RIOK2 transcriptionally regulates TRiC and dyskerin complexes to prevent telomere shortening

Ghosh, Shrestha; Nguyen, Mileena T.; Choi, Ha Eun; Stahl, Maximilian; Kühn, Annemarie Luise; Van der Auwera, Sandra; Grabe, Hans J.; Völzke, Henry; Homuth, Georg; Myers, Samuel A.; Hogaboam, Cory M.; Noth, Imre; Martinez, Fernando J.; Petsko, Gregory A.; Glimcher, Laurie H.

doi:10.1038/s41467-024-51336-3

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Published: 20 August 2024

RIOK2 transcriptionally regulates TRiC and dyskerin complexes to prevent telomere shortening

Nature Communications volume 15, Article number: 7138 (2024) Cite this article

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Abstract

Telomere shortening is a prominent hallmark of aging and is emerging as a characteristic feature of Myelodysplastic Syndromes (MDS) and Idiopathic Pulmonary Fibrosis (IPF). Optimal telomerase activity prevents progressive shortening of telomeres that triggers DNA damage responses. However, the upstream regulation of telomerase holoenzyme components remains poorly defined. Here, we identify RIOK2, a master regulator of human blood cell development, as a critical transcription factor for telomere maintenance. Mechanistically, loss of RIOK2 or its DNA-binding/transactivation properties downregulates mRNA expression of both TRiC and dyskerin complex subunits that impairs telomerase activity, thereby causing telomere shortening. We further show that RIOK2 expression is diminished in aged individuals and IPF patients, and it strongly correlates with shortened telomeres in MDS patient-derived bone marrow cells. Importantly, ectopic expression of RIOK2 alleviates telomere shortening in IPF patient-derived primary lung fibroblasts. Hence, increasing RIOK2 levels prevents telomere shortening, thus offering therapeutic strategies for telomere biology disorders.

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Introduction

Telomeres form the protective ends of chromosomal DNA that frequently undergo progressive shortening with each cell division¹. Telomere shortening results in a spectrum of disorders, such as aplastic anemia, idiopathic pulmonary fibrosis (IPF), Dyskeratosis Congenita (DC), and is also prevalent in aging individuals^2,3,4,5. Excessive telomere shortening is emerging as a causative factor for myelodysplastic syndromes (MDS)^6,7, a form of hematologic malignancy that is classically characterized by ineffective hematopoiesis and peripheral blood cytopenia^8,9. Thus, identifying regulators of telomere maintenance may uncover therapeutic approaches to alleviate telomere shortening in multiple disorders.

Telomerase is a ribonucleoprotein complex that synthesizes telomeric DNA repeats to prevent telomere shortening^10,11,12. Human telomerase includes a reverse transcriptase known as TERT (the Telomerase Reverse Transcriptase), an essential RNA core called TERC (the Telomerase RNA Component or hTR) and several essential protein cofactors, such as dyskerin complex and TCAB1 (Telomerase Cajal Body Protein 1)^1,13,14. Catalytically active telomerase elongates chromosomal DNA ends by reverse transcribing part of the RNA component TERC that functions as a template for TERT. Optimum enzymatic activity of telomerase is ensured by proper processing and stability of its components¹⁵. In this regard, the TRiC (TCP-1 Ring Complex) chaperonin complex functions as an integral component of telomere maintenance by properly folding and stabilizing the telomerase cofactor TCAB1^16,17,18. The TRiC complex consists of 8 homologous subunits, CCT1-CCT8, forming two octameric rings that remain stacked together¹⁹. Depletion of one TRiC subunit is sufficient to destabilize TCAB1 protein, thus impairing telomerase activity^16,17. Pathogenicity of autosomal recessive mutations of TCAB1 in the telomere biology disorder Dyskeratosis Congenita (DC)²⁰ further emphasizes the vital roles of this telomerase holoenzyme subunit in telomere biogenesis. The dyskerin complex is another key component of the telomerase enzyme that is required to properly fold and assemble TERC^21,22,23. This complex consists of 4 members: dyskerin (encoded by DKC1), NHP2, NOP10 and GAR1²⁴. Mutations in DKC1 were identified as the first pathogenic mutations associated with DC, followed by NHP2 and NOP10^15,25,26. Loss of or mutation in the dyskerin subunits decreases TERC stability, telomerase activity and induces telomere shortening²⁷. Although expression of telomerase complex subunits remains tightly controlled to prevent various disorders and malignancies^15,28,29, the upstream regulation of these telomerase holoenzyme subunits is not well understood.

We recently reported that RIOK2 (right open reading frame kinase 2), an understudied atypical protein kinase^30,31,32, also functions as a master transcription factor to regulate human blood cell development³³. Loss of RIOK2 in primary human hematopoietic stem and progenitor cells (HSPCs), and in erythroblast cell lines TF-1 and K562, severely dampens erythroid (red blood cell) differentiation. RIOK2 controls the mRNA expression of its target genes via its DNA-binding (DBD) and transactivation (TAD) sub-domains contained within its N-terminal winged helix-turn-helix (wHTH) domain³³.

Here, we show that RIOK2 is a key transcription factor in the regulation of human telomere maintenance. Loss of RIOK2 triggers telomere shortening in primary human stem cells, as well as in erythroid and non-erythroid cells. We used transcriptomics, ATAC-sequencing, chromatin-immunoprecipitation, TRAP assays, fluorescence in-situ hybridization (FISH), and reconstitution experiments to establish that RIOK2 transcriptionally regulates the gene expression of TRiC and dyskerin complex subunits to maintain telomerase activity and prevent telomere shortening. Furthermore, samples from aged individuals and patients with MDS and IPF revealed deregulated RIOK2-dependent transcription of TRiC and dyskerin complexes that contributes to telomere shortening in these disorders. Our findings highlight the importance of RIOK2-driven transcriptional networks in both telomere biogenesis and human blood cell development with substantial therapeutic implications in aging, MDS and IPF.

Results

Loss of RIOK2 triggers telomere shortening in cells

To gain deeper insights into RIOK2-dependent genetic programs, we generated both knockdown (KD) and knockout (KO) of RIOK2 using CRISPR-Cas9 based gene editing (Fig. 1a). Loss of RIOK2 substantially reduced cell proliferation in two different erythroblast cell lines, TF-1 and K562 cells (Fig. 1b and S1a). Deficiency of RIOK2 also arrested progression of cells at S and G2/M phases of cell cycle (Fig. 1c). Additionally, we noted distinct upregulation of DNA damage responses upon loss of RIOK2 (Fig. S1b). However, our previously characterized functions of RIOK2 in erythroid differentiation³³ could not explain these phenotypes. Hence, we used unbiased gene-set enrichment analysis (GSEA) to investigate the possible roles of RIOK2 in other cellular processes. Unexpectedly, GSEA analysis of both RNA and ATAC sequencing datasets revealed compromised telomere maintenance upon RIOK2 deficiency in primary human HSPCs (Fig. 1d,e). Indeed, loss of RIOK2 induced telomere shortening in primary human HSPCs, TF-1 and K562 cells (Fig. 1f–h), as measured by a quantitative PCR-based method^34,35. To further validate this finding, we performed fluorescence in-situ hybridization (FISH) to visualize telomeric puncta³⁶. Consistently, loss of RIOK2 markedly decreased the intensity of telomeric puncta formation, thus demonstrating critical roles of RIOK2 in telomere maintenance (Figs. 1i, j and S1c,d). The observed DNA damage foci at telomeres (Fig. S1k) as well as the decreased telomeric hybridization at chromosome ends upon deficiency of RIOK2 could be caused by loss of telomeric protection, lower telomerase action on telomeres, and combinations of these.

