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Tissue-selective effects of nucleolar stress and rDNA damage in developmental disorders

Many craniofacial disorders are caused by heterozygous mutations in general regulators of housekeeping cellular functions such as transcription or ribosome biogenesis. Although it is understood that many of these malformations are a consequence of defects in cranial neural crest cells, a cell type that gives rise to most of the facial structures during embryogenesis, the mechanism underlying cell-type selectivity of these defects remains largely unknown. By exploring molecular functions of DDX21, a DEAD-box RNA helicase involved in control of both RNA polymerase (Pol) I- and II-dependent transcriptional arms of ribosome biogenesis, we uncovered a previously unappreciated mechanism linking nucleolar dysfunction, ribosomal DNA (rDNA) damage, and craniofacial malformations. Here we demonstrate that genetic perturbations associated with Treacher Collins syndrome, a craniofacial disorder caused by heterozygous mutations in components of the Pol I transcriptional machinery or its cofactor TCOF1 (ref. 1), lead to relocalization of DDX21 from the nucleolus to the nucleoplasm, its loss from the chromatin targets, as well as inhibition of rRNA processing and downregulation of ribosomal protein gene transcription. These effects are cell-type-selective, cell-autonomous, and involve activation of p53 tumour-suppressor protein. We further show that cranial neural crest cells are sensitized to p53-mediated apoptosis, but blocking DDX21 loss from the nucleolus and chromatin rescues both the susceptibility to apoptosis and the craniofacial phenotypes associated with Treacher Collins syndrome. This mechanism is not restricted to cranial neural crest cells, as blood formation is also hypersensitive to loss of DDX21 functions. Accordingly, ribosomal gene perturbations associated with Diamond–Blackfan anaemia disrupt DDX21 localization. At the molecular level, we demonstrate that impaired rRNA synthesis elicits a DNA damage response, and that rDNA damage results in tissue-selective and dosage-dependent effects on craniofacial development. Taken together, our findings illustrate how disruption in general regulators that compromise nucleolar homeostasis can result in tissue-selective malformations.

ribosomal protein genes in the nucleoplasm 5 . Induction of nucleolar stress by inhibition of Pol I leads to DDX21 relocalization from the nucleolus to the nucleoplasm and to its simultaneous loss from Pol I and Pol II target promoters 5 . Furthermore, single-cell measurements revealed a strong correlation between the DDX21 nucleolar/nucleoplasmic ratio and pre-rRNA levels, both in unperturbed HeLa cells and in those treated with the Pol I inhibitor CX-5461 (hereafter iPol I) (Fig. 1a, b).
We asked whether perturbations in TCS-associated genes elicit disruption of DDX21 functions. Downregulation of TCOF1 or POLR1D in HeLa cells (Extended Data Fig. 1b, c) led to relocalization of DDX21 to the nucleoplasm ( Fig. 1c and Extended Data Fig. 1d), and this was accompanied by eviction of DDX21 from the rDNA and Pol II target promoters, as determined by chromatin immunoprecipitation followed by sequencing (ChIP-seq) (Fig. 1d-f) and confirmed in independent ChIP-qPCR experiments (Extended Data Fig. 1e, f). Analysis of TCOF1 genomic occupancy showed that although it binds the rDNA (Fig. 1g), unlike DDX21, it does not associate with Pol II promoters (Fig. 1h, i). Even within the nucleolus, DDX21 and TCOF1 may not act as a part of the same complex, as they do not readily co-immunoprecipitate (Extended Data Fig. 2a, b). Taken together, our data suggest that DDX21 can respond to TCOF1 dysfunction indirectly, through a pathway that is sensitive to the status of rRNA synthesis.
TCS craniofacial anomalies originate primarily from diminished allocation of cranial neural crest cells (cNCCs) into the first and second pharyngeal arches 3,4,7 . If loss of DDX21 from chromatin is an important downstream mediator of TCOF1 and POL1 dysfunction, then first and second arch structures should be sensitive to DDX21 knockdown. To test this, we established Xenopus as a model for TCS. tcof1 knockdown in Xenopus embryos with morpholinos targeting either translation or splicing of tcof1 resulted in hypoplasia and deformation of the mandibular and hyoid stream cartilage structures, which are derived from first and second arches ( Fig. 1j and Extended Data Fig. 2c-e), a phenotype consistent with both TCS and published zebrafish phenotypes [8][9][10] . Notably, at higher morpholino doses, overall growth defects were evident in tcof1 morphants (Extended Data Fig. 2e). Next, we designed and injected morpholino targeting ddx21. At high doses, this morpholino impeded embryonic growth, but at lower doses we observed craniofacial phenotypes remarkably similar to those seen in tcof1 morphants ( Fig. 1j and Extended Data Fig. 2c, e). Furthermore, injection of mRNA encoding a catalytically defective human DDX21 (ref. 11) (DDX21 SAT ) also faithfully phenocopied TCS craniofacial defects, whereas injection of wild-type DDX21 mRNA had no appreciable effects ( Fig. 1j and Extended Data Fig. 2c, e, f). Thus, development of craniofacial structures is hypersensitive to the loss of Ddx21 or its RNA helicase activity. These results were corroborated in zebrafish embryos injected with morpholinos blocking translation of ddx21, where observed craniofacial phenotypes resembled those reported in the polr1d −/− and polr1c −/− models of TCS [8][9][10] , and were rescued by the co-injection of human DDX21 mRNA (Extended Data Fig. 2g-j).
Because TCS phenotypes were shown to result from defects in cNCCs 9,10,12,13 , we investigated whether Tcof1 loss cell-autonomously affects DDX21 functions in cNCCs. We used CRISPR-Cas9 genome editing to generate Tcof +/− and Tcof −/− mouse embryonic stem (ES) cells, which showed no appreciable defect in Ddx21 nucleolar localization (Fig. 2a, left, and Extended Data Fig. 3a). However, upon differentiation into cNCCs, we observed partial relocalization of Ddx21 to the nucleoplasm in Tcof1-mutant, but not in wild-type cells (Fig. 2a,  right). This defect was rescued by introduction of an inducible human GFP-tagged TCOF1 construct (GFP-TCOF1) ( Fig. 2b and Extended Data Fig. 3b). The severity of the defect was dependent on Tcof1 dosage, with more pronounced nucleolar exclusion observed in Tcof −/− cNCCs (Fig. 2a). Ddx21 relocalization was not observed in embryoid body outgrowths from Tcof1 −/− (Extended Data Fig. 3c), further suggesting cell-type selectivity. We also generated a TCOF1 +/− human ES cell line (Extended Data Fig. 3d-f) and observed partial relocalization of DDX21 only after differentiation of TCOF1 +/− human ES cells to cNCCs (Extended Data Fig. 3g).
Consistent with cNCC-selective DDX21 relocalization, we also observed partial loss of DDX21 from rDNA and its Pol II target promoters in Tcof −/− cNCCs, but not in mouse ES cells (Fig. 2c, d). Similar depletion of DDX21 from chromatin occurred in human TCOF +/− cNCCs (Extended Data Fig. 3h) and was accompanied by downregulation of DDX21-bound Pol I and Pol II target genes (Extended Data Fig. 3i). In addition to transcription, DDX21 is also required for rRNA processing 5,14 through its interaction with both rRNA and small nucleolar RNAs (snoRNAs). Pol I inhibition disengaged DDX21 from both the 5′ external transcribed spacer, a site of processing in the rRNA, and from the snoRNAs (Extended Data Fig. 4a-e). Accordingly, Tcof1-mutant cells also display cell-type-selective impairment of the 5′ external transcribed spacer processing, specifically cleavage of the A′ site, which is one the first events during maturation of the 18S rRNA and only accumulates when rRNA processing is defective (Fig. 2e). Accumulation of unprocessed A′ site was both dosage-dependent and rescued by GFP-TCOF1 overexpression (Fig. 2e). Taken together, our results demonstrate that TCOF1 mutations result in cell-type-selective and cell-autonomous perturbations of DDX21 functions in cNCCs.
In addition to rRNA synthesis and processing defects 15 , TCS is also characterized by nucleolar stress-mediated activation of p53 (refs 10, 16). Accordingly, we observed upregulation of the canonical p53 target Cdkn1a (p21) in Tcof1-deficient mouse and  Figure 1 | The functions of DDX21 are linked to rRNA synthesis levels and altered by TCS-associated perturbations. a, b, Quantification of the relationship between DDX21 nucleolar/nucleoplasmic ratio and/or pre-rRNA synthesis after 1 h treatment of HeLa cells with different dosages of iPol I. Cells were collected from n = 3 biologically independent experiments. ρ, Pearson correlation coefficient; EU, 5-ethynyl uridine; AU, arbitrary units. Boxes represent median value and 25th and 75th percentiles, whiskers are minimum to maximum, crosses are outliers. * * * P < 0.001, two-sided Wilcoxon-Mann-Whitney test. c, Representative immunofluorescence images depicting DDX21 localization changes upon siRNA-mediated knockdown of TCOF1 (siTCOF1) or POLR1D (siPOLR1D). n = 3 biologically independent experiments. d, Mapping of DDX21 ChIP-seq reads, from HeLa cells treated with dimethylsulfoxide (DMSO), iPol I, or siTCOF1, to the rDNA locus. e, Genome browser tracks depicting DDX21 ChIP-seq signal, from HeLa cells treated with DMSO, iPol I, or siTCOF1, at the RPS3A locus. f, Average signal profiles of DDX21 ChIP-seq from cells treated with DMSO, iPol I, or siTCOF1. g, TCOF1 ChIP-seq reads mapped to the rDNA locus. h, Genome browser tracks of TCOF1 ChIP-seq signal at the RPS3A locus. i, Average signal profiles comparing DDX21 (same as in f) and TCOF1 ChIP-seq, and background input reads. ChIP-seq has been extensively validated by ChIP-qPCR and in another cell type (data not shown and ref. 5). j, Representative stainings of Xenopus laevis cranial cartilages at stage 49. Traces display the mandibular and hyoid stream defects. MO, morpholino; OE, overexpression. Animals were collected from n = 3 biologically independent experiments.
Letter reSeArCH TCOF1-deficient human cNCCs (Extended Data Fig. 5a, b). Treatment of wild-type cNCCs with NSC146109, a small molecule that promotes p53 stabilization 17,18 (Extended Data Fig. 5c), resulted in DDX21 relocalization (Fig. 3a, b) and concomitant inhibition of rRNA synthesis (Fig. 3c), suggesting that p53 activation is sufficient to induce DDX21 nucleolar exclusion. This is also true in vivo, as dorsal-anterior neural tubes of mouse embryos with the neural-crest-specific knockout of Mdm2 (Wnt1-cre;Mdm2 fl/fl ) showed relocalization of DDX21 only in those cells containing high levels of p53, corresponding to the developing cNCCs ( Fig. 3d and Extended Data Fig. 5d).
Despite a broad function of p53 as a stress sensor, stabilizing p53 mutations lead to specific craniofacial defects in mice 19 . We investigated whether p53 mRNA levels are elevated in the developing cNCCs, which could result in a larger reservoir of the translated product being available for stabilization upon stress, when the E3 ligase-mediated degradation of p53 is no longer the limiting regulatory step. Though p53 mRNA is broadly expressed throughout the mouse embryo, substantially higher levels are detected in the neural tube and craniofacial region, especially the first arch ( Fig. 3e and Extended Data Fig. 5e-g). Furthermore, elevated p53 mRNA levels have previously been observed in the dorsal neural tubes of chick embryos before the onset of cNCC emigration 20 . This elevated expression could potentially contribute to the sensitivity of cNCCs and craniofacial development to p53 activity upon stress, although other mechanisms may also be at play. Regardless, Wnt1-cre;Mdm2 fl/fl embryos showed hypoplasia of the first and second pharyngeal arches, confirming that these facial structures are sensitive to p53 stabilization (Extended Data Fig. 6).
To directly address whether human cNCCs are more sensitive to p53 stabilization than other embryonic cell types, we differentiated human ES cells into endothelial cells, cardiomyocytes, and cNCCs, and further differentiated cNCCs to smooth muscle cells. Quantification of apoptosis after parallel treatment of this isogenic set of cell types with NSC146109 revealed the highest sensitivity of cNCCs (Fig. 3f). This effect was not due to changes in DDX21 protein level among the different cell types (Extended Data Fig. 7a). Moreover, loss of one TCOF1 allele exacerbated this sensitivity (Extended Data Fig. 7b).
We next investigated whether disruption of DDX21 underlies cNCC sensitivity to p53. Indeed, induction of DDX21 overexpression partly rescued cNCC sensitivity to p53 activation (Fig. 3g). Moreover, overexpression of DDX21 mRNA rescued the cranial cartilage defects associated with tcof1 dysfunction in Xenopus embryos to a similar extent as TCOF1 mRNA or p53 knockdown (Extended Data Fig. 7c, d). Consistently, overexpression of DDX21 in TCS cNCCs rescued DDX21 nucleolar localization and chromatin association (Extended Data Fig. 7e, f). Thus, preventing DDX21 loss from nucleolus and chromatin suppresses sensitivity of cNCCs to apoptosis and developmental defects associated with TCS.
Given that nucleolar stress and p53 activation are hallmarks of many ribosomopathies 6 , we reasoned that DDX21 dysfunction could contribute to these disorders. To explore this, we performed a small-scale short interfering RNA (siRNA) screen in HeLa cells to assess DDX21 nucleolar localization upon knockdown of a subset of ribomosopathyassociated genes ( Fig. 3h and Extended Data Fig. 7h, g). We observed that knockdown of genes implicated in TCS, Diamond-Blackfan anaemia (RPS19) and Shwachman-Diamond syndrome (SDS) caused DDX21 relocalization, while others had no effect (Fig. 3h). Although patients with Diamond-Blackfan anaemia and Shwachman-Diamond syndrome develop craniofacial/skeletal deformities, these syndromes are characterized primarily by bone marrow dysfunction 1,6 . To test whether loss of DDX21 results in anaemia, we performed haemoglobin stainings of ddx21 zebrafish morphants. Similar to embryos derived from the rpl11 −/− zebrafish model of Diamond-Blackfan anaemia 21 , ddx21 morphants were anaemic ( Fig. 3i and Extended Data Fig. 7i), suggesting that hypersensitivies to loss of DDX21 exist beyond the craniofacial development and may contribute to the pathogenesis of multiple ribosomopathies. TCOF1 dysfunction has also been linked to elevated DNA damage 22 . To explore potential relationships between Pol I transcriptional stress, DNA damage, and DDX21, we examined induction of γ H2A.X in TCOF1 +/− cNCCs, in cNCCs treated with iPol I or low levels of actinomycin D, and in HeLa cells treated with iPol I. In all cases, we observed elevated γ H2A.X signals in a subset of cells (Extended Data Fig. 8a-g).
DNA damage induction in response to Pol I inhibition also occurs in vivo, as we detected elevated γ H2A.X signals in polr1d −/− and polr1c −/− zebrafish embryos at 24 h post-fertilization (Fig. 4a). The staining was more pronounced in the anterior parts of the embryo, but also evident in non-cranial tissues (Fig. 4a). However, the severity of cranial cartilage malformations was correlated with the γ H2A.X signals (Fig. 4a-c).
We hypothesized that rDNA damage caused by Pol I transcriptional stress may be a trigger for p53 activation and DDX21 relocalization.

