediatric patients (aged 1.3–12.7 years) who reached complete remission following induction therapy, as per the treatment protocols of the Chinese Children's Cancer Group study ALL-2015 3,25. From these patients, we obtained a total of 44 cryopreserved plasma aspirates, 22 collected at initial diagnosis (Day 0) and 22 post-induction chemotherapy (Day 46) (Fig. 1A). Detailed participant characteristics are provided in Table S1.

Principal component analysis/PCA revealed distinct clustering patterns between samples collected on Day 0 and Day 46 (Fig. S1A). Lipidomic profiling identified TG, SM, and phosphatidylcholine/PC as the most abundant lipid species (Fig. S1B). Among them, SM exhibited the most significant differential regulation, with elevated levels in ALL patients at diagnosis compared to remission (Fig. 1B and S1C). Further analysis
<figure><img src="image_2.png" /><figcaption>**Fig. 1** SM is elevated in pediatric patients with ALL. (A) The untargeted lipidomic workflow for identifying metabolic signature of pediatric ALL. (B) Lipidomic profiling of pediatric ALL at the timepoint of initial diagnosis (day 0) and after induction chemotherapy (day 46). The relative abundance of lipid is color-coded from red indicating high signal intensity to blue indicating low intensity. (C) Correlation analysis between detected lipid levels and indicated clinical parameters in pediatric ALL. WBC: white blood cell counts, LDH: lactate dehydrogenase, Blast counts: bone marrow blast counts. (D) The sphingomyelin concentrations in PBMCs of 6 healthy controls, 6 ALL patients, or ALL cell lines (NALM-6, Reh and CEM/C1) and AML cell lines (THP-1, HL60 and Kasumi-1). Data shown as mean ± SD (n = 3). P values were determined by unpaired two-tailed t-test. \*-P < 0.05; \*-\*P < 0.01; \*-\*-P < 0.001; \*-\*-\*P < 0.0001; NS: no significance. (E) Heatmap showing the transcriptomic expression of genes involving SM synthase and sphingomyelinase in healthy control, ALL patients, ALL cell lines (NALM-6 and Reh) and AML cell lines (K562 and HL60). Data shown as mean ± SD (n = 3). (F) Western blotting results of SGMS1 and SMPD3 in healthy control, ALL patients, ALL cell lines (NALM-6 and Reh) and AML cell lines (K562 and HL60). Quantification of the results are provided in Fig. S1D. Gel source data are presented in the Source Data file</figcaption></figure>

demonstrated a positive correlation between plasma SM levels and several key clinical indicators associated with leukemia burden [26], namely peripheral white blood cell/WBC counts, bone marrow blast counts, and lactate dehydrogenase/LDH levels (Fig. 1C).

SM is a sphingolipid found in animal cell membranes, particularly in the membranous myelin sheath

that surrounds nerve cell axons [27]. These findings prompted us to explore whether elevated SM levels denote a novel metabolic signature of leukemia cells. Elevated SM levels were observed in primary pediatric ALL cells and established ALL cell lines (NALM-6, Reh and CEM/C1), whereas such increases were not detected in AML cell lines (THP-1, HL60 and
Kasumi-1) (Fig. 1D), suggesting that the SM signature is specific for ALL.

To mechanistically explore the SM elevation in ALL cells, we examined the expression of key genes involved in SM metabolism using real-time quantitative polymerase chain reaction (qPCR). SGMS1 (sphingomyelin synthase 1) and SGMS2<sup>28</sup> can be contrasted with sphingomyelinase enzymes (SMPDs), which include five types: SMPD1 (sphingomyelin phosphodiesterase 1), SMPD2, SMPD3, SMPD4, and ENPP7 (ectonucleotide pyrophosphatase/phosphodiesterase 7) [29]. We observed the selective upregulation of SGMS1 and the downregulation of SMPD3 in primary ALL patient samples, as well as in human ALL cell lines (Fig. 1E). These mRNA expression changes were corroborated at the protein level by immunoblotting (Fig. 1F and S1D). As to be expected, intracellular SM levels were reduced following SGMS1 knockdown (SGMS1 KD) or SMPD3 overexpression (SMPD3 OE) in human ALL cell lines (NALM-6 and Reh), confirming the functional relevance of these enzymes (Fig. S1E).

Collectively, our findings suggest that an elevation in SM synthase SGMS1 and a reduction in sphingomyelinase SMPD3 are responsible for increasing intracellular SM levels in pediatric ALL.

## SM promotes the proliferation of ALL cells

In the next step, we cultured ALL, NALM-6, and Reh cells in the presence of natural SM purified from egg, brain, or milk. Although all SMs share a common structure comprising a ceramide backbone linked to a phosphocholine head group, their fatty acid compositions and biological properties vary significantly depending on their source [30].

Cell counts revealed that only brain SM significantly promoted cell proliferation (Fig. 2A). Given the diverse array of SM species differing in fatty acid chain lengths and degrees of unsaturation that occur naturally, we decided to determine their abundance in each source. C18:0 SM emerged as the most abundant species within brain SM (Fig. S2A). The intricate nature of isomeric or isobaric lipid levels precluded the identification of individual acyl chains for all lipid species. Nevertheless, among spectrally resolvable SM species, C18:0-containing SM was identified as one of the most prevalent SM in the context of ALL (Fig. S2B).

Because sphingosine (d18:1) is the predominant sphingoid base in natural SMs [31, 32], a synthetic C18:0 SM (d18:1/18:0) was used for further experiments with ALL cell lines. C18:0 SM significantly promoted the proliferation of ALL cell lines, closely replicating the effects observed with brain SM (Fig. 2B C). Additionally, C18:0 SM enhanced the

colony-forming capacity of ALL cells (Fig. 2D), contrasting with C16:0 SM, which did not affect proliferation (Fig. S2C). These results suggest a unique role for C18:0 SM in promoting the growth of ALL cells.

To determine a potential tumor-suppressive effect of SM deficiency on ALL, we utilized ALL cell lines (NALM-6 and Reh) that were subjected to the knockdown of SGMS1 or the overexpression of SMPD3 to deplete SM (Fig. 2E). Both SGMS1 KD and SMPD3 OE reduced cell proliferation in both cell lines (Fig. 2F), and this inhibitory effect on proliferation was reversed by addition of exogenous C18:0 SM (Fig. S2D). Furthermore, SGMS1 KD and SMPD3 OE induced a $G_0/G_1$ cell cycle arrest in ALL cells (Fig. S2E). In sharp contrast, no changes in cell proliferation were noted in healthy peripheral blood mononuclear cells (PBMCs) following SGMS1 knockdown or SMPD3 overexpression (Fig. S2F). Thus, depletion of SM selectively inhibits ALL cell proliferation, but does not affect normal PBMCs.

In the next step, we investigated whether SGMS1 and SMPD3 might serve as prognostic biomarkers in ALL patients. Analysis of a clinical cohort from the adult B-ALL ECOG E2993 trial (NCT00002514) revealed that higher mRNA levels of SGMS1 were associated with shorter overall survival (OS) (Fig. 2G). Similarly, elevated SGMS1 mRNA levels and low SMPD3 mRNA levels both correlated with reduced OS in pediatric ALL cohorts (TARGET-ALL-P2/P3 dataset) (Fig. 2H). In contrast, the expression levels of SGMS1 and SMPD3 did not correlate with clinical outcomes in AML (TARGET AML dataset) (Fig. S2G). These clinical data support a role for SM metabolism-related genes in the progression of ALL, but not AML.

## Pro-proliferation effect of SM is glucose dependent

Previous research has indicated that SGMS1-mediated SM synthesis favors the proliferation of lymphoma cells through a mechanism involving the upregulation of intracellular TF (transferrin) [33]. However, the genetic inactivation of SGMS1 in ALL cells did not yield significant alteration in intracellular TF levels (Fig. S3A), indicating that SGMS1-mediated cell proliferation in ALL cells does not involve changes in TF expression.