**Fig. 1: Loss of RIOK2 results in telomere shortening.**

To distinguish between these possibilities, next, we explored whether RIOK2 similarly promotes telomere maintenance in non-erythroid cells. To this end, we examined HeLa and HEK293 cells and found attenuated cell proliferation and telomere shortening with dose-dependent loss of RIOK2 (Figs. 1k, l, S1e–h). Thus, RIOK2 regulates telomere maintenance in cells of both erythroid and non-erythroid origin. Interestingly, complete loss of RIOK2 (RIOK2 KO) more prominently affected proliferation in telomerase positive cells (HeLa and HEK293) than in the passage-matched ALT cells (U-2 OS and Saos-2) (Fig. S1e, f, i and j). This suggests that RIOK2-mediated impact on telomerase activity contributes to cell proliferation defects.

We recently reported that hematopoietic cell-specific heterozygous depletion of Riok2 in mice recapitulates MDS-associated anemia³⁷. We also showed that the transcript levels of RIOK2 significantly correlate with those of its target transcription factors in MDS patient-derived bone marrow (BM) cells, thus partly explaining the anemia observed in MDS patients³³. Given the involvement of telomere shortening in MDS^6,7, we next investigated the expression of RIOK2 and corresponding telomere lengths in MDS patient-derived BM cells (Fig. 1m). Notably, the mRNA expression of RIOK2 positively correlated with telomere lengths in BM cells isolated from MDS patients, thus indicating that low RIOK2 expression associates with shortened telomere lengths in these patients (r = 0.5086, P = 0.0008; Fig. 1n). These findings confirm that RIOK2 plays a critical role in preventing telomere shortening.

RIOK2 regulates TRiC subunits to maintain TCAB1 stability

We next investigated the mechanisms underlying RIOK2-dependent telomere maintenance and interrogated pathways that are associated with telomere extension or protection. Our bulk RNA-sequencing analysis in primary human HSPCs revealed decreased mRNA levels of genes encoding TRiC complex subunits upon RIOK2 depletion: TCP1 (CCT1), CCT2, CCT3, CCT4, CCT5, CCT6, CCT7 and CCT8 (Fig. 2a). Deficiency of RIOK2 led to transcriptional downregulation of TRiC subunits in a dose-dependent manner (Figs. 2b, c and S2a). Loss of RIOK2 also reduced the mRNA expression of TRiC subunits in HeLa and HEK293 cells (Fig. S2b,c). We noted dramatically reduced chromatin accessibility at the promoter regions of TRiC complex subunits upon loss of RIOK2 (Fig. 2d). Furthermore, de novo RIOK2 binding motifs were observed in the promoter regions of TCP1 and CCT8 (Fig. S2d). This prompted us to investigate whether RIOK2 functions as a transcription factor to regulate the expression of TRiC subunits. Chromatin immunoprecipitation using both monoclonal and polyclonal antibodies against RIOK2 revealed that it binds to the promoter regions of TCP1 and CCT8 (Fig. 2e). RIOK2 also transactivated the expression of TCP1 and CCT8 in luciferase reporter assays (Fig. 2f). To further explore the functional implications of RIOK2-driven TRiC expression, we analyzed TCAB1 protein levels^16,17. As speculated, we observed a stark decrease in TCAB1 protein expression upon RIOK2 deficiency in both TF-1 and K562 cells (Figs. 2g, h, and S2e). Thus, RIOK2 transcriptionally regulates TRiC complex subunits to maintain TCAB1 protein levels.

**Fig. 2: RIOK2 transcriptionally regulates TRiC complex expression.**

RIOK2 promotes telomerase activity to prevent telomere shortening

Shelterin and CST are two distinct protein complexes that are critically involved in human telomere replication and chromosome end-capping^38,39. Human shelterin is a 6-membered complex (TRF1, TRF2, RAP1, TIN2, POT1 and TPP1) that binds telomeric DNA to protect them from activating DNA damage response (DDR) pathways^40,41. In contrast, CST is a heterotrimeric protein complex (CTC1-STN1-TEN1) that facilitates telomeric C-strand fill-in processes to prevent lengthening of the telomeric tail (G-overhang)^42,43. Evaluation of shelterin and CTC complex subunits yielded no significant change of their mRNA levels upon loss of RIOK2 (Fig. S3a and b). However, RIOK2 deficiency led to a significant drop in the gene expression of dyskerin complex subunits DKC1, NHP2, NOP10 and GAR1 in primary human HSPCs, erythroid and non-erythroid cells (Figs. 3a–c, S3c–e). Loss of RIOK2 reduced chromatin accessibility at the promoter regions of genes encoding dyskerin complex subunits, indicating that RIOK2 may also function as a transcription factor for the dyskerin complex (Fig. 3d). De novo RIOK2 binding motifs were similarly observed in the promoters of DKC1 and NHP2 (Fig. S2f). We found that RIOK2 bound to the promoter regions of DKC1 and NHP2, and also transactivated their expression (Figs. 3e,f). These findings substantiate that RIOK2 transcriptionally regulates the expression of TRiC and dyskerin complex subunits, but not shelterin or CTC complexes. Since dyskerin complex subunits maintain the stability of TERC^44,45, we next analyzed TERC levels to validate functional outcomes of RIOK2-driven dyskerin expression. Indeed, TERC stability decayed with gradual loss of RIOK2 in primary human HSPCs (Fig. 3g,h). This phenomenon was also observed in erythroid (TF-1, K562) and non-erythroid (HeLa, HEK293) cells (Fig. S3f–i). To further test whether RIOK2-driven transcription of TRiC and dyskerin complexes is reflected in telomerase activity, we performed the telomerase repeated amplification protocol (TRAP) assay and observed a dramatic decline in telomerase activity coupled with dose-dependent inhibition of RIOK2 (Fig. 3i–3l). Thus, RIOK2 transcriptionally regulates TRiC and dyskerin complex subunits to promote telomerase activity, thereby maintaining telomere length homeostasis. Corroborating this, we noted positive correlation of RIOK2’s mRNA levels with that of TRiC and dyskerin complex subunits, but not shelterin subunits, in MDS patient-derived BM cells (Fig. S4a–c). These findings strongly indicate that telomere shortening in MDS is in part due to reduced expression and/or transcriptional abilities of RIOK2 to regulate mRNA levels of TRiC and dyskerin complexes.

**Fig. 3: RIOK2 regulates mRNA expression of dyskerin complex subunits.**

RIOK2’s transcriptional functions maintain telomerase activity

RIOK2 plays an indispensable role in cytoplasmic protein translation via its kinase domain^30,32. Since global inhibition of cytoplasmic translation will inevitably affect telomere biogenesis, we sought to differentiate the contribution of RIOK2’s transcriptional functions from its translational ones in telomere maintenance. To achieve this, we used DNA-binding mutant (DBM) and transactivation domain 1 and 2 deleted (ΔTAD1 and ΔTAD2) mutants of RIOK2, as previously described³³. We observed that reconstitution of WT RIOK2 markedly rescued cell proliferation in endogenous RIOK2 KO cells, whereas DBM RIOK2 failed to do so (Fig. 4a). Interestingly, depleting either of the transactivation domains rendered RIOK2 incapable of stimulating cell proliferation. The DBM and ΔTAD mutants of RIOK2 also failed to rescue arrested cells at the S and G2/M phases of cell cycle (Fig. 4b). Chromatin immunoprecipitation assays showed reduced binding ability of DBM RIOK2 to the promoter regions of TCP1 and DKC1, as compared to WT RIOK2 (Fig. 4c). Luciferase reporter assays confirmed impaired transactivation functions of DBM, ΔTAD1 and ΔTAD2 RIOK2 as compared to WT RIOK2 in stimulating TRiC and dyskerin complex subunits (Fig. 4d). Corroborating this, DBM, ΔTAD1 and ΔTAD2 mutants of RIOK2 impaired rescue of the reduced expression of TRiC (TCP1, CCT6A) and dyskerin (DKC1, NHP2) complex subunits in RIOK2 KO cells (Fig. 4e,f). In line with this, neither DBM nor ΔTAD1/ΔTAD2 RIOK2 mutants could rescue TCAB1 expression in RIOK2 KO cells (Fig. S5a). These RIOK2 mutants also compromised rescue of TERC expression in RIOK2 KO cells (Figs. 4g and S5b). Consequently, DBM or ΔTAD mutants of RIOK2 could not revive telomerase activity as compared to WT RIOK2 in endogenous RIOK2 KO cells (Figs. 4h and S5c,d). The kinase dead mutant of RIOK2 K123A also impeded rescue of TERC expression and telomerase activity in RIOK2 KO cells (Figs. 4g,h, S5b–d), largely due to impaired cytoplasmic translation. We next performed fluorescence in-situ hybridization (FISH) of telomeric DNA to validate the involvement of RIOK2’s transcriptional functions in telomere shortening. Although WT RIOK2 largely restored telomeric puncta in endogenous RIOK2-depleted (KO) cells, DBM/ΔTAD1/ΔTAD2 mutants failed to rescue telomere shortening (Figs. 4i and S5e). The kinase-dead mutant K123A could also not rescue telomere shortening as compared to WT reconstitution, confirming that loss of cytoplasmic translation impairs telomere biogenesis. Moreover, DBM/ΔTAD1/ΔTAD2 mutants failed to alleviate DNA damage responses triggered by loss of RIOK2 (Fig. S6a). Taken together, these data demonstrate that the transcriptional functions of RIOK2 are pivotal in promoting telomere elongation (Fig. S6b).