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To test this, we used tamoxifen-inducible U2OS lines expressing either I-PpoI, an endonuclease which preferentially cleaves the rDNA 23,24 , or AsiSI, which causes DNA damage outside rDNA 25 . Consistently, the γ H2A.X signal was predominantly perinucleolar in I-PpoI-treated cells, and nucleoplasmic in AsiSI-treated cells (Fig. 4d). Only cells expressing I-PpoI relocalized DDX21 (Fig. 4d, e). Furthermore, although both I-PpoI and AsiSI induced auto-phosphorylation of DNA-dependent protein kinase catalytic subunit (pDNA-PKcs) and stabilized p53 (Fig. 4f), rDNA damage induced p53 protein to higher levels ( Fig. 4f), despite overall higher γ H2A.X levels in AsiSI-treated cells (Extended Data Fig. 8k). Single-cell analysis revealed that p53 stabilization is much more correlated with the number of the DNA damage foci in I-PpoI than in AsiSI-treated cells, suggesting an intricate link between the rDNA damage and p53 stabilization (Extended Data Fig. 8h-j). We next investigated temporal relationships and dependencies between Pol I inhibition, DNA damage signalling, and DDX21 relocalization. We observed a gradual increase in pDNA-PKcs within 15-30 min of iPol I treatment, coinciding with timing of DDX21 relocalization (Extended Data Fig. 9a-c). Inhibition of DNA-PKcs and ataxia-telangiectasia mutated (ATM) abolished the displacement of DDX21 from the nucleolus upon rDNA damage (Extended Data Fig. 9d, e). We also observed a reciprocal dependency, as knockdown of DDX21 resulted in elevated pDNA-PKcs (Extended Data Fig. 9f), consistent with a cross-talk between DDX21, DNA damage, and Pol I transcription 26 .
If rDNA damage contributes to TCS pathology, it would suggest that even a general insult such as rDNA damage can result in tissueselective phenotypes. We leveraged that I-PpoI cleavage site is conserved in Xenopus rDNA and injected various amounts of I-PpoI mRNA into Xenopus embryos. Overall growth and morphology of the resulting tadpoles appeared relatively unaffected with exception of the head development (Extended Data Fig. 9g). However, analysis of the cranial cartilage showed that low doses of I-PpoI mRNA result in phenotypes resembling those seen in TCS and upon ddx21 knockdown ( Fig. 4g). At higher doses, we observed a more severe and highly variable spectrum of phenotypes that were predominantly limited to the head region, although an overall effect on growth was also evident ( Fig. 4g and Extended Data Fig. 9h).
Because craniofacial development is selectively sensitive to rDNA damage, our results suggest that phenotypic variability observed in patients with TCS with the same mutation might be due, among other factors, to variable levels of rDNA damage resulting from in utero exposure, genetic modifiers, and/or stochastic events. Furthermore, various genetic and/or environmental perturbations leading to defects Letter reSeArCH in rRNA synthesis and rDNA damage will probably be associated with craniofacial malformations (Extended Data Fig. 10). In this context, our results provide a unified molecular framework that has the potential to explain, at least in part, why craniofacial malformations are very common, often arise through mutations in generic regulators, and show high sensitivity to environmental modulation and phenotypic variability among affected individuals.
Online Content Methods, along with any additional Extended Data display items and Source Data, are available in the online version of the paper; references unique to these sections appear only in the online paper.  Supplementary Information is available in the online version of the paper.