In the context of ALL, significant metabolic shifts and the mobilization of energy sources are commonly thought to drive malignant proliferation [34–36]. We determined whether the proliferative effect of SM is intertwined with cellular energy production. Of note, treatment of ALL cells with C18:0 SM resulted in an increase in total adenosine triphosphate (ATP) levels (Fig. 3A). Considering that ATP can arise from both glycolytic and mitochondrial pathways, we evaluated
<figure><img src="image_3.png" /><figcaption>**Fig. 2** SM is required for ALL cell proliferation (A) Growth curves in leukemic cells that were incubated with three different types of natural sphingomyelins (SM). Cell counts were measured at the indicated times using an automatic cell counter. Data shown as mean ± SD (n = 3). (B) Growth curves in leukemic cells that were incubated with C18:0 sphingomyelins (SM) (40 µM) (n = 3). (C) Growth curves in leukemic cells that were incubated with C18:0 sphingomyelins (SM) at the indicated concentrations (n = 3). (D) Colony-forming cell assay of ALL cells incubated with or without C18:0 sphingomyelin (SM) (40 µM). Colony numbers were assessed on day 14 after seeding in methylcellulose medium. Representative images are shown. Scale bar: 50 µm and 20 µm. The data are representative of three independent experiments. P values were calculated for comparison of colony numbers and colony size using unpaired two-tailed t test (n = 3). Data are shown as mean ± SD (n = 3). (E) Western blotting results showing the inducible effects of SGMS1 knockdown (SGMS1 sh) or SMPD3 overexpression (SMPD3 OE) in ALL cells. (F) Growth curves in SGMS1 knockdown (SGMS1 sh) ALL cells or control (Ctrl sh) and growth curves in SMPD3-overexpressing (SMPD3 OE) ALL cells or control (Ctrl cDNA). Data shown as mean ± SD (n = 3). (G–H) Kaplan-Meier curves for survival outcomes striated by adult ALL transcriptome datasets (GSE34861) and pediatric ALL transcriptome datasets (TARGET). P values in this figure were determined by two-way ANOVA with multiple comparisons analysis (A–C, F) or log-rank test (G–H). \*: P < 0.05; \*: P < 0.01, \*:*: P < 0.001, and \*:\*:\*: P < 0.0001. Gel source data are presented in the Source Data file</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3** SM removal limits ALL cell expansion in vitro (A) Total ATP levels were measured in the indicated cells. ATP values were normalized to viable cell counts. *P* values were determined by unpaired two-tailed t-test. (B) Extracellular acidification rate (ECAR) analysis in ALL cells with or without C18:0 sphingomyelin (40 µM) incubation. Each experiment was performed in triplicate and data are presented as mean ± SD. (C) ECAR analysis in *SGMS1* knockdown (*SGMS1* sh) ALL cells or control (Ctrl sh) and ECAR analysis in *SMPD3*-overexpressing (*SMPD3* OE) ALL cells or control (Ctrl cDNA). (D) Lactate levels were measured in the indicated cells. Lactate values were normalized to viable cell counts. (E) Relative mRNA change of glycolytic pathway transporters and enzymes in ALL cells with or without C18:0 sphingomyelin (40 µM) incubation. (F) Western blotting results for SLC2A1 in ALL cells with or without C18:0 sphingomyelin (40 µM) incubation. (G) Western blotting results for SLC2A1 in *SGMS1* knockdown (*SGMS1* sh) ALL cells or control (Ctrl sh) and western blotting results for SLC2A1 in *SMPD3*-overexpressing (*SMPD3* OE) ALL cells or control (Ctrl cDNA). (H) Glucose uptake level in Reh cells with or without C18:0 sphingomyelin (40 µM) incubation. (I) Western blotting results showing the inducible effects of SLC2A1 knockdown (SLC2A1 sh) or *HIF1A* knockdown (*HIF1A* sh) in Reh cells. And Growth curves in indicated cells that were incubated with or without C18:0 sphingomyelins (SM). *P* values in this figure were determined by unpaired two-tailed t-test (A, D) or two-way ANOVA with multiple comparisons analysis (B, C, I). \*: *P* < 0.05; \*: \*: *P* < 0.01, \*: \*: *P* < 0.001, and \*: \*: \*: *P* < 0.0001. Gel source data are presented in the Source Data file</figcaption></figure>
the extracellular acidification rate (ECAR) and mito-
chondrial function (oxygen consumption rate [OCR])
in C18:0 SM-treated ALL cells. Using the Seahorse flux
analyzer, we observed a C18:0 SM-triggered elevation
of ECAR in ALL cells (Fig. 3B). Additionally, SGMS1
KD and SMPD3 OE resulted in lower ECAR in ALL
cells cultured in glucose (Fig. 3C). However, we did not
detect significant alterations in OCR upon C18:0 SM
incubation or SMPD3 OE (Fig. S3B). These findings
indicate that SM supports ATP generation by glycoly-
sis rather than by oxidative phosphorylation.

Lactate, a byproduct of glycolysis, has emerged as a
signaling molecule that regulates multiple pathways
[37, 38]. Commensurate with the inhibition of glycoly-
sis, intracellular and extracellular lactate concentra-
tions were reduced in SGMS1 KD and SMPD3 OE ALL
cells (Fig. 3D, Fig S3C), and C18:0 SM resulted in an
increase in lactate levels (Fig. S3D). To investigate the
mechanisms underlying the observed glycolytic phe-
notypes linked to SM in ALL, we examined the mRNA
levels of genes encoding glycolytic pathway transport-
ers and enzymes. This analysis highlighted the distinc-
tive role of SLC2A1/GLUT1 (solute carrier family 2
member 1), a glucose transporter responsible for glu-
cose uptake in various tissues [39]under conditions of
SM incubation and SM metabolic disruption. Supple-
mentation of C18:0 SM yielded increased mRNA levels
of SLC2A1 in both NALM-6 and Reh cells, while ALL
cells subjected to SGMS1 KD or SMPD3 OE exhibited
the opposite trend (Fig. 3E and S3E). Analysis of pub-
licly available datasets, specifically the Therapeutically
Applicable Research to Generate Effective Treatments
(TARGET) program from the National Cancer Insti-
tute (https://www.cancer.gov/ccg/research/genome
-sequencing/target), revealed a positive correlation
between SGMS1 expression and SLC2A1 expression in
pediatric ALL patients (Fig. S3F). Conversely, SMPD3
expression negatively correlated with SLC2A1 levels
(Fig. S3F). Consistent with mRNA levels, western blot
analysis demonstrated that the protein expression of
SLC2A1 increased upon SM addition but decreased
upon SM depletion by SGMS1 KD or SMPD3 OE in
both NALM-6 and Reh cells (Fig. 3F and G). SM sup-
plementation reversed the downregulation of SLC2A1
observed in SGMS1 KD and SMPD3 OE ALL cells
(Fig. S3G), indicating that the expression of SLC2A1
is indeed regulated by SM. Interestingly, in line with
our observations, C18:0 SM facilitates glucose uptake
in Reh cells (Fig. 3H).

The transcription factor HIF1A (hypoxia inducible
factor 1 subunit alpha) plays a key role in the meta-
bolic reprogramming of tumors, including the hypoxic
response, cell cycle regulation, and glycolysis [40]. To
further validate the SM-regulated glycolysis pathway,

we measured the expression of HIF1A confirming
its upregulation upon addition of SM to the cultures
(Fig. S4A). As a master regulator of glycolysis, HIF1A
reportedly regulates the expression of SLC2A1 in
solid cancers [40]. HIF1A was also predicted to affect
SLC2A1 mRNA expression according to the Tran-
scriptional Regulatory Relationships Unravelled by
Sentence-based Text-mining/TRRUST database (https
://www.grnpedia.org/trrust/) (Fig. S4B). Western blots
demonstrated that SLC2A1 protein is downregulated
in response to HIF1A knockdown in Reh cells (Fig.
S4C), indicating that HIF1A is required for SLC2A1
expression in ALL cells. Furthermore, ChIP-qPCR
assay revealed that the binding of HIF1A on SLC2A1
promoter was significantly enhanced upon SM stimu-
lation (Fig. S4D).

To determine whether glycolysis influences SM-
dependent ALL cell proliferation, we cultured cells
in a glucose-depleted medium. Glucose deprivation
attenuated the proliferation advantage observed for
SM-incubated cells (Fig. S4E). In addition to glucose,
glutamine is also crucial for cell growth of many cancer
cells, including ALL [41]. Unlike glucose deprivation,
however, exogenous SM sustained cell survival under
conditions of glutamine deprivation stress (Fig. S4E).
Consistent with these findings, SM treatment-medi-
ated proliferation advantage was significantly attenu-
ated in SLC2A1 KD or HIF1A KD Reh cells (Fig. 3I).

Taken together, these findings highlight the glucose-
dependent pro-proliferative effect of SM, suggesting that
SM-promoted glucose uptake constitutes a novel mecha-
nism driving ALL cell growth.

SM-deficient ALL cells undergo apoptosis

Resistance to apoptosis is a hallmark of cancer, includ-
ing ALL [42, 43]. Glycolytic alterations in cancer cells
directly influence their proliferative capacity and sus-
ceptibility to apoptosis [44, 45]. Flow cytometry assays
using ANXA5/annexin V and PI staining revealed
an elevated rate of apoptosis in both SGMS1 KD and
SMPD3 OE ALL cells (Fig. 4A and B). This pro-apop-
totic effect was reversed by pretreatment with C18:0
SM (Fig. 4A and B). Consistent with the proliferative
phenotype, no substantial apoptotic changes were
observed in SGMS1 KD and SMPD3 OE PBMCs (Fig.
S4F). Additionally, immunoblots revealed that SGMS1
KD and SMPD3 OE ALL cells activated apoptosis-
associated processes, such as the proteolytic activation
of CASP3, a cysteine-aspartic acid protease central to
cell apoptosis, and its substrate PARP1 (poly(ADP-
ribose) polymerase 1) (Fig. 4C and D), without signifi-
cant alterations in other cell death markers, including
GPX4 (glutathione peroxidase 4) for ferroptosis and
phosphorylated (p-)MLKL (mixed lineage kinase
<figure><img src="image_5.png" /><figcaption>**Fig. 4** SM-deficient ALL cells undergo apoptosis (A) Apoptotic rates in SGMS1 knockdown (SGMS1 sh) ALL cells or control (Ctrl sh); the SM incubation was performed to check the variation of apoptosis. (B) Apoptotic rates in SMPD3-overexpressing (SMPD3 OE) ALL cells or control (Ctrl sh); the SM incubation was performed to check the variation of apoptosis. (C-D) Western blotting results showing the variations of protein expression levels of PARP1 and CASP3 in response to (C) SGMS1 knockdown or (D) SMPD3 overexpression. (E) Apoptotic rates in SGMS1 knockdown (SGMS1 sh) or SMPD3-overexpressing (SMPD3 OE) ALL cells treated with or without lactate (10 mM). *P* values in this figure were determined by unpaired two-tailed t-test. ***: *P* < 0.001, and ****: *P* < 0.0001. Gel source data are presented in the Source Data file</figcaption></figure>
domain like pseudokinase) for necroptosis (Fig. S4G and S4H). Thus, SM depletion through SGMS1 KD and SMPD3 OE induces apoptosis in ALL cells. Supplementation of exogenous lactate mitigated apoptosis of SGMS1 KD and SMPD3 OE ALL cells (Fig. 4E), suggesting that lactate depletion contributes to SM-depletion-induced ALL cell death.

## Lactylation of CASP3 is decreased under conditions of SM depletion

Lactylation is a post-translational modification (PTM) involving the addition of lactyl groups to lysine residues (Kla) on histones or other proteins. This PTM links cellular metabolism and gene regulation [46, 47]. Therefore, we investigated whether SM depletion affects protein lactylation in ALL cells.

Using a robust label-free quantification approach, we conducted global lactylome and proteome analyses to evaluate protein lactylation dynamics in response to SM removal by mass spectrometry. Among the 6,585 proteins that were identified, 2,117 proteins exhibited lactylation in 6,979 distinct lysine residues (Fig. S5A, S5A, and S5B). Most modified proteins harbored only one or two Kla sites (Fig. S5B). Quantitative comparisons of the lactylome revealed a downregulation of lactylated proteins and sites in both SGMS1 KD and SMPD3 OE Reh cells compared to controls (Fig. 5C and S5C). Of note, the downregulation of lactylation was more prominent in SMPD3 OE than in SGMS1 KD Reh cells (Fig. S5C), which is in line with the stronger reduction of intracellular lactate in SMPD3 OE than in SGMS1 KD cells (Fig. 3D). Among the top 10 down-regulated lactylation sites in the SMPD3 OE vs. control cells, we found CASP3 (Fig. 5D). The lactylation of the same site on CASP3 was also reduced in SGMS1 KD cells (Fig. 5D and S5D), indicating that SM and glycolytic metabolism affect the lactylation of this apoptotic effector protein.

