Lack of MDA5 delays hematopoietic aging by modulating inflammaging and proteostasis in mice

Domain: neuroscience
Published: 2026-02-12
Editorial outcome: major_revision

Abstract
“Inflammaging”, the chronic increase in inflammatory signaling with age, remains poorly understood in hematopoietic aging. Here, we identify the innate immune RNA sensor melanoma differentiation–associated protein 5 (MDA5) as an important factor of hematopoietic stem cell (HSC) aging. Aged Mda5-/- mice exhibit reduced HSC accumulation and myeloid bias. Importantly, aged Mda5-/- HSCs retain greater quiescence and superior repopulation capacity in non-competitive transplants compared to wild-type counterparts. Multiomic analyses—including chromatin accessibility, transcriptomics, and metabolomics—reveal decreased inflammatory signaling, a youthful metabolic profile, and improved proteostasis in Mda5-/- HSCs, through regulation of HSF1 and phospho-EIF2A, key proteostasis regulators. Activation of HSF1 in aged wild-type HSCs partially restores youthful features, supporting a causal role for proteostasis maintenance. Collectively, our findings demonstrate that attenuating MDA5-dependent inflammation preserves HSC function during aging by maintaining metabolic fitness and proteostasis and provide insight into potential therapeutic strategies for mitigating hematopoietic aging.

Introduction
Hematopoietic stem cells (HSC) lie at the top of the hematopoietic hierarchy and give rise to most of the differentiated hematopoietic cells. HSCs are normally quiescent within the bone marrow and can only be activated by external stimuli to exit quiescence and enter the cell cycle1. Inflammation holds a prominent role throughout the lifespan of HSCs. Indeed, it has been shown that IFNα (interferon α), IFNγ, TNF (tumor necrosis factor), and IL-1β (interleukin 1β) are responsible for the exit of HSCs from quiescence2–6. IL-1 mediates microbiome-induced inflammaging of HSCs in mice7. It has been shown that inflammatory signaling promotes clonal expansion in cases of clonal hematopoiesis (CH)8,9. At the same time, it was recently proposed that the mutant clones are resistant to the detrimental effects of the inflammatory milieu, thus leading to their expansion10. During aging, several features of HSCs become compromised. Aged HSCs exhibit reduced self-renewal and repopulation capacity, a myeloid bias, and an increase in their frequency, while proteostatic mechanisms are compromised concomitant with chronic low-grade inflammation11–13. “Inflammaging” refers to the low-grade, sterile chronic inflammation affecting the aged hematopoietic system and HSC function, including metabolic capacity, differentiation, and self-renewal capacity in aged organisms, leading to various diseases11,12,14. Indeed, it has been shown that continuous exposure to inflammation in the form of the viral mimic Poly I:C leads to impairment of HSC self-renewal capacity and premature aging15. However, how inflammation drives hematopoietic aging and the mechanisms through which the two phenomena are linked remain understudied.

An inflammatory pathway whose role in “inflammaging” has not been studied before is the RIG-I-like receptor (RLR) pathway16. RLRs are a group of three intracellular innate immune receptors: RIG-I (retinoic acid-inducible gene I) like receptor, MDA5 (melanoma differentiation-associated protein 5), and LGP2 (laboratory of genetics and physiology 2). Upon viral RNA recognition, RIG-I and MDA5 form filaments and interact with the downstream adapter protein MAVS (mitochondrial antiviral signaling protein), thus creating a signaling platform that permits the recruitment and activation of multiple signaling molecules. Ultimately, activation of the RLR signaling pathway leads to induction of a type-I IFN response and the secretion of proinflammatory cytokines16,17. We have previously shown that RLRs play a key role in hematopoietic stem and progenitor cell (HSPC) formation during development18, and that MDA5 modulates adult HSC activation after chemotherapy19. Whether RLRs are involved in hematopoietic aging remains unknown. Recently, it was reported by us and others that not only viral RNA, but also other types of RNA, including mitochondrial and transposable element RNA, can also be a stimulus for RLR receptors20–25. Since transposable element expression in HSCs increases with age26, MDA5 may play a role in HSC aging.

Protein homeostasis, or proteostasis, encompasses all the processes that work together to maintain the proper levels, structure, and function of proteins within living organisms27. Although inflammaging and impaired proteostasis are both hallmarks of aging HSCs, a link between them remains unexplored. Young adult HSCs have lower protein synthesis rates compared to downstream progenitors28,29. Heat shock transcription factor 1 (HSF1) is a master regulator of proteostasis30–32 which was recently reported to promote proteostasis and the regenerative activity of HSCs in response to culture stress and aging33. HSF1 protein levels decline with age in many tissues34. Protein translation inhibition through PKR (protein kinase R) or PERK (protein kinase R-like endoplasmic reticulum kinase)-induced phospho-EIF2a (eukaryotic translation initiation factor 2a) is also important for proteostasis and the induction of autophagy35. Autophagy helps to remove cytosolic protein aggregates and damaged organelles inside cells36. Loss of autophagy in HSCs leads to metabolic stress and altered functionality. While most HSCs lose autophagic capacity with age, a fraction maintains high autophagy and resembles young HSCs37. Chronic low-grade inflammation is the signal that leads to autophagy maintenance through metabolic adaptations38. Finally, aggrephagy has been shown as the preferred method of protein degradation by young HSCs39.

Here, we show that, in the absence of MDA5, HSCs are protected from age-associated functional decline. Loss of MDA5 preserves stem cell quiescence, limits myeloid bias, and maintains repopulation capacity during aging by restraining inflammatory signaling and sustaining metabolic fitness and proteostasis. These findings establish innate RNA sensing as an intrinsic driver of hematopoietic stem cell aging and highlight modulation of MDA5-dependent pathways as a potential strategy to mitigate age-related hematopoietic dysfunction.

Results
Reduced inflammation in Mda5-/- HSCs and bone marrow serum
Since activation of MDA5 induces a type-I IFN response and the secretion of pro-inflammatory cytokines, we isolated bone marrow serum from aged (18 to 24 months old) wild-type WT (or Mda5+/+ hereafter WT) and Ifih1-knockout mice (also known as Mda5; hereafter Mda5-/-; B6.Cg-Ifih1tm1.1Cln/J)40, to measure secreted cytokines. Indeed, IFN-β concentration was significantly lower in the bone marrow of aged Mda5-/- mice (Fig. 1A). Concomitantly, other cytokines such as IL-1α and β, IL-10, IL-6, and others exhibited lower concentrations in Mda5-/- bone marrow serum, while other cytokines like TNF and IFNγ showed no differences (Fig. 1B and Supplementary Fig. 1A, B). We repeated the experiment with young (2–3 months old) and middle-aged (10–14 months old) mice and observed various cytokines that had lower concentrations in the bone marrow serum of young and middle-aged Mda5-/- animals compared to controls (Supplementary Fig. 1C, D). When comparing these results at different ages, we noticed that cytokine concentration was increasing with age, with IL-1α, IL-10, IFNβ, and TNF being the most highly secreted in the bone marrow. This effect was dampened in aged Mda5-/- bone marrow serum (Fig. 1C).

We then wanted to check whether chromatin accessibility for inflammatory motifs is different in Mda5-/- HSCs. To explore the chromatin accessibility landscape of aged WT and Mda5-/- HSCs, we performed an assay for transposase-accessible chromatin followed by sequencing (ATAC-seq). We isolated HSCs using EPCR (endothelial protein C receptor) SLAM markers (EPCR+CD150+CD48−), as EPCR has been proven to be a stable marker that is insensitive to stress signals41,42. Indeed, more than 97% of the EPCR SLAM HSCs are also Lin-cKit+Sca+(LSK)CD150+CD48−(LSKSLAM)(SupplementaryFig.1E). Similar results were obtained for middle-aged and young HSCs (Supplementary Fig. 1F, G). Therefore, in this text, “HSC” will henceforth refer to EPCR SLAM HSCs, unless stated otherwise. Clustering of samples by age was observed (Supplementary Fig. 1H). We compared the accessible chromatin regions between young or aged WT and Mda5-/- HSCs. In the comparison of the young WT and Mda5-/- HSCs, we found 1.723 regions that were mostly accessible in WT (reduced peaks), and 10.694 mostly accessible in Mda5-/- HSCs (induced peaks) (Fig. 1D and Supplementary Data 1–4). Aged Mda5-/- HSCs on the other hand have less accessible chromatin than the WT, with 11.904 sites induced in WT and 5.336 sites induced in Mda5-/- HSCs (Fig. 1E and Supplementary Data 1–4). Both induced and reduced peaks were distributed in similar proportions across transcription start sites (TSS), exons, introns, and intergenic regions (Fig. 1D, E). Only the reduced peaks in young Mda5-/- HSCs were found mostly around the TSS (Fig. 1D). Additionally, when we compared young and aged WT or Mda5-/- HSCs, we observed an opening of chromatin in the aged populations that was distributed similarly in different genomic regions (Fig. 1F, G). Comparison of young and aged samples together revealed that most of the common induced regions were between the aged WT and Mda5-/- HSCs, and thus the aged Mda5-/- HSCs do not resemble younger WT HSCs (Fig. 1H).

Known motif analysis revealed that inflammatory motifs like IRF3 (IFN regulatory factor 3) were mainly enriched in aged Mda5-/- reduced peaks (p-value 1e-21 in reduced and 1e-05 in induced sites), but not young WT or Mda5-/- regions (Fig. 1I). In detail, in the young WT versus Mda5-/- regions, we found NFY (nuclear transcription factor Y, 1e-49), KLF1 (Krueppel-like factor 1, 1e-47), SP1 (specificity protein 1, 1e-38), FLI1 (friend leukemia integration 1, 1e-21) in the reduced regions and ZSCAN1 (zinc finger and SCAN domain containing 1), P53 (both 1e-02) in the induced regions (Fig. 1I). In aged WT versus Mda5-/- regions we found CTCF (CCCTC-binding factor, 1e-911), ETV2 (Ets variant 2, 1e-248), ERG (ETS transcription factor ERG, 1e-232), IRF3 (1e-21), PU1 (1e-133), JUN-AP1 (1e-72) in the reduced regions and CTCF (1e-139), ETV2 (1e-69), ETS1 (1e-55), PU1 (1-36), RUNX (1e-31), IRF3 (1e-05) in the induced regions (Fig. 1I). Thus, the inflammatory IRF3 motif is less present in induced Mda5-/- regions. The young WT HSCs harbor motifs for Pax7 (1e-2) while the induced regions in the aged WT HSCs harbor CTCF (1e-1965), ETV2 (1e-1063), ERG (1e-1032), FLI1 (1e-993), PU1 (1e-577), AP1 (activating protein-1, 1e-300), and IRF3 motifs (1e-131) (Fig. 1I). In contrast young Mda5-/- HSCs harbor ZSCAN (1e-02) motifs while for the induced regions in aged Mda5-/- HSCs we observed CTCF (1e-1177), ETV2 (1e-1057), ERG (1e-1003), FLI1 (1e-1015), PU1 (1e-552), AP1 (1e-211), IRF3 (1e-86) motifs (Fig. 1I).