**Fig. 4: Loss of RIOK2’s transcriptional functions results in telomere shortening.**

The mRNA expression of RIOK2 declines with aging

Since telomere shortening is a major hallmark of aging⁴⁶, we investigated the transcript levels of RIOK2 in young versus aging individuals. To this end, we analyzed a publicly available microarray dataset of PBMCs derived from young individuals (19–30 years) and nonagenarians ( > 90 years of age)⁴⁷. Strikingly, the mRNA expression of RIOK2 was significantly reduced in nonagenarians as compared to young individuals (Fig. 5a). Since RIOK2 transcriptionally regulates the expression of TRiC and dyskerin complexes, but not shelterin (Figs. 2, 3, S2 and S3), we next assessed their expression in this cohort of young individuals and nonagenarians. Indeed, the transcript levels of RIOK2 positively correlated with those of TRiC (CCT2, CCT4, CCT5, CCT8) and dyskerin (DKC1, NHP2) complex subunits, but not shelterin subunits (Figs. 5b, c and S6c). Of note, young individuals exhibited higher expression of RIOK2 with correspondingly high expressions of both TRiC and dyskerin complex subunits as compared to the nonagenarians. To further validate these findings, we studied the associations between age and transcript levels of RIOK2 in whole blood samples derived from 987 individuals from the population-based Study of Health in Pomerania (SHIP)^48,49. Unbiased analysis of this large dataset revealed a gradual decline in mRNA expression of RIOK2 with increasing age of the individuals (β = −0.0012, P = 0.042) (Fig. 5d). We further stratified this sample into younger ( < 40 years; 232 individuals) and older individuals ( > 60 years; 249 individuals). Consistent with our assessment in nonagenarians, the mRNA expression of RIOK2 was reduced in older individuals in the SHIP cohort as compared to the younger group (β = −0.0538, P = 0.034) (Fig. 5e). Additionally, mRNA levels of RIOK2 were positively associated with TRiC (CCT2, CCT4, CCT6A, CCT7, CCT8) and dyskerin (DKC1, NHP2, NOP10) complex subunits in this cohort (Fig. 5f,g). Collectively, these findings suggest that attenuated RIOK2-driven expression of TRiC and dyskerin complexes may underlie telomere shortening in aging individuals.

Fig. 5: mRNA expression of RIOK2 is reduced in aging individuals. — **Fig. 5: mRNA expression of *RIOK2* is reduced in aging individuals.**

Ectopically expressed RIOK2 rescues telomere shortening in IPF

Idiopathic Pulmonary Fibrosis (IPF) is an age-associated interstitial lung disease that is characterized as a telomere biology disorder (TBD). This is attributable to the presence of mutations in telomere-associated genes and prominent telomere shortening in PBMCs and lung tissues of IPF patients^3,50,51. However, the mechanistic basis of telomere shortening in IPF is not clearly understood since the progressive loss of chromosomal ends is also observed in IPF patients harboring no mutations in telomere-associated genes^52,53. This observation indicates the presence of additional genetic programs underlying telomere shortening in IPF that remain unexplored. To this end, we investigated the involvement of RIOK2 in IPF-associated telomere shortening. Our independent assessment of a transcriptomic dataset in IPF patient-derived PBMCs (n = 70 IPF patients)⁵⁴ revealed that the mRNA levels of RIOK2 were significantly reduced in IPF patients as compared to healthy individuals (Fig. 6a).

**Fig. 6: Ectopic expression of RIOK2 rescues telomere shortening in IPF patient-derived lung fibroblasts.**

Interestingly, RIOK2 transcript levels positively correlated with prognostic factors for survival in IPF patients, such as diffusing capacity for carbon monoxide in the lungs (dlco%) and forced vital capacity (FVC%) (Fig. S7a). Hence, reduced expression of RIOK2 correlates with poor prognosis in IPF patients. Since loss of RIOK2 triggers telomere shortening in non-erythroid cells as well, we next studied the transcriptional landscape of lung explants from IPF patients⁵⁵. Indeed, RIOK2’s expression was markedly downregulated in lung tissues isolated from IPF patients as compared to healthy controls (Fig. 6b). In parallel, we also noted that RIOK2 transcript levels positively correlated with those of the genes encoding TRiC and dyskerin complex subunits, but not shelterin complex, in IPF patients (Fig. S7b–f). These findings led us to speculate that loss of RIOK2-driven transcription of TRiC and dyskerin complex subunits may underlie telomere shortening in IPF patients. To directly test this hypothesis, we obtained lung explant-derived primary fibroblasts from 3 IPF patients and 5 healthy controls. The mRNA expression of RIOK2 was consistently reduced in IPF fibroblasts as compared to passage-matched control fibroblasts (Fig. 6c), along with TRiC and dyskerin complex subunits (Fig. 6d,e). Also, TERC expression was reduced in IPF fibroblasts (Fig. 6f), in part due to decrease in dyskerin complex expression. Consistent with previous findings^3,50,51, we confirmed telomere shortening in IPF fibroblasts as compared to controls (Fig. 6g,h).

To investigate direct roles of RIOK2 in blocking IPF-associated telomere shortening, we lentivirally expressed HA-tagged RIOK2 in primary lung fibroblasts derived from both IPF patients and healthy controls (Fig. 6i). Ectopic expression of RIOK2 modestly enhanced mRNA expression of both TRiC (TCP1) and dyskerin (DKC1) complex subunits in IPF fibroblasts, but not control fibroblasts (Figs. 6j and S8a). This indicates RIOK2’s tight regulation of TRiC and dyskerin complex subunits in healthy controls, which prevents ectopic RIOK2 from disturbing a balanced environment.

Remarkably, ectopic expression of RIOK2 significantly rescued telomere shortening in IPF fibroblasts, as measured by qPCR-based quantification and FISH assay (Fig. 6k,l). However, telomere lengths were unperturbed in control fibroblasts upon ectopic expression of RIOK2 (Fig. S8b,c), thus confirming that RIOK2 tightly controls TRiC and dyskerin complex expression to maintain telomere length homeostasis. Furthermore, we noted increased DNA damage responses (measured by γ-H2AX foci formation) in IPF fibroblasts (Fig. 6m), which is partly attributable to shortened telomere lengths. Consistent with RIOK2-driven rescue of telomere shortening, ectopically expressed RIOK2 markedly alleviated DNA damage responses in IPF fibroblasts (Fig. 6m,n).

Taken together, these results establish that RIOK2 transcriptionally regulates the expression of TRiC and dyskerin complex subunits to promote telomerase activity and prevent telomere shortening in the context of aging, MDS and IPF (Fig. 7).