In the next series of experiments, we measured CASP3 lactylation by immunoprecipitation of CASP3 and immunoblot-based quantification of its lactylation using a pan anti-Kla antibody. CASP3 lactylation increased upon the culturing of cells in the presence of extracellular lactate but decreased upon treatment with oxamate—an LDH inhibitor that impedes lactate production (Fig. 5E and S5E). Notably, SM treatment increased CASP3 lactylation, whereas glucose deprivation abolished this effect (Fig S5F). Furthermore, the SM-induced increase in CASP3 lactylation was attenuated by knockdown of either SLC2A1 or HIF1A (Fig S5G). In contrast, lactylation of CASP3 was reduced in both SMPD3 OE and SGMS1 KD ALL cells (Fig. 5F and S5H) and this effect was reversed by supplementation of C18:0 SM or lactate (Fig. 5F and S5H). The

proteolytic activation of CASP3 and its substrate PARP1 observed in SMPD3 OE and SGMS1 KD ALL cells were also abolished by addition of C18:0 SM or lactate (Fig. 5F and S5H). Thus, lactylation of CASP3 correlates with reduced CASP3 activation.

Overall, these experiments indicate that CASP3 lactylation represents a previously unreported post-translational modification resulting from SM-induced lactate production.

## Delactylation of CASP3 at K14 regulates SM depletion-induced apoptosis

The principal site of reduced lactylation on CASP3 due to SM removal was identified as lysine (K) residue 14 (Fig. 5D). This K14 site is situated at the N-terminal prodomain of CASP3, which must be cleaved for its activation [48]. K14, along with K11 and K19, was highly conserved across various mammalian species including *Homo sapiens* (Fig. 6A). Moreover, CASP3 K57 lactylation was detected in our lactylome analysis (Fig. S5D). To determine the function of these sites, we introduced HA-tagged CASP3 (wild type or mutated) into 293FT cells to convert the four K sites (K11, K14, K19 and K57) to arginine (R) residues, which cannot be lactylated. Only the CASP<sup>K14R</sup> mutant exhibited decreased lactylation compared to CASP3 wild type (WT) (Fig. 6B and S6A). We next engineered CASP3 to mimic a lactylated state of K14. Prior studies replaced K by glutamine (Q) to simulate K lactylation [49] but this approach failed to reverse CASP3 cleavage upon SM removal by SMPD3 OE or SGMS1 KD (Fig. S6C and S6D). Instead, we substituted K with threonine (T), a polar amino acid with a hydroxyl group and neutral electrical properties similar to those of Kla [50]. This K14T mutation prevented CASP3 activation, which occurred in CASP3<sup>K14R</sup> or CASP3<sup>K14Q</sup>-transfected Reh cells (Fig. S6E). Moreover, the K14R mutation of CASP3 resulted in the proteolytic activation of CASP3 and PARP1 (Fig. 6C). As a control, no such effect was observed for the K14 to T mutation (Fig. 6C and S6B). Together, these observations suggest that lactylation state of K14 in CASP3 limits its pro-apoptotic activity. Functional studies confirmed that expression of CASP3<sup>K14R</sup> (but not that of CASP3<sup>K14T</sup>) induced apoptosis in ALL cells (Fig. 6D). Expression of CASP3<sup>K14T</sup> in SGMS1 KD and SMPD3 OE Reh cells, followed by immunoblotting, confirmed that the K14T mutation hindered the cleavage of CASP3 and PARP1 (Fig. 6E F) as well as the induction of apoptosis (Fig. 6G).

Collectively, these findings suggest that K14 lactylation prevents the proteolytic activation of CASP3. SM removal reduces intracellular lactate levels and CASP3
<figure><img src="image_6.png" /><figcaption>**Fig. 5** Lactylation of CASP3 is decreased during SM removal (A) Heatmap showing differentially expressed proteins in response to SGMS1 knockdown or SMPD3 overexpression. (B) Heatmap showing differentially modified lactylation sites in response to SGMS1 knockdown or SMPD3 overexpression. (C) Distribution of SMPD3 OE: Ctrl or SGMS1:Ctrl ratio of Kla sites. The displayed Kla sites all satisfy *P* < 0.05 in the comparison between the indicated groups. (D) Heatmap showing TOP10 differently modified lactylation sites in response to SMPD3 overexpression, and the lactylation levels of the same 10 sites in response to SGMS1 knockdown. (E) The indicated cells were lysed and immunoprecipitated using anti-CASP3 antibody followed by detection of Pan Kla and CASP3 in the presence of lactate (10 mM) or oxamate (10 mM). Quantification results are provided in Supplementary Fig. S5E. (F) The indicated cells were lysed and immunoprecipitated using anti-CASP3 antibody followed by detection of Pan Kla and CASP3 in the presence of C18:0 SM (40 µM) or lactate (10 mM). Quantification results are provided in Supplementary Fig. S5H</figcaption></figure>

K14 lactylation, thereby facilitating CASP3 activation and apoptosis induction.

## SM removal limits ALL cell expansion in vivo

To evaluate the therapeutic efficacy of SM removal in vivo, we utilized two mouse models of leukemia that involve either orthotopic or subcutaneous xenografts. For orthotopic xenograft transplantations, firefly luciferase labeled WT, SGMS1⁻/⁻ (Fig S7A) or SMPD3 OE Reh cells were injected into the tail veins of severely immunodeficient

NOD/ShiLtJGpt-Prkdc<sup>em26Cd52</sup>II2rg<sup>em26Cd22</sup>/Gpt (NCG) mice. Bioluminescence imaging indicated rapid leukemic dissemination of WT cells, whereas almost no tumors were detected in the SGMS1⁻/⁻ (Fig. 7A) or SMPD3 OE (Fig. 7B) groups at day 21. Both SGMS1⁻/⁻ and SMPD3 OE groups also exhibited a significantly lower tumor burden at day 35 (Fig. 7A and B). Additionally, SM depletion via SGMS1⁻/⁻ or SMPD3 OE prevented splenomegaly (Fig. S7B and S7C) and prolonged overall survival compared to the WT controls (Fig. 7C and D). As to be expected, circulating SM
<figure><img src="image_7.png" /><figcaption>**Fig. 6** Delactylation of CASP3 at K14 regulates SM removal-induced apoptosis (A) Lysine 14 (K14) of CASP3 is evolutionarily conserved across species. Partial sequences of CASP3 in five species were aligned. K14 of CASP3 was highlighted in red. (B) Four candidate mutations (lysine/K to arginine/R) of CASP3 were transfected into 293FT cells. Proteins were immunoprecipitated by CASP3 and detected by anti-Pan Kla antibody. Quantification results are provided in Supplementary Fig. S6A. (C) K14R (lysine/K to arginine/R) or K14T (lysine/K to threonine/T) mutations of CASP3 were transfected into 293FT cells. Proteins were immunoprecipitated by CASP3 and detected by anti-Pan Kla antibody. Quantification results are provided in Supplementary Fig. S6B. (D) Apoptotic rates in the indicated Reh cells that were transfected with plasmids expressing WT CASP3 or CASP3<sup>K14R</sup>. (E-F) CASP3<sup>K14T</sup> plasmids were transfected into SMPD3-overexpressing (SMPD3 OE) Reh cells (E) or SGMS1 knockdown (SGMS1 sh) Reh cells (F). Proteins were detected with the indicated antibodies. (G) Apoptotic rates in SGMS1 sh or SMPD3 OE Reh cells that were transfected with or without plasmids expressing CASP3<sup>K14T</sup>. P values in this figure were determined by unpaired two-tailed t-test. **: P < 0.001, and ****: P < 0.0001; NS: no significance</figcaption></figure>

levels were reduced in mice injected with SGMS1⁻/⁻ or SMPD3 OE Reh cells compared to WT controls (Fig. S7D). Moreover, CASP3 lactylation was reduced but CASP3 and PARP1 cleavage were enhanced in bone marrow leukemic blasts arising from SGMS1⁻/⁻ or SMPD3 OE Reh cells with respect to WT controls, confirming that SM removal suppresses ALL progression by enhancing apoptosis (Fig. 7E F).

To further investigate the impact of SM on metabolism, we assessed glucose metabolism in subcutaneously implanted WT control or SM-depleted (SGMS1⁻/⁻ and SMPD3 OE) leukemia cells using

¹⁸F-fluorodeoxyglucose (¹⁸F-FDG), which was administered by the intravenous route. MicroPET/CT imaging demonstrated reduced glucose uptake and retention in SM-depleted tumors (Fig. 7G H). As compared to WT controls, SM removal by SGMS1⁻/⁻ and SMPD3 OE was associated with downregulation of HIF1A and SLC2A1 mRNAs (Fig. S7E), as well as with an increase in CASP3 and PARP1 cleavage (Fig. S7F and S7G).

Collectively, these in vivo findings suggest that targeting SM metabolism through SGMS1 inhibition or
<figure><img src="image_8.png" /><figcaption>**Fig. 7** SM removal limits ALL cell expansion in vivo (A-B) In vivo bioluminescence imaging of xenotransplanted NCG mice (n=6) transplanted with SGMS1 knockout (SGMS1-/-) or SMPD3-overexpressing (SMPD3 OE) Reh cells. Luminescent intensity of photons emitted from each mouse was quantified during the period of the experiment. (C-D) Kaplan-Meier survival curves of the overall survival for each group (n=6) are shown, and comparison of survival between the indicated groups was performed by log-rank test. (E-F) Western blotting results showing the variations of protein expression levels of CASP3 lactylation, CASP3 and PARP1 in bone marrow leukemic blasts from orthotopic xenograft mice that were transplanted with SGMS1 knockout (SGMS1-/-) Reh cells (E) or SMPD3-overexpressing (SMPD3 OE) Reh cells (F). (G-H) Representative ¹⁸F-FDG microPET/CT imaging of subcutaneous tumor xenograft mice that were derived from SM-depleted leukemia cells (SGMS1-/- [G] and SMPD3 OE [H]) (n=6). Images were acquired 1 h after injection of ¹⁸F-FDG and the tumors are indicated with arrows. The tumor standard uptake value (SUV<sub>max</sub>) in xenograft mice was displayed in the column graph. P values in this figure were determined by unpaired two-tailed t-test. **: P < 0.01, ***: P < 0.001, and ****: P < 0.0001</figcaption></figure>
SMPD3 overexpression reduces CASP3 lactylation and promotes apoptosis.

