Epigenetic profiling of hematopoietic stem cells from male mice identifies KDR and PU.1 as regulators of aging transcriptome and caloric restriction response

Domain: chemistry
Published: 2026-02-20
Editorial outcome: major_revision

Abstract
Caloric restriction (CR) provides anti-aging benefits but has also been reported to be associated with reduced immune function, and how hematopoietic stem cells (HSCs) potentially contribute to this decline remains unclear. Using life-long and short-term CR in male mice, we found reducing the energy supply decreases total white blood cell production and shifts hematopoiesis towards myeloid and thrombo-erythroid lineages, prioritizing cells essential for survival (red blood cells, platelets, innate immune cells) over adaptive immunity. HSCs under CR enter cell cycle to support myeloid differentiation rather than self-renewal. Lifelong CR inhibits age-associated transcriptome changes in HSCs, though age-associated profiles appear shortly after ad libitum feeding. Epigenetic profiling identified KDR as a key CR response regulator, and Kdr knockdown in aged HSCs recapitulated the youthful transcriptome of lifelong CR HSCs. Finally, we show PU.1 acts as an intracellular regulator of CR response, controlling HSC self-renewal and differentiation through increased target gene binding under CR conditions.

Introduction
Caloric restriction (CR) is defined as a significant reduction in caloric intake without malnutrition (commonly applied as 20%−40% reduction) and has been reported to extend lifespan and delay age-associated diseases in multiple species1. Experimental paradigms using different levels of restriction, timing of the restriction, and composition of the diets show that each of these parameters can contribute to some extent of positive benefits, but overall dietary restriction is associated with mostly beneficial effects on aging phenotypes2. C57BL/6J male mice on 40% CR showed increased median and maximum lifespan, whereas this lifespan extension was not seen in female C57BL/63. 40% CR on male C57BL/6J mice also improved metabolic efficiency4 and 30% reduction in these male mice was associated with more robust metabolic flexibility and carbohydrate utilization5. While this level of restriction in C57BL/6 males mitigated the loss of splenic CD8+ T-cells in aged mice, with increased cycling after stimulation (compared to old ad libitum fed)6, CR has also been associated with suppressed immune response7–9.

Aging of the hematopoietic system (which is responsible for immune response) is associated with decreases in lymphoid potential, bias toward myeloid cell generation, increased inflammation, and increased susceptibility to infection and disease. As tissue-specific stem cells are responsible for the maintenance and repair of their system throughout life, age-associated changes in the hematopoietic stem cell (HSC) compartment are thought to contribute to these hematopoietic alterations. While CR studies have shown positive benefits on various other adult tissue-specific stem cell compartments such as intestinal stem cells, skeletal muscle stem cells, and neural stem and progenitor cells during aging10–13, manuscripts examining the effect of dietary restriction on HSCs have conflicting findings9,14,15. Variations in these studies included the duration of caloric restriction (9-21 months), the level of restriction (30%-40%), and potentially the sex of the animals.

As positive life- and health-span benefits have been consistently reported at 40% CR in C57BL/6J males, we sought to investigate this level of lifelong dietary restriction on hematopoiesis, with a focus on the aged HSC compartment’s response to this sustained stress to understand the molecular signatures that may arise contributing to global effects on the blood system.

Results
Reduced white blood cell production in lifelong CR male mice
To investigate the impact of CR on the hematopoietic system during aging, C57BL/6J male mice were subjected to lifelong 40% CR (60% of ad libitum diet) starting at the age of 3–4 months (Fig. 1a). Additionally, short-term CR (4 weeks) was performed to examine the effects of CR without the influence of aging (Supplementary Fig. 1a). Complete blood count analysis of peripheral blood (PB) isolated from old mice (24–26 months) showed lifelong CR (OCR, Old Caloric Restricted) resulted in a significant reduction in white blood cell (WBC) production compared to lifelong ad libitum access to chow (OAL, Old Ad Libitum) (Fig. 1b). We also evaluated the response to 4 weeks ad lib access to food after lifelong CR (OCRRF, Old Caloric Restricted Re-Fed) and found WBC production restored to levels similar to the OAL mice (Fig. 1b). Lifelong CR affected the production of both myeloid (neutrophil) and lymphoid (lymphocyte) PB cells, with a larger reduction in lymphocyte numbers (Fig. 1c, d). Consistently, the weight of the spleen, an organ that produces lymphocytes, was significantly reduced in OCR mice and recovered after refeeding (Fig. 1e). These changes in PB parameters were mirrored in the short-term CR (YCR, Young Caloric Restricted) results (Supplementary Fig. 2a–d). Age associated changes were also observed, with reduction in red blood cells and increase in platelets in ad lib fed old mice compared to ad lib fed young mice (Fig. 1f, g). However, the number of red blood cells and platelets in PB were maintained under CR conditions (Fig. 1h, i).

Biased differentiation towards myeloid cells in lifelong CR mice
To further examine changes in the frequency of specific blood cell types, we analyzed blood cell frequencies using flow cytometry (Supplementary Fig. 1b). The results showed an increase in Mac-1+ myeloid cells (Fig. 1j and Supplementary Fig. 2e), particularly granulocytes (Fig. 1k and Supplementary Fig. 2f), and a decrease in B cells in both lifelong and short-term CR mice (Fig. 1l and Supplementary Fig. 2g). The frequency of T cells was largely maintained, although the absolute number of T cells was reduced under both short-term and lifelong CR (Supplementary Fig. 2h, i). In contrast, CR decreased both the absolute number and frequency of B cells (Fig. 1l, Supplementary Fig. 2g and j), suggesting that B cells are more selectively suppressed than T cells under CR conditions. Overall, the mice maintained a myeloid-biased PB composition under CR conditions (Supplementary Fig. 2k). Analysis of whole bone marrow (WBM) cells revealed an increased frequency of megakaryocyte-erythroid progenitors (MEP) in CR mice (Fig. 1m, and Supplementary Fig. 1c and 3a), consistent with the maintained red blood cell and platelet production in PB (Fig. 1h, i). The frequencies of common myeloid progenitors (CMP) and granulocyte-monocyte progenitors (GMP) remained unchanged under CR conditions (Supplementary Fig. 3b, c). However, the frequency of common lymphoid progenitors (CLP) was reduced (Fig. 1n and Supplementary Fig. 3d), along with decreased frequencies of Lin-Sca-1+c-Kit+ (LSK) cells and MPP Flk2+ cells (lymphoid-biased multipotent progenitors) in CR mice (Fig. 1o, p and Supplementary Fig. 3e, f), consistent with the reduced lymphoid output seen in the PB (Supplementary Fig. 2k). Interestingly, lifelong CR ameliorated age-dependent HSC expansion (Fig. 1q and Supplementary Fig. 3g), a well-defined HSC aging phenotype16. CR also altered HSC composition, with a decreased frequency of CD150high (myeloid-biased) HSCs and maintenance of CD150low (balanced lineage output) HSCs (Supplementary Fig. 3h). Upon refeeding, the frequency of CD150high HSCs returned to ad lib levels, whereas CD150low HSC frequency decreased (Supplementary Fig. 3h). These results suggest that under CR, myeloid-biased CD150high HSCs are preferentially consumed to sustain myeloid differentiation, while CD150low HSCs contribute to the burst of lymphoid differentiation upon refeeding.

Importantly, overall bone marrow cellularity remained largely unchanged after CR (Supplementary Fig. 3i), in contrast to the peripheral blood, where total white blood cell counts were significantly reduced under CR (Supplementary Fig. 2a). This distinction highlights a key difference between the bone marrow and peripheral blood in their responses to CR. Together, our data indicate that myeloid differentiation is preferentially maintained, while lymphoid differentiation is repressed under CR conditions, despite an overall reduction in white blood cell output (Supplementary Fig. 3j). These results suggest that, under CR stress, male mice modify their differentiation programs to prioritize the production of cell types essential for survival.

CR drives HSCs into cell cycle to maintain myeloid differentiation rather than self-renewal
We then performed RNA-seq on HSCs (LSK CD34⁻ Flk2⁻ CD150⁺) isolated from both lifelong and short-term CR mice to investigate how CR regulates HSC function. Principal component analysis (PCA) showed that samples from the same treatment group clustered together, and samples from the refeeding group were closer to the ad lib group (Fig. 2a and Supplementary Fig. 4a). Differentially expressed gene (DEG) analysis identified similar number of up- and down-regulated genes (up 897 vs down 743) in OCR HSCs (Fig. 2b, and Supplementary Data 1). However, although the overall number of DEGs in YCR HSCs was lower compared to OCR HSCs, we observed a significant bias toward down-regulation of transcripts in YCR versus YAL (31 up vs 105 down of 136 DEGs; two-sided exact binomial test, p=1.33×10-10) (Supplementary Fig. 4b, Supplementary Data 1). The transcriptome largely reverted to the levels of similarly aged ad lib HSCs after refeeding for both YCR and OCR groups (Fig. 2b and Supplementary Fig. 4b). Surprisingly, pathway analysis revealed that upregulated genes in OCR HSCs were significantly enriched in cell cycle related pathways (Fig. 2c and Supplementary Fig. 4c). Gene Set Enrichment Analysis (GSEA) confirmed that OCR HSCs were enriched for the expression of cell cycle and checkpoint related genes (Fig. 2d). Analysis of short-term CR data showed that YCR HSCs were also enriched for the expression of cell cycle and checkpoint related genes, although to a lesser extent compared to OCR HSCs (Supplementary Fig. 4d). Direct examination of cell cycle (including Mki67, Cdk1, and Ccna2) and checkpoint (including Chek1, Chek2, and Brca1) related transcripts confirmed their upregulation in OCR HSCs (Fig. 2e, f and Supplementary Fig. 4e, f). These results were unexpected as CR led to an overall reduction in white blood cell production and reduced age-dependent HSC expansion (Fig. 1b, q). To confirm HSCs were indeed cycling more under CR conditions, we restricted young Ki67-RFP reporter mice with a CR diet for three months then examined the ratio of Ki67+ cycling HSCs (Supplementary Fig. 4g). The result showed that CR led to an ~2-fold increase in the ratio of actively cycling HSCs compared to ad lib feeding (Supplementary Fig. 4h). We then investigated whether the increased cycling HSCs were involved in self-renewal or differentiation. CellRadar (software to visualize cell-type gene enrichment based on normal mouse or human hematopoiesis expression) analysis of up- and down-regulated genes in OCR HSCs showed downregulated genes were mainly enriched for long-term HSC (LT-HSC) related genes, while the upregulated genes were enriched for short-term HSC (ST-HSC), granulocyte progenitor (pre-GM and GMP), and erythrocyte progenitors (CFUE, ProE) (Fig. 2g). In short-term CR mice, the downregulated genes in YCR HSC were also mainly LT-HSC related genes, and the upregulated genes were specifically enriched in ST-HSC and pre-GM (Supplementary Fig. 4i). These results suggest under CR stress, HSCs enter cell cycle and preferentially express transcripts directing differentiation over self-renewal. Together, our data suggest that CR drives HSCs into the cell cycle to maintain myeloid differentiation rather than self-renewal.