We performed digital footprinting analysis to identify motif occupancy for transcription factors IRF3, NF-κB (p65), and STAT1 in aged compared to young WT and Mda5-/- HSCs. We observed a substantial increase in occupancy between young and aged WT or Mda5-/- footprints, although the total number of footprints was lower in the knockout. Additionally, we did not detect many footprints for inflammatory factors in young HSCs (Supplementary Fig. 1I). To further investigate this, we compared aged WT and Mda5-/- footprints, and we observed less occupancy of the WT footprints in the Mda5-/- samples (Supplementary Fig. 1J).

Taken together, these results further support that inflammatory signaling activation, a hallmark of aging, is significantly dampened in Mda5-/- HSCs and bone marrow.

Reduced HSC accumulation and myeloid bias in aged Mda5-/- animals
We reasoned that changes in cytokine concentrations and inflammatory signaling activation could impact HSC functionality and maintenance during aging. We did not detect changes in bone marrow cellularity in young, middle-aged, or aged WT and Mda5-/- animals (Supplementary Fig. 2A). When evaluating the HSC compartment in middle-aged and aged WT animals, we observed an increased frequency of EPCR SLAM, LSK SLAM, and LT-HSCs, indicative of HSC expansion, an established hallmark of aging, that was more prominent in aged compared to the young or middle-aged animals. However, this accumulation was significantly reduced in Mda5-/- animals (Fig. 2A–C and Supplementary Fig. 2B–D). Concomitantly, metabolomic analysis showed significant differences between aged WT and Mda5-/- HSCs. The concentration of glutathione disulfide (GSSG), which is known to increase during aging43, was lower in the aged Mda5-/- HSCs (Fig. 2D). The nicotinamide adenine dinucleotide (NAD+) concentration has been shown to decline upon aging. Similarly, a decline in NADP+ is also expected during aging, since it is exclusively synthesized from NAD+ by cytoplasmic and mitochondrial NAD+ kinases44. The concentration of these two metabolites, and especially NADP+, was higher in aged Mda5-/- HSCs (Fig. 2D). Minimal differences were observed in the nucleotide metabolism and the TCA cycle between aged Mda5-/- and WT HSCs (Supplementary Fig. 2E, F). Additionally, we measured HSC mitochondrial mass and membrane potential using MitoTracker green dye (MTG) and tetramethylrhodamine methyl ester (TMRM), respectively, in the presence of verapamil45,46, a known blocker of xenobiotic efflux pumps, which fosters dye retention in the organelles. This allowed us to observe a higher mitochondrial mass and membrane potential in aged Mda5-/- HSCs, a characteristic of young HSCs47–49, albeit in studies utilizing verapamil (Fig. 2E, F).

Next, we evaluated the MPP (multipotent progenitor) composition in the bone marrow of young, middle-aged and aged mice. We observed an expansion of the MPP1 population in both aged WT and Mda5-/- animals, but to a lesser degree in the aged Mda5-/- (Supplementary Fig. 2G). MPP2 cells also accumulated with age, to a similar extent between WT and Mda5-/- animals (Supplementary Fig. 2H). The frequency of MPP3 cells, which represent myeloid-biased progenitor cells50, increased in aged WT mice compared to aged Mda5-/- animals (Supplementary Fig. 2I). Lastly, the MPP4 population, comprised of the lymphoid-prone progenitor cells50, equally declined with age in WT and Mda5-/- animals (Supplementary Fig. 2J). Taken together, these results indicate a myeloid bias within the WT progenitor compartment during aging, which is ameliorated by Mda5 deficiency.

We also assessed the frequency of committed progenitors, including common myeloid progenitors (CMP), granulocytic monocytic progenitors (GMP), megakaryocytic-erythroid progenitors (MEP) and common lymphoid progenitors (CLP), as well as differentiated lineage populations in the bone marrow. While all the population frequencies were comparable in young animals, CMPs and MEPs were decreased while CLPs were increased in middle-aged and aged Mda5-/- animals compared to age-matched WT mice (Fig. 2G–L).

In conclusion, HSCs tend to accumulate less, and there is less myeloid bias during aging in Mda5-/- animals.

Aged Mda5-/- HSCs are more quiescent and have better repopulation capacity
To functionally investigate the effects of the lack of Mda5 in aging, we first asked whether their quiescent status differs from that of the aged WT HSCs. We isolated HSCs from young, middle-aged, and aged WT and Mda5-/- animals, and we counted the number of cell divisions after 24 and 48 h in culture. The percentage of WT HSCs undergoing at least one division increased during aging, but overall, Mda5-/- HSCs divided less than their WT counterparts at all ages (Fig. 3A and Supplementary Fig. 3A). Similarly, more Mda5-/- HSCs remained in G0 phase at all ages when comparing the cell cycle status of the WT to the Mda5-/- HSCs (Fig. 3B and Supplementary Fig. 3B).

Next, we assessed the repopulation capacity in non-competitive transplantations. In these experiments, we transplanted the volume of whole bone marrow containing 300 HSCs from aged WT or Mda5-/- animals (CD45.2) into lethally irradiated recipients (CD45.1). The overall chimerism of Mda5-/- HSCs was significantly higher compared to their WT counterparts, both in primary and secondary transplantations (Fig. 3C–J). However, this reconstitution advantage was lost in competitive transplantations, where the volume of whole bone marrow containing 300 HSCs of either aged WT or Mda5-/- animals (CD45.2) was transplanted with 0.5 million bone marrow cells from a young donor (CD45.1) into lethally irradiated recipients (CD45.1/.2) (Supplementary Fig. 3C–J). Thus, aged Mda5-/- HSCs have better repopulation capacity in a non-competitive setting, but they lose this advantage when competing with young HSCs.

To examine the capacity of WT and Mda5-/- HSCs to repopulate lethally irradiated recipients, we sorted and transplanted 100 LSK-SLAM HSCs from young or aged WT or Mda5-/- mice together with aged-matched CD45.1 competitors into CD45.1 lethally irradiated recipients, followed by whole bone marrow competitive secondary transplantations. Overall, we observed similar total chimerism in the primary competitive transplantations of young and aged WT and Mda5-/- HSCs (Fig. 3K–O and Supplementary Fig. 3K–O). However, the chimerism of young Mda5-/- HSCs was higher in the secondary competitive transplantations when compared to young WT HSCs (Supplementary Fig. 3K), with a significantly higher contribution of CD45.2 donor-derived cells to B cell, T cell, monocyte, and granulocyte populations (Supplementary Fig. 3L–O). One potential explanation for the significantly increased chimerism of the secondary transplants of the young Mda5-/- is the increase in the frequency of the LSKs and HSCs in the primary transplant (not significant due to the transplanted animals with very low chimerism). In contrast, the total and lineage-specific chimerism of aged Mda5-/- HSCs was comparable to the WT counterpart (Fig. 3K–P).

In line with these results, analysis of the bone marrow of primary recipients revealed no significant differences between WT and Mda5-/- HSCs in the overall chimerism and the chimerism of lineage-specific and hematopoietic progenitor populations (Supplementary Fig. 4A, C). Yet, the contribution of CD45.2 donor-derived cells in the bone marrow of secondary recipients was higher for young and aged Mda5-/- HSCs (Supplementary Fig. 4B, D). This confirms that both have better reconstitution capacity upon secondary transplantations and indicates that Mda5-/- HSCs show diminished differentiation, potentially due to their more quiescent nature.

Taken together, these results show that aged Mda5-/- are more quiescent and have a better reconstitution capacity in a non-competitive setting, as well as in the bone marrow of secondary competitive transplantations.

Proteostatic pathways are deregulated in middle-aged and aged Mda5-/- HSCs
To investigate the molecular mechanism underlying the youthful phenotype of Mda5-/- HSCs, we performed bulk RNA-sequencing (RNA-seq) to compare young, middle-aged, and aged WT and Mda5-/- HSC transcriptomes. Multiple genes were deregulated (fold change 1.5 and FDR < 0.05) in all comparisons, but only ifih1 was significantly deregulated in the comparison between young and ifih1 and another gene were significantly deregulated in middle-aged WT and Mda5-/- HSCs (Supplementary Data Set 5). Concomitantly, principal component analysis (PCA) showed a clear separation of the WT and Mda5-/- HSC aged transcriptomes from the other transcriptomes, while Mda5-/- middle-aged and young HSC transcriptomes clustered together with the WT (Fig. 4A). In detail, one WT and one Mda5-/- young transcriptome clustered closer to the aged transcriptomes (Fig. 4A). We next depicted the top 500 most differentially expressed genes clustered by age, where the aged Mda5-/- HSCs clustered next to the young Mda5-/- HSCs (Fig. 4A). Of note, Ifih1 alias Mda5 was not transcriptionally upregulated during aging in our datasets (Supplementary Data 5) nor in an examined, publicly available, human dataset51. Gene set enrichment analysis (GSEA) showed enrichment for the dormant HSC signature52,53 in young, middle-aged, and aged Mda5-/- HSCs in comparison to the respective WT (Fig. 4B–D), verifying our single-cell division and cell cycle assays. Additionally, the aging signature of Svendsen54 (aging signature 1) and the aging signature hub (https://agingsignature.webhosting.rug.nl/agingsignature) (aging signature 2) were enriched in middle-aged and aged WT in comparison to middle-aged and aged Mda5-/- HSCs (Fig. 4B–D). Ingenuity pathway analysis55 of genes deregulated between aged WT and Mda5-/- HSCs showed enrichment for “eukaryotic translation elongation” and “EIF2 signaling” (Fig. 4E). Of note, we observed that a significant number of deregulated genes (247 genes) were close to induced peaks upon aging (Fig. 4F), suggesting that they experience changes in chromatin accessibility. Both aging signatures were enriched in the middle-aged or aged WT populations when compared to young WT (Fig. 4G). Interestingly, these signatures were enriched in middle-aged Mda5-/- when compared not only with young, but surprisingly also with aged Mda5-/- HSCs (Fig. 4H, I). Collectively, this comprehensive molecular analysis supports our functional analysis and shows that aged Mda5-/- HSCs are transcriptionally younger than their aged WT counterpart.