## Discussion

Metabolic alterations, such as the Warburg effect, provide cancer cells with the energy and biosynthetic precursors necessary for rapid proliferation and survival [4]. These metabolic changes create a microenvironment that suppresses apoptosis and favors immunoevasion, allowing cancer cells to strive. In ALL, metabolic alterations are particularly critical, as they drive malignant transformation and therapeutic resistance [51]. Here, we identify a novel mechanism through which glycolysis-driven lactate production confers apoptosis resistance to ALL cells by modulating post-translational modifications of key apoptotic regulators. Specifically, we demonstrate that lactate-induced lactylation of CASP3 at lysine 14 (K14) inhibits its proteolytic activation, thereby suppressing apoptosis. This process is regulated by SM metabolism, revealing a previously unrecognized metabolic crosstalk between glycolysis and lipid signaling in leukemia progression. We provide direct evidence that SM removal lowers intracellular lactate levels, leading to CASP3 delactylation and restoration of its pro-apoptotic activity.

SM, a critical structural component of lipid bilayers, has been implicated in various cellular processes, including membrane integrity, signaling, and tissue growth. Although disruptions in SM metabolism are linked to disorders such as Niemann-Pick disease and metabolic conditions like atherosclerosis and insulin resistance, its role in leukemia remains underexplored. Studies in solid tumors have highlighted altered SM metabolism as a driver of proliferation and survival, but its specific contribution to hematological malignancies, particularly ALL, has not been well-characterized. Our study bridges this gap by demonstrating that elevated SM levels promote ALL cell proliferation and survival through glycolysis-mediated metabolic reprogramming. SM regulates the expression of HIF1A and the glucose transporter SLC2A1, thereby enhancing glycolysis and lactate production. Furthermore, we found that SM depletion disrupts this metabolic axis, leading to CASP3 delactylation and apoptosis induction.

Normal lymphoid precursor cells are tightly constrained in their glucose and energy metabolism, but metabolic dysregulation, such as enhanced glycolysis, is a key driver of malignant transformation in ALL. We provide new insights into the interplay between lipid and glucose metabolism in leukemia progression. Because SM is a fundamental component of mammalian cell membranes, cells deficient in SM synthesis fail

to proliferate without SM supplementation from external sources, and mice lacking enzymes required for SM synthesis arrest their development during embryogenesis [33, 52]. Our study suggests that elevated SM levels may contribute to the increased energy demands of ALL cells by promoting glycolysis through the regulation of HIF1A and the glucose transporter SLC2A1. These findings highlight SM as a central regulator of glycolysis and apoptosis resistance in ALL. Whereas lipids such as triglycerides and sterol esters primarily support leukemic cell proliferation and survival, SM confers a distinct metabolic advantage by directly modulating lactate production and CASP3 activity. Thus, targeting SM metabolism could represent a novel therapeutic strategy to overcome apoptosis resistance in ALL.

Lactylation affects lysine residues of human histones [46] and non-histone proteins, hence influencing gene expression and protein activity in a glycolysis-dependent manner [47]. Our study reinforces the role of lactylation in the pathological context of ALL. CASP3 delactylation works as a critical post-translational switch governing apoptotic activation in ALL cells. The identification of SM-regulated lactylation as a rheostat controlling CASP3 activation unveils novel therapeutic opportunities to pharmacologically mimic delactylation and reinstate apoptotic sensitivity in ALL.

In summary, we elucidates the pivotal role of SM in promoting leukemic cell proliferation and disabling apoptosis through glycolysis-mediated lactate production. By uncovering a novel mechanism linking SM metabolism to CASP3 lactylation, our findings advance the understanding of metabolic vulnerabilities in ALL and provide a rationale for developing metabolic-targeted therapies for acute lymphoblastic leukemia.

## Materials and methods

### Reagents are described in table S2

#### Patient samples

Peripheral blood samples were collected from 22 pediatric patients diagnosed with ALL at two time points: at diagnosis (day 0) and following induction therapy (day 46). Additionally, samples from six healthy donors were collected as controls. All samples were processed within 24 h of collection. Peripheral blood mononuclear cells and plasma were isolated using density gradient centrifugation (Ficoll-Paque, GE17-1440-02) following the manufacturer's protocol. PBMCs were freshly cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS). The study was approved by the Institutional Review Board of the Third Xiangya Hospital of Central South University (No. 21034), and written informed consent was
obtained from all participants. A summary of patient characteristics is provided in Table S1.

## Cell lines

The human leukemia cell lines Reh (CRL-8286), CEM/ C1(CRL-2265), THP-1 (TIB-202), HL-60 (CCL-240) and Kasumi-1 (CRL-2724) were obtained from ATCC, and NALM-6 (FH1161) was acquired from Fu Heng Biology. Leukemia cells were cultured in RPMI 1640 medium (Thermo Fisher Scientific, 22400097) supplemented with 10% fetal bovine serum (Thermo Fisher Scientific, A3840001) and 1% penicillin-streptomycin (Thermo Fisher Scientific, 15070-063). HEK 293FT cells (Thermo Fisher Scientific, R70007) were maintained in high-glucose DMEM (Thermo Fisher Scientific, 11995073) supplemented with 10% FBS and 1% penicillin-streptomycin. All cells were cultured at 37 °C in a humidified incubator with 5% CO₂. Prior to experimental use, all cell lines were tested and confirmed to be free of mycoplasma contamination.

## Establishment of immortalized B-lymphocytes

Immortalized B-lymphocytes were generated by infecting PBMCs isolated from healthy donors with Epstein-Barr virus (EBV) [53]. The EBV-containing supernatant was produced using the marmoset cell line B95-8 (Procell, CL-0476), which secretes transforming, infectious EBV particles. After isolating PBMCs through Ficoll-Paque density gradient centrifugation, the cells were washed and resuspended in RPMI 1640 medium supplemented with 20% FBS. A total of $1 \times 10^7$ PBMCs were pelleted by centrifugation and resuspended in 5 mL of B95-8 supernatant. The cells were incubated with the EBV-containing supernatant at 37 °C for 2 h to allow viral infection.

Following infection, EBV-transformed cells were cultured in complete RPMI 1640 medium with 10% FBS, in the presence of cyclosporine A at a concentration of 0.1 µg/mL. Cyclosporine A was used to inhibit T-cell activation, preventing the clearance of EBV-infected B-cells during the initial three-week period. The cultures were maintained at 37 °C in a humidified incubator with 5% CO₂ and fed weekly by replacing half of the culture medium with fresh RPMI 1640 supplemented with 10% FBS, but without cyclosporine A. As the cultures expanded, the cells were gradually transferred to larger wells to accommodate growth.

## Chromatin Immunoprecipitation (ChIP) assay

ChIP experiments were conducted utilizing the EZ-Magna ChIP A/G Kit (17-10086, Millipore) as per the manufacturer's protocol. To summarize, Reh cells were subjected to cross-linking with 1% formaldehyde at ambient temperature for a duration of 10 min,

succeeded by quenching with 125 mM glycine. Following this, the cells underwent washing, after which they were lysed in lysis buffer at 4 °C for 30 min and subsequently sonicated (60% amplitude, 10-second pulse, 40-second rest on ice, repeated for 4 cycles) to produce DNA fragments ranging in length from 200 to 1,000 bp. The chromatin fragments were subjected to pre-clearing and then underwent immunoprecipitation with Protein A/G Magnetic beads that had been conjugated with anti-HIF1A antibodies or corresponding isotype-matched IgG. The DNA that had been immunoprecipitated was then purified and employed for qPCR analysis.

## Western blot assay

Cells were lysed on ice using RIPA buffer (Cell Signaling Technology, 9803) supplemented with a cocktail of protease inhibitors (Cell Signaling Technology, 5872) to prevent protein degradation. The cell lysates were then subjected to sonication using an ultrasonic processor, with three cycles of 3-second pulses to ensure efficient cell disruption. Following sonication, the lysates were centrifuged at 14,000 rpm for 15 min at 4 °C to pellet cell debris. The supernatant containing soluble proteins was carefully collected, and protein concentration was determined using the BCA Protein Assay Kit (Thermo Fisher Scientific, 23225) according to the manufacturer's instructions.

Equal amounts of protein (based on BCA assay results) were resolved on 10–12% SDS-PAGE gels (Epizyme, PG112, PG113) to separate proteins by molecular weight. The proteins were then transferred onto 0.2–0.45 µm polyvinylidene difluoride (PVDF) membranes (Millipore, IPVH00010) using a semi-dry or wet transfer system, depending on the molecular weight of the target proteins. Following the transfer, membranes were blocked in 5% skimmed milk diluted in Tris-buffered saline with Tween 20 (TBST) at room temperature for 1 h to prevent nonspecific antibody binding.

After blocking, the membranes were incubated with primary antibodies at 4 °C overnight with gentle agitation. Primary antibodies were diluted in Primary Antibody Dilution Buffer. The following day, membranes were washed with TBST and incubated with species- and isotype-matched secondary antibodies, conjugated to horseradish peroxidase (HRP), for 2 h at room temperature. After thorough washing, protein detection was carried out using enhanced chemiluminescence/ECL Western Blotting Substrate (Thermo Fisher Scientific, 34095). Signal detection and imaging were performed using the ChemiDoc Touch Imaging System (BIO-RAD).

Protein band intensities were quantified using ImageJ software, normalizing to the corresponding loading
control. A list of all primary antibodies used in the study is provided in Table S2.

## qPCR assay

Total RNA was extracted from cells using RNAiso Plus reagent (TAKARA, 108-95-2) following the manufacturer's protocol. Briefly, cells were lysed in RNAiso Plus, and the homogenates were subjected to phase separation by adding chloroform. The aqueous phase containing RNA was carefully transferred to a new tube, and RNA was precipitated with isopropanol, washed with 75% ethanol, and dissolved in RNase-free water. RNA concentration and purity were assessed using a NanoDrop spectrophotometer.