Lifelong CR preserves a youthful transcriptome in aged HSCs
We next investigated whether CR explicitly affects established age-associated changes in the HSC transcriptome. Towards this, we used the previously defined HSC aging signature (AS) gene list, a consensus group of differentially expressed transcripts identified by at least four independent studies17. Our results showed that lifelong CR significantly reduced the expression of AS genes upregulated in aged HSCs and significantly increased the expression of AS genes downregulated in aged HSCs (Fig. 3a and Supplementary Data 2). GSEA analysis confirmed that OCR HSCs exhibited lower expression of upregulated AS genes and were enriched for higher expression of downregulated AS genes (Fig. 3b). PCA revealed that lifelong CR shifted the transcriptome toward the young HSC state along PC1 (Supplementary Fig. 5a). Unsupervised clustering analysis showed that YAL and YCR grouped together, while OAL and OCR formed a separate cluster (Supplementary Fig. 5b). Nonetheless, the transcriptomic profile of OCR HSCs clearly shifted toward-but did not fully converge with-the YAL state, indicating a partial transcriptional reversion under CR. Examining the AS genes which were differentially expressed in the opposite direction in OCR compared to OAL HSCs showed that nearly half of the AS genes exhibited a significant mitigation in expression changes under CR conditions (Fig. 3c). This included Clu and Osmr, which are upregulated HSC aging signature genes (Fig. 3d). Clu (clusterin) encodes a secreted glycoprotein that drives myeloid bias in aged HSCs by regulating mitochondrial function18, while Osmr (oncostatin M receptor) encodes a cytokine receptor involved in the maintenance of erythroid and megakaryocyte progenitors19. In contrast, Ect2 and Mcm5 are downregulated HSC aging signature genes (Fig. 3e). Ect2 (epithelial cell transforming 2) encodes a guanine nucleotide exchange factor required for cytokinesis and cell cycle progression, whereas Mcm5 (minichromosome maintenance complex component 5) encodes a subunit of the MCM helicase complex essential for DNA replication initiation and serves as a marker of proliferative potential. Additionally, elevated expression of transposable elements (TEs) has been reported in aged HSCs20. In OCR HSCs compared to OAL, we observed mostly downregulation of TE expression (Fig. 3f), again suggesting that lifelong CR helps preserve a more youthful transcriptome in HSCs.

Epigenetic repression of Kdr and Bmpr1a expression contributes to the improved transcriptome in OCR HSCs
Given the global changes to transcription, we wanted to screen for altered epigenetic regulation underlying the mitigated HSC aging signature under CR conditions. Most literature focuses on histone acetylation in CR conditions as sirtuin expression is typically altered. However, we did not observe changes in the sirtuin family expression, but we found significant changes in other regulators of histone modifications, particularly those involved with histone methylation (Supplementary Data 3). Thus, we performed histone ChIP-seq on OAL and OCR HSCs on two histone methylation marks H3K4me3 and H3K27me3 which are key regulators of transcription. Though stem cell numbers are limiting in ChIP assays, QC analysis of our samples showed high correlation between replicates for both H3K4me3 (permissive mark) and H3K27me3 (repressive mark) antibodies within treatment groups (Supplementary Fig. 6a). Global comparisons of all histone peaks in OAL and OCR HSCs revealed a general trend of reduced histone modification levels for both H3K4me3 and H3K27me3, with a more pronounced loss for H3K4me3 in the OCR (Fig. 4a), there were also decreased levels of both H3K4me3 and H3K27me3 at gene promoters (Supplementary Fig. 6b) in OCR HSCs compared to the OAL. To identify the candidate loci that contribute to altered transcription, we analyzed the most differentially modified promoter regions, selecting loci with the greatest relative increase or decrease in ratio of histone peaks in OAL and OCR HSCs (Fig. 4a). Consistent with the concept that H3K4me3 positively corelates with gene expression and H3K27me3 negatively correlates with gene expression, the genes that were in the top 10% of relative H3K4me3 levels between OCR and OAL (H3K4me3-high) and the loci with the lowest 10% of relative H3K27me3 levels (H3K27me-low) had higher expression levels in OCR HSCs (Supplementary Fig. 6c). Conversely, the H3K4me3-low and H3K27me3-high related genes had lower expression levels in OCR HSCs (Supplementary Fig. 6c). Given that the expression of AS genes trends toward a younger state in OCR HSCs (Fig. 3a, b), we examined whether changes in histone modifications in OCR HSCs are associated with the mitigation of AS gene expression changes in the OCR HSCs. For the 180 upregulated AS genes in aged HSCs17, 61 genes were associated with relatively lower H3K4me3 in OCR, and 17 genes overlapped with H3K27me3-high genes in OCR HSCs (Supplementary Fig. 6d). For 39 downregulated AS gene17, 7 genes overlapped with H3K4me3-high genes, and 1 gene overlapped with H3K27me3-low genes in OCR HSCs (Supplementary Fig. 6d). We focused on the 17 AS genes that showed increased levels of H3K27me3 in the OCR HSCs, as these H3K27me3 regions went against the global trend of overall decreased H3K27me3 level suggesting concerted mechanisms to increase the repression at these locations. Further, aged HSCs have global decreases in H3K27me3 compared to young HSCs (manuscript under review), and thus this increase of H3K27me3 might be reflective of a younger epigenetic status. These 17 AS genes also had lower levels of H3K4me3, and almost all showed reduced gene expression (Fig. 4b). We focused on Kdr and Bmpr1a which showed the highest fold increase in H3K27me3 and also had robust change in gene expression. Increased levels of H3K27me3 and decreased levels of H3K4me3 were at the promoters of Kdr and Bmpr1a, associated with reduced gene expression in the OCR HSCs compared to the OAL (Fig. 4c, d), suggesting this altered epigenetic regulation contributed to the changes in expression. As both Kdr and Bmpr1a encode cell-surface receptors involved in multiple signaling pathways, the regulatory genes have potential to globally impact gene transcription, and we felt these would be strong candidates for contributing to altered transcriptional profiles of the OCR HSCs. Additionally, Bmp4, the gene coding a ligand for Bmpr1a, also showed similar histone modification changes and decreased gene expression in OCR HSCs (Supplementary Fig. 6e).

To explore the role of KDR and BMPR1A in aged HSCs, we explored if knockdown of Kdr and Bmpr1a in aged HSCs could recapitulate the improved transcriptome signature observed in OCR HSCs. To investigate the effects of combinational and individual knockdowns, we infected purified, aged HSCs with both GFP-labeled Kdr shRNA virus (shRNA5) and mCherry-labeled Bmpr1a shRNA virus (shRNA1, 2), or both GFP- and mCherry-labeled scramble shRNAs. These cells were then transplanted into lethally irradiated recipient mice with a radioprotective dose of myeloid progenitor cells (Fig. 4e and Supplementary Fig. 6f, g). This transplant paradigm allowed for continuous knockdown of these transcripts in HSCs, not feasible in vitro. At the end of the transplantation period, Kdr and Bmpr1a double knockdown HSCs, as well as Kdr single knockdown HSCs, were sorted for RNA-seq analysis (Fig. 4e). Scramble shRNA infected HSCs (GFP+mCherry+, or GFP+) were purified from transplanted mice as controls. GSEA analysis of RNA-seq data revealed that both the Kdr and Bmpr1a double knockdown, as well as the Kdr single knockdown resulted in decreased expression of upregulated AS genes (Supplementary Fig. 6h, left). Conversely, these knockdowns were enriched for increased expression of downregulated AS genes (Supplementary Fig. 6h, right). The effect was more pronounced with the double knockdown, as indicated by a higher enrichment score (Supplementary Fig. 6h). These findings suggest that knockdown of Kdr and Bmpr1a can shift the aging transcriptome of HSCs towards a more youthful state and imply that epigenetic repression of Kdr and Bmpr1a expression contributes to the improved transcriptome in OCR HSCs.

Cell surface receptor Kdr is a regulator of the CR response in OCR HSCs
When performing GSEA analysis with the DEGs identified in OCR HSCs, we observed that while both the Kdr and Bmpr1a double knockdown and the Kdr single knockdown can mimic the CR-associated gene downregulation in OCR HSCs, only the Kdr single knockdown recapitulated the CR-associated gene upregulation (Fig. 4f). A direct comparison of DEG expression levels showed that the DEGs downregulated in OCR HSCs were significantly downregulated in both the double and single knockdown HSCs (Fig. 4g, upper panel). However, DEGs upregulated in OCR HSCs were only significantly upregulated in the Kdr single knockdown HSCs (Fig. 4g, lower panel).