**Fig. 7: RIOK2 regulates transcription of TRiC and Dyskerin complex subunits in the context of aging, MDS and IPF.**

Discussion

Proper telomerase activity dictates the continued extension of chromosomal DNA ends to decelerate telomere shortening that induces DNA damage responses, replicative senescence, and apoptosis^4,46. Although regulation of TERT and TERC are studied extensively^29,56, the upstream regulators of other core components of the telomerase holoenzyme remain largely undefined. Here, we identify RIOK2 as a novel transcriptional regulator of TRiC and dyskerin complex subunits that promote telomerase activity to block telomere shortening.

TCAB1 is an indispensable component of the human telomerase holoenzyme^57,58. The TRiC complex acts as a chaperone to properly fold nascent TCAB1 protein and prevent its proteolysis^16,17. On the other hand, the dyskerin complex preserves the stability of TERC, the crucial RNA component of the human telomerase enzyme that functions as a template for reverse transcription to extend telomere ends¹⁵. Our findings demonstrate that RIOK2 functions as a key transcription factor for both TRiC and dyskerin complex subunits via the DNA-binding and transactivation sub-domains located within its N-terminal winged helix-turn-helix (wHTH) domain. We show that RIOK2 binds to the promoter regions of the genes encoding TRiC complex subunits and stimulates their expression to maintain TCAB1’s protein stability. Similarly, RIOK2 transcriptionally controls the expression of dyskerin complex subunits to sustain TERC stability. Consequently, loss/mutations in the transcriptional domains of RIOK2 dramatically impair human telomerase function, thereby causing telomere shortening. However, the stark defects in cell proliferation upon KD and KO of RIOK2 may not solely result from shortened telomeres, since RIOK2 is a master transcription factor and other unidentified factors may have been affected that could have contributed to cell proliferation defects. Although proliferation defects were observed in both ALT cells and telomerase-positive cells upon loss of RIOK2 (Fig. S1), our data demonstrates that the impact of complete loss of RIOK2 on proliferation is more prominent in telomerase-positive cells (HeLa and HEK293) than in the ALT cells (Saos-2 and U-2 OS). Interestingly, it is reported that loss of DKC1 in telomerase-negative U-2 OS cells also results in a marked reduction of cell proliferation, signifying that DKC1-driven effects are not restricted to telomerase maintenance alone^59,60. Since deficiency of RIOK2 leads to diminished DKC1 expression, this partly explains the reduced proliferation of ALT cells upon loss of RIOK2. Notably, RIOK2 deficiency led to significantly downregulated TRiC and dyskerin complex gene expression and resultant telomere shortening only upon later passages in immortalized cell lines, such as TF-1, K562, HEK293 and HeLa. In contrast, loss of RIOK2-driven telomere shortening was evident within 7-10 days in primary human HSPCs. This may be attributable to the inherently high telomerase activity in these immortalized cell lines^61,62,63, thus rendering them more resistant to impaired TRiC and dyskerin complex expression. We, however, did not observe RIOK2-mediated regulation of shelterin complex subunits, thus suggesting that RIOK2 specifically controls TRiC and dyskerin complexes, but not shelterin, to preserve human telomerase activity. Given that RIOK2 is a master transcription factor, it is plausible that RIOK2 may regulate other unidentified targets associated with telomere maintenance. Hence, diminished telomerase activity may not be solely responsible for telomere shortening in RIOK2 KO cells. Additionally, since RIOK2 plays an indispensable role in ribosome biogenesis and cytoplasmic protein translation, we believe that markedly low protein turnover inadvertently affects telomere maintenance mechanisms, which also reflects in the shortened telomeres upon RIOK2 deficiency. This is reflected in the RIOK2 KO cells reconstituted with a kinase-dead K123A mutant RIOK2, that specifically lacks translational abilities and is unable to rescue telomere shortening. However, we also show that reconstitution of neither DNA-binding mutant (DBM) nor transactivation domain deleted (ΔTAD1/2) mutants of RIOK2 were able to rescue telomere shortening in RIOK2 KO cells, thus suggesting that the transcriptional roles of RIOK2 are also important in preventing telomeric attrition.

Anemia and telomere shortening are predominant in aging individuals^64,65. Although telomere shortening and anemia remain closely associated with aging and a spectrum of hematological disorders such as MDS^66,67,68, the molecular underpinnings of this intriguing association have remained elusive thus far. We recently discovered the fundamental role of RIOK2 as a master transcription factor in human blood cell development and employed various in vitro and in vivo models to show that deficiency of RIOK2 blocks erythroid differentiation, thereby causing anemia^33,37. Therefore, our identification of RIOK2 as a key transcriptional regulator of both red blood cell differentiation and telomere maintenance provides one explanation for the association of telomere shortening and anemia with aging and hematological disorders and distinguishes RIOK2 as the first-in-class protein to directly link anemia with telomere shortening.

Idiopathic pulmonary fibrosis (IPF) is an age-associated interstitial lung disorder with a poorly understood etiology. However, it is classified as a telomeropathy since IPF patients exhibit shorter telomere lengths as compared to age-matched healthy controls, and telomere shortening correlates with worse prognosis and survival in these patients⁵³. Given that both familial and sporadic IPF patients with telomere shortening outnumber the total IPF patients harboring mutations in telomere-associated genes^3,53,69, there must be additional genetic programs that contribute to telomere shortening in IPF patients. In this regard, we discovered that compromised RIOK2 expression underlies telomere shortening in IPF patient-derived lung fibroblasts at late passages (Passage 13–15). Most importantly, ectopic expression of RIOK2 potently ameliorated telomere shortening and associated DNA damage responses in these patient-derived fibroblasts at later passages. However, downregulated RIOK2 expression may not be the sole reason behind telomere shortening in these IPF cells tested, and other unidentified transcriptional/mutational alternations may have contributed to the telomeric attrition. Nevertheless, RIOK2-driven transcriptional programs present a novel mechanism to control telomere shortening in IPF patients. Taken together, our findings suggest that RIOK2-driven transcriptional programs may be therapeutically targeted to alleviate anemia, aging and other telomere biology disorders.

Methods

Ethics

The research complies with all relevant ethical regulations approved by Dana-Farber Cancer Institute and Harvard Medical School. All human donors provided informed consent.

Primary and secondary cell culture

CD34+ primary human hematopoietic stem and progenitor cells (HSPCs) were obtained from Fred-Hutchinson Cancer Research Center, Seattle, USA. CD34+ cells were isolated from G-CSF-mobilized peripheral blood of adult healthy donors by magnetic sorting and cryopreserved. The cells were thawed and washed with 1× PBS constituting 1% human AB serum, followed by revival in expansion medium constituting StemSpan SFEM II medium supplemented with 1× CC100 (STEMCELL Technologies), 1% penicillin-streptomycin (P/S), 1% Glutamine and 10 ng/ml TPO. Post 3–5 days of genetic editing in the expansion phase, the cells were subjected to differentiation medium containing IMDM reconstituted with 3% human AB serum, 2% human AB plasma, 1% P/S, 0.06% heparin solution (STEMCELL Technologies), 1 ng/ml IL-3 (Peprotech, Inc.), 10 ng/ml SCF (Peprotech, Inc.), 200 µg/ml holo-transferrin (Sigma-Aldrich) and 3U/ml erythropoietin (DFCI pharmacy). Cells were cultured at a density of 0.1–0.2 × 10⁶ cells/ml and incubated at 37 °C with 5% CO₂. Media were changed every alternate day, as required. These protocols have been described previously³³.