The master regulator of heat shock response and proteostasis is the transcription factor HSF1, which has been found to increase its concentration in the nucleus of aged and middle-aged HSCs33. We performed GSEA analysis using the transcriptomics signature from the study by Kruta et al.33 (HSF1 signature), and we observe enrichment in middle-aged WT HSCs when compared to young WT or middle-aged Mda5-/- HSCs (Fig. 4J). Additionally, upstream regulator analysis from Ingenuity pathway analysis revealed HSF1 as an upstream regulator in all ages in WT and Mda5-/- HSCs (Fig. 4K), suggesting that it is an important regulator of HSC aging. We then performed Ingenuity pathway analysis for all the different comparisons. The categories for “eukaryotic translation elongation” or “EIF2 signaling” were enriched especially in Mda5-/- transcriptomes and at advanced ages. Finally, we observed enrichment of PKR signaling in middle-aged Mda5-/- HSCs (Supplementary Fig. 5A).

Thus, translation and cellular response to heat stress, both maintaining proteostasis, are important in Mda5-/- HSCs.

Transposable elements are upregulated during aging
Since transposable elements (TE) are deregulated during aging26, and we and others have previously shown that they can act as ligands for MDA519,20,22,23, we next analyzed our RNA-seq data for TE expression (Fig. 5A). We found that TE families were upregulated when comparing aged to young or to middle-aged HSCs. We observed 165 up and 3 downregulated TE families in aged WT HSCs compared to young HSCs, and 622 up and 2 down when comparing aged versus middle-aged WT HSCs (fold change 1.5, FDR < 0.05) (Supplementary Data 6). Interestingly, more TEs were upregulated in Mda5-/- HSCs. Indeed, 631 TE families were up, and 6 were downregulated in aged versus young Mda5-/- HSCs, and 764 TE families were up and 2 downregulated when we compared aged to middle-aged Mda5-/- HSCs. Very few or no differentially expressed TEs were detected between young (0), middle-aged (0), or aged (1) WT and Mda5-/- HSCs (Supplementary Data 6). Lastly, to verify the presence of double-stranded RNA in the cytoplasm of young, middle-aged, and aged WT and Mda5-/- HSCs, we performed a staining with anti-double-stranded RNA antibody. Indeed, we observed an increase in mean fluorescence intensity (MFI) in both WT and Mda5-/- HSCs upon aging (Fig. 5B–D).

Single-cell RNA sequencing reveals a link between Mda5 and translation
To understand the cellular and molecular heterogeneity, we performed single-cell RNA sequencing (scRNA-seq) in LSK cells isolated from young and aged WT and Mda5-/- animals. We applied known gene signatures to assign the cells to HSCs and MPP1–5 populations in both young and aged WT and Mda5-/- datasets56–58 (Fig. 6A, B, and Supplementary Fig. 6A, B). Cells were then ordered by slingshot59 pseudotime. The HSC population was placed at the top of the pseudotime ordering, followed by the MPP5/MPP1 or MPP5 populations, in agreement with previous findings that placed these populations in close proximity to HSCs both molecularly and biologically56 (Fig. 6C, D and Supplementary Fig. 6C, D). Additionally, we performed pseudobulk analysis on the HSC/MPP1 cluster on young and aged Mda5-/- versus WT datasets, detecting 214 upregulated and 21 downregulated in the young and 57 upregulated and 25 downregulated in the aged HSCs (Fig. 6E and Supplementary Data 7, cut-offs: Log2FC 0.5 and FDR < 0.05). Concomitantly, analysis of aged WT and Mda5-/- versus young HSC/MPP1 populations revealed numerous deregulated genes (Fig. 6E and Supplementary Data 7).

To assess whether we could observe similar results in the multipotent progenitor populations, we clustered the MPP3 and MPP4 top 500 genes (Supplementary Fig. 6E, F and Supplementary Data 8–12). We observed 23 upregulated genes in aged Mda5-/- versus WT, while 276 were up and 49 were downregulated when comparing young Mda5-/- and WT MPP3 populations. Similarly, 20 genes were up, and 12 were downregulated in aged Mda5-/- versus WT, and 162 were up, and 66 were downregulated in young Mda5-/- and WT MPP4 populations (Supplementary Data 8–12). Ingenuity pathway analysis revealed an enrichment of “EIF2 signaling”, “PKR-mediated signaling”, and “unfolded protein response” categories in both HSC/MMP1 and MPP3, but not MPP4, aged populations. Additionally, “aggrephagy” and “cell cycle checkpoints” were enriched in young HSC/MPP1, MPP3, and MPP4 populations. Lastly, HSF1 was identified as an upstream regulator of deregulated genes in young HSC/MPP1, MPP3, and MPP4 and aged MPP3 (Fig. 6F).

Taken together, these results are in line with our bulk RNA-seq analysis and further show that the MPP3 population may undergo transcriptional changes similar to the HSC population, especially in terms of proteostasis and protein translation.

Aged Mda5-/- HSCs exhibit enhanced PERK signaling
We have previously observed that EIF2 signaling is transcriptionally enriched in Mda5-/- HSCs. Stress in the endoplasmic reticulum (ER) leads to the unfolded protein response (UPR). The PERK-induced branch of the UPR is responsible for the phosphorylation of the EIF2a, which is in turn is responsible for blocking translation60. To delineate the order of events in our datasets, we performed GSEA analysis and observed that the endoplasmic-reticulum-associated protein degradation (ERAD pathway) is enriched both in the middle-aged and aged WT and Mda5-/- HSCs (Fig. 7A, B). However, the PERK branch was enriched only in the middle-aged and aged Mda5-/- HSCs (Fig. 7C). To assess EIF2a phosphorylation status downstream of the PERK pathway, we performed phospho-EIF2a staining in young, middle-aged aged and aged WT and Mda5-/- HSCs. Interestingly, phospho-EIF2a levels were comparable in young and middle-aged WT and Mda5-/- HSCs, but we detected an increased signal in aged Mda5-/- HSCs, verifying that this signaling is activated primarily in Mda5-/- HSCs (Fig. 7D).

Aged Mda5-/- HSCs exhibit enhanced proteostasis
Since HSF1 and EIF2a are involved in the maintenance of protein homeostasis, we next assessed proteostasis in aged Mda5-/- and WT HSCs. We exploited two previously established assays to evaluate the abundance of misfolded and unfolded proteins, by quantifying ubiquitinated proteins and tetraphenylethene maleimide (TPE-MI)39,61 labeled proteins by flow cytometry, respectively. Aged Mda5-/- HSCs exhibited a significantly lower amount of misfolded and unfolded proteins compared to the WT counterparts (Fig. 8A, B). We detected a similar trend in middle-aged Mda5-/- HSCs compared to WT (Supplementary Fig. 7A, B). The protein levels in young and aged WT and Mda5-/- HSCs were similar, albeit a bit more in the Mda5-/- HSCs (Supplementary Fig. 7C). To investigate whether the enhanced quiescence of Mda5-/- HSCs leads to fewer misfolded proteins, we combined the misfolded protein assay with the cell cycle staining. Interestingly, we observed reduced accumulation of misfolded proteins in G0 aged Mda5-/- HSCs compared to the WT counterpart (Supplementary Fig. 7D). Additionally, protein synthesis rate, measured using O-propargyl-puromycin (OP-Puro) incorporation, was significantly lower in aged Mda5-/- HSCs compared to aged WT HSCs (Fig. 8C). We hypothesized that these translational changes might have an impact on intracellular amino acid availability. Indeed, our intracellular metabolomic analysis on aged WT and Mda5-/- HSCs showed an increased concentration of amino acids in Mda5-/- HSCs (Fig. 8D). Collectively, these results align with the increased phospho-EIF2a observed in aged Mda5-/- HSCs (Fig. 7D) and support the hypothesis that amino acid accumulation in Mda5-/- HSCs is due to a reduced protein synthesis rate.

Autophagy has been implicated in the maintenance of protein homeostasis in response to stress62,63. Additionally, PERK signaling has been shown to lead to an induction of autophagy35. Recently, it has been shown that quiescent HSCs have a high autophagic flux, while aging has also been associated with impaired autophagy37,39,64. Consistent with these results, we observed a significant increase in the autophagic flux in aged Mda5-/- HSCs compared to WT (Fig. 8E). Taken together, these data indicate that aged Mda5-/- HSCs retain enhanced proteostasis and autophagic activity.

As a master regulator of protein homeostasis, HSF1 is activated under stress conditions and translocates into the nucleus to trigger a transcriptional response30,31. To evaluate HSF1 localization and abundance, we performed immunofluorescence staining on isolated young, middle-aged, and aged WT and Mda5-/- HSCs. HSF1 levels were similar in young WT and Mda5-/- HSCs, increased in middle-aged WT but not Mda5-/- HSCs, and significantly increased in aged Mda5-/- HSCs compared to age-matched WT counterparts (Fig. 8F, G). As we did not observe changes in HSF1 transcript levels upon aging (Supplementary Data 5), our results agree with Kruta et al.33, in which they described HSF1 upregulation, at least at the protein level, upon aging and already in middle-aged cells. To further strengthen these results, we performed a similar staining using an antibody against phosphorylated HSF1 (pSer326), which marks HSF1 activation. Similarly, we observed reduced accumulation of phospho-HSF1 in middle-aged Mda5-/- HSCs. Increased levels of phospho-HSF1 were detected in aged Mda5-/- HSCs, albeit this result was not significant (Supplementary Fig. 6E, F).

To investigate whether MDA5 can retain HSF1 in the cytoplasm, we overexpressed MDA5 in HEK293T cells and stained for HSF1 to assess its localization. In HEK293T cells, HSF1 is localized in the nucleus. We observed that overexpression of full-length MDA5 was enough to keep a portion of HSF1 in the cytoplasm (Supplementary Fig. 7G, H). However, HSF1 was not tethered in the cytoplasm upon overexpression of a mutant that lacks both CARD domains (Supplementary Fig. 7G, H). Lastly, proximity ligation assay revealed that MDA5 and HSF1 are found in proximity (Fig. 8H), suggesting a potential interaction between these two proteins. To our surprise, we observed both nuclear and cytoplasmic staining for the proximity ligation assay, hinting at the fact that MDA5 may also be in the nucleus.