Under CR stress, HSCs are driven into cell cycle and maintain circulating myeloid cells (Fig. 2). To investigate whether Kdr plays a role in regulating the response of HSCs to CR, we performed DEG analysis using RNA-seq data from control (GFP-Scramble) and Kdr knockdown (Kdr-shRNA5) HSCs (Supplementary Fig. 6i). Pathway analysis indicated that DEGs in the Kdr knockdown HSCs were involved in cell cycle related pathways (Fig. 4h and Supplementary Fig. 6j). CellRadar analysis showed a clear overlap between expression plots generated with DEGs identified in OCR HSCs compared to OAL HSCs and those from Kdr knockdown old HSCs compared to scramble control old HSCs (Fig. 4i). In Kdr knockdown HSCs, upregulated DEGs were enriched in genes related to ST-HSC and myeloid/erythroid cells (preGM, GMP, nucleated erythrocytes), while downregulated DEGs were enriched in LT-HSC and monocyte related genes (Fig. 4i). These findings suggest that Kdr knockdown induces a transcriptome that favors cell cycle progression and myeloid differentiation while inhibiting HSC self-renewal, mimicking the effects of CR. This indicates that Kdr not only contributes to the improved aging transcriptome in HSC under CR conditions but also plays a crucial role in regulating the CR response in HSCs.

PU.1 regulates the response of HSCs to CR by increased binding to its target genes
To identify potential intracellular regulators of the CR response we performed ATAC-seq on OAL, OCR, and OCRRF HSCs. PCA analysis revealed that samples from the same treatment group clustered together (Supplementary Fig. 7a). Differentially accessible region (DAR) analysis identified a similar number of DARs with increased (663) or decreased (673) accessibility in OCR HSCs (Fig. 5a and Supplementary Fig. 7b). Notably, the changes in chromatin accessibility were largely reversible, with accessibility in OCRRF HSCs returning towards the levels observed in OAL HSCs (Fig. 5a). Transcription factor motif analysis of these DARs highlighted a significant enrichment of PU.1 and SPIB binding motifs in regions with increased accessibility in OCR HSCs (Fig. 5b). Given the similarity in PU.1 and SPIB binding motifs and the higher expression levels of Spi1 (the gene encoding PU.1) compared to SpiB in HSCs (Supplementary Fig. 7c), our results suggested PU.1 may be a key regulator of the CR response in HSCs.

To test this, we conducted PU.1 ChIP-seq on OAL, OCR, and OCRRF HSCs. Genome browser viewing of our data confirmed the reported self-regulation of PU.121, with binding of PU.1 upstream of its promoter (Supplementary Fig. 7d). Consistent with the motif enrichment analysis, we observed increased PU.1 binding in OCR HSCs at DARs with a PU.1 binding motif, which returned to OAL levels upon refeeding (Fig. 5c). Genome-wide comparison of PU.1 binding demonstrated an overall trend of increased binding in OCR HSCs compared to OAL HSCs, which normalized after refeeding (Fig. 5d). Consistent with this, we identified more peaks with increased PU.1 binding (1942) rather than decreased binding (236) in OCR HSCs (FC>1.5) (Fig. 5e). Comparisons of genes associated with increased PU.1 binding and DEGs in OCR HSCs showed a balanced number of upregulated and downregulated DEGs with increased PU.1 binding (Fig. 5f), consistent with the reported activity of PU.1 functioning as both an activator and repressor of gene transcription22. Pathway analysis of these overlapping DEGs revealed an enrichment of cell cycle related pathways among the upregulated DEGs in OCR HSCs (Fig. 5g and Supplementary Fig. 7e), indicating that increased PU.1 binding is involved in regulating the cell cycle response to CR in HSCs. CellRadar analysis further showed that the upregulated overlapping DEGs in OCR HSCs were enriched for erythroid related genes (nucleated erythrocyte), while the downregulated overlapping DEGs were enriched for LT-HSC related genes (Fig. 5h). This suggests that increased PU.1 binding in OCR HSCs is also involved in regulating differentiation choices and inhibiting HSC self-renewal. To investigate potential binding partners of PU.1, we performed transcription factor motif enrichment analysis using HOMER, examining sequences within ±200 bp of the centers of PU.1 peaks associated with genes either upregulated or downregulated after CR. Both de novo and known motif analyses were conducted. As expected, the PU.1 motif was the most significantly enriched motif in both up- and down regulated gene-associated peaks (Supplementary Fig. 8a), confirming the specificity and robustness of our dataset. Among known motifs, we observed similar enrichment of ETS family transcription factors-including ELF4, ETS1, FLI1, GABPA, and ERG-across both up- and down regulated gene-associated peaks (Supplementary Fig. 8a, left). These factors share the ETS DNA-binding domain and are known to act as either activators or repressors in a context-dependent manner, consistent with PU.1’s dual regulatory roles. Interestingly, de novo motif analysis revealed modest enrichment of distinct motifs between the two gene sets: NFIA and HMGA1 motifs were more enriched in PU.1 peaks linked to upregulated genes, whereas ZFP335 and MYBL2 motifs were more enriched in those associated with downregulated genes, although their enrichment p-values were substantially higher than that of PU.1 (Supplementary Fig. 8a, right). These results suggest that PU.1 may engage different co-regulatory partners in distinct transcriptional contexts. Alternatively, PU.1’s dual activity may involve sequence-independent mechanisms, such as recruitment of different chromatin modifiers to regulate transcription. To further explore the potential link between KDR and PU.1 pathways, we analyzed whether genes differentially expressed following Kdr knockdown were regulated by PU.1. ~50% of these DEGs had PU.1 binding within 50kb of the transcription start site (TSS) (Supplementary Fig. 8b), indicating substantial potential regulatory overlap. Moreover, chromatin profiling revealed increased PU.1 binding after CR (Supplementary Fig. 8c), suggesting that PU.1 contributes to the transcriptional regulation of KDR-responsive genes under CR conditions. We also found that both CR and Kdr knockdown led to a modest increase in Spi1 expression (Supplementary Fig. 8d), and ChIP-seq data showed PU.1 occupancy near the Kdr promoter (Supplementary Fig. 8e), supporting a possible reciprocal regulatory interaction between KDR signaling and PU.1 activity. In summary, our data support a role for PU.1 as an intracellular regulator of the CR response in HSCs and suggest that it may cooperate with KDR signaling to orchestrate the corresponding transcriptional program under CR conditions.

Methods
Animals
Life-long ad lib feeding and CR mice (male, C57BL/6, 24-26 months) were acquired from the NIA Aged Rodent Colony. Young C57BL/6 (CD45.2) male mice (3-4 months), Ki67-RFP reporter mice, and young female transplant recipient B6.SJL-Ptprca Pepcb/BoyJ (CD45.1) mice were obtained from The Jackson Laboratory. Lifelong CR mice were subjected to 40% CR (60% of AL diet) starting at the age of 3-4 months. Short-term CR mice (3-4 months) were subjected to 20% CR for the first week, then 40% CR for another 3 weeks (C57BL/6) or 3 months (Ki67-RFP reporter mice). For refeeding, CR mice were returned to ad lib feeding for 4 weeks following either lifelong or short-term CR. All CR regimens were applied continuously, with the same restricted feeding maintained on both weekdays and weekends. Mice were maintained under a 12 h light/12 h dark cycle, at an ambient temperature of 69–75°F, with relative humidity of 30–70%. All animal experiments were performed under protocols approved by NIA Institutional Animal Care and Use Committees (469-TGB-2025).

Antibodies
Flow cytometry. The following antibodies are from Biolegend: Biotin anti-TER119 (Cat# 116204, 1:100 dilution), Biotin anti-B220 (Cat# 103204, 1:100 dilution), Biotin anti-CD3 (Cat# 100244, 1:100 dilution), Biotin anti-Mac-1 (Cat# 101204, 1:100 dilution), Biotin anti-Gr-1 (Cat# 108404, 1:100 dilution), PB anti-TER119 (Cat# 116232, 1:200 dilution), PB anti-B220 (Cat# 103227, 1:200 dilution), PB anti-Mac-1 (Cat# 101224, 1:200 dilution), PB anti-CD3 (Cat# 100214, 1:200 dilution), BV421 anti-Gr-1 (Cat# 108445, 1:200 dilution), BV421 anti-IL7Rα (Cat# 135027, 1:200 dilution), APC/Cy7 anti-Sca-1 (Cat# 108126, 1:200 dilution), PE anti-c-Kit (Cat# 105808, 1:200 dilution), BV421 anti-c-Kit (Cat# 105827, 1:200 dilution), APC anti-Flk2 (Cat# 135310, 1:50 dilution), PE/Cy7 anti-CD150 (Cat# 115914, 1:200 dilution), APC anti-CD48 (Cat# 103412, 1:200 dilution), APC anti-CD45.1 (Cat# 110714, 1:200 dilution), PerCP/Cy5.5 anti-CD45.1 (Cat# 110728, 1:100 dilution), PB anti-CD45.2 (Cat# 109820, 1:200 dilution), PerCP/Cy5.5 anti-TER119 (Cat# 116228, 1:200 dilution), APC/Cy7 anti-B220 (Cat# 103224, 1:200 dilution), PE anti-CD3 (Cat# 100206, 1:200 dilution), PE/Cy7 anti-Mac-1 (Cat# 101216, 1:200 dilution), FITC anti-Gr-1 (Cat# 108406, 1:200 dilution), BV510 anti-Gr-1 (Cat# 108457, 1:200 dilution). FITC anti-CD34 (Cat# 11-0341-85, 1:50 dilution) and PerCP/Cy5.5 anti-FcγRα (Cat# 45-0161-82, 1:100 dilution) are from Thermo Fischer.

ChIP-seq. Anti-H3K4me3 (Sigma, Cat# 07-473, 1.5 ul per IP), anti-H3K27me3 (Diagenode, Cat# C15410195, 1.5 ul per IP), anti-PU.1 (ThermoFischer, Cat# MA5-15064, 9 ul per IP), anti-PU.1 (abcam, Cat# ab227835, 9 ul per IP).

Peripheral blood analysis
Complete blood count (CBC) data were collected using Hemavet 950FS. Frequencies of PB cell populations were generated using flow cytometry after ACK treatment and staining with Ter119, B220, Mac-1, CD3, and Gr-1 for CR experiments; staining with CD45.1, CD45.2, Ter119, B220, Mac-1, CD3, and Gr-1 for transplantation experiments.