Complementary DNA (cDNA) was synthesized from 1 µg of total RNA using the PrimeScript™ RT Master Mix (TAKARA, RR036A), according to the manufacturer's instructions. The reverse transcription reaction was carried out in a thermal cycler with the following settings: 37 °C for 15 min, followed by 85 °C for 5 s to inactivate the reverse transcriptase enzyme.

qPCR was performed using the synthesized cDNA as a template. The qPCR reactions were set up using SYBR Green Master Mix (Vazyme, Q711-03) and specific primers (Table S3). Amplification and detection were carried out on a C1000 Touch Thermocycler equipped with a CFX96 Real-Time System (Bio-Rad). The qPCR conditions were as follows: initial denaturation at 95 °C for 30 s, followed by 40 cycles of denaturation at 95 °C for 10 s, and annealing/extension at 60 °C for 30 s. A melting curve analysis was performed at the end of the run to ensure the specificity of amplification.

## Gene transduction, RNAi and gene editing

Lentivirus was produced by transfecting 293FT cells with the indicated plasmid, along with packaging plasmids psPAX2 and pMD2.G, using Lipofectamine 3000 transfection reagent (Thermo Fisher Scientific, L3000-015) following the manufacturer's protocol. Briefly, 293FT cells were seeded into appropriate culture plates one day prior to transfection to achieve optimal confluency (70–80%) at the time of transfection. For each transfection, plasmid DNA and Lipofectamine 3000 were diluted in Opti-MEM (Thermo Fisher Scientific), mixed, and added dropwise to the cells. After 12 h of incubation at 37 °C with 5% CO₂, the medium was replaced with fresh complete growth medium.

At 24 h post-transfection, the virus-containing supernatant was harvested, filtered through a 0.45 µm syringe filter to remove cell debris, and concentrated using a viral concentration kit or ultracentrifugation, depending on the desired viral titer. The concentrated viral supernatant was stored at -80 °C for future use or

used immediately to transduce target cells. Target cells were transduced by incubating them with the lentiviral supernatant in the presence of 8 µg/mL polybrene (Sigma-Aldrich) to enhance transduction efficiency. Following transduction, cells expressing vectors with a puromycin-resistance marker were selected by adding 2 µg/mL puromycin (YEASEN, 60210ES72) to the culture medium for 48–72 h, ensuring the survival of successfully transduced cells.

CRISPR-Cas9-mediated knockout of *SGMS1* was performed as previously described [51]. Lentiviral vectors encoding short hairpin RNAs (shRNAs) targeting *SGMS1* and lentiviral vectors encoding *SMPD3* cDNAs were purchased from Genechem. For gene knockout and overexpression studies, cells were transduced with these lentiviral constructs, followed by puromycin selection.

Additionally, plasmids encoding point mutations in the *CASP3* gene, such as K11R, K14R, K19R, K57R, K14Q, and K14T, were also created and purchased from Genechem for functional studies of specific mutations. The detailed information on plasmids, shRNAs, and guide RNAs (gRNAs) used in this study is provided in Table S4.

## Immunoprecipitation assay

Protein extracts were incubated with protein A/G magnetic beads (Selleck, B23201) for pre-clearing to reduce nonspecific binding. This pre-incubation was carried out for 2 h at 4 °C with gentle rotation. Following pre-clearing, the whole cell lysates were divided into two equal parts. One part was incubated with a control IgG antibody (Abcam, ab172730), while the other part was incubated with an anti-CASP3 antibody (Abcam, ab32351). The samples were gently shaken overnight at 4 °C to allow specific antibody-protein interactions.

The next day, fresh protein A/G magnetic beads were added to the lysate-antibody mixtures, and the incubation continued for an additional 4 h at 4 °C with gentle rotation to capture the antibody-protein complexes. After incubation, the magnetic beads were washed five times with ice-cold lysis buffer to remove any nonspecifically bound proteins. The washing steps were crucial to ensure the specificity of the immunoprecipitation.

After the final wash, the bound protein complexes were eluted by boiling the beads in SDS-PAGE loading buffer for 5 min at 95 °C. The eluted proteins were then analyzed by western blotting to detect the immunoprecipitated proteins. The eluates were resolved on SDS-PAGE gels, transferred to PVDF membranes, and probed with the appropriate primary and secondary antibodies for detection of the proteins of interest.
## Colony-forming assay

A total of 1,000 ALL cells were resuspended in 1.5 mL of H4230 semisolid methylcellulose medium (STEM-CELL Technologies), designed to support hematopoietic colony growth. The cell suspension was gently mixed to ensure even distribution and then plated into 3-cm diameter culture dishes. To maintain optimal humidity and prevent excessive evaporation during incubation, an additional water-filled dish was placed inside the incubator.

The dishes were incubated at 37 °C in a humidified atmosphere with 5% CO₂ for 7 to 14 days. During this period, colonies formed in the methylcellulose matrix. After the incubation period, colony formation was assessed manually under a microscope. Colonies containing 40 or more cells were classified as true colonies and counted [54]. The number of colonies was recorded, and any significant morphological differences or clustering were noted during the analysis.

This assay was repeated as necessary to ensure reproducibility, and colony numbers were averaged across experiments to assess the clonogenic potential of the ALL cells.

## Cell proliferation assay

The proliferation rate of cells was measured using the hemocytometer counting method. Initially, cells were counted manually using a hemocytometer to ensure accurate seeding density. A total of 5 × 10⁵ cells were seeded into 6-well plates in triplicate to provide biological replicates. The cells were cultured under standard conditions (37 °C, 5% CO₂) and allowed to grow.

To assess proliferation, cell numbers were counted every 24 h over a defined period using an automatic cytometer (Bodboge), which ensures high precision and reduces manual counting errors. For each time point, the cell number in each well was determined in triplicate, and the average value was recorded.

The relative cell counts were calculated by normalizing the cell numbers at each time point to the baseline value (the number of cells seeded at time 0). This normalization allowed for accurate assessment of cell proliferation over time. Data were averaged across the triplicate wells to ensure reproducibility and minimize variability, and growth curves were plotted to visualize proliferation trends.

## Cell death assay

The apoptotic rate of cells was measured using the FITC Annexin V Apoptosis Detection Kit I (Abclonal, RK05875), following the manufacturer's protocol [55]. Briefly, cells were harvested, counted, and resuspended in 1X ANXA5/annexin V binding buffer at a concentration of 1 × 10⁶ cells/mL to ensure optimal staining conditions.

For each sample, 100 µL of the cell suspension (containing 1 × 10⁵ cells) was transferred to a clean tube. To label apoptotic cells, 5 µL of FITC-conjugated ANXA5 and 10 µL of propidium iodide (PI) were added to the cell suspension. The samples were gently vortexed and incubated at room temperature for 15 min in the dark to protect the fluorophores from light degradation.

Following the incubation period, 400 µL of ANXA5 binding buffer were added to each tube to bring the final volume to 500 µL. The stained cells were immediately analyzed using the Attune Nxt flow cytometer (Thermo Fisher Scientific) within 1 h to ensure accurate detection of apoptotic and necrotic cells.

Flow cytometry data were collected, and the percentages of early apoptotic cells (ANXA5 positive, PI negative) and late apoptotic/necrotic cells (ANXA5 positive, PI positive) were quantified. Data analysis was performed using flow cytometry software to assess the apoptotic rate across different samples.

## Cell cycle analysis

To assess the cell cycle profile, cells were fixed in 2 mL of 75% ice-cold ethanol at -20 °C overnight to preserve their structure and ensure efficient permeabilization for subsequent staining. After fixation, the cells were centrifuged at 1,000 g for 5 min at 4 °C to pellet the fixed cells, and the ethanol was carefully aspirated.

The cell pellets were then resuspended in 500 µL of PBS containing 25 µL of PI (Beyotime, C1052) and 10 µL of RNase A (to a final concentration of 50 µg/mL) to ensure the degradation of RNA, allowing for accurate DNA staining [56]. The samples were gently vortexed to ensure thorough mixing and incubated at room temperature for 30 min in the dark to protect the PI from light, ensuring uniform DNA staining.

Following the incubation, the stained cells were analyzed using an Attune Nxt flow cytometer (Thermo Fisher Scientific). The fluorescence emitted by PI, which intercalates with the DNA, was detected in the FL2 channel to differentiate cells in different phases of the cell cycle (G₀/G₁, S, and G₂/M).

Data from the flow cytometer were processed using FlowJo V10 software to generate cell cycle profiles. The percentage of cells in each phase of the cell cycle (G₀/G₁, S, and G₂/M) was calculated based on the PI fluorescence intensity, allowing for the quantification of cell cycle distribution.

## Cellular SM measurement

Cellular SM levels were measured from cell lysates using a Sphingomyelin Quantification Colorimetric Assay Kit (Abcam, ab287856), following the manufacturer's instructions. Briefly, 1 × 10⁶ cells were harvested, washed with PBS, and then resuspended in 0.5
mL of SM Assay Buffer. The cells were lysed by sonication on ice for 5–10 s, followed by thorough homogenization to ensure complete lysis. The cell lysates were then centrifuged at 10,000 g for 5 min at 4 °C to pellet any remaining debris, and the supernatant containing the SM was collected.

For the assay, 20 µL of the clarified supernatant was aliquoted into a microcentrifuge tube, and an additional 20 µL of SM Assay Buffer was added to dilute the sample. The mixture was briefly heated at 70 °C for 2 min to enhance solubility and remove protein aggregates. After heating, the sample was centrifuged again at room temperature for 2 min at 10,000 g to remove any remaining debris or insoluble material.

The resulting supernatant was then adjusted to a final volume of 50 µL with SM Assay Buffer, and 50 µL of the provided Reaction Mix was added to each well of a 96-well plate. The plate was incubated at 37 °C for 1 h to allow the reaction to occur. After the incubation, the absorbance was measured at 570 nm using a microplate reader (Biotek) to determine the SM concentration.

Data were analyzed by comparing the absorbance values to a standard curve generated from known SM concentrations, allowing for precise quantification of cellular SM levels in each sample.

## Lactate measurement

Cellular lactate levels were measured using the Lactate Assay Kit (Sigma-Aldrich, MAK065) according to the manufacturer's instructions. Briefly, $2 \times 10^6$ cells were harvested and washed twice with ice-cold PBS to remove any residual culture medium that could interfere with the assay. The washed cell pellet was then resuspended in 4X volumes of Lactate Assay Buffer to ensure sufficient lysis and extraction of lactate.