Because HSF1 protein levels were increased and localized in the nucleus in Mda5-/- HSCs, we reasoned that HSF1 activation could lead to the alteration of Mda5-/- and WT phenotypes, respectively. HSF1A is a small molecule inhibitor of TRiC chaperonin (chaperonin that keeps HSF1 in the cytoplasm) and functions as HSF1 activator (HSF1A), by preventing TRiC binding to HSF1, thus permitting its trimerization and translocation to the nucleus33,65. Therefore, we reasoned that the HSF1A inhibitor could induce aged WT HSCs to behave like aged Mda5-/- HSCs. Indeed, HSF1A treatment enhanced aged WT HSC quiescence by decreasing their ability to divide in culture (Fig. 8I). In addition, we performed serial colony-forming unit (CFU) assays by plating HSCs in methylcellulose and replating a similar number of cells every week. This assay evaluates the in vitro repopulation capacity of HSCs. Indeed, the inhibitor of HSF1 that made the cells more quiescent led to the enhanced capacity of aged WT HSCs to produce colonies after the third replating, suggesting that they can keep their in vitro self-renewal capacity (Fig. 8J).

These data suggest that a lack of Mda5 can delay the aging process in the HSC compartment by retaining effective proteostasis through HSF1 engagement and reduction of protein translation.

Methods
Mice
All mouse experiments were carried out in accordance with the guidelines of the Federation of European Laboratory Animal Science Association and following legal approval of the Regierungspräsidium Freiburg and the Animal Care and Use Committee of the French authorities-Ciepal Azur (35-9185.81/G-18/127, 35-9185.81/G-22/058, PEA845 or apafis 2022062916507603_v5). All of the animals were maintained at the animal facility of the Max Planck Institute of Immunobiology and Epigenetics or at the IRCAN facility under specific pathogen-free conditions in individually ventilated cages with a light-dark cycle of 12–12h at 20–24°C under 45–65% humidity. For all genotypes, gender matched female or male mice were used in the experiments, and experimental and control mice were co-housed in the same facility rooms. Mice were euthanized with cervical dislocation or CO2 followed by cervical dislocation. HSC transplantations were performed at St. Jude Children’s Hospital. The transplantation procedure was performed according to protocols approved by the St. Jude Children’s Research Hospital Institutional Animal Care and Use Committee, under animal protocol #3096.

Mda5-/- mice or B6.129X1(C)-Ifih1tm1.1Cln/J were purchased from Jackson Laboratory Strain #: 015812. CD45.1 BL6 mice were bred in MPI-IE unless otherwise stated (Jax Strain #: 002014). CD45.1 JAX Boy mice were purchased from Jackson Laboratory (C57BL/6J-Ptprcem6Lutzy/J, JAX stock #033076).

Antibodies
The following antibodies were purchased from BioLegend and used at a dilution 1:500 dilution unless stated otherwise: anti-CD45.2/Ly5.2 (Pacific Blue or FITC, 104); anti-CD45.1/Ly5.1 (Alexa Fluor 700 or Pe/Cy7, A20); anti-CD45 (FITC, 1:1000, 30-F11); anti-CD45R/B220 (BV650 or Alexa Fluor 700 or FITC or PE/Cy7 or biotin, RA3-6B2); anti-Ly6G/Ly6C (Gr1, BV650 or PE/Cy7 or APC, 1:1000, FITC or biotin, 1:1600, RB6-8C5); anti-CD11b (BV650 or PE/Cy7 or APC/Cy7, 1:1000, FITC or biotin, 1:1600, M1/70); anti-TER119 (BV650 or PE/Cy7, 1:1000, FITC or biotin, 1:1600, TER119); anti-CD3ε (BV650 or PE/Cy7, 1:1000, FITC or biotin, 1:1600, 145-2C11); anti-NK-1.1 (FITC, PK136); anti-CD19 (FITC, 1D3/CD19; anti-CD4 (PE/Cy5 or FITC, 1:1000, RM4-5); anti-CD8a (PE/Cy5 or FITC, 1:2000, 53-6.7); anti-CD117 (cKit, BV711, 1:1000, 2B8); anti-Ly-6A/E (Sca1, PE/Cy7 or APC/Cy7, 1:400, E13-161.7); anti-CD201 (EPCR, PE or APC, 1:200, RCR-16); anti-CD150 (SLAM, PE/Dazzle™ 594 or BV421, 1:400, TC15-12F12.2); anti-CD48 (PE/Cy7 or APC/Cy7, 1:400, or BV421, 1:1000, HM48-1); anti-CD34 (FITC, 1:50, SA376A4); anti-CD135/Flk2 (PE or APC, 1:200, A2F10); anti-Ki67 (FITC or PE, 1:100, 16A8); anti-CD16/32 (APC or PE, 1:1000, 93); anti-CD127 (IL-7Rα, APC or PE, 1:1000, A7R34); goat anti-mouse IgG, IgM (H+L) secondary (1:2500, Alexa Fluor 488, A-10680, Invitrogen) See also Supplementary Table 1.

Sorting strategy
Throughout the text, HSCs refer to EPCR SLAM cells (Lin−EPCR+CD150+CD48− unless otherwise stated. LSK SLAM: Lin−Sca1+cKit+(LSK) CD150+CD48−; LT-HSCs: LSKCD150+CD48−CD34−CD135−; MPP1: LSKCD150+CD48−CD34+CD135−; MPP2: LSKCD150+CD48+CD34+CD135−; MPP3: LSKCD150−CD48+CD34+CD135−; MPP4: LSKCD150−CD48+CD34+CD135+.

HSC isolation, flow cytometry and cell sorting
Tibiae, femurs, and hip bones were isolated and crushed in PBS. Cells were washed with FACS buffer (PBS, 2% FCS, 1 mM EDTA) and counted Vi-cell XR counter (Beckman Coulter).
- For lineage quantification, HSPC characterization, 3×106 cells were stained with antibody mix in FACS buffer for 30 min at 4°C. Sample acquisition was performed using a Fortessa FACS analyzer (BD Biosciences). All data were analyzed using FlowJo (BD) software.
- For sorting, samples were enriched by lineage depletion using a biotin-conjugated lineage antibodies cocktail (CD3ε, CD11b, CD45R/B220, Ly6G/Ly6C, TER-119) for 20 min at 4°C. Streptavidin nanobeads (MojoSort, 480016, BioLegend) were added for 20 min at 4°C, with subsequent magnetic separation for 5 min at room temperature. Enriched samples were stained with an antibody mix in FACS buffer against EPCR, CD150, and CD48 for 30 min at 4°C. Cells were washed and resuspended in 1 ml FACS buffer, and sorting was performed on a BD FACSAria III or a BD FACSAria Fusion (BD Biosciences).

Ex vivo HSC culture
HSCs or lineage-depleted cells were cultured in StemPro-34 SFM medium with 2.5% StemPro-34 Supplement (Gibco), 50 ng/ml mSCF, 25 ng/ml mTPO, 30 ng/ml mFlt3L, 1% penicillin-streptomycin, and 2 mM l-glutamine.

Single-cell division assay
HSCs were single sorted on Terasaki microtest plates (654102, Greiner) in the medium described above. For HSF1 treatment, DMSO or HSF1A (8 μM final concentration, Axon 1890, Axon MedChem) was added to the medium before sorting. The frequencies presented in the single cell division assay plots were calculated for each Terasaki plate separately and independently as follows:
Frequency of HSCs that did not divide = number of wells with one sorted HSC that did not divide/total number of wells in the Terasaki plate with live sorted HSCs
Frequency of HSCs that divided at least once = number of wells with sorted HSC that did divide/total number of wells in the Terasaki plate with live sorted HSCs

Empty wells or wells containing dead cells were not included in the frequency calculations. Fisher’s exact test was performed considering the total number of wells across all different independent experiments.

Cell cycle staining
Samples were isolated and enriched as described above. 3×106 lineage-negative cells were stained with the antibody mix in FACS buffer for 30 min at 4°C to identify HSCs. Then, cells were washed and resuspended in fixed intracellular Fixation Buffer (00-8222-49, Thermo Fisher Scientific) for 10 min at 4°C. Samples were then washed and resuspended in permeabilization buffer (00-8333-56, Thermo Fisher Scientific) with anti-Ki67 antibodies for 2 h at 4°C. Lastly, cells were washed and resuspended in PBS with Hoechst 33258 (H3569, Life Technologies) at room temperature for at least 15 min before acquisition. Sample acquisition was performed using a Fortessa FACS analyzer (BD Biosciences). All data were analyzed using FlowJo (BD) software.

Colony-forming unit assay
Two hundred HSCs were sorted in 1 ml Mouse MethoCult GF medium (M3434, StemCell Technologies) and plated in 35 mm petri dishes. DMSO, HSF1A (8 μM final concentration, Axon 1890, Axon MedChem) was added to the medium before each plating. Colonies were counted after 5–7 days at 37°C and 5% CO2. For replating, cells were washed with PBS, counted, and replated (10,000 cells of each replicate) in MethoCult GF (M3434, StemCell Technologies). Colonies were quantified after 4–5 days, after the second and third replating.

Whole bone marrow transplantations
Aged WT and Mda5-/- mice (CD45.2+/Ly5.2) were used as donors, young WT mice (CD45.1+/Ly5.1) were used as competitors in competitive transplantations and as recipients in non-competitive transplantations. Young WT mice (CD45.1+/CD45.2+) were used as recipients in competitive transplantations. We calculated the HSC frequency of the donors and intravenously injected the volume of bone marrow containing 300 HSCs into lethally irradiated (9.5 Gy) recipients. In competitive transplantations, we additionally injected 500,000 competitor-derived bone marrow cells. Peripheral blood chimerism was checked every 4 weeks after transplantation for 16 weeks. Briefly, 20 μl of blood was obtained from the submandibular vein using a lancet. Erythroid cells were lysed in ammonium-chloride-potassium (ACK, BP10-548E, Lonza Bioscience) for 5 min at room temperature. Cells were washed with PBS and stained for 30 min at 4°C with anti-CD45.1/Ly5.1, anti-CD45.2/Ly5.2, anti-CD4, anti-CD8a, anti-CD11b, anti-Ly6G/Ly6C, anti-CD45R/B220, and anti-Ter119 antibodies. After washing and resuspending in FACS buffer, samples were acquired on a Fortessa FACS analyzer (BD Biosciences). All data were analyzed using FlowJo (BD) software.