Whole bone marrow analysis
Bone marrow cells were treated with ACK and then stained with antibodies. For staining WBM cells from ad lib and CR mice: lineage (Ter119, B220, Mac-1, CD3, Gr-1), Sca-1, c-Kit, CD34, Flk2, CD150, FcγRα. For staining WBM cells from post-transplant recipient mice: lineage (Ter119, B220, Mac-1, CD3, Gr-1), Sca-1, c-Kit, CD34, Flk2, CD150, CD45.1, CD45.2.

HSC purification
Bone marrow cells were collected from crushed femurs, tibias, pelvises, humeri and filtered through 100µm filters. Isolated BM cells were positively selected with PE c-Kit antibody and EasySep™ PE Positive Selection Kit II (17684, STEMCELL). For CR experiments, enriched cells were stained with antibodies against lineage (Ter119, B220, Mac-1, CD3, Gr-1, IL-7Rα), Sca-1, c-Kit, CD34, Flk2, CD150. For transplantation experiments, enriched cells were stained with antibodies against lineage (Ter119, B220, Mac-1, CD3, Gr-1), Sca-1, c-Kit, CD48, CD150, CD45.1, CD45.2. Propidium Iodide (PI) was used to exclude dead cells. HSCs were sorted as PI-Lin-Sca-1+c-Kit+(LSK) CD34-Flk2-CD150+ for CR experiments and PI-LSK CD48-CD150+CD45.2+ for transplantation experiments with BDFACSAriaII Cell Sorter.

HSC in vitro culture and virus transduction
HSCs were cultured in media described before38: F12 medium, 10 mM HEPES, 1% ITSX, 1% P/S/G, 100 ng/ml mouse TPO, 10 ng/ml mouse SCF and 0.1% PVA (P8136, Sigma) in 96-well U-bottom plates at 37°C with 5% CO2 and O2. The lentivirus shRNA knockdown vector construction and virus packaging were performed by VectorBuilder. The target sequences of shRNA are 5′-GCATCTCATCTGTTACAGCTT-3’ (Kdr-shRNA5), 5′-CAATTTGTGCAACCAGTA-TTT-3’ (Bmpr1a-shRNA1), and 5′-TCAAGACTCCAATCCTGATAA-3’ (Bmpr1a-shRNA2). For virus transduction, 10,000 HSCs purified from aged mice were cultured in 50 μl of media with the virus overnight (12–16 h). The next morning, 150 μl of fresh media was added to each well. For knockdown efficiency test, HSCs were cultured for 3.5 days before sorting for RNA-seq. For transplantation, HSCs were cultured for 20–24 h before the procedure.

HSC transplantation
For transplantation of HSCs from OAL, OCR and OCRRF mice, 200 HSCs (CD45.2) were transplanted into lethally irradiated (9.56 Gy) recipients (CD45.1) together with 2×105 WBM cells (CD45.1) via retro-orbital injection. For transplantation of virus transfected HSCs, about 500 cultured HSCs (CD45.2) were transplanted into lethally irradiated (9.56 Gy) recipients (CD45.1) together with 30,000 myeloid progenitor cells (CD45.1) via retro-orbital injection.

RNA-seq
HSCs were directly sorted into TRIzol Reagent (15596026, ThermoFisher) and stored at −80°C. RNA was purified with Direct-zol RNA Microprep (R2060, Zymo) for CR experiments. RNA from transplanted HSCs was extracted using phenol-chloroform extraction and isopropanol precipitation. cDNA libraries were prepared with SMART-Seq RNA Kit (634891 for CR experiments, 634772 for RNA from transplanted HSCs, TaKaRa) according to the manufacturer’s protocol. Sequencing libraries were constructed using Nextera XT DNA Library Preparation Kit (FC-131-1024, Illumina) with 125pg input cDNA. Sequencing was done with Illumina HiSeq 2500 instrument for CR experiments libraries, and Illumina NovaSeq 6000 System for libraries from transplanted HSCs.

ATAC-seq
ATAC-seq was performed as described previously39. 6,000 sorted HSCs were spun down using a fixed angle rotor at 500g, 4°C for 10 min. Cells were resuspended with 50 µl of cold resuspension buffer (10 mM Tris-HCl, 10 mM NaCl, 3 mM MgCl2 in water) containing 0.1% NP40, 0.1% Tween-20, and 0.01% Digitonin by pipetting up and down 3 times, and incubated on ice for 3 min. 1 ml of cold resuspension buffer containing 0.1% Tween-20 (without NP40 or Digitonin) was added to wash the cells and tubes were inverted 3 times to mix. The nuclei were pelleted by spinning at 500g, 4°C for 10 min with a fixed angle rotor. The pellet was then resuspended in 50 µl of transposition mix (25 µl of 2x TD buffer, 2.5 µl of Tn5 Transposase (20034197, Illumina), 22.5 µl of H2O) by pipetting up and down 6 times. The reaction was Incubated at 37°C for 30 min in a thermomixer with 1000 rpm mixing and cleanup was performed with DNA Clean & Concentrator-5 (D4013, Zymo Research). DNA was eluted with 21 µl of H2O and amplified for 5 cycles with NEBNext Ultra II Q5 Master Mix (M0544L, NEB). We determined the optimum additional PCR cycles by qPCR as described previously40. The final PCR reaction was purified with DNA Clean & Concentrator-5 (D4013, Zymo Research). Libraries were size selected (120−800 bp) by running TBE gel (EC6265BOX, ThermoFisher) and sequenced on Illumina HiSeq 2500 instrument.

ChIP-seq
ChIP-seq was performed as described previously41 with Chromatin Immunoprecipitation (ChIP) Assay Kit (17-295, Millipore). For histone ChIP-seq, 10,000 sorted HSCs were mixed with 1×108 bacteria (18258012, ThermoFisher), then crosslinked with 1% formaldehyde (F8775, Sigma-Aldrich) at room temperature (RT) for 10 min. Crosslinking was stopped by adding 2 M glycine to a final concentration of 0.125 mM and incubated at RT for 5 min. After one wash with cold 1x PBS, cells were resuspended in SDS lysis buffer with 1x proteinase inhibitor cocktail (Millipore, 4693132001) and incubated on ice for 10 min. Sonication was performed with Bioruptor Plus (Diagnode) using the following setting: 3×10 cycles of 30 sec ON and 30 sec OFF at HIGH setting. After sonication, fragmented chromatin was diluted by adding 9 volumes of ChIP dilution buffer with 1x proteinase inhibitor cocktail. Corresponding antibodies were added and incubated overnight at 4°C with rotation. 20 µl of Dynabeads Protein A (10002D, ThermoFisher) were added and incubated at 4°C for 2 h with rotation. Before washing, 5 ng biotin-DNA carrier immobilized with 10 µl of Dynabeads M-280 Streptavidin (11205D, ThermoFisher) was added. Sample was washed with 1 ml washing buffer at 4°C for 5 min in the following order: 1x with low salt buffer, 1x with high salt buffer, 1x with LiCl washing buffer, 2x with TE. Beads were resuspended with decrosslinking buffer (0.5% SDS, 0.2 M NaCl in TE) and incubated at 65°C for 4–5 hour or overnight with 1000 rpm mixing. 20 mg/ml proteinase K (final concentration 0.8 mg/ml, 26160, ThermoFisher) was added and incubated at 55°C for 2 h. ChIPed DNA was purified with 1.6x SPRIselect beads (B23318, Beckman Colter). For PU.1 ChIP-seq, 20,000 sorted HSCs were mixed with 5×107 bacteria, and beads were washed for 1 min instead of 5 min in the sample wash step. Libraries were prepared with TruSeq Nano DNA Low Throughput Library Prep Kit (20015964, Illumina) according to manufacturer’s instructions. Sequencing was done with Illumina HiSeq 2500 instrument.

RNA-seq analysis
For analysis of transcriptome datasets, we built an index for STAR using the GENCODE M22 reference feature including protein-coding and non-coding genes. Prior to sequence alignment, we applied trim galore (version 0.4.3) with cutadapt (version 1.12)42 to remove any unnecessary genomic fragments (e.g., adapter dimers) and low-quality nucleotide sequences from the raw reads. We mapped adapter trimmed sequencing reads to the mouse reference genome (mm10) using STAR aligner43 and calculated the raw count using featureCounts software (gene-level)44. Differentially expressed gene (DEG) lists were generated with DESeq245. For transposable element (TE) detection, we utilized SQuIRE46 and used limma based edgeR package of R47 to find differential transposable elements.

ATAC-seq analysis
All sequencing reads were trimmed using cutadapt42, and trimmed reads (>36 bp minimum alignment length) were mapped against the mm10 genome using BWA aligner48. We used de-duplicated and uniquely mapped reads for peak calling analysis after excluding high-sensitive black-list regions defined by ENCODE. The candidate peaks were predicted by MACS peak calling tool (FDR<0.01)49. After identifying narrow peaks from all replicates, we created a merged set of consensus peaks and generated a matrix of open chromatin regions (OCRs). This OCR matrix was then imported into the R package DESeq245, and we determined differentially accessible regions (DARs) with cutoff: FC>1.5, logCPM>1.5, FDR<0.05. Finally, the candidate differential open chromatin regions were submitted to search for potential transcription factor binding sites using HOMER software50 with non-DARs as background regions. In this analysis, de novo motif and known motif searches were performed, and we reported the top five significant de novo motif results. Composite plots and heatmaps were generated by customized Python script and Java TreeView software.