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Summary. HeLa cells were cultured in DMEM 10% FBS and maintained under standard tissue culture conditions unless otherwise specified. Mouse ES cells were cultured in 2i medium as previously described (see Methods). H9 human ES cells were maintained feeder-free and cultured in mTESR-1 (Stem Cell Technologies). U2OS cells were maintained in phenol-free DMEM supplemented with 10% Tetfree FBS. ChIP-seq and ChIP-qPCR analyses were conducted with commercially available antibodies described in the Methods sections. Nascent rRNA synthesis was performed using a Click-iT Nascent RNA Capture Kit (Thermo Fisher Scientific) following the manufacturer's instructions. For immunofluorescence studies, cells were fixed with 4% paraformaldehyde and/or methanol, unless otherwise specified, and stained with the indicated antibodies. Small-molecule treatments with DMSO, actinomycin D, or CX-5461 (Tocris) were done for 1 h, unless otherwise specified. Treatments with NSC146109 (Tocris), and DNAPK (Tocris, NU 7441) and ATM inhibitors (Tocris, KU 55933), were performed at the indicated time points in the figure legends. For annexin V staining, we used an Annexin V/ Dead Cell Apoptosis Kit (Thermo Fisher Scientific) as recommended by the manufacturer. All animal work was done in accordance with the Stanford University Administrative Panel on Laboratory Animal Care. Zebrafish lines were housed in AAALAC-approved facilities and maintained according to protocols approved by the Massachusetts Institute of Technology Committee on Animal Care. Cell lines. HeLa cells were obtained from American Type Culture Collection (ATCC) and grown under standard conditions in DMEM 10% FBS supplemented with antibiotics. U2OS cells were also obtained from ATCC and maintained in phenol-free DMEM supplemented with 10% Tet-free FBS. Human ES cells (H9 line) were obtained from WiCell, maintained and expanded feeder-free, and cultured in mTESR-1 (StemCell Technologies). Cells were passaged approximately 1:6 every 5-6 days with ReLeSR (StemCell Technologies) onto tissue culture dishes coated with growth-factor-reduced matrigel (BD Biosciences). Mouse ES cells were cultured in 2i medium as previously described 27 . All cell lines used in this study were mycoplasma-free. Vectors and plasmids. For generating GFP-TCOF1 and GFP-DDX21 expression vectors, gBlocks gene fragment (IDT) encoding the human TCOF1 and DDX21 coding region were synthetized and cloned into a GFP-expressing and tetracycline-inducible PiggyBac vector (SBI). The PiggyBac was stably integrated into pluripotent cells and several clones were expanded for further analyses. For generation of the cNCCs, we used our previously published protocol 28,29 (described below). To express the transgenes, doxycycline was added to the medium at a concentration of 2.5 μ g ml −1 for 24-48 h. For the generation of Tcof1 mutant ES cells and mouse ES cells, single guide RNAs (sgRNAs) were developed at http:// crispr.mit.edu/ and cloned into the PX458 vector (Addgene, 48138). Cells were transfected with Lipofectamine 2000 (Life Technologies), and sorted on the basis of GFP. The resulting clones were expanded and screened by PCR to search for Tcof1 loss-of-function alleles. I-PpoI construct (Addgene, 32565) was developed in M. Kastan's laboratory. AsiSI was synthetized as a gBlock gene fragment (IDT). Differentiation of pluripotent cells into cNCCs and other lineages. Pluripotent lines were differentiated into cNCCs as previously described 28,29 . In brief, human ES cells/mouse ES cells were incubated with 5 mg ml −1 collagenase (Life Technologies) for 1 h. Clusters of 100-200 cells were generated by manual pipetting and plated in a Petri dishes (BD Biosciences) containing cNCC differentiation medium (see methods in ref. 29 for details). The medium was changed every other day for 7 days or until the neuroepithelial spheres attached, before the induction and emigration of the cNCCs. To further purify the cNCCs, cells were dissociated with Accutase (Life Technologies) and passaged onto fibronectin-coated (Thermo Fisher Scientific) plates. cNCCs were then transitioned to maintenance media (see methods in ref. 29 for details). For propagating, cNCCs were passaged onto fibronectin-coated plates every three days. After two passages, they were transitioned to cNCC long-term maintenance medium (see methods in ref. 29 for details). For differentiation into smooth muscle cells, cNCCs were cultured in DMEM (Thermo Fisher Scientific) supplemented with 10% FBS (Omega). Differentiation of pluripotent cells into endothelial 30 cells and cardiomyocytes 31 has been previously described by Wu's laboratory at Stanford University. Embryoid body differentiation was performed as previously described 27 . ChIP-qPCR and ChIP-seq. ChIP assays were performed as previously described 5 . In brief, cells were cross-linked with 1% formaldehyde for 10 min and quenched with glycine to a final concentration of 0.125 M for another 10 min. Chromatin was sonicated with a Bioruptor (Diagenode), cleared by centrifugation, and incubated overnight at 4 °C with 7.5 μ g of the desired antibodies: anti-DDX21 (Novus Biologicals BP100-1781), anti-TCOF1 (Novus Biologicals NBP1-86909). Immunocomplexes were immobilized with 100 μ l of Protein G Dynal magnetic beads (Life Technologies) for 4 h at 4 °C, followed by stringent washes and elution. Eluates were reverse cross-linked overnight at 65 °C and de-proteinated with proteinase K at 56 °C for 30 min. DNA was extracted with phenol-chloroform, followed by ethanol precipitation. ChIP-seq libraries were prepared according to the NEBNext protocol and sequenced using Illumina HiSeq 2500. ChIP-qPCR analyses were performed in a Light Cycler 480II machine (Roche). ChIP-qPCR signals were calculated as the percentage of input. All primers used in qPCR analyses are shown in Supplementary Table 1. ChIP-seq analyses. Sequences were mapped to the respective genomes using Bowtie2 and analysed by QuEST and MACS2. For QuEST, ChIP-seq peaks were determined using a kernel density estimate bandwidth of 30, a ChIP candidate threshold of = 20, a ChIP extension fold enrichment of 3, and a ChIP-tobackground fold enrichment of 3. WIG files were generated with QuEST and used for visualization in the University of California, Santa Cruz (UCSC) Genome Browser. Average ChIP-seq signal profiles around the centre of DDX21 ChIP-seq peaks were generated with the Sitepro tool.
All genomic datasets have been deposited in the Gene Expression Omnibus under accession number GSE89420: https://www.ncbi.nlm.nih.gov/geo/query/ acc.cgi?token=abcjeymyvbkrnap&acc=GSE89420. Mapping ChIP signal to rDNA locus. For mapping ChIP-seqs to the rDNA locus, we obtained the DNA consensus sequence of the 43 kb ribosomal locus NCBI (GeneBank accession number U13369.1). Using this unique 43 kb region, we used the Bowtie algorithm to map ChIP-seq reads with standard parameters used for mapping to the Hg19 human genome build (see methods in ref. 5 for details). RNA extraction and RT-qPCR. RNA was isolated using TRIzol (Life Technologies) and an RNAeasy mini kit (Qiagen) according to the manufacturer's protocol. All RNA samples were DNase treated in column (Qiagen). cDNA was generated using Sensifast (Bioline) according to the manufacturer's instructions. qPCR analyses were performed on a Light Cycler 480II (Roche). All primer sequences can be found in Supplementary Table 1. Immunofluorescence. Cells were seeded into 24-well plates containing glass cover slips. For HeLa cells, mouse ES cells, and cNCCs, cover slips were coated with fibronectin and cultured for 16 h before treatment. After this, cells were treated for the indicated drugs (refer to the corresponding figure legends for drug concentration and timescale of the experiment). Cells were fixed in 4% PFA for 10 min at room temperature, washed three times for 5 min with PBS. For methanol fixation, after PFA fixation, cells were incubated with ice-cold methanol 2 min and washed twice for 5 min with PBS. Cells were permeabilized in PBS containing 0.3% (v/v) Triton X-100 for 5 min, and blocked overnight at 4 °C in PBT buffer (PBS with 1% bovine serum albumin (BSA), 0.1% Triton X-100 (v/v), 0.05% sodium azide (w/v)). After blocking, coverslips were incubated in PBT with the corresponding antibody. For DDX21 (Novus Biologicals NBP1-83310), TCOF1 (Novus Biologicals NBP1-86909), γ H2A.X (Abcam ab11174-50ug), and p53 (Vector Laboratories VP-P953), all antibodies were diluted 1:100 and incubated at room temperature for at least 2 h. Alexa Fluor 488, 568, and 647 secondary antibodies (1:1,000; Life Technologies) for 1 h. Cells were washed three times for 5 min with PBT, twice for 5 min with PBS, rinsed briefly with water, and mounted onto glass slides using VECTASHIELD mounting medium with DAPI. All images were taken and processed using a Zeiss LSM700 confocal microscope. Western blots and co-immunoprecipitation. Nuclear extracts were prepared according to the Dignam and Roeder protocol 32 . For immunoprecipitations, extracts were incubated overnight with 3-5 μ g of the desired antibody pre-bound to Protein G Sepharose (Pierce). In some cases, protein extracts were treated with RNaseA (20 μ g ml −1 ). Immunocomplexes were eluted in 2× Laemmli buffer and resolved in a 4-20% pre-casted tris-glycine gel (Life Technologies). For western blots, the following antibodies were used according to the manufacturer's instructions: anti-GFP (Thermo Fisher Scientific A-6455), anti-TCOF1 (Novus Biologicals NBP1-86909 and Abnova H00006949-B01P), anti-Flag (Sigma-Aldrich), anti-DDX21 (Novus Biologicals NB100-1781), anti-p53 (Vector Laboratories VP-P953), anti-ACTIN (Abcam ab49900). All antibodies had been previously validated unless otherwise specified. siRNA and antisense oligonucleotide knockdown. For knockdowns, HeLa cells were plated at a defined density (2.5 × 10 5 ) and transfected in DMEM supplemented with 5% FBS without antibiotics using RNAiMAX (Life Technologies) with 20 nM of the desired pools of siRNA. siRNA diced pools were generated in J.W. 's laboratory using recombinant Giarda lamblia Dicer. Xenopus embryology. X. laevis embryos were staged in accordance with standard procedures 33 . For knockdowns, morpholinos were injected into both blastomeres of two-cell-stage embryos at the indicated final concentrations. Embryo viability was scored throughout development to the tadpole stage. To make inferences about the observed phenotypes and determine the appropriate dosages, we conducted pilot experiments for each individual morpholino or in vitro transcribed RNA. We then determined the concentration at which the phenotypic variability was minimized within the sample and at the same time had minimal impact on the viability of the embryos. Once the dosages were determined, all embryos that survived were scored. No randomization of the sample or blinding were applied.