The cells were lysed by vortexing, and the lysates were centrifuged at maximum speed (approximately 12,000 g) for 5 min at 4 °C to remove any insoluble material or cell debris. The resulting supernatant, containing cellular lactate, was collected for further processing. To prevent interference from LDH, which could convert lactate to pyruvate, the supernatant was deproteinized using a 10-kDa molecular mass cut-off spin filter. This step ensures that only free lactate is present in the sample for quantification.

After deproteinization, 50 µL of the sample was added to a 96-well plate. Next, 50 µL of the provided Reaction Mix from the assay kit, which includes the lactate probe and lactate enzyme, was added to each well. The plate was incubated at room temperature for 30 min in the dark to allow the reaction to proceed, during which the lactate present in the samples reacts with the probe, producing a colorimetric signal.

After the incubation period, the absorbance was measured at 450 nm using a microplate reader (Biotek). The lactate concentration in each sample was calculated by comparing the absorbance values to a standard curve generated from known concentrations of lactate. This allowed for accurate quantification of lactate levels in the cell lysates.

## Seahorse assay

Metabolic profiling was assessed using the Seahorse XF24 Flux Analyzer (Agilent Technologies), as previously described [54]. Two key parameters, the extracellular acidification rate and oxygen consumption rate, were measured to evaluate glycolytic activity and mitochondrial function, respectively.

For glycolytic activity assessment, the ECAR was measured using the Seahorse XF Glycolysis Stress Test Kit (Agilent, 103020-100) following the manufacturer's instructions. Briefly, $1.5 \times 10^5$ cells per well were seeded onto Seahorse XF24 culture plates pre-coated with Cell-Tak (Corning, 354240) to ensure cell adherence. The cells were then incubated for 1 h at 37 °C in a glucose-free XF Base Medium supplemented with 2 mM glutamine in a non-CO₂ incubator to maintain a controlled environment without external CO₂ interference. The basal ECAR was first measured under glucose-deprived conditions to establish the glycolytic baseline.

To evaluate the capacity of the glycolytic pathway, the ECAR was continuously monitored as compounds were sequentially injected. The injection protocol included the following steps: Glucose (10 mM) was injected to stimulate glycolysis and measure the glycolytic response. Oligomycin (1.5 µM), an ATP synthase inhibitor, was injected to measure the glycolytic capacity by inhibiting mitochondrial ATP production and driving cells to rely entirely on glycolysis. 2-Deoxy-D-glucose (50 mM), a glycolysis inhibitor, was injected to measure non-glycolytic acidification by blocking glycolysis.

For mitochondrial function assessment, the OCR was measured over time using the Seahorse XF Cell Mito Stress Test Kit (Agilent, 103015-100). Similar to the glycolysis stress test, $1.5 \times 10^5$ cells per well were seeded onto Cell-Tak coated Seahorse XF24 plates. The cells were incubated for 1 h at 37 °C in an XF Base Medium supplemented with 10 mM glucose and 1 mM glutamine in a non-CO₂ incubator.

The mitochondrial function was evaluated by sequential injection of the following compounds: Oligomycin (2 µM) to inhibit ATP synthase and assess ATP-linked respiration. FCCP (1 µM), an uncoupler, to measure the maximum respiratory capacity by collapsing the mitochondrial membrane potential and
driving maximal electron transport. Rotenone + anti-
mycin A (0.5 µM), inhibitors of complexes I and III,
respectively, were injected to shut down mitochondrial
respiration, allowing the measurement of non-mito-
chondrial respiration.

The data from both assays were collected and analyzed
using Seahorse Wave software, enabling the calculation
of key parameters such as glycolytic capacity, glycolytic
reserve, basal respiration, and maximal respiration. This
analysis provided a comprehensive assessment of both
glycolytic and mitochondrial metabolic activity in the
cells.

Lipidomic assay

Serum samples were collected using the Ficoll den-
sity gradient centrifugation method, and lipids were
extracted following the methyl-tert-butyl ether
(MTBE) method as previously described [51, 57].
Briefly, 100 µL of serum was spiked with internal lipid
standards for quantification. The samples were homog-
enized twice using an MP homogenizer (6.0 M/S, 60 s)
in a mixture of 200 µL water and 240 µL methanol.
After homogenization, 800 µL of MTBE was added to
the mixture, which was then subjected to ultrasound
treatment for 20 min at 4 °C to enhance lipid extrac-
tion. The mixture was allowed to stand at room tem-
perature for 30 min to facilitate phase separation.

Following the extraction, the samples were centri-
fuged at 14,000×g for 15 min at 10 °C to separate the
phases, and the upper organic layer containing lipids
was collected. The lipid extracts were dried under
nitrogen gas to remove the solvent. Once dried, the
lipid residue was re-dissolved in 200 µL of isopropa-
nol: acetonitrile (1:1, v/v) and centrifuged again at
14,000×g for 15 min to remove any remaining particu-
lates. A final volume of 3 µL of the clarified solution
was injected into the ultra-high-performance liquid
chromatography (UHPLC) system for analysis. Solvent
A was acetonitrile-water (6:4, v/v) with 0.1% formic
acid and 0.1 mM ammonium formate, and solvent B
was acetonitrile-isopropanol (1:9, v/v) with 0.1% for-
mic acid and 0.1 mM ammonium formate. The initial
mobile phase was consisted of 30% solvent B at a flow
rate of 300 µL/min. It was held for 2 min, and then lin-
early increased to 100% solvent B in 23 min, followed
by equilibrating at 5% solvent B for 10 min.

Quality control (QC) samples were prepared by pooling 10-µL aliquots from each individual sample. These pooled QC samples were run initially to condition the chromatography column and subsequently every ten injections to monitor and correct for retention time shifts or variations in ion intensity during the analysis.

The lipidomic analysis was performed using a UHPLC
Nexera LC-30 A system (SHIMADZU, Japan) coupled

to a Q-Exactive Plus mass spectrometer (Thermo Scien-
tific). The resulting lipidomic datasets are presented in
Table S5.

Lactylproteomics analysis

Lactylproteomics analysis was performed at Jingjie PTM BioLab, following protocols previously described [47, 58]. For 4D label-free quantitative lactylproteomics, the preparation of proteins involved three main steps: protein extraction, trypsin digestion, and enrichment of lactyl-modified peptides.

Briefly, Reh cells were collected and lysed in a lysis buffer containing protease inhibitors and phosphatase inhibitors to prevent protein degradation and dephosphorylation. The cell lysates were sonicated on ice three times to ensure complete cell disruption, followed by centrifugation at 12,000 rpm for 10 min at 4 °C to remove insoluble debris. The protein concentration in the supernatant was measured using the BCA protein assay kit.

Next, the protein solution was reduced with 5 mM dithiothreitol (DTT) at 56 °C for 30 min to break disulfide bonds, and then alkylated with 11 mM iodoacetamide in the dark for 15 min to prevent reformation of disulfide bonds. After the alkylation step, 100 mM triethylammonium bicarbonate/TEAB was added to buffer the protein sample, which was then digested overnight with trypsin to generate peptides.

To enrich lactyl-modified peptides, the digested pep-
tides were dissolved in NETN buffer (100 mM NaCl, 1
mM EDTA, 50 mM Tris-HCl, 0.5% NP-40, pH 8.0) and
incubated with pre-washed antibody-conjugated beads
at 4 °C overnight with gentle agitation. The beads were
washed four times with NETN buffer and twice with
distilled water to remove nonspecifically bound pep-
tides. Lactyl-modified peptides were then eluted from
the beads using 0.1% trifluoroacetic acid, and the elu-
ates were combined and vacuum-dried.

For LC-MS/MS analysis, the dried peptides were
reconstituted in solvent A and separated using a
nanoElute UHPLC system (Bruker Daltonics). The
peptides were ionized using a capillary source and
analyzed with the timsTOF Pro mass spectrometer
(Bruker Daltonics). The resulting data sets for both the
proteome and lactylome are provided in Table S6 and
S7, respectively.

Bioluminescence imaging

Female NOD/ShiLtJGpt-Prkdc<sup>em26Cd52</sup>Il2rg<sup>em26Cd22</sup>/Gpt (NCG) mice, aged 4–6 weeks and weighing 18–20 g, were obtained from the Department of Laboratory Animals, Central South University. All animal experiments were conducted in accordance with the ethical standards set by the Ethics Committee of Central
South University (approval No. XMSB-2024-0138) and adhered to the National Institutes of Health guidelines for the care and use of laboratory animals. The mice were housed individually in ventilated cages under a 12-h light/dark cycle with ad libitum access to food and water.

To establish an ALL cell line-derived xenograft model, 3.0 × 10⁶ Reh cells, which were labeled with firefly luciferase for in vivo tracking, were injected into the NCG mice via the tail vein. The progression of leukemia was monitored weekly through bioluminescence imaging (BLI) using the IVIS® Spectrum imaging system (PerkinElmer). Bioluminescent signals were detected by administering 150 mg/kg of the luciferase substrate D-luciferin (YEASEN, 40902ES03) via intraperitoneal injection 10 min before imaging.

During imaging, the mice were anesthetized using isoflurane to minimize movement and stress. After anesthesia, the mice were placed into the IVIS imaging chamber, and the bioluminescent signals from the tumor-bearing areas were captured. The images were processed and analyzed using Living Image® software (PerkinElmer), allowing for precise quantification of the leukemia burden in each mouse. Imaging continued weekly until the experimental endpoint was reached.

## Micro-PET/CT imaging

Control, *SGMS1* knockout or *SMPD3*-overexpressing Reh cells (5.0 × 10⁶) were subcutaneously implanted into the left and right flanks of NCG mice to establish xenograft tumor models. Each mouse received a subcutaneous injection of Reh cells in both flanks to allow for comparative analysis of tumor growth and metabolic activity between the different cell lines.

After 15 days, micro positron emission tomography (PET) combined with computed tomography (CT) scanning (Mediso) was performed to assess tumor metabolic activity. All mice were subjected to overnight fasting (12–16 h) prior to the micro-PET/CT scan to minimize background glucose uptake and enhance tumor-specific glucose metabolism during imaging.

To evaluate the glucose uptake of the subcutaneous tumors, ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) was administered intravenously via tail vein injection at a dose of 155.1 ± 15.31 µCi [54, 59]. After injection, the mice were allowed to rest for 1 h to ensure adequate ¹⁸F-FDG uptake by the tumors. During this period, mice were kept under standard conditions with minimal disturbance.