HSC transplantations
Transplantations were performed using the CD45.1/CD45.2 congenic system. Recipient CD45.1 JAX Boy mice (C57BL/6J-Ptprcem6Lutzy/J, JAX stock #033076) (10 weeks old) were lethally irradiated with 11 Gy delivered in a split dose of 5.5 Gy each, at least 3 h apart, using a gamma irradiator with a 137Cs source. Recipients were treated with Sulfamethoxazole/Trimethoprim in drinking water for 7 days before the transplant and for 21 days post-transplant. On the day of the transplant, 100 FACS-sorted donor HSCs (Lin-Sca1+c-kit+CD135−CD48−CD150+) were injected into the tail vein along with 2×105 of whole bone marrow cells isolated from age-matched CD45.1 JAX Boy mice. Hematopoietic reconstitution in peripheral blood was monitored by flow cytometry every 4 weeks, and the primary recipients were euthanized 16 weeks post-transplant. For the secondary transplantation, bone marrow of primary recipients injected with HSCs from the same donor mice was pooled, and 3×106 of pooled whole bone marrow cells were delivered via tail vein injections into lethally irradiated CD45.1 JAX Boy secondary recipients. Hematopoietic reconstitution in peripheral blood was monitored by flow cytometry every 4 weeks. The secondary recipients were euthanized 16 weeks post-transplant. The following cell populations were analyzed in the peripheral blood and bone marrow of transplant recipients using flow cytometry: granulocytes (Mac1+Grihi), monocytes (Mac1+Grilo), B cells (CD19+), T cells (CD3+), LSK (Lin−Sca1+c-kit+), SLAM LSK (Lin−Sca1+c-kit+CD135−CD48−CD150+), MPP2 (Lin−Sca1+c-kit+CD135−CD48+CD150+), MPP3 (Lin−Sca1+c-kit+CD135−CD48+CD150−), MPP4 (Lin−Sca1+c-kit+CD135+CD150−). Lineage (Lin) cocktail contained the following antibodies: CD19, B220, CD3, Mac1 (CD11b), Gr1 (Ly-6G/Ly-6C), CD11c, and Ter119. The following antibodies were used for flow cytometry: APC anti-mouse/human CD11b (Biolegend, 101212, M1/70, 1/250), APC anti-mouse Ly-6A/E (Sca-1) (Biolegend, 108112, D7, 1/250), APC/cyanine7 anti-mouse CD19 (Biolegend, 115529, 6D5, 1/250), brilliant violet 421 anti-mouse CD117 (c-Kit) (Biolegend, 105828, 2B8, 1/250), brilliant violet 421 anti-mouse CD3ε (Biolegend, 100341, 145-2C11, 1/250), brilliant Violet 605 anti-mouse CD150 (SLAM) (Biolegend, 115927, TC15-12F12.2, 1/1000), brilliant violet 605 anti-mouse CD19 (Biolegend, 115539, 6D5, 1/250), brilliant violet 605 anti-mouse CD45.1 (Biolegend, 110737, A20, 1/1000), brilliant violet 605 anti-mouse CD45.2 (Biolegend, 109841, 104, 1/250), eBioscience Fixable Viability Dye eFluor 780 (Thermofisher, 65-0865-14, 1/2000), FITC anti-mouse CD11c (Biolegend, 117306, N418, 1/250), FITC anti-mouse CD19 (Biolegend, 152404, 1D3/CD19, 1/250), FITC anti-mouse CD3ε (Biolegend, 100306, 145-2C11, 1/250), FITC anti-mouse CD45.2 (Biolegend, 109806, 104, 1/250), FITC anti-mouse Ly-6G/Ly-6C (Gr-1) (Biolegend, 108406, RBC6-8C5, 1/500), FITC anti-mouse TER-119 (Biolegend, 116206, TER-119, 1/250), FITC anti-mouse/human CD11b (Biolegend, 101206, M1/70, 1/250), FITC anti-mouse/human CD45R/B220 (Biolegend, 103206, RA3-6B2, 1/250), PE anti-mouse CD135 (Biolegend, 135306, A2F10, 1/500), PE anti-mouse Ly-6G/Ly-6C (Gr-1) (Biolegend, 108408, RBC6-8C5, 1/2000), PE/cyanine7 anti-mouse CD45.2 (Biolegend, 109830, 104, 1/250), and PerCP/cyanine5.5 anti-mouse CD48 (Biolegend, 103422, HM48-1, 1/500). See also Supplementary Table 1.

Cytokine quantification
The LEGENDplex mouse inflammation panel (740150, BioLegend) was used according to the manufacturer’s instructions. Briefly, after crushing the bones, the cell suspension was centrifuged at 1500 rpm for 5 min at 4°C. The bone marrow supernatant was then collected in a separate tube and stored at −80°C until further use. Samples were diluted 1:1 in PBS, incubated with beads conjugated with the respective antibodies, and analyzed on a Fortessa FACS analyzer (BD Biosciences). All data were analyzed using FlowJo (BD) software.

Misfolded (polyubiquitinated) protein quantification
2×106 lineage-depleted cells were stained with the antibody mix in FACS buffer for 30 min at 4°C to identify HSCs. Cells were washed and resuspended in 1% PFA for 10 min at room temperature. Samples were then washed and incubated overnight with 1:500 primary pan-ubiquitin antibody (clone FK2, 04-262, Sigma) in permeabilization buffer (00-8333-56, Thermo Fisher Scientific). Then, cells were washed and resuspended with 1:2500 goat anti-mouse IgG1 secondary antibody in permeabilization buffer at 4°C for at least 2 h. After washing and resuspending in FACS buffer, samples were acquired on a Fortessa FACS analyzer (BD Biosciences). All data were analyzed using FlowJo (BD) software. For measurement of misfolded proteins in G0 HSCs cells were washed and resuspended with 1:2500 goat anti-mouse IgG1 secondary antibody in permeabilization buffer at 4°C for 2 h, along with anti-Ki67 antibody at 4°C for at least 2 h (H3569, Life Technologies). After washing and resuspending in FACS buffer, samples were acquired on a Fortessa FACS analyzer (BD Biosciences). All data were analyzed using FlowJo (BD) software.

Measurement of protein concentration
For total protein concentration measurement, 1000 EPCR SLAM cells were sorted from young and aged WT and Mda5-/- mice. Cells were lysed in RIPA buffer (89900, Thermo Fisher) with protease inhibitor (1x) for 10 min at 4°C. Samples were centrifuged at 13,000×g for 15 min to pellet the cell debris. The protein concentration in the supernatant was measured by Qubit 3.

Unfolded protein quantification
Tetraphenylethene maleimide39,61 (TPE-MI, HY-143218, MedChemExpress) was reconstituted according to the manufacturer’s instructions, diluted in PBS (50 mM final concentration), and added to each sample. 2×106 lineage-depleted cells were incubated for 45 min at 37°C. Samples were washed and stained with the antibody mix in FACS buffer for 30 min at 4°C to identify HSCs. Cells were washed and acquired on a Fortessa FACS analyzer (BD Biosciences). All data were analyzed using FlowJo (BD) software.

Mitochondrial mass and membrane potential quantification
2×106 lineage-depleted cells were resuspended in complete StemPro-34 SFM medium with either MitoTracker Green (MTG, 50 nM final concentration, M7514, Thermo Fisher Scientific) or tetramethylrhodamine methyl ester perchlorate (TMRM, 100 nM final concentration, I34361, Thermo Fisher Scientific), supplemented with verapamil (V4629, Sigma Aldrich). After 30 min incubation at 37°C, samples were washed and stained with the antibody mix in FACS buffer with verapamil for 30 min at 4°C to identify HSCs. Cells were washed and acquired on a Fortessa FACS analyzer (BD Biosciences). All data were analyzed using FlowJo (BD) software.

Measurement of protein synthesis
2×106 lineage-depleted cells were stained with the antibody mix in FACS buffer for 30 min at 4°C to identify HSCs. Then, cells were resuspended in complete StemPro-34 SFM medium (10639011 Thermo Fisher) with OP-Puro at 50 μM and incubated at 37°C for 1 h. Samples were then washed, fixed with 1% PFA in PBS for 10 min at 4°C, and permeabilized with permeabilization buffer (00-8333-56, Thermo Fisher Scientific) for 20 min at 4°C. The azide-alkyne cycloaddition was performed using the Click-iT Plus OPP Alexa Fluor 488 Kit (C10456, Thermo Fisher Scientific), according to the manufacturer’s instructions. After the 30 min reaction, the cells were washed with PBS and acquired on a Fortessa FACS analyzer (BD Biosciences). All data were analyzed using FlowJo (BD) software.

Autophagic flux detection
This assay was performed using the CYTO-ID autophagy detection kit (ENZ-KIT175-0050, ENZO Life Sciences) according to manufacturer’s instructions. Briefly, 2×106 lineage-depleted cells were resuspended with CYTO-ID green detection reagent diluted in assay buffer and incubated for 30 min at 37°C. Samples were then stained with the antibody mix in FACS buffer for 30 min at 4°C to identify HSCs. After washing, cells were resuspended in FACS buffer and acquired on a Fortessa FACS analyzer (BD Biosciences). All data were analyzed using FlowJo (BD) software.

Targeted metabolomics
For low-input targeted metabolomics (of polar metabolites), 5000 HSCs were isolated as described above. Samples were then washed with 1 mL of 9 g/L NaCl and filtered through a 30 mM cell strainer before proceeding immediately with sorting using 2 g/L NaCl in deionized water as sheath liquid. Cells were sorted directly into a 96-well plate containing 25 μL of extraction solution, which consisted of 0.1% 13C internal standard stock (1 aliquot of U-13C yeast extract (Isotopic solutions, Cat# ISO-1) in 10 mL of 25% v/v methanol in water) in acetonitrile (LC-MS grade). To assess background levels, 5000 cell debris (low light-scattering signals) were collected. Targeted metabolite quantification by LC-MS was carried out as previously described90, and data processing was performed using the R package automRm91. Metabolites were only included if they were detected above the cell debris detection levels, and the retention time was identical to the 13C yeast standard qualifier peak. The metabolic differences were calculated by determining the area under the curve (AUC). Data were normalized to the mean of every detected metabolite in the aged WT population per experiment.