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No statistical methods were used to predetermine sample size. The 5′ -capped and 3′ -tailed mRNAs were synthesized with a Message Machine Ultra kit (Ambion) and injected along with the indicated morpholino at the two-cell stage. For phenotypic analyses of craniofacial defects, stage 49 embryos were stained with alcian blue as previously described 28 . The I-PpoI construct was deposited in Addgene (32565) by M. Kastan's laboratory. For injections, I-PpoI was amplified (see Supplementary  Table 1 for oligonucleotides), followed by in vitro transcription using Message Machine Ultra kit (Ambion) and injected at the indicated concentrations. Mouse husbandry. All mouse work was done in accordance with the Stanford University Administrative Panel on Laboratory Animal Care. Mice were maintained on a mixed 129/Sv-C57BL/6 background. The Wnt1-cre transgene and Mdm2 flox alleles have been described previously 34,35 . To obtain embryos, mice were mated overnight and the day a vaginal plug was observed was considered E0.5. Yolk sac DNA, extracted using the HotSHOT method 36 , was used for genotyping. Neural tube immunofluorescence. Mouse embryos were fixed in 4% PFA overnight at 4 °C, dehydrated in ethanol, paraffin embedded, and sectioned at 5 μ m. For immunofluorescence, antigen retrieval was performed in citrate buffer (10 mM sodium citrate, pH 6.0, 0.05% Tween 20; 5 min at 'high pressure' in a pressure cooker) and slides were permeabilized with Tris-buffered saline containing 0.025% Triton X (TBS-TX). Blocking was performed for 1 h at room temperature with 10% goat serum and 1% BSA in TBS-TX. Primary antibody incubations were performed overnight at 4 °C and secondary antibody incubations for 1 h at room temperature. The following antibodies were used and were diluted in 1% BSA in TBS-TX: mouse anti-p53 (1C12, Cell Signaling, 1:300), rabbit anti-DDX21 (NB100-1718, Novus, 1:100), fluorescein goat anti-rabbit (FI-1000, Vector Labs, 1:200), and Alexa Fluor 546 goat anti-mouse (A-11030, Thermo Fisher Scientific, 1:200). Whole-mount in situ hybridization. In brief, embryos were collected in cold PBS and immediately fixed in 4% paraformaldehyde/PBS overnight at 4 °C. Embryos were then dehydrated in methanol and bleached in a methanol/H 2 O 2 (5:1) solution for 5 h at room temperature. Then embryos were rehydrated in PBS-T (0.1% Tween 20/PBS) before being incubated in a proteinase K solution (10 mg ml −1 in PBS) for 15 min at room temperature and fixed in 0.2% glutaraldehyde/4% paraformaldehyde/PBS solution for 20 min at room temperature. Embryos were then incubated for 2 h at 65 °C in the hybridization solution (composed of 2% Blocking Powder (Roche), 50% formamide (Sigma-Aldrich), 5× SSC (Sigma-Aldrich), 0.1% CHAPS (Sigma-Aldrich), 5 mM EDTA (Sigma-Aldrich), 0.1% Triton X-100 (Fisher) to which was added yeast tRNA (Sigma-Aldrich, 1 mg ml −1 ) and Heparin (Sigma-Aldrich, 50 g ml −1 )). After this step, the solution was replaced and the digoxigenin-labelled mRNA probe against p53 (Genebank, AB021961.1) added to the hybridization solution. Embryos were incubated in this solution overnight at 65 °C. The next day, embryos were first washed in 50% formamide, 1× SSC, and 0.1% Tween 20, then they were washed several times in MABT (20 mM malic acid, 30 mM NaCl, 0.1% Tween 20) before being incubated for 2 h at room temperature in the blocking solution (2% Blocking Powder, 20% Inactivated Lamb Serum (Gibco) in MABT). Blocking solution was then diluted by half in MABT and the anti-digoxigenin antibody (Roche, 1/4,000) was added. Embryos were incubated in the antibody solution overnight at 4 °C. Embryos were next washed for 2 days in MABT before proceeding to the revealing using BM Purple (Roche). Image processing/analysis. All image analysis was performed using customwritten MATLAB scripts. For all images, the nuclear mask was generated by performing segmentation on DAPI images as follows. log-transformed images were convolved with a rotationally symmetric Laplacian of Gaussian filter, and objects were defined as contiguous pixels exceeding a threshold filter score calculated by Otsu's method. Specifically, for cNCC, a special segmentation method was used to de-clump the cells that clustered together and the poorly de-clumped cells were discarded for downstream analysis. In brief, a hierarchical watershed transform was performed where cells that had different levels of overlaps were separated by watershed with different parameters and the joint masks were defined to maximally separate the cells. Raw images were then subtracted with background signal defined by the average pixel intensity of the non-masked regions in the whole image, and all the following image analyses were done with background-subtracted images.
For nucleolus segmentation, a custom-written MATLAB function was implemented to reliably segment out the nucleoli regions from each individual cell by leveraging some or all main features of nucleoli in DAPI staining, including (1) low intensity of DAPI staining; (2) frequent appearance of DAPI high ridges surrounding nucleoli (separate nucleoli from Cajal bodies); (3) circularity (that nucleoli tend to be rounded). For each different cell type, four parameters were tuned to reach the maximum level of detection while limiting the false segmentation level.
For quantification of DDX21 shuttling, the ratio between the mean intensity of DDX21 signal within the defined nucleolar mask and the mean intensity of DDX21 signal within the nucleoplasmic mask (nuclear mask − nucleolar mask) was calculated for each individual cell.
For rRNA content quantification, the mean intensity of 5-ethynyl uridine staining within the defined nucleolar mask was calculated for each individual cell and a ratio-metric measurement performed to normalize the rRNA content to the mRNA content, where the nucleoli mean 5-ethynyl uridine intensity was divided by the nucleoplasmic 5-ethynyl uridine staining mean intensity for each individual cell.
For DNA damage analysis, a custom-written MATLAB function was implemented to segment out the γ H2A.X puncta. In brief, puncta were segmented by tophat filter and only those above the 75th percentile intensity of all puncta were called γ H2A.X foci. Each focus was then assigned with the nuclear mask identified with previous method and the number of foci/nuclei were then calculated. In particular, the perinucleolar γ H2A.X foci were defined by the foci identified above within a perinucleolar ring mask defined by a dilated nucleoli mask subtracting the nucleoli mask. See Supplementary Fig. 2 for details about the masks and segmentation methods. Zebrafish embryology. Zebrafish polr1c +/− , polr1d +/− , and rpl11 +/− were obtained from the Zebrafish International Resource Centre. For crosses, we performed pilot experiments to determine that the published phenotypes were observed at the expected Mendelian ratios. Injection of ddx21 morpholino or mRNA was performed on one-cell-stage zebrafish embryos at the indicated concentrations. Injected embryos were incubated at 28 °C. To make inferences about the observed phenotypes and to determine the appropriate dosages, we conducted pilot experiments for ddx21 morpholino or in vitro transcribed RNA. We then determined the concentration at which the phenotypic variability was minimized within the sample and at the same time had minimal impact on the viability of the embryos. Once the dosages were determined, all embryos that survived were scored. For cartilage staining with alcian blue, embryos were collected 5 days post-fertilization. Alcian blue staining was performed as described 10 . For haemoglobin staining with o-dianisidine and immunofluorescence, embryos were collected 24 h postfertilization. o-Dianisidine staining was performed as described 21 . The sequence of ddx21 morpholino was ATTCTGGGAGACTCTTTGACGGCAT. DDX21 mRNA was transcribed from PCR products using a T7 mMessage Ultra Kit (Thermo Fisher Scientific) and purified using a MegaClear Kit (Thermo Fisher Scientific). For γ H2A.X immunofluorescence, fish of the indicated genotypes were harvested 24 h post-fertilization, fixed in 4% PFA diluted in PBS overnight, washed three times with 0.1% Tween-20 in PBS (PBS-T), and blocked with 1% BSA in PBS-T (PBS-TA) for 3 h. Embryos were stained in PBS-TA with zebrafish anti-γ H2A.X (GeneTex: GTX127340) overnight, washed three times with PBS-TA, and imaged using a Leica M205 stereomicroscope. No randomization of the sample or blinding were applied. No statistical methods were used to predetermine sample size. iCLIP data analysis. FAST-iCLIP was performed 37 on HeLa cells by ultravioletcrosslinking to a total of 0.35 J cm −2 . Whole-cell lysates were generated in iCLIP lysis buffer (50 mM HEPES, 200 mM NaCl, 1 mM EDTA, 10% glycerol, 0.1% NP-40, 0.2% Triton X-100, 0.5% N-lauroylsarcosine) and briefly sonicated using a probe-tip Branson sonicator to solubilize chromatin. Each iCLIP experiment was normalized for total protein amount, typically 1 mg, and partly digested with RNase I (Thermo Fisher Scientific, catalogue number AM2294) for 10 min at 37 °C and quenched on ice. DDX21 was isolated with anti-DDX21 antibody (Novus Biologicals NB100-1718) for 8 h at 4 °C on rotation. Samples were washed sequentially in 1 ml for 5 min each at 4 °C: 2× high stringency buffer (15 mM Tris-HCl, pH 7.5, 5 mM EDTA, 2.5 mM EGTA, 1% Triton X-100, 1% sodium deoxycholate, 120 mM NaCl, 25 mM KCl), 1× high salt buffer (15 mM Tris-HCl pH 7.5, 5 mM EDTA, 2.5 mM EGTA, 1% Triton X-100, 1% sodium deoxycholate, 1 M NaCl), 1× NT2 buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1 mM MgCl 2 , 0.05% NP-40). After the NT2 wash, DDX21-bound RNA-protein complexes were dephosphorylated with T4 PNK (NEB, catalogue number M0210) for 30 min in an Eppendorf Thermomixer at 37 °C, 15 s at 1,400 r.p.m., 90 s rest in a 30 μ l reaction, pH 6.5, containing 10 units of T4 PNK, 0.1 μ l SUPERase-IN, and 6 μ l of PEG-400 (16.7% final). After 30 min, beads were rinsed once with NT2 buffer and 3′ end ligated with T4 RNA Ligase 1 (NEB, catalogue number M0204) overnight in an Eppendorf Thermomixer at 16 °C, 15 s at 1,400 r.p.m., 90 s rest in a 30 μ l reaction containing 10 units of T4 RNA Ligase, 1 pmole pre-adenylated-DNA-adaptor, 0.1 μ l SUPERase-IN, and 6 μ l of PEG400 (16.7% final). The following day, samples were again rinsed with NT2 buffer and 5′ radiolabelled by adding 1 μ l of T4 PNK, 0.5 μ l g32-ATP (Perkin Elmer), 2 μ l 10× T4 PNK buffer, 0.5 μ l SUPERase-In, and 16 μ l of water for 15 min at 37 °C. To this reaction, 1 μ l of 100 mM DTT and 6 μ l of 4× LDS Buffer (Thermo Fisher Scientific) were added, samples headed to 75 °C for 10 min, and released RNA-protein complexes were separated on SDS-PAGE using NuPAGE 4-12% Bis-Tris Gels (1.0 mm × 12 wells) at 180 V for 45 min. Resolved ribonucleoprotein complexes were wet-transferred to nitrocelluose at 400 mA for 60 min at 4 °C.
RNA was recovered and processed for library preparation as in the irCLIP protocol 38 . Membranes were cut into approximately 0.5 × 1 mm narrow strips Letter reSeArCH that easily came to rest in the bottom of a siliconized 1.5 ml Eppendorf tube. To each tube, we added 0.2 ml of proteinase K reaction buffer (100 mM Tris, pH 7.5, 50 mM NaCl, 1 mM EDTA, 0.2% SDS) and 10 μ l of proteinase K (Thermo Fisher Scientific, catalogue number AM2546). The reaction was incubated for 60 min at 50 °C in an Eppendorf Thermomixer. Next, 200 μ l of saturated phenol-chloroform, pH 6.7, was added to each tube and incubated for 10 min at 37 °C in an Eppendorf Thermomixer, 1,400 r.p.m. Tubes were briefly centrifuged and the entire contents transferred to a 2 ml Heavy Phase Lock Gel (5Prime, catalogue number 2302830). After 2 min centrifugation at more than 13,000 r.p.m., the aqueous layer was re-extracted with 1 ml of chloroform (invert tube ten times to mix; do not vortex, pipet or shake) in the same 2-ml Phase Lock Gel tube and centrifuged for 2 min at more than 13,000 r.p.m. The aqueous layer was then transferred to a new 2-ml Heavy Phase Lock Gel tube and extracted again with an additional 1 ml of chloroform. After 2-min centrifugation at more than 13,000 r.p.m., the aqueous layer was transferred to a siliconized 1.5-ml Eppendorf tube and precipitated overnight at − 20 °C by addition of 10 μ l 5 M NaCl, 3 μ l Linear Polyacrylamide (Thermo Fisher Scientific, catalogue number AM9520), and 0.8 ml ethanol.
cDNA synthesis primers were purchased from IDT: cDNA-barcode1 (6 bp TruSeq barcode in bold type): /5phos/WWWNNNXXXXXXNNNNNTACCCTT CGCTTCACACACAAG/iSp18/GGATCC/iSp18/TACTGAACCGC. P3short RNA fragments were pelleted at more than 13,000 r.p.m. for 45 min at 4 °C, washed once with 1 ml of ice-cold 75% ethanol and air dried. Pellets were resuspended in 12 μ l water. RNA (12 μ l) was mixed with 1 μ l of 1 μ M cDNA and 1 μ l of 10 mM dNTPs and heated to 70 °C for 10 min, then rapidly cooled to 4 °C. Six microlitres of cDNA Master Mix (4 μ l 5× SSIV buffer, 1 μ l 100 mM DTT, 1 μ l SSIV) was added to the annealed RNA and incubated for 30 min at 55 °C. cDNA:RNA hybrids were captured by addition of 5 μ l of MyOne Streptavidin C1 Dynabeads (Thermo Fisher Scientific, catalogue number 65001) that had been rinsed and suspended in 30 μ l of biotin-IP buffer (100 mM Tris, pH 7.5, 1 M NaCl, 1 mM EDTA, 0.1% Tween), and end-over-end rotation for 30 min at room temperature. Beads were placed on a 96-well magnet and washed sequentially with 0.1 ml of Biotin-IP buffer and PBS. Beads were resuspended in 10 μ l of cDNA elution/ RNA degradation buffer (8.25 μ l water, 1 μ l of 1 μ M P3short oligo, and 0.75 μ l of 50 mM MnCl 2 ) and placed in a thermocycler with the following program: 5 min at 95 °C, 1 min at 75 °C, ramp 0.1° s −1 to 60 °C forever. After 15 min, tubes were removed and mixed with 5 μ l of Circligase-II reaction buffer (3.3 μ l water, 1.5 μ l 10× Circligase-II buffer, and 0.2 μ l of Circligase-II, Epicentre, catalogue number CL9021K). cDNA was circularized in a thermocycler for 1.5 h at 60 °C. cDNA was captured by addition of 30 μ l of Ampure XP beads (Beckman Coulter, catalogue number A63880), 75 μ l of isopropanol, and incubation for 15 min (the solution was remixed after 7.5 min). Beads were washed once with 80% ethanol, dried for 5 min, and resuspended in 14 μ l of water. For maximal elution, tubes were placed in a 95 °C thermocycler for 2 min and immediately transferred to a 96-well magnet. The 14 μ l eluate was transferred to a new 0.2 ml PCR tube containing 15 μ l of 2× Phusion HF-PCR Master Mix (NEB, catalogue number M0531), 0.5 μ l of 30 μ M P3/P6 PCR1 oligo mix, and 0.5 μ l of 15× SYBR Green I (Thermo Fisher Scientific, catalogue number S7563). The tubes were then placed in a Stratagene MX3000P qPCR machine with the following program: 98 °C for 2 min, 15 cycles of 98 °C for 15 s, 65 °C for 30 s, 72 °C for 30 s, with data acquisition set to the 72 °C extension. PCR1 reactions were then subjected to one round of magnetic bead size selection by addition of 4.5 μ l of isopropanol, 54 μ l of Ampure XP beads, and incubation for 10 min. Beads were washed once with 80% ethanol, dried for 5 min, and eluted in 10 μ l of water. PCR1 products were subjected to a second round of size selection by addition of 1.5 μ l of isopropanol, 18 μ l of Ampure XP beads, and incubation for 10 min. Beads were washed once with 80% ethanol, dried for 5 min, and eluted in 10 μ l 500 nM P3solexa/P6solexa oligonucleotide mix. Ten microlitres of 2× Phusion HF-PCR Master was added to each tube and placed in a thermocycler with the following program: 98 °C for 2 min, three cycles of 98 °C for 15 s, 65 °C for 30 s, 72 °C for 30 s. Final libraries were purified by addition of 36 μ l of Ampure XP beads and incubation for 5 min. Beads were washed twice with 70% ethanol, dried for 5 min, and eluted in 20 μ l of water. One microlitre of each library was quantitated by HS-DNA Bioanalyzer.
Samples were sent for deep sequencing on an Illumina NextSeq machine for single-end 75-bp cycle runs. FAST-iCLIP data were processed using the FAST-iCLIP analysis pipeline (https://github.com/ChangLab/FAST-iCLIP). PCR duplicates were removed using unique molecular identifiers in the RT primer region. Adaptor and barcode sequences were trimmed, and reads were mapped stepwise to repetitive and non-repetitive genomes. Specific parameters used were as follows: -f 18 (trims 17 nt from the 5′ end of the read), -l 15 (includes all reads longer than 15 nt), -bm 25 (minimum MAPQ score from Bowtie2 of 25 is required for repeat element mapping), -sr 0.08 (STAR mismatch-per-base ratio; 0.08 corresponds to 2 mismatches per 25 bases), and -tr 2,3 (repetitive genome) and -tn 2,3 (non-repetitive genome) RT stop intersection (n,m; where n = replicate number and m = number of unique RT stops required per n replicates). Using the -tr/tn 2,3 parameters, a minimum of six RT stops were required to support any single nucleotide identified as crosslinking site. Code availability. All custom codes are available upon request from the corresponding author. Data availability. Data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession number GSE89420. Source Data for Figs 1-4 and Extended Data Figs 8 and 9 have been provided. Source Data for western blots shown in Fig. 4 and Extended Data Figs 2-5, 7 and 9 have been provided in Supplementary Fig. 1. All other relevant data that support the findings of this study are available from the corresponding author upon reasonable request.