ChIP-seq analysis
All sequencing reads were trimmed using cutadapt42, and trimmed reads (>36 bp minimum alignment length) were mapped against the mm10 genome using BWA aligner48. We used de-duplicated and uniquely mapped reads for peak calling analysis after excluding high-sensitive black-list regions defined by ENCODE. For H3K4me3 ChIP-seq, signals within ±1kb of the transcription start site (TSS) were used for downstream analysis. For H3K27me3 ChIP-seq, signals from 0 to 2 kb downstream of the TSS were used for further analysis. For PU.1 ChIP-seq, the candidate peaks were predicted by MACS peak calling tool (FDR<0.05)49. Composite plots and heatmaps were generated with customized Python script and Java TreeView software.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Dr. Zong examines mechanisms underlying HSC responses to CR which lead to loss of immune function. They use both life-long and short-term CR in mouse models and found a decrease in WBC counts with preservation in myeloid-biased and megakaryocyte/platelet-erythroid biased progeny. They conclude that this CR response prioritizes red cells, platelets, and innate immune cells at the expense of adaptive immune cell differentiation. HSC under CR are driven into cell cycle to support myeloid bias over self-renewal. Intriguingly, they also find that CR mitigates aging-associated transcriptomic changes despite the myeloid and megakaryocyte/platelet-erythroid biases. These transcriptomic changes are lost after re-feeding ad lib. Using epigenetic profiling, they found that KDR is a central regulator of the CR response and knock-down of Kdr in aged, murine HSCs enforces the youthful transcriptome that was observed in lifelong CR. Using ATACseq, they also identify PU.1 as a regulator of the CR response by binding to downstream target genes. Overall, this study uncovers exciting mechanistic insight relevant to CR and HSC with aging, although some additional analyses that provide insight into how these changes are linked would substantiate the results and strengthen the conclusions.\nGeneral comments:\nThe research focus is of great interest in the field because it unveils previously unknown hematologic responses to CR and aging. Additional bioinformatic analyses to establish a potential link between KDR and PU.1 would strengthen the manuscript. Further examination of the long-term HSC pool with OACR would also improve the paper since it is not clear how CR promotes a decrease in LT-HSC together with HSC cycling and myeloid bias while also repressing Aging Signature (AS) genes; perhaps listing the specific AS genes in a Table would be illuminating. Examination of quiescent (CD41 low/Procr high) HSC compared to cycling (CD41 high/Procr low) HSC could help to further define changes in HSC subsets with CR since the finding that HSC cycling and myeloid bias is difficult to understand in the context of mitigating aging transcriptional changes unless quiescent HSC are maintained while the remaining HSC that cycle are biased toward myeloid lineages. Once these issues are addressed, this interesting work would be suitable for publication.\nMajor issues:\nFigure 1:\nFig 1f-g: The text in paragraph 1 of results states that RBC and platelet numbers are maintained under CR conditions and Fig 1f-g show no significant difference between OAL and CR. Are RBC and platelet numbers similar to those observed in young mice? (A bar graph in the main figure would be helpful here to avoid searching for comparisons in the supplemental data).\nFig 1: Could you also add comparisons between OAL and OCRRF? (OAL vs. OCR and OCR vs. OCRRF are shown, presumably OAL vs. OCRRF are similar, but please show for clarity)\nFigure 2:\nPlease provide details in text for HSC markers used in RNAseq so readers do not need to search through methods.\nFig 2d: It may be helpful to perform and GSEA comparing OCR (on left) versus OAL (on right) which would yield positive (+) NES (normalized enrichment scores) for cell cycling pathways, and better match the text and Fig. 1c stating that OCR activates cell cycle progression genes. The data is convincing; it would be easier to follow if the enrichment scores match the text and conclusions.\nFig. 2g: OCR seems to expand preB –is this significant? If yes, could this be discussed and investigated further? Is there a block in differentiation since B cells are down-regulated?\nExtended Figure 4:\nThe trend noted with more down-regulated genes in YCR versus YAL seems significant from the heat maps (Extended Fig 4) – are there other statistical approaches to compare the proportion of up versus down-regulated genes?\nFigure 3:\nFig. 3a: AS dysregulated genes are shown in a bar graph, but not the specific genes. A table/excel file of these genes and their differential expression and P values would be of interest.\nPlease discuss the genes Clu, Osmr, Ect2, Mcm5 further since they were discussed in the text and examined in Fig. 3e, although their full gene names and function may not be well-known to many readers.\nFigure 4:\nThe text mentions that sirtuin genes were not altered with CR, although genes regulating histone methylation were reported to be differentially regulated. Please elaborate further and show the genes and data supporting this.\nPlease also provide rationale for focusing on H3K4me3 and H3K427me3. Did the gene expression results suggest these marks would be affected?\nFig. 4c-i: Gene silencing experiments suggest that KDR is a key regulator of the CR response and effects on aging signature.\nText Line 200 – Please clarify: Alternatively, we could have examined the 7 genes with increased H3K4me3 that mitigated the decreased expression associated with aging in HSC. I would either examine these genes or not mention that you could examine them.\nFigure 5:\nBased on ATACseq, PU.1 is implicated as a primary regulator of the CR response which was validated by ChiPseq. What is the link between KDR signaling and PU.1? Could experiments or bioinformatic analyses be performed to identify potential pathways that link these 2 factors?\nSome additional issues to address:\nThe findings that CR increases HSC cycling with a myeloid bias while mitigating AS genes seems contradictory. How does the LT-HSC in OCR compare to Young – is CR restraining HSC expansion that occurs with aging?\nAlong these lines, are the expanded HSC acquiring mutations as occurs with human HSC aging? Although this is beyond the scope of this paper, could the authors speculate on whether CR may modulate acquisition of mutations that allow HSC expansion?\nHow can we reconcile increased HSC cycling and myeloid bias (2 aging phenotypes) with dampening of the AS? What are the specific AS genes?\nAre the quiescent LT-HSC being preserved with CR? Examination of quiescent (CD41 low/Procr high and/or other markers of quiescence) versus cycling LT-HSC (CD41 high/Procr low and/or other markers of cycling) could provide further insight into how CR represses AS while promoting cycling and myeloid bias.\nWhile beyond the scope of this paper, transcriptomic analyses of spleens and spleen/bone marrow single cell analyses would provide further insight into mechanisms underlying CR with aging.", "reviewer_2": "Summary\nThis manuscript provides a mechanistically detailed and well-executed investigation into how caloric restriction (CR) remodels the aging hematopoietic stem cell (HSC) compartment. The authors report that CR promotes a myeloid-biased transcriptional program at the expense of lymphoid differentiation, driven in part by epigenetic silencing of Kdr and Bmpr1a and increased PU.1 chromatin occupancy. The findings are timely and impactful; however, several areas require further clarification or additional data to strengthen the manuscript’s claims and contextualization.\nSpecific suggestions:\nDefinition of \"Youthfulness\"\nWhile the manuscript asserts that CR preserves a youthful HSC transcriptome, the observed phenotype is also characterized by reduced lymphoid output—particularly naive T cells—which are hallmarks of youth. The current phenotype may instead reflect a regulated or functionally adaptive response to energetic stress. I recommend refining the language throughout to differentiate between transcriptional rejuvenation and functional youthfulness. A deeper analysis of which aged HSC genes are reversed by CR and which persist would clarify the extent of this rejuvenation.\nDirect comparison across age and CR groups\nAlthough the authors refer to a youthful signature, they do not show a PCA or clustering analysis that includes both young ad libitum (YAL) and young CR (YCR) HSCs alongside old CR (OCR) HSCs. Including these groups in PCA or trajectory analyses would allow the readers to evaluate whether OCR HSCs genuinely converge toward a youthful transcriptional profile or instead form a unique CR-adapted state.\nClarification of CR protocol\nMore detail is needed on the CR paradigm, particularly concerning the weekend feeding regimen. Were mice subjected to continuous daily restriction, or did they receive multi-day rations? Such patterns could lead to unintentional refeeding-fasting cycles, influencing immune and HSC outcomes. Clarifying whether CR was continuous or intermittent is important for interpreting discrepancies with previous reports and for translational relevance.\nT cell output\nThe authors quantify CD3+ cells in their gating strategy, yet T cell data are not reported. This omission is notable, as it precludes comparison with studies (e.g., Di Francesco et al. Nature 2024) that observed CR-mediated preservation of T cells. Reporting absolute and relative T cell numbers would help resolve whether CR compromises adaptive immunity broadly or selectively suppresses B cells.\nFunctional evidence for immune suppression\nThe claim that CR may compromise immune function would be strengthened by functional validation, such as infection or tumor challenge models. In the absence of these experiments, the authors should clearly acknowledge this limitation and revise language in the abstract and discussion to avoid overinterpretation.\nBiological replicates in transcriptomic analyses\nIn Figure 2a, only n=2 samples are included for the OCRRF group, while other groups have n=4. The authors should explain why OCRRF replicates were reduced and clarify whether conclusions drawn from this group are robust. If possible, additional replicates should be included.\nBone marrow cellularity and absolute counts\nThe manuscript reports frequencies of HSCs and progenitor populations but omits data on total BM cellularity. Without absolute counts, it is difficult to distinguish between genuine expansion/contraction of cell populations and shifts due to altered gating proportions. Including BM cellularity and absolute numbers of cell populations would substantially strengthen the phenotypic conclusions.\nKdr ligand expression\nThe authors should evaluate whether expression of Kdr ligands (e.g., VEGFs) is altered with aging or CR, either in their dataset or via comparison with public datasets. This would strengthen the case for Kdr as a physiologically relevant modulator of HSC aging.\nPU.1 and Kdr Pathway Integration\nThe manuscript presents parallel evidence for PU.1 and Kdr as CR response regulators but does not clarify whether these pathways intersect mechanistically. Is there evidence that PU.1 binding is altered at loci with CR-induced histone changes? Are any PU.1-bound genes among those regulated by Kdr? Exploring overlap in differential chromatin accessibility and histone methylation could uncover a more unified mechanism.