HSF1, phospho-HSF1, phospho-eIF2α, and dsRNA staining
HSCs were isolated and sorted as previous described. Cells were loaded into cytospin (700 rpm, 5 min) to allow them to adhere to glass slides. Fixation was performed with 4% PFA in PBS for 10 min at room temperature, slides were air-dried and kept at +4°C until the next step. Permeabilization and blocking steps were done with 0.1% Triton X-100 and 10% goat serum (Sigma G9023), respectively. Indirect immunofluorescent analysis was conducted using primary anti-HSF1 antibodies (Enzo Life Sciences ADI-SPA-901-D, 1:500 or StressMarq SMC-118D for Rat Ab 1:500), anti-dsRNA (Sigma Aldrich MABE1134, 1:500), phosphorylated HSF1 (Enzo Life Sciences [pSer326]HSF1 ADI-SPA-902-D 1:500) and phosphorylated EIF2a (cell signaling phospho-EIF2a (Ser51) 9721S 1:500) and secondary anti-rabbit or anti-mouse antibodies conjugated with Alexa Fluor-488 dye (Thermo Fisher Scientific, 1:1000). Samples were mounted with Fluroshield mounting solution containing DAPI (Sigma F6057). Images were acquired using an LSM880 confocal microscope (Carl Zeiss, Germany).

HSF1 staining in HEK293T cells
HEK293T cells are cultured in IMDM (10% FBS, 2% penicillin, streptomycin, and glutamine). 20.000 cells were plated in each well of 12-well plates, on coverslips. pFLAG_WTMDA5 (pFLAG_WTMDA5 was a gift from Sun Hur (Addgene plasmid # 216798; http://n2t.net/addgene:216798; RRID:Addgene_216798) and pFLAG_MDA5Δ2CARD (pFLAG_MDA5Δ2CARD was a gift from Sun Hur (Addgene plasmid # 216800; http://n2t.net/addgene:216800; RRID:Addgene_216800) were transfected using Lipofectamine overnight according to the manufacturer’s instructions. Fixation was performed with 4% PFA in PBS for 20 min at room temperature. Permeabilization and blocking steps were done with 0.1% Triton X-100 and 1% BSA, respectively. Indirect immunofluorescent analysis was conducted using primary anti-HSF1 antibodies (Enzo Life Sciences ADI-SPA-901-D, 1:1000) and Alexa Fluor-488 dye (Thermo Fisher Scientific, 1:1000). Samples were mounted with Fluroshield mounting solution with DAPI (Sigma F6057). Images were acquired using an LSM880 confocal microscope (Carl Zeiss, Germany).

Proximity ligation assay
Human BJ fibroblasts, untreated or treated with 24 h at 10 μg/mL Poly I:C, were fixed with 4% PFA for 20 min at 37°C and permeabilized with 2% Triton X-100 for 10 min at room temperature. PLA was performed according to the manufacturer’s protocol (Sigma Aldrich DUO94104). Primary antibodies were used at the following dilutions: anti-HSF1 (Santa Cruz sc-17757, 1:100), anti-MDA5 (Abcam ab79055, 1:200). Cells were mounted with DAPI-containing mounting medium (Sigma Aldrich DUO82040). Interaction between HSF1 and MDA5 was visualized in situ using a Carl Zeiss LSM880 confocal microscope (Carl Zeiss). Presented images represent the maximum intensity projection of the z-stack.

RNA-seq library preparation
Two thousand five hundred EPCR SLAM cells were sorted from young, middle-aged, and aged WT and Mda5-/- mice. RNA was then isolated using the Arcturus PicoPure RNA isolation Kit (KIT0204, Applied Biosystems). Low-input RNA-seq libraries were prepared using SMART-Seq Stranded Kit (634442, Takara Bio). First, random priming allowed the generation of cDNA from all RNA fragments in the sample. Next, a first round of PCR amplification (5 cycles) added full-length Illumina adapters (Indexing Primer Set HT for Illumina v2), including barcodes. Then, the ribosomal cDNA (originating from rRNA) was cleaved. The cDNA fragments were enriched via a second round of PCR amplification (14 cycles) using primers universal to all libraries. Libraries were cleaned up using AMPure XP beads (A63881) following the manufacturer’s protocol. Libraries were sequenced at Vanderbilt.

RNA-seq analysis of genes
Paired-end 300 bp reads were generated using the Illumina NovaSeq 6000 system and sequenced at a depth of 48–70 M. Raw fastq read files were assessed for quality with FastQC v0.11.9 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and trimmed of Illumina sequencing adapters with Trim Galore v0.3.7 (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). Alignment to the reference mouse genome (GENCODE GRCm38.p6 release M23) was done using STAR v2.7.10a92 with default parameters. Aligned bam files were indexed with samtools v1.1293, and gene count matrices for each sample were generated from the alignment bam files using HTSeq-count v0.12.494 with the parameter –intersection-non-empty, and –stranded=“reverse” was specified for RNA libraries prepared with the Takara directional kit. Samples were hierarchically and PCA clustered to determine sample congruence. Normalization, scaling, and differential expression analysis were performed in R v4.1.3 (R Core Team, 2022) using the edgeR v3.34.195 qlf test for each pair-wise comparison. Genes were considered significantly and differentially expressed between conditions if they met FDR and fold-change cutoffs of 0.05 and 1.5, respectively. Plotting of results was performed using heatmap.2 function of the gplots package v3.1.1 and ggplot2 v3.3.596 packages in R.

RNA-seq analysis of transposable element families
Read generation and data pre-processing were performed as described above for RNA-seq analysis of genes. Alignment to the reference mouse genome (GENCODE GRCm38.p6 release M23) was done using STAR v2.7.10a92 with the following parameters to retain multi-mapping reads: --outFilterMultimapNmax 100 --winAnchorMultimapNmax 100 --outMultimapperOrder Random --outSAMmultNmax 1. Alignment BAM files were indexed with samtools index93. Expression of transposable element families was quantified by sample using TECounts v2.0.397 with parameter –mode=multi to estimate transposable element abundances from multimapped alignments, and –stranded reverse to properly process the stranded reads, using the pre-generated GENCODE GRCm38 TE GTF RepeatMasker file from the Hammellab FTP site (https://labshare.cshl.edu/shares/mhammelllab/www-data/TEtranscripts/TE_GTF) and the GENCODE GRCm38 v23 annotation GTF. Counts were assembled into a single table in R, were normalized to counts per million (CPM), and differential expression was assessed in R v3.1.4 (R Core Team, 2022) on the combined transposable element/gene counts using the edgeR qlf test, as described above, determining TEs to be differentially expressed if they met the cutoffs of FC 1.5 and FDR 0.05.

Functional enrichment and upstream regulator analysis
Ingenuity pathway analysis (IPA) for upstream regulators and canonical pathways was performed on log-transformed differential expression values for genes found to be significantly differentially expressed in edgeR, using the default settings of IPA v.24.0.255.

Gene set enrichment analysis
Normalized log CPM (counts per million) matrices for all genes of each comparison were extracted from the edgeR DGE object in R, generated as described above, and saved as a tab-separated text file for input into GSEA (v4.4.0 build 18)98,99. GSEA was run using the gene_set permutation with the max and min set sizes specified at 1000 and 1, respectively. Gene sets tested were obtained from MSigDB (https://www.gsea-msigdb.org/gsea/msigdb)100 and 85,101,102.

Single-cell RNA-seq
100,000 LSK cells from young and aged WT or Mda5-/- mice were isolated and sorted as previously described. Samples were then barcoded using the 10x Genomics 3′ CellPlex Kit. Cell multiplexing was performed before sorting. Pooled gene expression and feature barcoding fastq files obtained via NovaSeq 6000 sequencing platform were demultiplexed and aligned to the GRCm38/mm10 reference genome using cellranger count103. The resulting gene expression, barcode, and hashtag matrices were imported into R v4.1.3, and analysis was performed using the Seurat package v4.1.0104. Cells were filtered for nFeature_RNA > 1000 and a mitochondrial gene percentage of at most 5%. Hashtag data was used to trace pooled cells of a sample back to individual mice via the HMOdemux function in Seurat. Of 38,137 WT aged cells output by cellranger count, 21,241 were singlets, and 12,616 cells were retained after filtering for further analysis. Of 34,017 Mda5-/- aged cells output by cellranger count, 18,248 were singlets, and 12,084 cells were retained after filtering for further analysis. Of 32,441 WT young cells output by cellranger, 18,788 were singlets, and 9986 cells were retained after filtering for further analysis. Of 34,636 Mda5-/- young cells output by cellranger count, 14,007 were singlets, and 8178 cells were retained after filtering for further analysis. Samples were individually regularized using SCTransform V2 before Seurat anchor-based integration using 2000 anchors. Based on individual gene expression results, aged sample replicates CMO303 (WT) and CMO306 (Mda5-/-) were found to be unfit for further analysis and were excluded. After PCA dimensionality reduction, an elbow plot was used to determine the optimal number of dimensions to proceed with (30), and SNN clustering was performed at several resolutions (0.1–1, increment of 0.1) for input into clustree105 to determine the optimal clustering resolution. Differential expression between clusters was performed with the FindAllMarkers function, and cell populations were determined based on the expression of HSC and MPP markers56. To perform a comparison at the sample level, pseudobulk analysis was carried out by collapsing cell population clusters by condition and differential expression analysis in edgeR qlf test95. Functional enrichment, GSEA, and IPA analysis were performed on the results as described for RNA-seq analysis above.

ATAC-seq library preparation
For ATAC-seq (3000–5000 cells per sample, 3 biological replicates per condition), HSCs were sorted in FACS buffer. HSCs were spun down at 500g, 4°C for 10 min, and incubated with Illumina TAGMENT DNA enzyme and buffer (20034210, Illumina) or with tagmentase (Diagenode, C01070012) and the respective buffer (Diagenode C01019043) and Digitonin (0.01%) for 30 min at 37°C on a thermo mixer shaking at 700 rpm. Transposed DNA was purified using MinElute Cleanup (28004, QIAGEN). Adapter indices were ligated using the Nextera XT Index Kit (15055289, Illumina), and libraries were amplified using NEBNext High-Fidelity 2X PCR Master Mix (M0541S, NEB).