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Extended Data Figure 7 | DDX21 overexpression rescues TCS and its function is deregulated by knockdown of other ribosomopathyassociated genes. a, Representative western blot for DDX21 in different cell types from n = 3 biologically independent experiments. b, FACS analyses to determine the sensitivity of TCOF1 +/− cNCC to p53-mediated apoptosis. cNCCs were treated with NSC146109 for 4-6 h (note that this time point is significantly shorter than the one used in Fig. 3f and g). Apoptosis was quantified by FACS of annexin V staining. Bars represent the average of n = 3 independent experiments; error bars, s.e.m. c, Quantification of Xenopus cranofacial development rescue experiments by measuring the length of the hyoid stream upon overexpression of TCOF1, DDX21, or p53 knockdown. Embryos were collected from n = 3 biologically independent experiments. Boxes represent median value and 25th and 75th percentiles, whiskers are minimum to maximum, crosses are outliers. * * * P < 0.001, two-sided Wilcoxon-Mann-Whitney test. d, Rescue of TCS-associated craniofacial malformations in Xenopus by injection of the embryos with the indicated in vitro transcribed mRNAs and/or morpholinos (quantification is shown in c). e, Representative immunofluorescence images of mouse Tcof1 −/− cNCCs upon overexpression of human GFP-tagged DDX21. n = 3 biologically independent experiments. f, ChIP-qPCR analysis, in mouse cNCCs sampling Ddx21 genomic occupancy, at a representative panel of Ddx21 target promoters and at the rDNA locus. n = 3 biologically independent experiments. Bars represent the average of n = 3 independent experiments; error bars, s.e.m. g, siRNA pools against a subset of ribosomopathyassociated genes were transected into HeLa cells. qPCR was used to determine the efficiency of the knockdowns. Bars represent the average of n = 3 independent experiments; error bars, s.e.m. h, Representative immunofluorescence images showing DDX21 localization changes in HeLa cells transfected with the indicated pools of siRNAs (quantification is on Fig. 3i). n = 3 biologically independent experiments. i, Tables quantifying the number of embryos stained for haemoglobin with o-dianisidine for the indicated genotypes. In the case of rpl1 zebrafish, embryos were collected from three independent matings. For DDX21, three independent batches of embryos were injected and stained.  For experiments with animals we collected enough animals to ensure reproducibility and statistical power. All of our experiments comply with the analysis of variance (ANOVA). E= total number of animals-total number of groups. E is a measure of the degree of freedom and should lie between 10 and 20.

Data exclusions
Describe any data exclusions.
No data was excluded.

Replication
Describe whether the experimental findings were reliably reproduced.
Once experiments and procedures were fully optimized, all attempts at replication were successful.

Randomization
Describe how samples/organisms/participants were allocated into experimental groups.
For the mouse and zebrafish experiments: embryos were allocated into experimental groups based on their genotype. For frogs MO and/or mRNA injections, embryos were allocated into experimental groups based on the treatment.