\nPU.1 Binding Partners\nFor genes with differential PU.1 occupancy, are different adjacent transcription factor motifs enriched among upregulated vs downregulated genes? This could suggest context-specific co-factors that shape PU.1's dual roles as an activator and repressor. This analysis would provide important mechanistic depth.\nMinor points\nCurrently the text mentions B220+ cells (line 93). Is B220 used as a marker for B cells? If so, the text should state that there is decrease in B cells with both short-term and long term CR for clarification.\nOWT is used in the text starting on line 258. This most likely refers to the Old Ad Lib (OAL) mice and should be corrected for consistency.", "reviewer_3": "In this manuscript, Zong et al. investigate the mechanisms underlying the effects of caloric restriction (CR) on the immune function of aged mice. They first confirm that CR leads to reduced white blood cell counts and myeloid bias. They then eexamied the effects of short term and long term CR as well as refeeding on transcription aging signatures in hematopoietic stem cells (HSCs) and demonstrate that CR preserves the 'youthful' signature. They examined epigenetic changes in specific histone methylation marks which identified Kdr and Bmpr1a as candidate regulators of the CR effects. Subsequent knockdown experiments confirmed that Kdr is able to recapitulate the CR effect. Examination of differential open chromatin regions further implicated Pu.1 as a candidate inhibitor of HSC renewal.\nThis work substantially advances our understanding the molecular mechanisms underlying immune response to CR.\nThe experiments are clearly described and follow a logical progression that supports the conclusions.\nMy only major concern is that the study is limited to male C57BL/6J mice. Including female mice should be considered essential, despite previous studies reporting absence of CR effects. Further the use of a single genetic background limits the scope of the conclusions as it is unclear if these responses are universal or limited to (male) C57 mice. Nonetheless the study is valuable and advances new hypotheses that can be explored in other contexts.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We thank the reviewer for the positive assessment of our work and for the thoughtful and constructive comments. The questions and suggestions raised are highly valuable and have helped us further improve the clarity and depth of our study. We have addressed all these points in detail in the following point-by-point responses.\nMajor issues:\nFigure 1:\nFig 1f-g: The text in paragraph 1 of results states that RBC and platelet numbers are maintained under CR conditions and Fig 1f-g show no significant difference between OAL and CR. Are RBC and platelet numbers similar to those observed in young mice? (A bar graph in the main figure would be helpful here to avoid searching for comparisons in the supplemental data).\nResponse: We thank the reviewer for this helpful comment. Compared to young C57BL/6J male mice, the number of RBCs is decreased (Fig. 1f), while the number of platelets is increased in aged mice (Fig. 1g). As suggested, we have moved the RBC and platelet data from young mice to the main figure to facilitate direct comparison and improve clarity (Fig. 1h,i).\nFig 1: Could you also add comparisons between OAL and OCRRF? (OAL vs. OCR and OCR vs. OCRRF are shown, presumably OAL vs. OCRRF are similar, but please show for clarity)\nResponse: We thank the reviewer for this suggestion. Comparisons between OAL and OCRRF have been added as requested, and all relevant figures have been updated in the revised manuscript to reflect these changes.\nFigure 2:\nPlease provide details in text for HSC markers used in RNAseq so readers do not need to search through methods.\nResponse: We thank the reviewer for this helpful suggestion. The HSC markers (LSK CD34⁻ Flk2⁻ CD150⁺) have been added to the text to provide clarity, allowing readers to understand the populations used for RNA-seq without referring to the Methods section.\nFig 2d: It may be helpful to perform and GSEA comparing OCR (on left) versus OAL (on right) which would yield positive (+) NES (normalized enrichment scores) for cell cycling pathways, and better match the text and Fig. 1c stating that OCR activates cell cycle progression genes. The data is convincing; it would be easier to follow if the enrichment scores match the text and conclusions.\nResponse: We thank the reviewer for this helpful suggestion. We have regenerated the GSEA comparing OCR (left) versus OAL (right) to ensure that the enrichment scores are consistent with the text (Fig. 2d). In addition, the corresponding GSEA plots for young mice have also been updated in Extended Data Fig. 4d.\nFig. 2g: OCR seems to expand preB –is this significant? If yes, could this be discussed and investigated further? Is there a block in differentiation since B cells are down-regulated?\nResponse: We thank the reviewer for this insightful comment. The CellRadar tool (https://karlssong.github.io/cellradar/) provides a qualitative assessment of relative gene expression enrichment, but it does not allow formal statistical testing to determine significance. We do observe enrichment of pre-B–related genes in OCR HSCs (Fig. 2g), although this enrichment is not apparent in young CR HSCs (Extended Data Fig. 4i). Since young CR mice also show decreased lymphoid differentiation, it is unclear whether the pre-B gene enrichment in OCR HSCs reflects a block in differentiation toward mature B cells. However, we can confirm that CR causes a block in lymphoid differentiation overall: lymphoid cell numbers and frequency decrease after CR, whereas refeeding leads to a rebound in lymphoid production, sometimes exceeding levels seen in ad libitum–fed mice (Fig. 1d, 1l, 1n).\nExtended Figure 4:\nThe trend noted with more down-regulated genes in YCR versus YAL seems significant from the heat maps (Extended Fig 4) – are there other statistical approaches to compare the proportion of up versus down-regulated genes?\nResponse: We thank the reviewer for this suggestion. We formally tested whether the observed imbalance between up- and down-regulated genes in YCR versus YAL was statistically significant. Among 136 DEGs, 31 were up-regulated and 105 were down-regulated. A two-sided exact binomial test confirmed a significant deviation from a 50:50 distribution (p = 1.33 × 10⁻¹⁰), and a chi-square goodness-of-fit test yielded a very similar result (p = 2.22 × 10⁻¹⁰). These analyses support the observation of a strong bias toward down-regulation in YCR HSCs.\nWe have added this comparison and conclusion in the revised manuscript:\n“We observed a significant bias toward down-regulation of transcripts in YCR versus YAL (31 up vs 105 down of 136 DEGs; two-sided exact binomial test, p = 1.33×10-10).”\nFigure 3:\nFig. 3a: AS dysregulated genes are shown in a bar graph, but not the specific genes. A table/excel file of these genes and their differential expression and P values would be of interest.\nResponse: Thank you- In response, we have added Extended Data Table 2, which lists the AS genes shown in Fig. 3a, along with their log2 fold-change (log2FC) and false discovery rate (FDR).\nPlease discuss the genes Clu, Osmr, Ect2, Mcm5 further since they were discussed in the text and examined in Fig. 3e, although their full gene names and function may not be well-known to many readers.\nResponse: As requested, we have added a brief introduction to these four genes in the revised manuscript:\n“Examining the AS genes that were differentially expressed in opposite directions in OCR compared to OAL HSCs showed that nearly half of the AS genes exhibited a significant mitigation in expression changes under CR conditions (Fig. 3c). This included Clu and Osmr, which are upregulated HSC aging signature genes (Fig. 3d). Clu (clusterin) encodes a secreted glycoprotein that drives myeloid bias in aged HSCs by regulating mitochondrial function (Sun et al., Nature Aging, 2025, https://doi.org/10.1038/s43587-025-00908-z), while Osmr (oncostatin M receptor) encodes a cytokine receptor involved in the maintenance of erythroid and megakaryocyte progenitors (Tanaka et al., Blood, 2003, https://doi.org/10.1182/blood-2003-02-0367). In contrast, Ect2 and Mcm5 are downregulated HSC aging signature genes (Fig. 3e). Ect2 (epithelial cell transforming 2) encodes a guanine nucleotide exchange factor required for cytokinesis and cell cycle progression, whereas Mcm5 (minichromosome maintenance complex component 5) encodes a subunit of the MCM helicase complex essential for DNA replication initiation and serves as a marker of proliferative potential.”\nFigure 4:\nThe text mentions that sirtuin genes were not altered with CR, although genes regulating histone methylation were reported to be differentially regulated. Please elaborate further and show the genes and data supporting this.\nResponse: We thank the reviewer for this helpful suggestion. In response, we have added Extended Data Table 3, which lists the expression levels of all sirtuin genes as well as histone methylation regulators. As shown in the table, sirtuin genes did not display significant changes under CR, whereas two H3K27me3 regulators, Ezh1 and Ezh2, were differentially expressed.\nPlease also provide rationale for focusing on H3K4me3 and H3K427me3. Did the gene expression results suggest these marks would be affected?\nFig. 4c-i: Gene silencing experiments suggest that KDR is a key regulator of the CR response and effects on aging signature.\nResponse: We focused on H3K4me3 and H3K27me3 because these histone marks are tightly linked to transcriptional regulation: H3K4me3 is enriched at active promoters, whereas H3K27me3 mediates transcriptional repression. Gene expression analysis of enzymes associated with H3K4me3 and H3K27me3 suggested potential dysregulation of these modifications in HSCs under CR conditions. Specifically, in OCR HSCs we observed an unexpected shift in the expression of H3K27me3 methyltransferases - Ezh1 was decreased and Ezh2 increased - which contrasts with the pattern seen during physiological aging, where Ezh1 increases and Ezh2 decreases. However, changes in enzyme expression alone cannot reveal which genes are directly affected or the nature of those changes. Therefore, we performed H3K4me3 and H3K27me3 ChIP-seq to define locus-specific alterations, enabling us to pinpoint the genomic targets of these histone modifications and to identify potential regulators of the CR response.\nText Line 200 – Please clarify: Alternatively, we could have examined the 7 genes with increased H3K4me3 that mitigated the decreased expression associated with aging in HSC. I would either examine these genes or not mention that you could examine them.\nResponse: We apologize for this poor phrasing. We agree that the original sentence was potentially confusing. Accordingly, we have removed this sentence in the revised manuscript.\nFigure 5:\nBased on ATACseq, PU.1 is implicated as a primary regulator of the CR response which was validated by ChiPseq. What is the link between KDR signaling and PU.1? Could experiments or bioinformatic analyses be performed to identify potential pathways that link these 2 factors?\nResponse: We thank the reviewer for this insightful question regarding the potential connection between KDR signaling and PU.1. To explore this link, we analyzed whether genes differentially expressed after Kdr knockdown were regulated by PU.1. Approximately 50% of the DEGs showed PU.1 binding within 50 kb of the transcription start site (TSS) (Extended Data Fig. 8b). Notably, chromatin profiling revealed that PU.1 binding increased after CR (Extended Data Fig. 8c), suggesting that PU.1 participates in the transcriptional regulation of KDR-responsive genes under CR conditions.