ATAC-seq differential accessibility analysis
Paired-end 100 bp reads were generated using the Illumina Hiseq 3000 system and sequenced at a depth of 60–96 M. Fastq read files were trimmed of Nextera adapters with Trim Galore v0.6.7 (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and aligned to GRCm38/mm10 with Bowtie2 v2.4.4106. The resulting BAM files had PCR duplicates, mitochondrial reads, and ENCODE blacklist107 features removed before further analysis using Picard v3.4.0 (Broad Institute, 2019), samtools v1.1393, and bedtools v2.30.0108, respectively. For differential accessibility analysis, BAM files were merged by sample using Samtools before calling peaks with MACS v2.2.9.1109. Induced/reduced peaks were determined by overlapping peaksets with bedtools intersect108, after which gene TSSs overlapping peaks (−100 kb, +50 kb) were determined with bedtools closest108, and motif enrichment was obtained with findMotifsGenome.pl script from the HOMER v5.1 package110. For visualization, filtered BAM files were converted to bigwig format via a bedgraph intermediate with bamCoverage and bedtobigwig, using scaling factors obtained from deeptools v3.5.6111 multiBamSummary. These bigwigs, along with the previously obtained induced and reduced peaks in bed format, were input into deeptools computeMatrix, and the output was plotted using deeptools plotHeatmap reference-point.