Blinding
Describe whether the investigators were blinded to group allocation during data collection and/or analysis.
Blinding was only performed during image analyses. For some of the images analyses such as those performed in figures 1, 3 and 4, the graduate student analyzing the images was blinded to all conditions. Note: all studies involving animals and/or human research participants must disclose whether blinding and randomization were used.

Statistical parameters
For all figures and tables that use statistical methods, confirm that the following items are present in relevant figure legends (or in the Methods section if additional space is needed).

n/a Confirmed
The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement (animals, litters, cultures, etc.) A description of how samples were collected, noting whether measurements were taken from distinct samples or whether the same sample was measured repeatedly A statement indicating how many times each experiment was replicated The statistical test(s) used and whether they are one-or two-sided (note: only common tests should be described solely by name; more complex techniques should be described in the Methods section) A description of any assumptions or corrections, such as an adjustment for multiple comparisons The test results (e.g. P values) given as exact values whenever possible and with confidence intervals noted A clear description of statistics including central tendency (e.g. median, mean) and variation (e.g. standard deviation, interquartile range)

Clearly defined error bars
See the web collection on statistics for biologists for further resources and guidance.
analyzed by QuEST and MACS2. For QuEST ChIP-seq peaks were determined using a kernel density estimate bandwidth of 30, a ChIP candidate threshold of = 20, a ChIP extension fold enrichment of 3, and a ChIP-to-background fold enrichment of 3. WIG files were generated with QuEST and used for visualization in the UCSC Genome Browser. Average ChIP-seq signal profiles around the center of DDX21 ChIP-seq peaks were generated with the Sitepro tool.
9. Describe the methods used to ensure data quality. 1) All files were generated by MACS version 2.1.0.20140616 2) Command line: callpeak -t sample.sam -c input.sam -f SAM -g hs -n sample_MACS2 -B -q 0.01 3) For DDX21 ChIPs: About 3238 peaks were identified. Of these 3173 were show over five fold enrichment over input. 4) For all ChIPs we use two peak callers MACS2 and QuEST and only used overlapping peaks in both analyses for further studies.
10. Describe the software used to collect and analyze the ChIP-seq data.


extfieldsofstudy: Psychology Medicine

text:
Does cross-generational epigenetic inheritance contribute to cultural continuity?

Abstract Human studies of cross-generational epigenetic inheritance have to consider confounding by social patterning down the generations, often referred to as ‘cultural inheritance’. This raises the question to what extent is ‘cultural inheritance’ itself epigenetically mediated rather than just learnt. Human studies of non-genetic inheritance have demonstrated that, beyond foetal life, experiences occurring in mid-childhood before puberty are the most likely to be associated with cross-generational responses in the next generation(s). It is proposed that cultural continuity is played out along the axis, or ‘payoff’, between responsiveness and stability. During the formative years of childhood a stable family and/or home permits small children to explore and thereby learn. To counter disruptions to this family home ideal, cultural institutions such as local schools, religious centres and market places emerged to provide ongoing stability, holding the received wisdom of the past in an accessible state. This cultural support allows the growing child to freely indulge their responsiveness. Some of these prepubertal experiences induce epigenetic responses that also transfer molecular signals to the gametes through which they contribute to the conception of future offspring. In parallel co-evolution with growing cultural support for increasing responsiveness, ‘runaway’ responsiveness is countered by the positive selection of genetic variants that dampen responsiveness. Testing these ideas within longitudinal multigenerational cohorts will need information on ancestors/parents’ own communities and experiences (Exposome scans) linked to ongoing Phenome scans on grandchildren; coupled with epigenome analysis, metastable epialleles and DNA methylation age. Interactions with genetic variants affecting responsiveness should help inform the broad hypothesis.

Introduction
The title Isabelle Mansuy chose for the 2017 Latsis Symposium was 'Transgenerational Epigenetic Inheritance: Impact for Biology and Society'. It was the inclusion of '. . . and Society' that made me think again about what underpins the enduring nature of human cultures. This was a topic I discussed at length with my friend, the late David Hart, who would pose such questions as: among early Greek cultures, how did the state of Sparta maintain its distinctive militaristic nature for 400 years? We considered epigenetics among other processes and wrote an article in 2009, but never published it-principally because environmental epigenomics was still in its infancy.
The degree to which culture is part and parcel of our biological makeup has been debated since the times of ancient Greece, yet remains an open question. The word culture can have many meanings-a debate I will put aside. For the purposes of this paper, culture can be defined as 'The ideas, customs, and social behaviour of a particular people or society [1]; or artefacts, social and symbolic behaviour, taste, intellectual development at a particular time or place. It is characterized by the transmission of ideas from one generation to the next. Not just the transmission of information but 'ways of thinking' that lead to social and intellectual coherence-'solidarity between generations'. But why and how do cultures endure-often for many generations?
The answer seems to lie in the formative years of childhood. Most people would say that the core features of a particular culture are acquired from birth by imitation and associative learning via the immediate family, and then also through behavioural adaptations to the ways of the local community, and through the wider society and the cultural institutions that underpin it.
There is a feeling that cultural transmission is more than imitation and learning, yet evolution based on inherited DNA sequence differences operates over too long a timescale to be the main explanation. Many authors express this sentiment without explicitly considering possible mechanisms. For example, in his authoritative 2017 book 'How Language Began' [2], Daniel Everett refers (p171) to the brain's preparedness for language whilst rejecting any (genetic) prespecified mental capacity (p205). Everett does accept that the recent primate evolution of the FOXP2 gene sequence [3] 'allows for better control of the vocal apparatus and mental processing of the kind used in modern human's language' (p. 194) but also adds later 'Maybe humans passed a lot of grammar down by example from millennium to millennium as the species continued to evolve (p. 202). Towards the end he writes 'Because language is a cultural artefact, we must understand what culture is in order to understand language . . . . . . The larger issue is how cultures hang together at all (p. 273). It is this larger issue of hanging together that the current paper is attempting to address. I use the term 'continuity' to imply an ongoing process of information transfer between generations that tends to counter marked behavioural deviations in the face of rapidly changing circumstances. Continuity has been defined as 'a connection or line of development with no sharp breaks' [4] or 'the fact that something continues to happen or exist, with no great changes or interruptions' [5]. My use of the term does not imply that no changes whatsoever can occur, or that an ongoing tendency across generations is either beneficial or adverse.
In terms of adversity, there is a recent example that again reveals a feeling that cultural transmission is more biologically embedded than just learning. It comes from the paper titled 'In the shadow of coal: How large-scale industries contributed to present-day regional differences in personality and well-being' [6]. 'There are compelling theoretical and empirical reasons for supposing that the massive concentration of large-scale coalbased industries could have left a lasting psychological imprint in the local cultures [my emphasis] of the old industrial regions'. And later, 'We argue that psychological adversity "runs deep" [my emphasis] in these regions, expressed not only in the actual well-being of these people but also in their personality traits that are linked to well-being'. The phrases 'lasting psychological imprint' and 'runs deep' capture the point I am making about an ill-defined sense that cultural transmission is more than just learning. The above quotes come from recent academic treatises on aspects of culture. The media has, for a decade or more, used the phrase 'in the DNA' (as a metaphor of stable, heritable difference) to qualify some cultural activity that 'runs deep'. 'Carols are part of our cultural DNA' said the presenter of the BBC's Today radio programme at Christmas time 2008; also 'Siding with protesters is in the Lib Dem DNA' [7]; and 'Opera is in the Italian DNA. It's not ornamental. It's vital for the success of the state of Italy' [8]. Thus, the phrase 'in the DNA' has become a signifier for, not just being biologically embedded, but also inherited. Could this deeply entrenched idea of 'genetic memory' (as some lay people call it) be cross-generational epigenetic inheritance? This is a difficult question to answer, but it is time to make a start.
What If Cross-Generational Epigenetic Inheritance Did Contribute to Human Development?
For many, learning from the prevailing social and various physical environments, plus our inherited genetic background, seems sufficient for cultures to endure for generations. The concept of the 'meme' introduced by Richard Dawkins in 1976 [9] as a unit of imitation does not challenge this view that culture is learnt. It just suggests that replication of behaviours by imitation has some parallels with genetic replication and can evolve by natural selection. So cultural transmission is the passing of information by non-genetic means whether or not you subscribe to the somewhat questionable meme-based view that there are discreet 'units' of culture. And for most people 'by non-genetic means' in this context equates with learning. Daniel Everett rightly points out, at least where language is concerned, that cultural continuity is not 'set in stone' (but see later discussion about cultural institutions) or hardwired into the DNA sequence. It is dynamic, like life in general. 'Culture is dynamic, shifting, reinterpreted moment by moment. The roles, knowledge and values of culture are only found in the bodies (the brain is part of the body) and behaviors of its members' [2,10] (see p. 67). Despite this plasticity we are back to the current view that there is learning and there is genetic inheritance through the DNA code. In my experience, such neat dichotomies, are usually wrong. Just because there appears to be a sufficient explanation does not mean that is the whole explanation. Many different threads from the distant evolutionary past have contributed to cultural homo sapiens.
One such thread is epigenetic regulation of gene expression during development and in response to the environment. This additional layer of (epi)genomic information can last a lifetime, and there is increasing evidence that epigenetic responses to (early-life) experience may also influence the development of the next generation(s) [11]. Mammalian research and human studies into the role of epigenetic processes in development, plus the enduring impact of early life exposures throughout adulthood, have been underway for over two decades, much under the umbrella of the Developmental Origins of Health and Disease (DOHaD) paradigm [12,13]. The impact of early-life adversity on later neurobehavioural outcomes has been one focus [14], and more recently these studies have incorporated the interactions with the (gut) microbiome [15]. There is still a lot to learn about the role of epigenetics in DOHaD [16], but it is already clear that epigenetic processes contribute to many experiences being biologically embedded or 'getting under the skin'. Human observations are in line with animal studies. For the purposes of this paper (and in this journal), I will take a role for epigenetics in the biological embedding of developmental experience within a single individual as a given, and just consider the impact on the subsequent generation(s).
If some form of cross-generational epigenetic inheritance is common in humans, it would contribute a biological transmission to descendants that is very different from genetic inheritance. Genetic inheritance comes just from the parents and their respective genetic lineages; and chromosomal behaviour during meiosis leads to genetic diversity in the offspring. On the other hand, in theory at least, non-genetic intergenerational signals (whatever their molecular basis), that were induced by epigenetic responses to (early-life) experiences in the previous generation, will tend to produce comparable developmental adjustments within the offspring. By definition this information that is passed to the offspring is 'in at the beginning'-inherited at conception and potentially able to modify development thereafter. This starting point has important implications for epigenome analysis later in the life of the offspring e.g. analysis of metastable epialleles [17] that are considered later. But even more importantly, the initial epigenetic response is not just garnered from the biological parents, but from interactions with any (and perhaps many) significant others in the community, e.g. a school teacher. To this must be added the shared experience of natural disasters (e.g. Canadian 1998 ice storm [18,19]) or blessings that befall their society as a whole (Fig. 1). Herein lies the epigenetic potential for social cohesion across the generations; that is, an enduring culture.