TF-1 human erythroblast cells were purchased from ATCC (ATCC® CRL-2003^TM) and maintained in RPMI-1640 medium (ATCC® 30-2001^TM) containing 10% fetal bovine serum (FBS), 2 ng/ml GM-CSF and 1% P/S. For differentiation, the cells were washed with 1× PBS and resuspended in RPMI-1640 medium containing 10% FBS, 5 U/ml erythropoietin and 1% P/S. K562 human erythroid cells were purchased from ATCC (ATCC® CCL-243) and maintained in IMDM medium (ATCC® 30-2005^TM) containing 10% FBS and 1% P/S. For differentiation, the cells were washed with 1× PBS and resuspended in IMDM medium containing 10% FBS, 40 µM hemin and 1% P/S. HEK293 cells were purchased from ATCC, and HeLa cells were a kind gift from Dr. Kai Wucherpfennig’s lab at Dana-Farber Cancer Institute (DFCI). Both HeLa and HEK293 cells were maintained in DMEM medium supplemented with 10% FBS and 1% P/S. U2-OS and Saos-2 human telomerase negative (ALT) cells were purchased from ATCC (ATCC® HTB-96^TM and HTB-85^TM, respectively) and maintained in McCoy’s 5A medium (ATCC® 30-2007^TM) containing 10% FBS and 15% FBS, respectively. Both U2-OS and Saos-2 cell lines were cultured in media containing 1% P/S.

Frozen bone marrow (BM) aspirates from de-identified and coded MDS patients were obtained from the Ted and Eileen Pasquarello Tissue Bank in Hematologic Malignancies at Dana-Farber Cancer Institute, Boston. The BM cells were thawed, washed with 1× PBS, and allowed to revive in StemSpan SFEM II medium supplemented with 1× CC100 (STEMCELL Technologies) and 1% penicillin–streptomycin (P/S), followed by genomic DNA and RNA isolation the next day.

Frozen primary lung fibroblasts from IPF patients and controls were obtained from Cedars-Sinai, Los Angeles. These primary lung fibroblasts were thawed, washed with 1× PBS and cultured in DMEM (Lonza) medium containing 15% FBS, 1% penicillin–streptomycin (P/S), Amphotericin B (PSA) (Lonza- dilution 1:100), L-Glutamine 2 mM and Primocin (Invivogen- dilution 1:500).

Cells were incubated at 37 °C with 5% CO₂. Media were changed every alternate day, as required.

Generation of lentiviral vectors and infection

RIOK2 was cloned in the pHAGE-MCS-IRES-ZsGreen lentiviral vector with an N-terminal Hemagglutinin (HA) tag. DNA binding mutant (DBM), Transactivation domain deletion 1 and 2 (ΔTAD1 and ΔTAD2) vectors were generated by site-directed mutagenesis in N-terminal HA-tagged RIOK2 construct (GenScript).

For lentiviral production, HEK293FT cells were transiently transfected with pVSV-G, pDelta8.9 and the required RIOK2 vectors using X-tremeGENE^TM HP DNA transfection reagent as per the manufacturer’s protocol. Viral supernatant was collected 48 h post transfection. Viral supernatant was centrifuged at 2544 xg for 5 min to remove cellular debris, then filtered using 0.45 µm filter and stored at −80 °C for further usage. Primary human HSPCs or secondary cells were transduced at a density of 0.1–0.2 × 10⁶ cells per well in 6-well plates. Spinfection was performed with viral supernatants added with 8 µg/ml polybrene (Millipore) at 3392 × g for 1.5 h at 32 °C and left overnight. The medium was changed the next morning. Lentiviral transduction efficiency reached 60–75% for primary human HSPCs and >95% for secondary cells after 48–72 h of infection. The positively transduced cells (ZsGreen + ) were FACS-sorted for further analysis.

CRISPR/Cas9 gene editing

Primary human HSPCs and human erythroblast cells (TF-1) were electroporated using the Lonza-Amaxa 4D nucleofector unit as per the manufacturer’s protocols. For ribonucleoprotein (RNP) formation, tracer RNAs (IDT) and crRNAs (IDT) were incubated at 95 °C for 5 min at equal molar ratios, brought to room temperature (RT) and mixed with equal molar concentration of Cas9 peptide (IDT Technologies). This was followed by incubation at 37 °C for 15 min and the RNP mix was kept at 4 °C until use within the next 2 h. Cells were mixed with the RNP mix at a density of 0.1–0.2 × 10⁶ cells per well of a 16-well electroporation strip and subsequently electroporated. Immediately after electroporation, fresh medium was added to the cells and incubated at RT for 10 min before transferring the cells to 37 °C. Genome editing efficiency was analyzed 48–72 hours after electroporation by nucleotide sequencing, quantitative PCR, and Western blotting. The crRNAs used for knockdown (KD) of RIOK2: GAACGGCGGGTTTCTTACCG and CATTTGTCAACCGATAGCCC, crRNAs used for knockout (KO) of RIOK2: TGACTTCAGGGTCTTGACCG and TGATTACAATCGTCATGCAG. crRNA against LacZ (Control): TTCTCCGCGGGAACAAACGG.

Luciferase reporter assay

For transactivation assays, the promoter regions of TCP1, CCT8, DKC1, and NHP2 (500–750 bps upstream of the ATG start codon) were cloned in pGL3.1 basic vector. Luminescence intensities were normalized using co-transfection of Renilla expression vector in HEK293 cells using Lipofectamine 3000 reagent. Dual luciferase assays (Promega) were performed as per the manufacturer’s instructions. Luminescence intensities were captured using EnVision^TM Multimode plate reader (Perkin Elmer).

Quantitative PCR

RNA was isolated from cells using the RNeasy Plus Micro kit (Qiagen) using the manufacturer’s protocols. Genomic DNA (gDNA) was removed using gDNA eliminator spin columns, followed by isolation of total RNA using phenol-free RNeasy MinElute spin columns. Reverse transcription was performed using qScript cDNA Synthesis kit (QuantaBio). Quantitative real-time polymerase chain reaction (RT-PCR) was performed using Quantstudio6 RT-PCR system (Applied Biosciences) and PerfeCTa SYBR Green FastMix Reaction Mixes (QuantaBio). Comparative C_T method has been used for all quantifications using corresponding ACTB (encoding for β-Actin) mRNA levels for normalization. The following primer sequences were used:

Name	Primers	Sequence
TCP1(CCT1)	FP	5ʹGTG GTC ATT ACA GAC CCT GAA AA 3ʹ
TCP1(CCT1)	RP	5ʹAAC TCT TCT AAC TGC CAT AGC AC 3ʹ
CCT2	FP	5ʹGCA CTA CCT CTG TTA CCG TTT T 3ʹ
CCT2	RP	5ʹCTT CTC TCC AAC CCG CTA TGA 3ʹ
CCT3	FP	5ʹACC GCA AGG CAT TGG ATG ATA 3ʹ
CCT3	RP	5ʹGAC CAC CGA CTG ATG GCT T 3ʹ
CCT4	FP	5ʹGTT CTG CTG AGT TAG CTG AGG 3ʹ
CCT4	RP	5ʹTTC AGT TAA TCG TAG GGC CAA C 3ʹ
CCT5	FP	5ʹTGG AAC TGT CCA AGT CTC AGG 3ʹ
CCT5	RP	5ʹCAT CGG CTA TTC TGA TTG GGTG 3ʹ
CCT6A	FP	5ʹACA CTC ACT CAG ATC AAA GAT GC 3ʹ
CCT6A	RP	5ʹCCC TTT ACA CTG GGC TTA TGT TT 3ʹ
CCT7	FP	5ʹGCT GGT GTT GCA TTC AAG AAG 3ʹ
CCT7	RP	5ʹTTG CCT GAT AAT CCT CAA CTG TG 3ʹ
CCT8	FP	5ʹAGG AGG GAG CGA AAC ACT TTT 3ʹ
CCT8	RP	5ʹGTT GCT GCA TCG TTT GTC ACA 3ʹ
DKCI	FP	5ʹCAC ACT ATA CAC CTC TTG CAT GT 3ʹ
DKCI	RP	5ʹGTA TCC GTC GAA TCC AGG CTA 3ʹ
NHP2	FP	5ʹATG GTT TTG GCA GGA GAC AC 3ʹ
NHP2	RP	5ʹCAT GGG GCT TGA CCA TTA TC3ʹ
NOP10	FP	5ʹCAG TAT TAC CTC AAC GAG CAG G 3ʹ
NOP10	RP	5ʹGGC TGA GCA GGT CTG TTG TC 3ʹ
GAR1	FP	5ʹCGT GGA GGC TTT AAT CGA GGT 3ʹ
GAR1	RP	5ʹGAC TAC ACG TTC TGG AGG TCC 3ʹ
CTC1	FP	5ʹTCC CTT CCT CCG AAC AAG C 3ʹ
CTC1	RP	5ʹTGG GAC AAC CAG ACA GTC TTC 3ʹ
POT1	FP	5ʹCAG CCA ATG CAG TAT TTT GAC C 3ʹ
POT1	RP	5ʹGGT GCC ATC CCA TAC CTT TAG AA 3ʹ
TRF1	FP	5ʹAAC AGC GCA GAG GCT ATT ATT C 3ʹ
TRF1	RP	5ʹCCA AGG GTG TAA TTC GTT CAT CA 3ʹ
TEN1	FP	5ʹCCC AAA CCT GGG ACC TAT TAC C 3ʹ
TEN1	RP	5ʹAGG CAC AAC CTG CCA AAT GT 3ʹ
TERC	FP	5ʹCAC CGC GAA GAG TTG GGC TCT G 3ʹ
TERC	RP	5ʹGCA TGT GTG AGC CGA GTC CTG G 3ʹ
28sRNA	FP	5ʹAGT AAC GGC GAG TGA ACA GG 3ʹ
28sRNA	RP	5ʹGCC TCG ATC AGA AGG ACT TG 3ʹ
ACTB	FP	5ʹGCG AGA AGA TGA CCC AGA TC3ʹ
ACTB	RP	5ʹCCA GTG GTA CGG CCA GAG G3ʹ
RIOK2	FP	5ʹGCT ATC GGT TGA CAA ATG CAG G3ʹ
RIOK2	RP	5ʹGAA ACG AGG TTC TTC CTA GTC TG3ʹ