\nFurther analysis indicated that both CR and Kdr knockdown led to a modest increase in Spi1 expression (Extended Data Fig. 8d), and ChIP-seq data showed PU.1 occupancy near the Kdr promoter (Extended Data Fig. 8e), supporting a possible reciprocal regulatory interaction between KDR signaling and PU.1 activity. However, GSEA did not detect significant enrichment of canonical KDR-related pathways after CR (Table R1), implying that the transcriptional impact of KDR signaling perturbation may be limited, as KDR primarily signals through phosphorylation cascades.\nSome additional issues to address:\nThe findings that CR increases HSC cycling with a myeloid bias while mitigating AS genes seems contradictory. How does the LT-HSC in OCR compare to Young – is CR restraining HSC expansion that occurs with aging?\nHow can we reconcile increased HSC cycling and myeloid bias (2 aging phenotypes) with dampening of the AS? What are the specific AS genes?\nAre the quiescent LT-HSC being preserved with CR? Examination of quiescent (CD41 low/Procr high and/or other markers of quiescence) versus cycling LT-HSC (CD41 high/Procr low and/or other markers of cycling) could provide further insight into how CR represses AS while promoting cycling and myeloid bias.\nResponse: Thank you for this insightful question. To clarify this seemingly contradictory observation, we analyzed CD150-high (myeloid-biased) and CD150-low (balanced) HSC subpopulations, as the frequency of cycling HSCs (CD41 high/Procr low) is higher in CD150-high HSCs compared to CD150-low HSCs (Fig. R1a). After CR, the frequency of CD150-high HSCs decreased, while CD150-low HSCs were maintained (Extended Data Fig. 3h), suggesting that CR primarily affects myeloid-biased HSCs, which may cycle more and differentiate under CR stress. Following refeeding, CD150-high HSCs returned to OAL levels, whereas CD150-low HSCs decreased (Extended Data Fig. 3h), which may reflect a burst of lymphoid differentiation. As CD150-low HSCs retain a younger transcriptome (Wang et al. 2025, Cell Research, https://doi.org/10.1038/s41422-024-01057-5), this shift likely contributes to the improved aging transcriptome observed in OCR HSCs.\nOverall, the transcriptome of OCR HSCs trends toward young HSCs but is not fully restored (Fig. R1b). CR partially restrains age-associated HSC expansion, though the population still increases, albeit to a lesser extent than in ad lib fed aged HSCs (Fig. R1c).\nWe have also added Extended Data Table 2, which lists all the AS genes, together with their log fold-change (log FC) and false discovery rate (FDR).\nAlong these lines, are the expanded HSC acquiring mutations as occurs with human HSC aging? Although this is beyond the scope of this paper, could the authors speculate on whether CR may modulate acquisition of mutations that allow HSC expansion?\nResponse: DNA replication is a major source of mutations, and CR increases HSC cycling, which could potentially influence mutation acquisition. We examined the RNA-seq data for mutations in the reported clonal hematopoiesis (CH) genes which acquire mutations associated with human aging, and do not see an increase in mutations in the CR HSCs. Our data also indicate that the cycling HSCs under CR mainly differentiate to maintain the myeloid lineage rather than self-renew. Therefore, we speculate that while CR may modulate mutation acquisition in HSCs, we don’t see an increase in mutation rate of the CH genes and posit that it is unlikely that acquisition of these mutations would result in substantial changes in murine HSC expansion in this experimental paradigm.\nWhile beyond the scope of this paper, transcriptomic analyses of spleens and spleen/bone marrow single cell analyses would provide further insight into mechanisms underlying CR with aging.\nResponse: We thank the reviewer for this thoughtful suggestion. We agree that transcriptomic analyses of spleen, as well as single-cell analyses of spleen and bone marrow, could provide additional insight into the mechanisms underlying CR with aging, and we will consider these experiments in future studies.\nReviewer #2 (Remarks to the Author):\nSummary\nThis manuscript provides a mechanistically detailed and well-executed investigation into how caloric restriction (CR) remodels the aging hematopoietic stem cell (HSC) compartment. The authors report that CR promotes a myeloid-biased transcriptional program at the expense of lymphoid differentiation, driven in part by epigenetic silencing of Kdr and Bmpr1a and increased PU.1 chromatin occupancy. The findings are timely and impactful; however, several areas require further clarification or additional data to strengthen the manuscript’s claims and contextualization.\nResponse: We thank the reviewer for the thoughtful and positive evaluation of our work and for recognizing the impact of our study. We appreciate the constructive feedback highlighting areas for clarification and additional context. In the revised manuscript, we have carefully addressed all comments point by point. We have provided additional analyses, expanded data presentation, and refined the discussion to better contextualize our findings and strengthen the overall conclusions.\nSpecific suggestions:\nDefinition of \"Youthfulness\"\nWhile the manuscript asserts that CR preserves a youthful HSC transcriptome, the observed phenotype is also characterized by reduced lymphoid output—particularly naive T cells—which are hallmarks of youth. The current phenotype may instead reflect a regulated or functionally adaptive response to energetic stress. I recommend refining the language throughout to differentiate between transcriptional rejuvenation and functional youthfulness. A deeper analysis of which aged HSC genes are reversed by CR and which persist would clarify the extent of this rejuvenation.\nResponse: We thank the reviewer for this important comment. We agree that our data support transcriptional rejuvenation rather than functional youthfulness. Accordingly, we have revised the manuscript to use “transcriptional rejuvenation” throughout.\nTo further clarify the extent of this rejuvenation, we added Extended Data Table 2, which lists all HSC aging signature (AS) genes together with their log₂ fold-change (log₂FC) and false discovery rate (FDR). Among these, 111 genes were reversed by CR (fold-change > 1.2, FDR < 0.05), whereas 98 genes persisted (fold-change < 1.2 or FDR > 0.05). These data delineate the subset of aging-associated transcriptional changes that are responsive to CR versus those that remain unaffected.\nDirect comparison across age and CR groups\nAlthough the authors refer to a youthful signature, they do not show a PCA or clustering analysis that includes both young ad libitum (YAL) and young CR (YCR) HSCs alongside old CR (OCR) HSCs. Including these groups in PCA or trajectory analyses would allow the readers to evaluate whether OCR HSCs genuinely converge toward a youthful transcriptional profile or instead form a unique CR-adapted state.\nResponse: We thank the reviewer for this valuable suggestion. As recommended, we performed PCA and unsupervised clustering analyses including YAL, YCR, OAL, and OCR HSCs. The PCA showed that lifelong CR (OCR) shifted the transcriptome toward the young HSC (YAL) state along PC1 (Extended Data Fig. 5a). In the unsupervised clustering analysis, YAL and YCR grouped together, while OAL and OCR formed a separate cluster (Extended Data Fig. 5b). Nonetheless, the transcriptomic profile of OCR HSCs clearly shifted toward, but did not fully converge with, the YAL state, indicating a partial transcriptional reversion under CR.\nClarification of CR protocol\nMore detail is needed on the CR paradigm, particularly concerning the weekend feeding regimen. Were mice subjected to continuous daily restriction, or did they receive multi-day rations? Such patterns could lead to unintentional refeeding-fasting cycles, influencing immune and HSC outcomes. Clarifying whether CR was continuous or intermittent is important for interpreting discrepancies with previous reports and for translational relevance.\nResponse: We appreciate the reviewer’s critical comment regarding the CR paradigm. In our study, CR was implemented as a continuous regimen, with mice receiving the same restricted feeding both on weekdays and weekends. Thus, the animals were not exposed to intermittent refeeding-fasting cycles. We have revised the Methods section to clearly describe this protocol.\nT cell output\nThe authors quantify CD3+ cells in their gating strategy, yet T cell data are not reported. This omission is notable, as it precludes comparison with studies (e.g., Di Francesco et al. Nature 2024) that observed CR-mediated preservation of T cells. Reporting absolute and relative T cell numbers would help resolve whether CR compromises adaptive immunity broadly or selectively suppresses B cells.\nResponse: We thank the reviewer for this valuable comment. We have now included both the absolute number and relative frequency of CD3⁺ T cells in the revised data. Our analysis shows that both short-term and lifelong CR significantly reduce the absolute number of CD3⁺ T cells (Extended Data Fig. 2i), while the relative frequency of T cells remains largely unchanged (Extended Data Fig. 2h). In contrast, CR decreases both the absolute number and frequency of B cells (Fig. 1l and Extended Data Fig. 2g, 2j), indicating that B cells are more selectively suppressed than T cells under CR conditions.\nFunctional evidence for immune suppression\nThe claim that CR may compromise immune function would be strengthened by functional validation, such as infection or tumor challenge models. In the absence of these experiments, the authors should clearly acknowledge this limitation and revise language in the abstract and discussion to avoid overinterpretation.\nResponse: We thank the reviewer for this important comment. We fully agree that functional validation, such as infection or tumor challenge models, would provide stronger evidence for the impact of CR on immune competence. We acknowledge that such experiments were not performed in the current study and have now explicitly stated this limitation in the revised Discussion.\nTo provide context, previous studies have indeed demonstrated impaired immune responses under CR conditions. For example, Tang et al. reported that CR led to reduced bacterial clearance following infection (Tang et al., J. Exp. Med., 2016, https://doi.org/10.1084/jem.20151100). Similarly, Kristan et al. found that CR increased susceptibility of laboratory mice to intestinal parasite infection (Kristan, Aging Cell, 2007, https://doi.org/10.1111/j.1474-9726.2007.00345.x), and Gardner et al. showed that CR decreased survival, increased viral titers, and reduced natural killer cell activity following influenza infection in aged mice (Gardner, J. Gerontol. A, 2005, https://doi.org/10.1093/gerona/60.6.688).\nIn line with the reviewer’s suggestion, we have revised the Abstract and Discussion to temper our interpretation. We now describe that “CR has been reported to be associated with reduced immune function in certain contexts” rather than stating directly that “CR compromises immune function.” We have also added a sentence in the Discussion explicitly acknowledging the absence of direct functional validation of immune suppression in our study.