Brief Update on Relevant Cross-Generational (Epigenetic) Responses in Humans and Mammals
There is growing evidence that some effects of exposures can be biologically transmitted to the next or subsequent generations in some way. These effects are called intergenerational if the exposure (or importantly the organism's response to it, e.g. response to DNA damage) could have reached the germ cells leading to the next generation(s), or transgenerational if this is not the case. The latter implies some molecular 'memory' of the ancestral experience, or its response to it, is being passed down via the gametes. And here, a prime candidate is inter/transgenerational epigenetic inheritance [20]. Clearly the transmission of ideas, attitudes and ways of thinking that underpin a particular culture could be through several different routes, a number not involving the germline. Some of these non-genetic transmission pathways may still involve epigenetic responses in a fundamental way, so for the purposes of discussing culture, I will use 'cross-generational responses' as a catchall term. One such cross-generational pathway is exemplified by the classic rat studies of the influence of maternal care style on offspring stress response and their own nurturing behaviour [21][22][23]. Thus, nurturing behaviour is perpetuated in what could be called a cross-generational epigenetic cascade.
Non-genetic intergenerational inheritance has recently focused on paternal pre-conceptional effects on offspring development-an emerging field labelled POHaD [24]. The few human studies are supported by relevant animal experiments. In summary, in addition to environmentally induced changes to the sperm epigenome, it is clear that sperm also carry induced noncoding RNAs (including tRNA-derived small RNAs) [25][26][27][28][29]. Noncoding RNAs can be transferred to maturing post-testis sperm from the somatic epididymal epithelial cells [30]. This by-passes meiosis and the testis-blood barrier-in other words breaches August Weismann's historic 'barrier'. Teleologically speaking, this appears to help address the apparent imbalance between the parents in non-genetic influences on their developing foetus. It now seems information can sail through from both of them.
In further consideration of inter/trans-generational (epigenetic) inheritance and cultural continuity, there are some general points to make first. It should be emphasized that cross-generational responses need not result in 'inheritance of acquired characteristics' although they often do. The outcome in the next generation can be in the opposite direction, as if it were an adaptation. We have observed this in our intergenerational studies of maternal smoking in pregnancy, when the maternal grandmother smoked in pregnancy, but the study mother did not. Against expectations (direct prenatal tobacco exposure leads to a smaller birth weight) the grandsons were bigger at birth than if neither grandmother nor mother had smoked [31]. These grandsons had greater cardiovascular fitness and lean mass when followed to adolescence [32]. There was no such outcome in granddaughters, illustrating a feature often seen in animal experiments [33] and human observations [11,34]; namely, there are often sex-specific (but not sex-limited) transmission routes and offspring/descendant outcomes.
Human inter/trans-generational studies up to 2014 have been reviewed in detail [11]. This includes all the transgenerational associations of paternal grandparent's food supply in childhood on the grandchild's longevity plus cardiovascular and diabetic deaths in the Ö verkalix population in northern Sweden. Since that time a large Taiwan study of betel-quid chewing and smoking (after childhood) reported that longer duration of paternal betel-quid chewing and of smoking, prefatherhood, independently predicted early occurrence of incident metabolic syndrome in their offspring [35]. An intergenerational Swedish study [36] of the association between the death of a parent at different periods in childhood and the birth characteristics of their own offspring is discussed below.

Exposure-Sensitive Period for Inducing Human Cross-Generational Responses; Relevance to Cultural Continuity
Various stages of foetal development have long been regarded as exposure-sensitive periods in terms of developmental 'programming' within the DOHaD paradigm [37]. However for inducing human inter/trans-generational responses, the mid-childhood period leading up to the growth spurt going into puberty is also a critical exposure-sensitive period. First demonstrated in the Ö verkalix studies [34,38,39], it has observed in other studies [40]. This period was 8 or 9-12 years a century or more ago but taken as 6-10 years for contemporary studies. More recently, a transgenerational study of exposure in midchildhood to the German 1916-18 famine looked at economic and related outcomes in later generations [41]. They assessed height, mental health and educational achievement in the descendants of boys exposed at 9-12 and girls at 8-10 years. Among the third generation, males tend to have better mental health scores if their paternal grandfather was exposed at 9-12 years. The Swedish study of the death of a parent found parental (F1) death occurring at age 8-12 years of F2 boys (and to a lesser extent at 13-17 years) was associated with premature birth and reduced birth weight in their own offspring (F3) [36].
This replication of the association of human crossgenerational responses with initial exposures in mid childhood (in different populations at different periods in history) suggests the underlying processes evolved because biological transfer of such information to the next generation was a selective advantage. If so, it makes sense that social and environmental information (or a protective response to it) is captured before puberty, so potentially all future offspring can benefit. By this reasoning the upper age limit of the sensitive period being roughly the onset of puberty, makes sense, but what about the lower age limit of 6 years, or 8 years historically? It may simply be due to many aspects of immaturity, including the development of the testes and ovaries, or the fact that the period from birth to 6 years is primarily geared to survival per se. In this context it is interesting that phenotypic concordance between monozygotic twins is relatively low at this age, reaching its maximum in adolescence or later, for example with IQ [42]. Again this could be interpreted as surviving first, before then revealing your genetic potential ready for the mating game. What can be said, in terms of culture, is that mid childhood represents the latter part of 'the formative years'. The Jesuit maxim 'Give me the child until he is seven, and I will give you the man' makes the point; and this is echoed by UNICEF who regard the formative years as birth until 8 years [43]. One can argue that for the first 5 years the cultural input is primarily from the family, but then, at least for settled communities the cultural institutions, such as market places, schools, religious centres come more into play, enhancing social cohesion. For example, school experience in mid-childhood has been shown to predict health in adulthood [44]. Importantly these increased societal influences coincide in timing with the exposure sensitive period for inducing cross-generational responses. The interplay between families and the local cultural institutions also needs to be considered. These elements include security-city walls, food supply/markets; sense of belonging-religions, gangs, sports; information-trading, tablets of stone, writing, schools, etc. These institutions hold the received wisdom of the past in an accessible state-available to all, or perhaps selected members or families, or at a price. The stability of a community over time depends on both the constituent families and the cultural institutions. Importantly, the cultural institutions may provide fall-back support and encouragement for children from dysfunctional families or other crises that occur in that critical mid-childhood 'transfer window' for cross-generational (epigenetic) inheritance.

The Pay off between Responsiveness and Stability
There is a fundamental pay off between stability and responsiveness. Reputedly, the Jaguar  was the last fighter jet to be really flown by the pilot. Modern fighter jets are now built so unstable in the air (needing constant computer adjustments of the flaps to stay airborne) in order to be responsive enough to take avoiding action against missiles [45]. I suspect this issue is also an important matter for cultural continuity. One definition of responsiveness is 'The quality of reacting quickly and strongly [46]. Alternatively, the word responsive can be defined as 'showing interest or emotion in reaction to someone or something' [47]. This captures the sensitivity element-whether one reacts easily or, at the other extreme, remain unresponsive. If cultural institutions encourage stability within a community and across the generations, then this permits the individuals within that community to adopt more responsive behaviours. This may be crucial for children in their formative years in order to adopt new ideas and technologies on which the future of their communities may depend-and perhaps transmit epigenetic adjustments to their gametes. However, such a permissive culture may also carry the risk of what one could call 'runaway' responsiveness'; ignoring the past for a horizontal drift into an unstable melting pot of behaviours ravaged by wave after wave of fashion, rather like the 'crazes' that sweep today's primary schools. Too large a swing of the pendulum and the latest fighter jet falls out of the sky! From a population genetics point of view it is likely the population would hedge its bets through the positive selection of DNA variants that reduce (extreme) responsiveness. A comparable evolutionary scenario has been modelled in a paper titled Evolutionary emergence of responsive and unresponsive personalities [48]. Their evolutionary model is based on two key ingredients; the benefits of responsiveness are frequency dependent, and positive-feedback mechanisms reduce the cost of responsiveness, permitting some selective balance. There are indeed DNA variants of dopamine and serotonin pathway genes that result (at the extreme combinations) in what I call 'responders' or 'non-responders' to early-life stress in terms of later adolescent self-regulation [49] or telomere length [50]. Past studies of depression and post-traumatic stress disorder (PTSD) after child abuse have identified a role for the glucocorticoid receptor as a pivotal nuclear receptor of the stress hormone system. The gene encoding the FK506-binding-protein-5(FKBP5) is an important functional regulator of the glucocorticoid receptor complex and a SNP (rs1360780) moderates the risk for PTSD after early trauma [51]. Furthermore, this SNP results in childhood trauma-dependent DNA demethylation in functional glucocorticoid response elements of FKBP5 [52]. Interestingly, although barely commented upon, the non-abused controls with what they called the 'protective allele' had more lifetime PTSD that the non-abused with the so called 'risk' allele. Perhaps 'protective allele' carriers are actually 'non-responders', less responsive to typical, healthy social support, as well as abuse when it happens? This general field of differential susceptibility of the developing brain to contextual adversity and stress was recently reviewed [53].