Western blotting

For collection of whole cell lysates, cells were washed twice with ice cold 1× PBS after removal of medium, then resuspended in RIPA lysis buffer (Life Technologies) supplemented with 1× complete protease inhibitor cocktail and 1× phosphatase inhibitor cocktail (Thermo Scientific). Lysis was carried out on a rocker at 4 °C for 15–30 min followed by removal of cellular debris by centrifugation at 15,871 × g for 20 min. The supernatant was collected in a fresh tube, mixed with Laemmli buffer and incubated at 95 °C for 10 min. Equal amounts of proteins were resolved by SDS-PAGE method using Novex Tris-Glycine minigels submerged in Tris-Glycine-SDS buffer in a mini gel tank (Life Technologies). The proteins were then transferred onto PVDF membranes (Thermo Fisher Scientific) using Tris-Glycine buffer followed by blocking in 5% skimmed milk solution in 1× PBST buffer at RT for 1 h. The membranes were then probed with the required primary antibodies diluted in fresh blocking buffer at 4 °C for overnight (O/N): β-Actin rabbit polyclonal antibody at 1:1000 (3967 S, Cell Signaling Technology), RIOK2 mouse monoclonal antibody at 1:1000 (OTI3E11, TA505140, Origene), TCAB1 rabbit polyclonal antibody at 1:1000 (Novus Biologicals #NB100-68252). After O/N incubation, membranes were washed four times with PBST buffer for 5 min each on a rocker at RT, followed by incubation with HRP-linked anti-mouse IgG (7076 S, Cell Signaling Technology) or HRP-linked anti-rabbit IgG (7076 S, Cell Signaling Technology) at 1:3000 dilution in fresh blocking buffer for 1 h at RT. After incubation with secondary antibodies, membranes were washed four times with PBST buffer for 5 min each on a rocker at RT, followed by incubation with Pierce Western blotting substrates (Thermo Scientific) mixed at 1:1 ratio for 2–5 min at RT. The protein bands were then visualized using the ChemiDoc Touch Imaging system (Bio-Rad). ImageJ was used to quantify protein bands. Uncropped blots are also provided in the Source Data file.

Cell cycle analysis

For analysis of cell cycle stages, the cells were washed with 1× PBS and fixed using 4% PFA for 30 min or 70% ethanol for 1 h at 4 °C. The cells were washed twice using 1× PBS and resuspended in DAPI containing staining buffer followed by FACS analyses using CytoFLEX Flow Cytometer (Beckman Coulter). Data were analyzed using FlowJo 10.0.7 and plotted using GraphPad Prism.

Telomerase Repeated Amplification Protocol (TRAP) assay

Analysis of telomerase activity was performed using the non-radioactive method with TRAPeze® Telomerase detection kit from Millipore (#S7700), as per manufacturer’s protocols. Cells were lysed in the provided CHAPS lysis buffer and 0.5/0.25/0.1 µg total protein lysate was used in a reaction mixture containing the provided TRAP reaction buffer, dNTP mix, TRAP and TS primers, and Taq polymerase enzyme. PCR reactions were performed in a thermal cycler using the following program:

Cycle 1 for 1 cycle: 30 °C for 30 min;

Cycle 2 for 1 cycle: 95 °C for 2 min;

Cycle 3 for 30 cycles: 94 °C for 15 secs, 59 °C for 30 s, 72 °C for 1 min.

4 °C ∞ for 1 cycle.

The PCR samples and 10 bp DNA ladder were then loaded onto a 10% polyacrylamide TBE gel and run at 100 V for 1 h and 45 min. Post electrophoresis, the TBE gels were stained with GELRED nucleic acid gel stain (#10154-212) for 30 min at RT in dark and visualized using ChemiDoc Touch Imaging system (Bio-Rad). ImageJ software was used to quantify relative telomerase activity by pairwise comparison of amplicons across samples containing same amounts of total protein, for example 0.5 ug total protein containing control sample was compared with 0.5 ug total protein containing RIOK2 KD and KO samples. These values were normalized to the respective internal control bands of the samples.

Bulk RNA-sequencing

RNA library preparations, sequencing reactions and initial bioinformatic analysis were conducted at GENEWIZ, LLC. (South Plainfield, NJ, USA) as follows:

Library Preparation with polyA selection and HiSeq Sequencing: RNA samples received were quantified using Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA) and RNA integrity was checked using Agilent TapeStation 4200 (Agilent Technologies, Palo Alto, CA, USA). RNA sequencing libraries were prepared using the NEBNext Ultra RNA Library Prep Kit for Illumina following the manufacturer’s instructions (NEB, Ipswich, MA, USA). Briefly, mRNAs were first enriched with Oligo(dT) beads. Enriched mRNAs were fragmented for 15 min at 94 °C. First strand and second strand cDNAs were subsequently synthesized. cDNA fragments were end-repaired and adenylated at 3’ends, and universal adapters were ligated to cDNA fragments, followed by index addition and library enrichment by limited-cycle PCR. The sequencing libraries were validated on the Agilent TapeStation (Agilent Technologies, Palo Alto, CA, USA), and quantified by using Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA) as well as by quantitative PCR (KAPA Biosystems, Wilmington, MA, USA). The sequencing libraries were clustered on 1 lane of a flowcell. After clustering, the flowcell was loaded on the Illumina HiSeq instrument (4000 or equivalent) according to the manufacturer’s instructions. The samples were sequenced using a 2x150bp Paired End (PE) configuration. Image analysis and base calling were conducted by the HiSeq Control Software (HCS). Raw sequence data (.bcl files) generated from Illumina HiSeq were converted into fastq files and de-multiplexed using Illumina’s bcl2fastq 2.17 software. One mismatch was allowed for index sequence identification.

Data analysis

After investigating the quality of the raw data, sequence reads were trimmed to remove possible adapter sequences and nucleotides with poor quality using Trimmomatic v.0.36. The trimmed reads were mapped to the Homo sapiens reference genome available on ENSEMBL using the STAR aligner v.2.5.2b. The STAR aligner is a splice aligner that detects splice junctions and incorporates them to help align the entire read sequences. BAM files were generated as a result of this step. Unique gene hit counts were calculated by using feature Counts from the Subread package v.1.5.2. Only unique reads that fell within exon regions were counted.