Prior to imaging, the mice were anesthetized using vaporized isoflurane, with induction at 5% isoflurane and maintenance at 2.5% isoflurane throughout the scanning process. Anesthesia ensured that the mice remained

still during the imaging process, which was essential for acquiring high-resolution images.

PET/CT imaging was conducted using the nanoScan PET/CT system, which provided both metabolic and anatomical information. PET imaging captured the ¹⁸F-FDG uptake in the tumors, reflecting glucose metabolism, while CT provided detailed structural information about the tumor and surrounding tissues. This combined modality allowed for accurate localization and quantification of glucose uptake in the control, *SGMS1* knockout, or *SMPD3*-overexpressing tumors.

## Statistical analysis

Statistical analysis was conducted using GraphPad Prism 9.3. Depending on the experimental design, either an unpaired two-tailed Student's t-test or one-way/two-way analysis of variance (ANOVA) was applied, as specified in the figure legends. Statistical significance was determined based on the following p-values: *P* < 0.05, *P* < 0.01, *P* < 0.001, and *P* < 0.0001. Data are presented as mean ± standard deviation (SD), and the number of biological replicates for each experiment is also provided in the figure legends. For survival analysis, Kaplan-Meier curves were used to calculate overall survival (OS), and the log-rank test was employed to compare differences between groups.

## Abbreviations

ALL Acute Lymphoblastic Leukemia

AML Acute Myeloid Leukemia

ECAR Extracellular Acidification Rate

KD Knockdown

OCR Oxygen Consumption Rate

OE Overexpression

PBMCs Peripheral Blood Mononuclear Cells

PI Propidium Iodide

qPCR Quantitative Polymerase Chain Reaction

SM Sphingomyelin

TGs Triglycerides

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12943-025-02392-w.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Supplementary Material 4

Supplementary Material 5

Supplementary Material 6

Supplementary Material 7

Supplementary Material 8
## Acknowledgements

M.Y. is supported by the National Natural Science Foundation of China (81974000 and 82270185) and the Science and Technology Innovation Program of Hunan Province (2022RC3077). Z.L. is supported by the National Natural Science Foundation of China (82400213), the Natural Science Foundation of Hunan Province (2024)J6614) and the "co-PI" project from The Third Xiangya hospital of Central South University (202414). F.L. is supported by the National Natural Science Foundation of China (82303255), the China Postdoctoral Science Foundation (2023M743950), the Natural Science Foundation of Hunan Province (2023J40894) and the "co-PI" project from The Third Xiangya hospital of Central South University (202417). G.K. is supported by the Ligue contre le Cancer (équipe labellisée); Agence National de la Recherche (ANR-22-CE14-0066 VIVORUSH, ANR-23-CE44-0030 COPPERMAC, ANR-23-R4HC-0006 Ener-LIGHT); Association pour la recherche sur le cancer (ARC); Cancéropôle Ile-de-France; Fondation pour la Recherche Médicale (FRM); a donation by Elior; European Joint Programme on Rare Diseases (EJPRD) Wilsonmed; European Research Council Advanced Investigator Award (ERC-2021-ADG, Grant No. 101052444; project acronym: ICD-Cancer, project title: Immunogenic cell death (ICD) in the cancer-immune dialogue); The ERA4 Health Cardinoff Grant Ener-LIGHT; European Union Horizon 2020 research and innovation programmes Oncobiome (grant agreement number: 825410, Project Acronym: ONCOBIOME, Project title: Gut OncoMicrobiome Signatures [GOMS] associated with cancer incidence, prognosis and prediction of treatment response, Prevaling (grant agreement number 101095604, Project Acronym: PREVALING EU, project title: Biomarkers affecting the transition from cardiovascular disease to lung cancer: towards stratified interception), Neutrocure (grant agreement number 861878: Project Acronym: Neutrocure; project title: Development of "smart" amplifiers of reactive oxygen species specific to aberrant polymorphonuclear neutrophils for treatment of inflammatory and autoimmune diseases, cancer and myeloablation); National support managed by the Agence Nationale de la Recherche under the France 2030 programme (reference number 21-ESRE-0028, ESR/Equipex + Onco-Pheno-Screen); Evolution Network on Senescence in Aging (reference HF-E Einstein Network); Institut National du Cancer (INCA); Institut Universitaire de France; LabEx Immuno-Oncology ANR-18-IDEX-0001; a Cancer Research ASPIRE Award from the Mark Foundation; PAIR-Obésité INCa_1873, the RHUs Immunolife and LUCA-pi (ANR-21-RHUS-0017 and ANR-23-RHUS-0010, both dedicated to France Relance 2030); Seerave Foundation; SIRIC Cancer Research and Personalized Medicine (CARPEM, SIRIC CARPEM INCa-DGOS-Inserm-ITMO Cancer_18006 supported by Institut National du Cancer, Ministère des Solidarités et de la Santé and INSERM). This study contributes to the IdEx Université de Paris Cité ANR-18-IDEX-0001. Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union, the European Research Council or any other granting authority. Neither the European Union nor any other granting authority can be held responsible for them. D.J.K. is supported by NIH grant GM131919.

## Author contributions

Z.L., F.L., and J.L. performed the simulations, prepared the samples, and analyzed the data. Z.L. and F.L. conducted the experiments. D.T. and M.Y. designed the project. and M.Y. supervised the experiments. Z.L. and D.T. wrote the paper. R.K., G.K. and D.J.K. assisted in data interpretation, and/or edited the manuscript.

## Data availability

Lipidomic data are available at Metabolomics workbench under accession number 5432. Lactylome data are available at Metabolomics workbench under accession number 5433.

## Declarations

### Competing interests

The authors declare no competing interests.

### Competing interests

G.K. has been holding research contracts with Daiichi Sankyo, Eleor, Kaleido, Lytix Pharma, PharmaMar, Osasuna Therapeutics, Samsara Therapeutics, Sanofi, Sutro, Tollys, and Vascage. GK is on the Board of Directors of the Bristol Myers Squibb Foundation France. GK is a scientific co-founder of everImmune, Osasuna Therapeutics, Samsara Therapeutics and Therafast Bio. GK is in the scientific advisory boards of Hevolution, Institut Servier, Longevity Vision

Funds and Rejuveron Life Sciences. GK is the inventor of patents covering therapeutic targeting of aging, cancer, cystic fibrosis and metabolic disorders. Among these patents, one "Methods for weight reduction" (US11905330B1) is relevant to this study. GK's brother, Romano Kroemer, was an employee of Sanofi and now consults for Boehringer-Ingelheim. IM is a consultant of everImmune. GK's wife, Laurence Zitvogel, has held research contracts with Glaxo Smyth Kline, Incyte, Lytix, Kaleido, Innovate Pharma, Daiichi Sankyo, Pilege, Merus, Transgene, 9 m, Tusk and Roche, was on the on the Board of Directors of Transgene, is a cofounder of everImmune, and holds patents covering the treatment of cancer and the therapeutic manipulation of the microbiota. The funders had no role in the design of the study; in the writing of the manuscript, or in the decision to publish the results.

Received: 2 March 2025 / Accepted: 18 June 2025

Published online: 23 July 2025

## References

1. Malard F, Mohty M. Acute lymphoblastic leukaemia. Lancet (London England). 2020;395:1146-62. https://doi.org/10.1016/s0140-6736(19)33018-1.

2. Ni X, et al. Socioeconomic inequalities in cancer incidence and access to health services among children and adolescents in china: a cross-sectional study. Lancet (London England). 2022;400:1020-32. https://doi.org/10.1016/s0140-6736(22)01541-0.

3. Yang W, et al. Pulse therapy with vincristine and dexamethasone for childhood acute lymphoblastic leukaemia (CCCG-ALL-2015): an open-label, multicentre, randomised, phase 3, non-inferiority trial. Lancet Oncol. 2021;22:1322-32. https://doi.org/10.1016/s1470-2045(21)00328-4.

4. Faubert B, Solmonson A, DeBerardinis RJ. Metabolic reprogramming and cancer progression. Sci (New York N Y). 2020;368. https://doi.org/10.1126/scie.nce.aaw5473.

5. Fendt SM. 100 years of the Warburg effect: A cancer metabolism endeavor. Cell. 2024;187:3824-8. https://doi.org/10.1016/j.cell.2024.06.026.

6. Fu Y et al. Lipid metabolism in cancer progression and therapeutic strategies. MedComm (2020) 2, 27-59 (2021). https://doi.org/10.1002/mco2.27

7. Wang YH, et al. Cell-state-specific metabolic dependency in hematopoiesis and leukemogenesis. Cell. 2014;158:1309-23. https://doi.org/10.1016/j.cell.2014.10.048.

8. Tian H, Cronstein BN. Understanding the mechanisms of action of methotrexate: implications for the treatment of rheumatoid arthritis. Bull NYU Hosp Joint Dis. 2007;65:168-73.

9. Ueno T, et al. Cell cycle arrest and apoptosis of leukemia cells induced by L-asparaginase. Leukemia. 1997;11:1858-61. https://doi.org/10.1038/sj.leu.2400834.

10. Vitale I, et al. Apoptotic cell death in disease-Current Understanding of the NCCD 2023. Cell Death Differ. 2023;30:1097-154. https://doi.org/10.1038/s41818-023-01153-w.

11. Lin Z, et al. The lipid basis of cell death and autophagy. Autophagy. 2024;20:469-88. https://doi.org/10.1080/15548627.2023.2259732.

12. Ling VY, et al. Targeting cell cycle and apoptosis to overcome chemotherapy resistance in acute myeloid leukemia. Leukemia. 2023;37:143-53. https://doi.org/10.1038/s41375-022-01755-2.

13. Bodaar K, et al. JAK3 mutations and mitochondrial apoptosis resistance in T-cell acute lymphoblastic leukemia. Leukemia. 2022;36:1499-507. https://doi.org/10.1038/s41375-022-01558-5.

14. Konopleva M, et al. Mechanisms of apoptosis sensitivity and resistance to the BH3 mimetic ABT-737 in acute myeloid leukemia. Cancer Cell. 2006;10:375-88. https://doi.org/10.1016/j.ccr.2006.10.006.