The Broad Hypothesis
I propose that cultural continuity and some adjustments over time are played out along the axis, or 'payoff', between responsiveness and stability. During the formative years of childhood a stable family and/or home permits small children to explore and thereby learn. To counter disruptions to this family home ideal, cultural institutions such as local schools, religious centres and market places emerged to provide ongoing stability, holding the received wisdom of the past in an accessible state. This cultural support allows the growing child to freely indulge their responsiveness. Some of these prepubertal experiences induce epigenetic responses that also transfer molecular signals to the gametes through which they contribute to the conception of future offspring. In parallel co-evolution with growing cultural support for increasing responsiveness, 'runaway' responsiveness is countered by the positive selection of genetic variants that dampen responsiveness. It is proposed that such genetic variants would reduce the biological embedding of the early life experience and thus the measurable intergenerational effects on offspring development.

Testing the Hypothesis
The first thing to say is that testing these ideas in humans is a huge challenge, and will need a number of comprehensive, multigenerational cohorts followed from before birth throughout development, into adulthood and on to the next generation. In the first instance, an assessment of just how plausible this cultural continuity hypothesis is, will be built up piecemeal. This type of patchwork approach has already paid off in testing the whole idea of human non-genetic inheritance in the first place. It is worth remembering that 10 years ago, studies of specific maternal peri-conceptional and intrauterine exposures on foetal development would regard lack of a paternal exposure effect as a useful 'control' for unknown social confounders [54,55]. This is because finding a paternal association would cast doubt on the maternal findings. Now we could be turning this view on its head and pose the question-can established paternal, preconceptional POHaD effects contribute to 'social patterning' down the generations through intergenerational epigenetic inheritance? Less speculatively, the demonstration of mid childhood as an exposure-sensitive period for cross-generational associations does help to reduce concern about a number of unknown confounders, including genetic pleiotropy. For example [40], are DNA variants associated with the father's mid childhood smoking habit, if transmitted, also associated with the risk of obesity in his sons at 17 years? If so, then why not with father's onset of smoking at 14 years? That an association was not found with father's onset at 14 years makes the genetic pleiotropy explanation less likely. The other aspect of animal and human cross-generational (epigenetic) inheritance is the often observed sex-specific outcomes in the next generation(s). Over time, the observed epidemiological correlates with such crossgenerational associations have been building a more specific picture of the phenomenon. One is moving slowly from a hypothesis-free exploration to some more specific prior hypotheses. However, we should not be complacent. A study [56] of Social Genetic Effects in mice detected associations between a trait of one mouse and the genetic makeup of its cage mates. It turned out that such social genetic effects explained up to 29% of variation in anxiety, wound healing, body weight and immune function, and in several cases contributed more to phenotypic variation than the mouse's own genes. These correlations appear to hold for both in-bred and out-bred strains. Recently Social Genetic Effects of friends and school classmates have been reported to contribute to educational achievement [57]. Such correlations presumably depend on phenotypic expression-ranging from direct effects like body odour to complex behavioural variations. Social Genetic Effects, of course, can play both ways in terms of social cohesion and cultural continuity, but much is likely to be mediated by epigenetics. Yes, it is going to be complicated! One way of reducing complications in the interpretation of human observational studies has been to include the father and his ancestors, in addition to the maternal side, in multigenerational prospective cohorts; as has been the case in the Avon Longitudinal Study of Parents and Children (ALSPAC) UK [58], for example. Paternal cross-generational associations are less confounded than maternal associations with respect to several potential intergenerational transmission routes including mitochondrial inheritance, cross-placental transfer of biochemical signals, and via the microbiome and breast milk. But perhaps most importantly for epigenomic studies, sperm are potentially available. As alluded to earlier, sperm-mediated transmission is by definition 'in at the beginning'-contributing to the conceptus. This coupled with the improved identification of metastable epialleles (epigenetic variants set 'stochastically' in the early embryo and maintained during subsequent cellular differentiation [59]) will facilitate human epigenetic analysis in the next generation(s), because metastable epialleles are not tissue specific. Recent data [17] from the study of monozygotic twins indicate that they share more metastable epialleles than expected from being genetically identical. This 'supersimilarity' implies the generation of such epialleles in the earliest stages of the embryo-or perhaps the arrival of such epialleles (or something that induces them) in the egg or sperm. More broadly, the chromatin state of the early embryo could potentially correlate with general genomic biomarkers such as epigenetic (DNA methylation) age [60] and chromosomal telomere length [61,62], in the new born and later in life. They are increasingly recognized as a measure of biological ageing. For human studies, variation in these two measures might provide useful outcome measures (that are clearly biologically embedded) in cross-generational analysis. Additionally, the role of genetic variants that influence responsiveness to stress, for example, could be tested on the above (epi)genomic markers in the next generation(s). However, the impact of genetic unresponsiveness on society will be complex and context-dependent. One could speculate that in the trenches of the First World War being a 'non-responder' helped maintain courage and therefore social cohesion and survival. This contributed to restoration of a core cultural way of life post-war; but then 'responders' contributed in turn to the adoption of important innovations. Whatever the approaches adopted for human studies going forward, there is always the need for replication of cross-generational associations with early-life experience in different populations. This can prove a particular challenge when considering cultural continuity because this needs ongoing quantitative measures of well-being and neurobehavioral traits right across the population and not just a (nested) case-control design focused on disease and adverse outcomes. Ideally multigenerational cohorts should start with an 'open', specific hypothesis-free approach through Phenome Scans [63] and Exposome scans on the previous generation(s) [64].
Before concluding this final section, it is worth standing back to look at the unknowns. Famously, Donald Rumsfeld stated [65] 'There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know'. Mindful of this, in any pie chart of factors contributing to developmental variation in humans, I usually add a 'yet to be discovered' slice, the size of which can be debated. What are the known unknowns in the current field of non-genetic cross-generational inheritance with respect to culture? There are three that I have followed with interest. Human intelligence, Pavlovian conditioning and genomic imprinting. There is consistent evidence in a number of industrialized countries that the IQ of the population has risen over many decades, known as the Flynn effect [66][67][68]. What combination of factors are responsible for this rise remains an open question. Interestingly from the cultural continuity perspective, Mark Shlyankevich hypothesized in 2011 that the relatively high intelligence of the Ashkenazi Jews is not so much a result of genetic selection, but a consequence of studying religious texts for at least 100 generations [69]. He speculates that intense early study had a cumulative inter-generational epigenetic effect resulting in a higher average intelligence. This idea is in line with the Flynn effect and implies some epigenetic neurological 'priming' of the next generation. In the context of 'priming', it has been reported [70] that fear conditioning with a novel odour in mice produces similar behaviour (and neuroanatomy) in the unconditioned offspring and is associated with sperm DNA methylation changes in the relevant specific olfactory receptor gene. If replicated, this would have real implications for cultural continuity; experiences in one generation 'priming' as it were, the next. It happens that there is a notorious history of this idea of crossgenerational effects of Pavlovian conditioning. Ivan Pavlov himself felt that repeated conditioning in each successive generation might eventually lead to unconditioned descendent mice (not dogs in this particular study) who react to a bell with food seeking behaviour as a 'hard-wired' instinct, just as new born chicks instinctively peck at seeds on the ground. Initial studies from his laboratory, performed by Nikolai Studentsov, showed a progressive reduction across the generations in the number of conditioning episodes needed to induce food seeking. This was reported in 1923 (under the spelling I. P. Pawlow) in Science [71]. Challenged by others Pavlov's laboratory eventually repeated the study, this time with controls, and the previously reported transgenerational 'priming' was not replicated, in the sense there was no difference from controls. An account of all this is discussed in Loren Graham's 2016 book Lysenko's Ghost: Epigenetics and Russia [72]. In terms of testing the cultural continuity hypothesis outlined in this paper, it is crucial that this conditioning work is pursued. Proving a negative is difficult, so we need several laboratories to invest in trying to answer this 'for once and all', combining modern neurobehavioural research techniques, imaging, gene expression and of course epigenetics. Hopefully, in years to come this particular idea will move into the known known category; it is shown to be extremely unlikely or very likely to be true.
Genomic imprinting also qualifies as a known unknown principally on the grounds that it is an established epigenetic gene-silencing process that involves two generations. Genomic imprinting (gene silencing dependent on the parent of origin) establishes the principle that epigenetic marks such as DNA methylation placed (in the developing gametes) in one generation can influence gene expression in the next [73]. This is possible because imprinted genes are protected from the genome wide de-methylation that occurs after conception in the preimplantation period. It was my research on human imprinting disorders in the early 1990's that made me speculate that imprinting plays a key role in human transgenerational responses [74]. It has been a long wait! However, genomic imprinting remains a known unknown because the phenomenon has not been ruled out as a player; and there are hints that genomic imprinting networks may be involved in a new field that doesn't really have a name yet, i.e. just emerging from the unknown unknown category. A study published a couple of years ago [75] demonstrated that mice with TRIM28 insufficiency resulted in polyphenism, wherein lean and obese phenotypes can arise from the identical genotypes through dysregulation of an imprinted gene network. They showed that human BMI distributions and transcriptomes also suggest TRIM28-associated subpopulations. They conclude that perturbation of imprinted genes Peg3 and Nnat trigger stochastic bi-stable obesity. It is interesting from the POHaD perspective that the long range physical interaction of several imprinted genes, is particularly marked in spermatogonia; 'suggesting that the 'imprintome' is primarily operating in stem cells of the male germline' [76]. Indeed, paternal obesity is associated with altered DNA methylation patterns at MEST, PEG3 and NNAT imprinted genes in his offspring [77]. Polyphenism implies that genetically identical embryos carry two or more potential developmental trajectories. The switch to one trajectory is currently regarded as 'stochastic'-that means a known unknown in my book. Watch this space!

Conclusion
It is time to seriously examine the potential role of crossgenerational epigenetic inheritance in our cultural continuity. It is recognized that this is a multifaceted question, and as such will involve diverse academic disciplines. It calls for what one could call the 'patchwork' approach, in the hope of one day realizing the whole; just like those tackling other big questions in science. The cultural patchwork is stitched together by history and experience. This is not my phrase-I heard diplomat Alexander Downer [78] on BBC radio discuss how one got to understand a country to which one had been posted. 'They feel as they feel because of their own history and experience. People are defined by these experiences'. The question 'where did I come from?' and its reciprocal 'deep sense of belonging' might relate to being tied to one's ancestors, not so much through genetic inheritance, but through their experiences. It may not all be down to storytelling, but underpinned by non-genetic gametic inheritance.