Gene set enrichment analysis (GSEA) was performed using Broad Institute’s GSEA Software. Sets of telomere-maintenance-associated genes were derived by accounting for the differentially regulated genes that passed the cut-off of adjusted p value < 0.05 in control vs RIOK2-depleted HSPCs. Additionally, canonical pathways such as KEGG, REACTOME and BIOCARTA were consulted.

Assay of transposase-accessible chromatin with high-throughput sequencing (ATAC-Seq)

ATAC-seq on an equal number of snap-frozen control and RIOK2-depleted HSPCs was performed at Center for Functional Cancer Epigenetics (CFSE) at DFCI. Briefly, the snap-frozen cells were thawed and their nuclei were isolated followed by a transposase reaction. Then high throughput Illumina sequencing of pair-ended reads was performed, and the raw reads were mapped to the reference genome (hg19) using alignment software (conFigureyaml). After alignment, the mapped reads were then normalized (by down-sampling) to 4 million reads. The uniquely mapped reads of the downsample were then piled-up. Nd is defined as the number of unique region locations. N1 is defined as the number of unique region locations with only one read. The PBC (a common method to determine sample complexity) is then simply calculated as N1/Nd. ATAC-seq samples with a good PBC score >= 0.90 were analyzed. Peaks were called using the MACS peak calling software. Chromatin accessibility was analyzed using IGV software. Gene set enrichment analysis (GSEA) was performed using Broad Institute’s GSEA Software.

Chromatin immunoprecipitation (ChIP)

Chromatin Immunoprecipitation was performed using EZChIP^TM kit (Millipore Sigma) according to the manufacturer’s instructions. Briefly, 1 × 10⁶ cells were incubated with growth medium supplemented with formaldehyde (methanol-free) at a final concentration of 1% for 10 minutes at RT for cross-linking. Then unreacted formaldehyde was quenched by adding Glycine at a final concentration of 0.125 M. The cells were then washed twice with 1× PBS, followed by resuspension in lysis buffer supplemented with protease inhibitors and incubated at 4 °C for 15–30 min. The lysed samples were then sonicated at 25% amplitude for 5 min at 1 sec on and 4 sec off cycles, followed by centrifugation at 12,000 g for 10 min at 4 °C. To the supernatant, 60 µl of protein agarose G beads slurry was added for pre-clearing and incubated at 4–°C for 1 h with rotation, followed by spinning at 94 × g for 1 min. 1% of the pre-cleared supernatant was saved as Input, and to the rest 2–4 µg primary antibodies: HA rabbit monoclonal antibody (C29F4, 3724 S, Cell Signaling Technology), RIOK2 mouse monoclonal antibody (OTI3E11, TA505140, Origene); RIOK2 rabbit polyclonal antibody (NBP130098, Thermo Fisher Scientific)) or control IgGs were added followed by incubation at 4 °C with rotation overnight. After overnight incubation, 60 µl of protein agarose G beads slurry was added to each sample and incubated at 4 °C for 2 h. The agarose beads were pelleted by spinning, and washed sequentially with low salt buffer, high salt buffer, LiCl wash buffer and TE buffer. After washing, elution of protein-DNA complexes was performed using elution buffer containing NaHCO₃ and SDS at RT for 15–30 min. The eluted supernatant was treated with RNase A for 30 mins at 37 °C and reverse-crosslinked using a buffer containing EDTA, Tris-HCl and Proteinase K for 1–2 h at 42 °C. After reverse-crosslinking, DNA was purified using Spin columns provided by Millipore Sigma/Qiagen and the purified DNA was then used for quantitative PCR using Quantstudio6 RT-PCR system (Applied Biosciences).

Fluorescence in-situ hybridization (FISH)

Fluorescence in-situ hybridization was performed using probes from PNA Biosciences as per the manufacturer’s protocols. Cells were fixed on slides and washed twice with 1× PBS. They were treated with RNase solution and incubated at 37 °C for 20 min, followed by 1x PBS wash and pepsin digestion. The cells were then re-fixed with 70%, 85% and 100% cold ethanol solutions sequentially and air dried. Hybridization buffer (20 mM Tris, pH7.4, 60% formamide, 0.5% blocking reagent) containing 500 nM PNA probes (TelC-PNA Bio) was added to the air-dried slides, followed by heating the slides at 85 °C for 10 min and incubation at room temperature for 2 h in the dark. The slides were then sequentially washed with wash solution (2× SSc, 0.1% Tween-20), 2× SSc, 1× SSc and water, followed by quick drying the slides and mounting with an anti-fade mounting reagent containing DAPI. The slides were then visualized using fluorescence microscopy.

Immunofluorescence staining

For immunofluorescence staining, the coverslips were coated with poly-L-Lysine solution (0.1 mg/ml) at 4 °C overnight. The coverslips were then washed with 1× PBS and cells were incubated on the coverslips. The cells on the coverslips were then fixed with 4% PFA in 1× PBS for 15 min at RT, washed with 1× PBS, permeabilized using 0.1% Triton in 1× PBS for 5 min at RT and blocked with 4% BSA in 1× PBS for 1 h at RT. The cells were then incubated with primary antibodies (TCAB1: Novus Biologicals #NB100-68252; γ-H2AX: Cell Signaling Technology #9718 S) diluted in blocking buffer for 1 h at RT, washed thrice with 1× PBS, and incubated with secondary antibodies (Abcam) for 1 h at RT. The coverslips were similarly washed thrice with 1× PBS, mounted with an anti-fade mounting reagent containing DAPI, and visualized using fluorescence microscopy.

Telomere length measurement

Telomere length/content was assessed by measuring T/S ratio (telomere/single copy gene expression) using primers and quantitative PCR conditions as previously described^34,35. Briefly, cells were harvested and lysed with RLT lysis buffer as per the manufacturer’s protocol (Qiagen). Following this, genomic DNA (gDNA) was isolated using Quick-DNA microprep kit (Zymo Research). The isolated gDNA concentrations were measured using Nanodrop and equal amounts of gDNA (2.5/5 ng/µl) across samples were loaded for qPCR (using SYBR green) based telomere length assessment using the PCR program as previously described^34,35. The final telomere primer concentrations were: tel1, 270 nM; tel2, 900 nM. The final 36B4 (single copy gene) primer concentrations were: 36B4u, 300 nM; 36B4d, 500 nM. The primer sequences were: tel1, GGTTTTTGAGGGTGAGGGTGAGGGTGAGGGTGAGGGT; tel2, TCCCGACTATCCCTATCCCTATCCCTATCCCTATCCCTA; 36B4u, CAGCAAGTGGGAAGGTGTAATCC; 36B4d, CCCATTCTATCATCAACGGGTACAA. The housekeeping single copy gene 36B4 encodes acidic ribosomal phosphoprotein PO and is located on chromosome 12. The telomere length measurements were carried out ~14 days after gene editing to achieve knockdown (KD) and knockout (KO) of RIOK2 in all cell lines (TF-1, K562, HeLa and HEK293), except for primary human HSPCs where telomere length measurements were performed post 7-10 days of gene editing.

Study of Health in Pomerania (SHIP) cohort and measurements

The Study of Health in Pomerania (SHIP) was conducted in accordance with the Declaration of Helsinki and was reviewed and approved by the University Medicine Greifswald Institutional Review Board. All subjects provided written informed consent. The analyses in this manuscript are based on a subset of n = 987 participants aged from 20 to 81 from the SHIP-TREND-0 cohort⁴⁸. For this sub-sample, microarray-based whole blood-derived transcriptome data as well as all required covariates were available. Analyses in SHIP were done via linear mixed effect models adjusting for sex, blood cell composition and technical parameters (more details in the statistical tests below). A detailed description of blood sample collection, RNA preparation, array processing and expression data generation can be found elsewhere⁴⁹.