ATAC-seq digital genomic footprinting
Footprinting analysis to determine TF occupancy was performed on sample-merged bam files (samtools merge93) and sample-merged narrow peak output from MACS2 using the Wellington algorithm from the pyDNase package v0.3.0112. Output bed regions were merged, then intersected among samples in order to plot profiles with dnase_average_profile.py.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_2": "The authors present a large body of work that convincingly show that Mda5 plays a role in HSC function. The changes in inflammatory cytokine levels, improved MMP, and a role for mitigating MDA5-/- to improve many aspects of HSC aging.\nPlease find suggestions / clarifications requested to improve the overall message of the manuscript.\nSuggestions / clarifications\nThe changes in chromatin accessibility between the young and old WT and the old MDA5-/- vs old WT are particularly relevant. The overall changes (from interpretation of the figure 1) in the chromatin accessibility between the KO and WT during aging suggest that, in general, the age associated increase in accessibility is maintained in both the WT and KO HSCs, but the wording that “the young Mda5-/- HSCs have more accessible chromatin, but this is reversed during aging, where 11.904 sites were induced in WT and 5.336 sites were induced in Mda5-/- HSCs” is a bit confusing as the old Mda5-/- HSCs have more accessible chromatin compared to the young Mda5-/-HSCs. Were the same sites that were more open in young Mda5-/- HSCs the ones that became closed in old Mda5-/-? Could the authors please explain in Fig 1E, why the reduced peaks in aged MDA5-/- (blue line plot) is higher relative to the peak in the aged WT? It is unclear if the Mda5-/- aged HSCs revert chromatin accessibility to something more similar to young WT (a venn diagram or upset plot could help clarify this) as the pathway enrichments have very similar reduced and induced pathways- this may be clarified a bit with showing the overlapping loci or a more clear depiction of if the aged Mda5-/- HSCs have chromatin accessibility more similar to young or just different from aged WT HSCs (which may be the interpretation from the clustering in Supp 1H)\nWith the significant enrichment of open chromatin in intergenic regions in the Mda5-/- young HSCs- if just comparing the TE expression between young WT HSC and Mda5-/- HSCs is there an increase in TE expression?\nThis single cell data is of course a nice additional for examining cellular heterogeneity, but the presentation of data does not follow with the manuscript- it is unclear how the Mda5-/- cells differ from their WT counterparts and the pseudotime trajectories include both Mda5-/- and WT cells in the young to old comparions (and in the supplemental files show the young and old KO and young and old WT, but not the matching aged WT and KO, which appear to be the most relevant. Were there differences in the pseduotime projections of the old KO vs old WT (or perhaps unclear labels on plots)\nMinor points:\n1. For the RNAseq clustering for PCA, it is a bit unusual that the young HSC (both the MDA-/- and WT) samples are the most variable compared to middle and old aged samples- as it is also unclear what the axis is- as the BVC: Bockfjorden volcanic complex is not a term I can find relative to principal component assays.\n2. Authors nicely include middle aged HSCs in ATAC seq data, but in clustering analysis these are not included (Supp Fig 1H). please do\n3. In evaluation of mitochondria- the authors measure mitochondrial mass, where they normalize to fold change relative to aged WT, yet the aged WT bar plot is not at 1. (fig 2e) this is the same for analysis of protein in figure Sup 7a/b.\n4. One potential explanation for the significantly increased chimerism of the secondary transplants of the young Mda5-/- that should be at least discussed is the increase in the frequency of the LSKs and HSCs in the primary transplant (not significant due to the transplanted animals with very low chimerism).", "reviewer_3": "In this study, the authors demonstrate that MDA5, an RNA sensor in innate immunity, is involved in the aging of haematopoietic stem cells (HSCs). The revised manuscript incorporates updated data analyses and new experimental results, strengthening the claim that MDA5 contributes to the regulation of HSC ageing. The reviewer acknowledges that the authors have responded to the previous comments.\nHowever, regarding comment (3), the authors provide new data using MDA5 mutants in HEK293 cells (Supplementary Figure 7G). These results indicate that the MDA5 G496R mutant, a gain-of-function variant, fails to retain HSF1 in the cytoplasm. This observation may contradict the authors' assertion that \"signaling activity of MDA5 is essential for its interaction with HSF1.\" Furthermore, the authors include proximity ligase assay data using human BJ fibroblasts (Figure 8H); however, the nuclear detection of MDA5 remains unclear and requires further clarification. These issues should be addressed.\nMinor points:\n1. On page 9, line 2, the manuscript states: ‘Ingenuity pathway analysis in genes deregulated between aged WT and Mda5⁻/⁻ HSCs showed enrichment for “eukaryotic translation elongation”, “EIF2 signaling”, and “cellular response to heat stress” among others (Figure 4E)”’, and on line 12: “We then investigated the transcriptomic signature of “cellular response to heat stress” (Figure 4E).” However, the description of \"cellular response to heat stress\" is missing from Figure 4E.\n2. The description of the mice from which the cells were harvested is absent in Supplementary Figure 6E.\n3. Spelling out certain technical terms would make the manuscript more accessible to a broader readership; for example: EIF2a, MPP, cGAS.", "reviewer_4": "It appears that the concerns of reviewer 1 were sufficiently addressed in the revised version of the manuscript. The authors added a large amount of additional experimental data, revised the bioinformatic analysis of obtained data and edited the text to make it more accessible for readers.\nReviewer 1 was particularly concerned whether MDA5 deficient cells are inherently more fit rather than able to maintain proteostasis during aging. To address this point, the authors have added several new data sets in the main text and the supplement. Taken together, these data sets point to altered proteostasis in the absence of MDA5 beginning at middle age – in line with their main conclusion. In addition, a more extensive discussion of this aspect has been included.\nIn response to reviewer 1, the authors now performed ATAC-seq experiments, repeated RNA-seq experiments with freshly isolated HSCs, and sequenced all samples together to minimize batch effects. Furthermore, the bioinformatic analysis has been substantially revised, now including Ingenuity Pathway Analysis (IPA) and Gene Set Enrichment Analysis (GSEA). The analysis further supports the main conclusion of the manuscript.\nImmunofluorescence microscopy was substantially extended and now includes the use of new antibodies and experimental approaches (proximity ligation) to verify a role of MDA5 in controlling the location and activation of HSF-1.\nFinally, additional experiments were performed to analyze the impact of MDA5 absence on protein synthesis and the ubiquitylation and aggregation of proteins at different age levels of HSCs and MPPs. This provides further evidence for a participation of the innate immune RNA sensor MDA5 in the regulation of proteostasis."}, "author_response": "Reviewer #2 (Remarks to the Author):\nThe authors present a large body of work that convincingly show that Mda5 plays a role in\nHSC function. The changes in inflammatory cytokine levels, improved MMP, and a role for\nmitigating MDA5-/- to improve many aspects of HSC aging. Please find suggestions /\nclarifications requested to improve the overall message of the manuscript.\nWe thank the reviewer for the kind words.\nSuggestions / clarifications\nThe changes in chromatin accessibility between the young and old WT and the old MDA5-\n/- vs old WT are particularly relevant. The overall changes (from interpretation of the figure\n1) in the chromatin accessibility between the KO and WT during aging suggest that, in\ngeneral, the age associated increase in accessibility is maintained in both the WT and KO\nHSCs, but the wording that “the young Mda5-/- HSCs have more accessible chromatin,\nbut this is reversed during aging, where 11.904 sites were induced in WT and 5.336 sites\nwere induced in Mda5-/- HSCs” is a bit confusing as the old Mda5-/- HSCs have more\naccessible chromatin compared to the young Mda5-/-HSCs.\nWe agree with the reviewer and we have reworded this phrase to “In the comparison of\nthe young WT and Mda5-/- HSCs, we found 1.723 regions that were mostly accessible in\nWT (reduced peaks), and 10.694 moslty accessible in Mda5-/- HSCs (induced peaks)\n(Figure 1D and Supplementary Table 1). Aged Mda5-/- HSCs on the other hand have less\naccesible chromatin than the WT, with 11.904 sites induced in WT and 5.336 sites\ninduced in Mda5-/- HSCs (Figure 1E and Supplementary Table 1).”\nWere the same sites that were more open in young Mda5-/- HSCs the ones that became\nclosed in old Mda5-/-?\nOnly 491 common regions are open in Young Mda5-/- and closed in Aged Mda5-/-(for this\nreason we only have the venn as an aswer to the reviewer but we can add it to the text if\nyou think it is helpful) .\nSites more open\nin Young Mda5-/- HSCs\n10199\n491\n11412\nSites closed\nin Aged Mda5-/- HSCs\nCould the authors please explain in Fig 1E, why the reduced peaks in aged MDA5-/- (blue\nline plot) is higher relative to the peak in the aged WT?\nThe reviewer is correct and there was a problem with the normalization of this sample.\nNow this problem is addressed.\nIt is unclear if the Mda5-/- aged HSCs revert chromatin accessibility to something more\nsimilar to young WT (a venn diagram or upset plot could help clarify this) as the pathway\nenrichments have very similar reduced and induced pathways- this may be clarified a bit\nwith showing the overlapping loci or a more clear depiction of if the aged Mda5-/- HSCs\nhave chromatin accessibility more similar to young or just dicerent from aged WT HSCs\n(which may be the interpretation from the clustering in Supp 1H)\nAs you can see from the new plot on Figure 1H the majority of the open regions in aged\nHSCs are common between the aged WT and the aged Mda5-/-. However 11779 are only\nopen in aged WT HSCs and 4460 regions are open only in aged Mda5-/-HSCs, in\ncomparison to all narrow peaks from all datasets. Very few genomic regions are common\nwith the young HSCs.\nWith the significant enrichment of open chromatin in intergenic regions in the Mda5-/-\nyoung HSCs- if just comparing the TE expression between young WT HSC and Mda5-/-\nHSCs is there an increase in TE expression?\nEven if some genomic regions are more open in the Mda5-/- the TE expression does not\nreflect this. There is no significanlty deregulated TE family between young or middle aged\nMda5-/- versus young or middle aged WT HSCs and only one family is upregulated in aged\nMda5-/-vesrus aged WT HSCs (See also Supplementary Table 3).\nThis single cell data is of course a nice additional for examining cellular heterogeneity,\nbut the presentation of data does not follow with the manuscript- it is unclear how the\nMda5-/- cells dicer from their WT counterparts and the pseudotime trajectories include\nboth Mda5-/- and WT cells in the young to old comparions (and in the supplemental files\nshow the young and old KO and young and old WT, but not the matching aged WT and KO,\nwhich appear to be the most relevant. Were there dicerences in the pseduotime\nprojections of the old KO vs old WT (or perhaps unclear labels on plots)\nThe reviewer is right and we did the pseudotime trajectories separately for each\npopulation without observing any significant dicerence between WT and MDA5 KO\nespecially when aged. We didn’t include this in the manuscript because we think it\ndoesn’t add much to our conclusions but we are happy to do that if the reviewer feels it\nis necessary.\nMinor points:\n1. For the RNAseq clustering for PCA, it is a bit unusual that the young HSC (both the\nMDA-/- and WT) samples are the most variable compared to middle and old aged\nsamples- as it is also unclear what the axis is- as the BVC: Bockfjorden volcanic complex\nis not a term I can find relative to principal component assays.\nWe are sorry for the confusion. Indeed the reviewer is correct and we should have less\nvariation in the young HSCs but this is not what we are observing. Regarding the BCV is\nbiological coecicient of variation (BCV) which is a term associated with PCA. We have\nnow explained the term in the figure legend.\n2. Authors nicely include middle aged HSCs in ATAC seq data, but in clustering analysis\nthese are not included (Supp Fig 1H). please do\nWe are sorry if we confused the reviewer but we have not performed the ATAC-seq on\nmiddle aged animals and that is why we have not included it in the clustering analysis.\n3. In evaluation of mitochondria- the authors measure mitochondrial mass, where they\nnormalize to fold change relative to aged WT, yet the aged WT bar plot is not at 1. (fig 2e)\nthis is the same for analysis of protein in figure Sup 7a/b.\nWe have corrected the graphs and thank you very much for the thorough comments.\n4. One potential explanation for the significantly increased chimerism of the secondary\ntransplants of the young Mda5-/- that should be at least discussed is the increase in the\nfrequency of the LSKs and HSCs in the primary transplant (not significant due to the\ntransplanted animals with very low chimerism).\nWe thank the reviewer for the comment and we have added this explanation to the\nmanuscript.\nReviewer #3 (Remarks to the Author):\nIn this study, the authors demonstrate that MDA5, an RNA sensor in innate immunity, is\ninvolved in the aging of haematopoietic stem cells (HSCs). The revised manuscript\nincorporates updated data analyses and new experimental results, strengthening the\nclaim that MDA5 contributes to the regulation of HSC ageing. The reviewer acknowledges\nthat the authors have responded to the previous comments.\nWe thank tha reviewer for the isnightful comments.\nHowever, regarding comment (3), the authors provide new data using MDA5 mutants in\nHEK293 cells (Supplementary Figure 7G). These results indicate that the MDA5 G496R\nmutant, a gain-of-function variant, fails to retain HSF1 in the cytoplasm. This observation\nmay contradict the authors' assertion that \"signaling activity of MDA5 is essential for its\ninteraction with HSF1.\"\nWe repeated this experiment multiple times and we lowered the dose of the constitutive\nactive mutant. However all the times we got similar results and we think that the\nconstitutively active mutant is causing too much inflammation and thus devastating to\nthe cells. Since this is an ambiguous results we decided to remove the constitutively\nactive mutant from our results and if the reviewer agrees.\nFurthermore, the authors include proximity ligase assay data using human BJ fibroblasts\n(Figure 8H); however, the nuclear detection of MDA5 remains unclear and requires further\nclarification. These issues should be addressed.\nWe agree with the reviewer and we were equally surprised about the localization of MDA5\nthat is also nuclear and not only cytoplasmic. For this reason we isolated mouse dermal\nfibroblasts for WT and Mda5-/- embryos and stained them with the MDA5 antibody under\nhomeostatic conditions and after PolyIC stimulation that increases the levels of MDA5.\nWe indeed show as you can see in the images below that our antibody is specific and that\nMDA5 is localized both in the cytoplasm and the nucleus. This is a novel observation that\nfavors better investigation in the future but we added a comment in our manuscript. We\ndon’t think that we should include these controls in the manuscript but we are happy to\ndo so if necessary. As a note, we would like to mention that also RIG-I has been found in\nthe nucleus (PMID: 30097581). In the figure MDA5 is stained in red and in blue we have\nDAPI staining.\nWT Ctrl Mda5 KO Ctrl\nWT poly I:C Mda5 KO poly I:C\nMinor points:\n1. On page 9, line 2, the manuscript states: ‘Ingenuity pathway analysis in genes\nderegulated between aged WT and Mda5⁻/⁻ HSCs showed enrichment for “eukaryotic\ntranslation elongation”, “EIF2 signaling”, and “cellular response to heat stress” among\nothers (Figure 4E)”’, and on line 12: “We then investigated the transcriptomic signature of\n“cellular response to heat stress” (Figure 4E).” However, the description of \"cellular\nresponse to heat stress\" is missing from Figure 4E.\nThe reviewer is right and the cellular response to heat stress was enriched but not\nsignificantly and thus we removed it from the figure and now from the text.\n2. The description of the mice from which the cells were harvested is absent in\nSupplementary Figure 6E.\nWe kindly disagree with the reviewer since the WT and Mda5-/- young and aged HSCs are\nclearly depicted in the Supplementary Figure 6E.\n3. Spelling out certain technical terms would make the manuscript more accessible to a\nbroader readership; for example: EIF2a, MPP, cGAS.\nThe reviewer is right and we have explained these abbreviations.\nReviewer #4 (Remarks to the Author):\nIt appears that the concerns of reviewer 1 were suciciently addressed in the revised\nversion of the manuscript. The authors added a large amount of additional experimental\ndata, revised the bioinformatic analysis of obtained data and edited the text to make it\nmore accessible for readers.\nReviewer 1 was particularly concerned whether MDA5 deficient cells are inherently more\nfit rather than able to maintain proteostasis during aging. To address this point, the\nauthors have added several new data sets in the main text and the supplement. Taken\ntogether, these data sets point to altered proteostasis in the absence of MDA5 beginning\nat middle age – in line with their main conclusion. In addition, a more extensive\ndiscussion of this aspect has been included.\nIn response to reviewer 1, the authors now performed ATAC-seq experiments, repeated\nRNA-seq experiments with freshly isolated HSCs, and sequenced all samples together to\nminimize batch ecects. Furthermore, the bioinformatic analysis has been substantially\nrevised, now including Ingenuity Pathway Analysis (IPA) and Gene Set Enrichment\nAnalysis (GSEA). The analysis further supports the main conclusion of the manuscript.\nImmunofluorescence microscopy was substantially extended and now includes the use\nof new antibodies and experimental approaches (proximity ligation) to verify a role of\nMDA5 in controlling the location and activation of HSF-1.\nFinally, additional experiments were performed to analyze the impact of MDA5 absence\non protein synthesis and the ubiquitylation and aggregation of proteins at dicerent age\nlevels of HSCs and MPPs. This provides further evidence for a participation of the innate\nimmune RNA sensor MDA5 in the regulation of proteostasis.\nWe thank the reviewer for thoroughly reading our manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_2": "Thank you for the clarifications and additional information. I agree the venn diagram overlap is not needed for the manuscript, and I may have been unclear about the comparisons I was interested in: I was curious about the overlap of aged MDA-/- more closed compared to aged WT with the chromatin chages in young vs old WT: However, the authors do state now that the aged MDA5-/- HSCs do not resemle more young HSCs, answering the question.\nAll other comments / suggestions were appropriately addressed- Please do double check through the newly added text as there were some minor typos- but the science looks interesting and well presented", "reviewer_3": "The reviewers acknowledge that the authors have addressed the comments and strongly commend this research for its contribution to the advancement of the field.\n>We kindly disagree with the reviewer since the WT and Mda5-/- young and aged HSCs are clearly depicted in the\nSupplementary Figure 6E.\nMy apologies. My previous comment was incorrect. The reviewer intended to refer to “Supplementary Figure 7E”, not “6E”. In Supplementary Figure 7E, it would be helpful for readers if the three panels were labelled to indicate which cells originate from the ‘young’, “middle”, and ‘aged’ groups."}, "author_response": "Reviewer #2 (Remarks to the Author):\nThank you for the clarifications and additional information. I agree the venn diagram\noverlap is not needed for the manuscript, and I may have been unclear about the\ncomparisons I was interested in: I was curious about the overlap of aged MDA-/- more\nclosed compared to aged WT with the chromatin chages in young vs old WT: However,\nthe authors do state now that the aged MDA5-/- HSCs do not resemle more young\nHSCs, answering the question.\nAll other comments / suggestions were appropriately addressed- Please do double\ncheck through the newly added text as there were some minor typos- but the science\nlooks interesting and well presented\nWe thank the reviewer for all the useful comments.\nReviewer #3 (Remarks to the Author):\nThe reviewers acknowledge that the authors have addressed the comments and\nstrongly commend this research for its contribution to the advancement of the field.\n>We kindly disagree with the reviewer since the WT and Mda5-/- young and aged HSCs\nare clearly depicted in the Supplementary Figure 6E.\nMy apologies. My previous comment was incorrect. The reviewer intended to refer to\n“Supplementary Figure 7E”, not “6E”. In Supplementary Figure 7E, it would be helpful for\nreaders if the three panels were labelled to indicate which cells originate from the\n‘young’, “middle”, and ‘aged’ groups.\nWe thank the reviewer for the useful comments and we have corrected the figure\naccording to the reviewer’s instructions."}]

Decision letter