15. Schimmer AD, et al. Functional blocks in caspase activation pathways are common in leukemia and predict patient response to induction chemotherapy. Cancer Res. 2003;63:1242-8.

16. Aid Z, et al. High caspase 3 and vulnerability to dual BCL2 family Inhibition define ETO2::GLIS2 pediatric leukemia. Leukemia. 2023;37:571-9. https://doi.org/10.1038/s41375-022-01800-0.

17. Zhou Z, et al. Diverse functions of cytochrome c in cell death and disease. Cell Death Differ. 2024;31:387-404. https://doi.org/10.1038/s41418-024-01284-8.

18. Czabotar PE, Lessene G, Strasser A, Adams JM. Control of apoptosis by the BCL-2 protein family: implications for physiology and therapy. Nat Rev Mol Cell Biol. 2014;15:49-63. https://doi.org/10.1038/nrm3722.
19. Ploumaki I, et al. Bcl-2 pathway Inhibition in solid tumors: a review of clinical trials. Clin Transl Oncol. 2023;25:1554-78. https://doi.org/10.1007/s12094-02-2-03070-9.

20. Fowler-Shorten DJ, Hellmich C, Markham M, Bowles KM, Rushworth SA. BCL-2 Inhibition in haematological malignancies: clinical application and complications. Blood Rev. 2024;65:101195. https://doi.org/10.1016/j.bfre.2024.101195.

21. Seymour JF, et al. Venetoclax-Rituximab in relapsed or refractory chronic lymphocytic leukemia. N Engl J Med. 2018;378:1107-20. https://doi.org/10.1016/j.neJMoa1713976.

22. Thol F, Döhner H, Ganser A. How I treat refractory and relapsed acute myeloid leukemia. Blood. 2024;143:11-20. https://doi.org/10.1182/blood.2023022481.

23. Zhu S, Liu J, Kang R, Yang M, Tang D. Targeting NF-kappaB-dependent alkalip-tosis for the treatment of venetoclax-resistant acute myeloid leukemia cells. Biochem Biophys Res Commun. 2021;562:55-61. https://doi.org/10.1016/j.bbrc.2021.05.049.

24. Sharma A, Boise LH, Shanmugam M. Cancer metabolism and the evasion of apoptotic cell death. Cancers (Basel). 2019;11. https://doi.org/10.3390/cancer11081144.

25. Shen S, et al. Effect of dasatinib vs Imatinib in the treatment of pediatric Phila-delphia Chromosome-Positive acute lymphoblastic leukemia: A randomized clinical trial. JAMA Oncol. 2020;6:358-66. https://doi.org/10.1001/jamaoncol.2019.5868.

26. Arguello F, et al. Two serologic markers to monitor the engraftment, growth, and treatment response of human leukemias in severe combined immuno-deficient mice. Blood. 1996;87:4325-32.

27. Nussbaum JL, Bieth R, Mandel P. Phosphatides in Myelin sheaths and reparti-tion of sphingomyelin in the brain. Nature. 1963;198:586-7. https://doi.org/10.1038/198586a0.

28. Huitema K, van den Dikkenberg J, Brouwers JF, Holthuis JC. Identification of a family of animal sphingomyelin synthases. EMBO J. 2004;23:33-44. https://doi.org/10.1038/sj.emboj.7600034.

29. Wiegmann K, Schütze S, Machleidt T, Witte D, Krönke M. Functional dichotomy of neutral and acidic sphingomyelinases in tumor necrosis factor signaling. Cell. 1994;78:1005-15. https://doi.org/10.1016/0092-8674(94)9027-5-5.

30. Jiang C, et al. Dietary sphingomyelin metabolism and roles in gut health and cognitive development. Adv Nutr (Bethesda Md). 2022;13:474-91. https://doi.org/10.1093/advances/nmab117.

31. Blaas N, Schüürmann C, Bartke N, Stahl B, Humpf HU. Structural profiling and quantification of sphingomyelin in human breast milk by HPLC-MS/MS. J Agric Food Chem. 2011;59:6018-24. https://doi.org/10.1021/jf200943n.

32. Valsecchi M, et al. Ceramide and sphingomyelin species of fibroblasts and neurons in culture. J Lipid Res. 2007;48:417-24. https://doi.org/10.1194/jlr.M600344-JLR200.

33. Shakor ABA, et al. Sphingomyelin synthase 1-generated sphingomyelin plays an important role in transferrin trafficking and cell proliferation. J Biol Chem. 2011;286:36053-62. https://doi.org/10.1074/jbc.M111.228593.

34. Liu T, et al. Glucose transporter 1-mediated glucose uptake is limiting for B-cell acute lymphoblastic leukemia anabolic metabolism and resistance to apoptosis. Cell Death Dis. 2014;5:e1470. https://doi.org/10.1038/cddis.2014.41.

35. Pan L, et al. PON2 subverts metabolic gatekeeper functions in B cells to promote leukemogenesis. Proc Natl Acad Sci USA. 2021;118. https://doi.org/10.1073/pnas.2016553118.

36. Müschen M. Metabolic gatekeepers to safeguard against autoimmunity and oncogenic B cell transformation. Nat Rev Immunol. 2019;19:337-48. https://doi.org/10.1038/s41577-019-0154-3.

37. Hui S, et al. Glucose feeds the TCA cycle via Circulating lactate. Nature. 2017;551:115-8. https://doi.org/10.1038/nature24057.

38. Liu W, et al. Lactate modulates iron metabolism by binding soluble adenylyl cyclase. Cell Metabol. 2023. https://doi.org/10.1016/j.cmet.2023.06.017.

39. Lee EE, et al. A protein kinase C phosphorylation motif in GLUT1 affects glucose transport and is mutated in GLUT1 deficiency syndrome. Mol Cell. 2015;58:845-53. https://doi.org/10.1016/j.molcel.2015.04.015.

40. Zhou L, et al. HOXA9 inhibits HIF-1α-mediated Glycolysis through interacting with CRIP2 to repress cutaneous squamous cell carcinoma development. Nat Commun. 2018;9:1480. https://doi.org/10.1038/s41467-018-03914-5.

41. DeBerardinis RJ, et al. Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc Natl Acad Sci USA. 2007;104:19345-50. https://doi.org/10.1073/pnas.0709747104.

42. Carneiro BA, El-Deiry WS. Targeting apoptosis in cancer therapy. Nat Rev Clin Oncol. 2020;17:395-417. https://doi.org/10.1038/s41571-020-0341-y.

43. Valentin R, Grabow S, Davids MS. The rise of apoptosis: targeting apoptosis in hematologic malignancies. Blood. 2018;132:1248-64. https://doi.org/10.1182/blood-2018-02-791350.

44. Rodriguez-Zabala M, et al. Combined GLUT1 and OXPHOS Inhibition elimi-nates acute myeloid leukemia cells by restraining their metabolic plasticity. Blood Adv. 2023. https://doi.org/10.1182/bloodadvances.2023009967.

45. Chen Z et al. Peptide-appended Nanosonosensizers Targeting Tumor Glycolysis for Synergistic Sonodynamic-Immunometabolic Therapy of Spinal-Metastasized Tumor. Advanced materials (Deerfield Beach, Fla.), e2304246 (2023). https://doi.org/10.1002/adma.202304246

46. Zhang D, et al. Metabolic regulation of gene expression by histone lacty-lation. Nature. 2019;574:575-80. https://doi.org/10.1038/s41586-019-1678-1.

47. Chen H, et al. NBS1 lactylation is required for efficient DNA repair and chemo-therapy resistance. Nature. 2024;631:663-9. https://doi.org/10.1038/s41586-024-07620-9.

48. Reed JC, Doctor KS, Godzik A. The domains of apoptosis: a genomics per-spective. Science's STKE: signal Transduct Knowl Environ. 2004;2004(re9). http://doi.org/10.1126/stke.2392004re9.

49. Zhou J, et al. Astrocytic LRP1 enables mitochondria transfer to neurons and mitigates brain ischemic stroke by suppressing ARF1 lactylation. Cell Metabol. 2024. https://doi.org/10.1016/j.cmet.2024.05.016.

50. Gao R, et al. Mitochondrial pyruvate carrier 1 regulates fatty acid synthase lactylation and mediates treatment of nonalcoholic fatty liver disease. Hepa-tology (Baltimore MD). 2023;78:1800-15. https://doi.org/10.1097/hep.00000000000000279.

51. Lin Z, et al. The lipid flippase SLC47A1 blocks metabolic vulnerability to fer-roptosis. Nat Commun. 2022;13:7965. https://doi.org/10.1038/s41467-022-35707-2.

52. Yamaoka S, Miyaji M, Kitano T, Umehara H, Okazaki T. Expression cloning of a human cDNA restoring sphingomyelin synthesis and cell growth in sphingo-myelin synthase-defective lymphoid cells. J Biol Chem. 2004;279:18688-93. https://doi.org/10.1074/jbc.M401205200.

53. Nagy N. Establishment of EBV-Infected lymphoblastoid cell lines. Methods in molecular biology. (Clifton N J). 2017;1532:57-64. https://doi.org/10.1007/978-1-4939-6655-4_3.

54. Lu MJ, et al. SLC25A51 decouples the mitochondrial NAD(+)/NADH ratio to control proliferation of AML cells. Cell Metabol. 2024;36:808-e821806. https://doi.org/10.1016/j.cmet.2024.01.013.

55. Song X, et al. Cytosolic cytochrome c represses ferroptosis. Cell Metab. 2025. https://doi.org/10.1016/j.cmet.2025.03.014.

56. Ji X, et al. Sphingolipid metabolism controls mammalian heart regeneration. Cell Metabol. 2024;36:839-e856838. https://doi.org/10.1016/j.cmet.2024.01.017.

57. Chen F, et al. CYP51A1 drives resistance to pH-dependent cell death in pancreatic cancer. Nat Commun. 2025;16:2278. https://doi.org/10.1038/s41467-025-57583-2.

58. Yan L, et al. Physical exercise mediates cortical synaptic protein lactylation to improve stress resilience. Cell Metabol. 2024. https://doi.org/10.1016/j.cmet.2024.07.018.

59. Sun H, et al. Sustained remission of type 2 diabetes in rodents by centrally administered fibroblast growth factor 4. Cell Metabol. 2023;35:1022-1037. e1026 https://doi.org/10.1016/j.cmet.2023.04.018.

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