\nBiological replicates in transcriptomic analyses\nIn Figure 2a, only n=2 samples are included for the OCRRF group, while other groups have n=4. The authors should explain why OCRRF replicates were reduced and clarify whether conclusions drawn from this group are robust. If possible, additional replicates should be included.\nResponse: We thank the reviewer for this valuable comment. We initially included four mice per group. However, upon dissection, we found that two mice in the OCRRF group presented with health abnormalities - one had a large liver tumor and the other had an abnormally small spleen. Because these conditions could introduce confounding effects unrelated to our experimental variables, we did not proceed with RNA-seq for these two animals, resulting in n=2 for the OCRRF group.\nTo assess the robustness of our conclusions, we later performed RNA-seq on additional OCRRF mice. The transcriptomic profiles of these new samples were consistent with those of the original OCRRF replicates (Fig. 2a,b and Fig. R2), supporting the reliability of our findings. These additional data are provided to the reviewers for reference.\nBecause RNA-seq data generated at different times can be influenced by batch effects, we present only samples processed and sequenced within the same experimental batch to ensure internal consistency across all groups. Importantly, the inclusion of additional OCRRF replicates did not alter the key findings, supporting the robustness of the conclusions despite the reduced sample size in the original dataset.\nBone marrow cellularity and absolute counts\nThe manuscript reports frequencies of HSCs and progenitor populations but omits data on total BM cellularity. Without absolute counts, it is difficult to distinguish between genuine expansion/contraction of cell populations and shifts due to altered gating proportions. Including BM cellularity and absolute numbers of cell populations would substantially strengthen the phenotypic conclusions.\nResponse: Thank you for this comment. We agree that bone marrow (BM) cellularity is important for distinguishing genuine expansion/contraction of cell populations and shifts due to altered gating proportions.\nTo address this concern, we quantified total BM cellularity and found that overall BM cellularity remained largely unchanged after CR, with no statistically significant differences between groups (Extended Data Fig. 3i). This contrasts with peripheral blood, where total white blood cell counts were significantly reduced under CR conditions (Extended Data Fig. 2a).\nThese findings suggest that while CR alters the composition of hematopoietic populations - reducing B cell frequencies and increasing myeloid frequencies in both BM and blood - the total number of BM cells is maintained. Thus, the observed shifts in BM lineage frequencies primarily reflect changes in lineage balance rather than overall cell loss. This distinction highlights a key difference between the bone marrow and peripheral blood in their responses to CR.\nKdr ligand expression\nThe authors should evaluate whether expression of Kdr ligands (e.g., VEGFs) is altered with aging or CR, either in their dataset or via comparison with public datasets. This would strengthen the case for Kdr as a physiologically relevant modulator of HSC aging.\nResponse: We thank the reviewer for this valuable suggestion. In mice, three known ligands bind to Kdr: Vegfa, Vegfc, and Vegfd. In our dataset, Vegfa and Vegfc were readily detected in HSCs, whereas Vegfd expression was very low (Fig. R3).\nWith aging, Kdr expression increased, accompanied by a decrease in Vegfa and no substantial change in Vegfc (Fig. R3). Under lifelong CR, Kdr expression was reduced compared to aged controls, while Vegfa showed a modest trend toward increased expression and Vegfc showed a trend toward decreased expression (Fig. R3). These patterns suggest that CR may mitigate age-associated upregulation of Kdr and subtly modulate its ligand balance. Nonetheless, Kdr signaling in HSCs may also be influenced by ligands derived from niche or systemic sources.\nPU.1 and Kdr Pathway Integration\nThe manuscript presents parallel evidence for PU.1 and Kdr as CR response regulators but does not clarify whether these pathways intersect mechanistically. Is there evidence that PU.1 binding is altered at loci with CR-induced histone changes? Are any PU.1-bound genes among those regulated by Kdr? Exploring overlap in differential chromatin accessibility and histone methylation could uncover a more unified mechanism.\nResponse: We thank the reviewer for this insightful question regarding the potential mechanistic integration between PU.1 and Kdr pathways. We examined the genomic distribution of PU.1 binding sites, regions of altered chromatin accessibility (DARs), and loci with CR-associated histone modification changes. PU.1 binding and DARs were predominantly located at distal regulatory elements rather than promoters, whereas changes in histone modifications (H3K4me3, H3K27me3) were mainly confined to promoter regions. Consequently, there was minimal direct overlap between PU.1 binding/DARs and promoter-associated histone changes. However, such non-overlap does not exclude functional interaction, as PU.1-bound enhancers can still regulate genes whose promoters show histone modification changes through enhancer–promoter interactions.\nTo further explore the potential link between these pathways, as the reviewer suggested, we analyzed whether genes differentially expressed following Kdr knockdown were regulated by PU.1. Approximately 50% of these DEGs had PU.1 binding within 50 kb of the transcription start site (TSS) (Extended Data Fig. 8b), indicating substantial potential regulatory overlap. Moreover, chromatin profiling revealed increased PU.1 binding after CR (Extended Data Fig. 8c), suggesting that PU.1 contributes to the transcriptional regulation of KDR-responsive genes under CR conditions.\nWe also found that both CR and Kdr knockdown led to a modest increase in Spi1 expression (Extended Data Fig. 8d), and ChIP-seq data showed PU.1 occupancy near the Kdr promoter (Extended Data Fig. 8e), supporting a possible reciprocal regulatory interaction between KDR signaling and PU.1 activity.\nPU.1 Binding Partners\nFor genes with differential PU.1 occupancy, are different adjacent transcription factor motifs enriched among upregulated vs downregulated genes? This could suggest context-specific co-factors that shape PU.1's dual roles as an activator and repressor. This analysis would provide important mechanistic depth.\nResponse: We thank the reviewer for this thoughtful suggestion. To address this, we classified PU.1 peaks showing altered occupancy into two groups according to whether they were associated with genes upregulated or downregulated after CR. We then performed transcription factor binding motif enrichment analysis using HOMER, analyzing sequences within ±200 bp of the PU.1 peak centers. Both de novo and known motif analyses were conducted.\nAs expected, the PU.1 motif was the top enriched motif in both groups, confirming the specificity and robustness of the dataset (Extended Data Fig. 8a). Among known motifs, we observed similar enrichment of ETS family transcription factors - including ELF4, ETS1, FLI1, GABPA, and ERG - across both up- and downregulated gene-associated peaks (Extended Data Fig. 8a, left). These factors share the ETS DNA-binding domain and are known to act as either activators or repressors in a context-dependent manner, consistent with PU.1’s dual regulatory roles.\nInterestingly, de novo motif analysis revealed modest enrichment of distinct motifs between the two gene sets: NFIA and HMGA1 motifs were more enriched in PU.1 peaks linked to upregulated genes, whereas ZFP335 and MYBL2 motifs were more enriched in those associated with downregulated genes, although their enrichment p-values were substantially higher than that of PU.1 (Extended Data Fig. 8a, right). These results suggest that PU.1 may engage different co-regulatory partners in distinct transcriptional contexts. Alternatively, PU.1’s dual activity may involve sequence-independent mechanisms, such as recruitment of different chromatin modifiers to regulate transcription.\nMinor points\nCurrently the text mentions B220+ cells (line 93). Is B220 used as a marker for B cells? If so, the text should state that there is decrease in B cells with both short-term and long term CR for clarification.\nResponse: We thank the reviewer for this helpful comment. Yes, B220 is used as a marker for B cells. As suggested, we have revised the text to replace “B220⁺ cells” with “B cells” to clarify that B cell frequency decreases under both short-term and lifelong CR conditions.\nOWT is used in the text starting on line 258. This most likely refers to the Old Ad Lib (OAL) mice and should be corrected for consistency.\nResponse: We thank the reviewer for catching this inconsistency. We have corrected “OWT” to “OAL” throughout the revised manuscript, including both the text and figure labels, to ensure consistency.\nReviewer #3 (Remarks to the Author):\nIn this manuscript, Zong et al. investigate the mechanisms underlying the effects of caloric restriction (CR) on the immune function of aged mice. They first confirm that CR leads to reduced white blood cell counts and myeloid bias. They then eexamied the effects of short term and long term CR as well as refeeding on transcription aging signatures in hematopoietic stem cells (HSCs) and demonstrate that CR preserves the 'youthful' signature. They examined epigenetic changes in specific histone methylation marks which identified Kdr and Bmpr1a as candidate regulators of the CR effects. Subsequent knockdown experiments confirmed that Kdr is able to recapitulate the CR effect. Examination of differential open chromatin regions further implicated Pu.1 as a candidate inhibitor of HSC renewal.\nThis work substantially advances our understanding the molecular mechanisms underlying immune response to CR.\nThe experiments are clearly described and follow a logical progression that supports the conclusions.\nMy only major concern is that the study is limited to male C57BL/6J mice. Including female mice should be considered essential, despite previous studies reporting absence of CR effects. Further the use of a single genetic background limits the scope of the conclusions as it is unclear if these responses are universal or limited to (male) C57 mice. Nonetheless the study is valuable and advances new hypotheses that can be explored in other contexts.\nResponse: We thank the reviewer for the positive evaluation of our work and for highlighting this important point. We fully agree that including female mice and additional genetic backgrounds would strengthen the generalizability of our conclusions. We recognize that sex differences play a significant role in the aging of the hematopoietic system. In fact, we are currently preparing a separate study focused on sex-dependent changes in hematopoiesis during aging. We will also examine the effects of CR in female mice in future investigations to determine whether the mechanisms identified here are conserved across sexes.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed the critiques and the revised manuscript is appropriate for publication.", "reviewer_2": "I appreciate the authors’ thorough revision of the manuscript and detailed responses to the previous comments. The manuscript has substantially improved and the new analyses and clarifications address the questions raised previously. In my opinion, the manuscript is suitable for publication in Nature Communications.", "reviewer_3": "No additional concerns to note."}, "author_response": ""}]

Decision letter