Statistical tests

Data are presented as mean ± SEM. Unpaired two-tailed t-test or unpaired non-parametric Mann-Whitney t-test were used to compare two groups. Analysis of variance (ANOVA) with Dunnett’s multiple comparisons test was used for comparisons amongst multiple groups, wherever applicable as per requirements of data and quantification. Two-tailed non-parametric Spearman correlation or two-tailed Pearson’s correlation were used for correlation analyses. GraphPad Prism v8.0/9.0 (GraphPad Software Inc., San Diego, CA) was used to perform statistical analyses. Analyses in SHIP were done via linear mixed effect models using either age as exposure and RIOK2 transcript level as outcome or using RIOK2 transcript level as exposure and the target gene’s mRNA levels as outcome. The analyses in SHIP were adjusted for sex, body mass index (kg/m2), white blood cells (Gpt/L), red blood cells (Tpt/L), platelets (Gpt/L), technical parameters (RNA integrity number, RNA amplification batch, sample storage time (time between blood donation and RNA isolation, days)), and blood cell composition (lymphocytes (%), monocytes (%), basophils (%), eosinophils (%)), and age (years) if age was not the exposure variable. Amplification batch was included as a random effect, all other variables as fixed effects. When multiple array probes mapped to the same gene, we reported the results of all probes but used the number of genes for the Bonferroni correction for multiple testing.

Sample size was not predetermined.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data generated from RNA-sequencing are deposited in the Gene Expression Omnibus (GEO) database under accession code GSE185922 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185922. ATAC–sequencing datasets are deposited in the GEO database under accession number GSE174714 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174714. The mRNA correlations in MDS patients and aging individuals have been drawn using publicly available GEO datasets under accession codes GSE19429 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19429 and GSE65219 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65219, respectively. The mRNA correlations in IPF patient-derived PBMCs and lung tissues have been drawn using publicly available GEO datasets under accession codes GSE133298 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133298 and GSE124685 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124685, respectively. Transcription data from SHIP is publically available under GSE36382 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36382. The covariates can be made available upon reasonable request at https://transfer.ship-med.uni-greifswald.de/FAIRequest/. Requests for any materials (plasmids generated in this study, crRNAs) and reagents should be directed to and will be fulfilled by Laurie H. Glimcher (Laurie_Glimcher@dfci.harvard.edu) with appropriate material transfer agreements. Source data are provided with this paper.

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Acknowledgements

We thank Dr. Jerome Ritz, Dr. Jacqueline S. Garcia (Dana-Farber Cancer Institute) and the Ted and Eileen Pasquarello Tissue Bank in Hematologic Malignancies for kind assistance in procuring primary MDS patient samples. We thank Dr. John S. Kim (University of Virginia) for his valuable input in the study of idiopathic pulmonary fibrosis (IPF). We are grateful to Dr. Kai W. Wucherpfennig (Dana-Farber Cancer Institute) for providing the HeLa cell line and his guidance on the overall study design. We also thank Dr. Elizabeth H. Blackburn for providing insightful edits to the manuscript. Figure 7 has been created using BioRender.com. Funding is from National Institutes of Health small grant program 1R03HL156574-01 (L.H.G.), The Edward P. Evans Foundation-EvansMDS Discovery Research Grant (L.H.G.), Institutional funding, Dana-Farber Cancer Institute (L.H.G.), The Helen Gurley Brown Presidential Initiative (S.G.), The Claudia Adams Barr award (S.G.), National Institutes of Health/NHLBI UG3HL145266 (I.N.). The Study of Health in Pomerania (SHIP) is part of the Community Medicine Research Network (CMR) (http://www.medizin.uni-greifswald.de/icm) of the University Medicine Greifswald, which is supported by the German Federal State of Mecklenburg-Western Pomerania. S.V.D.A. was supported by the Federal Ministry of Education and Research (BMBF, gr. no. 01KU2004) under the frame of ERA PerMed (TRAJECTOME project, ERAPERMED2019-108). The University of Greifswald is a member of the Caché Campus programme of the InterSystems GmbH.

Author information

Mileena T. Nguyen
Present address: Yale University, New Haven, CT, USA
These authors jointly supervised this work: Shrestha Ghosh, Laurie H. Glimcher.

Authors and Affiliations

Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
Shrestha Ghosh, Mileena T. Nguyen, Ha Eun Choi & Laurie H. Glimcher
Department of Immunology, Harvard Medical School, Boston, MA, USA
Shrestha Ghosh & Laurie H. Glimcher
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
Maximilian Stahl
Harvard Medical School, Boston, MA, USA
Maximilian Stahl
Department for Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
Annemarie Luise Kühn, Sandra Van der Auwera & Hans J. Grabe
German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
Sandra Van der Auwera & Hans J. Grabe
Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
Henry Völzke
German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
Henry Völzke
Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
Georg Homuth
La Jolla Institute for Immunology, La Jolla, CA, USA
Samuel A. Myers
Women’s Guild Lung Institute, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Cory M. Hogaboam
Division of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville, VA, USA
Imre Noth
Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
Fernando J. Martinez
Department of Neurology, Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Gregory A. Petsko
Department of Medicine, Harvard Medical School, Boston, MA, USA
Laurie H. Glimcher

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Shrestha Ghosh
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Mileena T. Nguyen
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Ha Eun Choi
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Maximilian Stahl
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Annemarie Luise Kühn
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Sandra Van der Auwera
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Hans J. Grabe
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Henry Völzke
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Georg Homuth
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Imre Noth
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Contributions

Conceptualization: S.G. and L.H.G.; Designing: S.G. and L.H.G.; Methodology: S.G., M.T.N. H.E.C.; Sample Acquisition: S.G., C.H., M.S., I.N., F.J.M., H.V. and G.H.. Data Analysis: S.G., M.T.N., H.E.C., G.A.P., S.A.M., A.L.K., S.V.D.A., H.J.G., and L.H.G.; Funding acquisition: S.G., H.V., G.H., I.N., and L.H.G.; Writing – original draft: S.G. and L.H.G.; Editing: S.G., L.H.G. and G.A.P. Supervision: S.G. and L.H.G.

Corresponding authors

Correspondence to Shrestha Ghosh or Laurie H. Glimcher.

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Competing interests

L.H.G. is a former Director of Bristol-Myers Squibb, GlaxoSmithKline Pharmaceuticals and the Waters Corporation and currently serves on the Board of Directors of Analog Devices, Inc. She also serves on the scientific advisory boards of Repare Therapeutics and Abpro Therapeutics. L.H.G. is a consultant for JP Morgan healthcare. G.A.P. is on the Scientific Advisory Boards of Amicus Therapeutics, MeiraGTx, Annovis Bio, Retromer Therapeutics, and Proclara Biosciences and holds equity or stock options in Denali Therapeutics, MeiraGTx, Annovis Bio, Retromer Therapeutics and Proclara Biosciences, companies that are developing therapies for neurodegenerative diseases. M.S. served on the advisory board for Novartis, Kymera, Sierra Oncology, GSK and Rigel; consulted for Boston Consulting and Dedham group and participated in GME activity for Novartis, Curis Oncology, Haymarket Media and Clinical care options. C.M.H. serves as Chief Scientist at Lung Therapeutics and is on the Scientific Advisory Boards of Lassen Therapeutics, Rubedo Life Sciences, and Structure Therapeutics. C.M.H holds stocks options in Lung Therapeutics, Lassen Therapeutics, and Rubedo Life Sciences. I.N. is a consultant for Boerhinger and Sanofi. H.J.G. has received travel grants and speakers honoraria from Fresenius Medical Care, Neuraxpharm, Servier and Janssen Cilag. All other authors declare no relevant competing interests. L.H.G. and S.G. have a patent filed with Dana-Farber Cancer Institute, Boston related to this work. The remaining authors declare no competing interests.

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Ghosh, S., Nguyen, M.T., Choi, H.E. et al. RIOK2 transcriptionally regulates TRiC and dyskerin complexes to prevent telomere shortening. Nat Commun 15, 7138 (2024). https://doi.org/10.1038/s41467-024-51336-3

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Received: 11 September 2023
Accepted: 02 August 2024
Published: 20 August 2024
Version of record: 20 August 2024
DOI: https://doi.org/10.1038/s41467-024-51336-3

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