Nascent Transcriptome Reveals Orchestration of Zygotic Genome Activation in Early Embryogenesis

Hui Chen; Matthew C Good

doi:10.1016/j.cub.2022.07.078

. Author manuscript; available in PMC: 2023 Oct 10.

Published in final edited form as: Curr Biol. 2022 Aug 24;32(19):4314–4324.e7. doi: 10.1016/j.cub.2022.07.078

Nascent Transcriptome Reveals Orchestration of Zygotic Genome Activation in Early Embryogenesis

Hui Chen ¹, Matthew C Good ^1,^2,^3,^*

PMCID: PMC9560990 NIHMSID: NIHMS1829064 PMID: 36007528

SUMMARY

Early embryo development requires maternal-to-zygotic transition, during which transcriptionally silent nuclei begin widespread gene expression during zygotic genome activation (ZGA)^1–3. ZGA is vital for early cell fating and germ layer specification^3,4, and ZGA timing is regulated by multiple mechanisms^1–5. However, controversies remain about whether these mechanisms are interrelated and vary among species^6–10 and whether the timing of germ layer-specific gene activation is temporally ordered^11,12. In some embryonic models, widespread ZGA onset is spatiotemporally graded^13,14, yet it is unclear whether the transcriptome follows this pattern. A major challenge in addressing these questions is to accurately measure the timing of each gene activation. Here, we metabolically label and identify the nascent transcriptome using 5-ethynyl uridine (EU) in Xenopus blastula embryos. We find that EU-RNA-seq outperforms total RNA-seq in detecting the ZGA transcriptome, which is dominated by transcription from maternal-zygotic genes, enabling improved ZGA timing determination. We uncover discrete spatiotemporal patterns for individual gene activation, a majority following a spatial pattern of ZGA that is correlated with a cell-size gradient¹⁴. We further reveal that transcription necessitates a period of developmental progression and that ZGA can be precociously induced by cycloheximide, potentially through elongation of interphase. Finally, most ectodermal genes are activated earlier than endodermal genes, suggesting a temporal orchestration of germ layer-specific genes, potentially linked to the spatially graded pattern of ZGA. Together, our study provides fundamental new insights into the composition and dynamics of the ZGA transcriptome, mechanisms regulating ZGA timing, and its role in the onset of early cell fating.

Keywords: Zygotic genome activation, nascent transcription, early embryogenesis, 5-ethynyl uridine, click chemistry, spatiotemporal patterning, cell size gradient, DNA:cytoplasm ratio, cell cycle elongation, germ layer

In Brief

By profiling the nascent transcriptome during zygotic genome activation (ZGA) from whole Xenopus blastula and dissected subregions, Chen et al. unveil predominant transcription from maternal-zygotic genes and distinct spatial patterns, reconcile regulatory mechanisms of ZGA and discover a link to sequential activation of germ layer-specific genes.

RESULTS AND DISCUSSION

Nascent EU-RNA-seq characterizes the composition and dynamics of ZGA with high sensitivity

Transcript levels in early embryos are dominated by maternal RNAs preloaded in the egg, whereas newly transcribed RNAs, including those from maternal-zygotic (MZ) and exclusively zygotic (Z) genes, constitute a small portion during ZGA (Figure 1A). Abundant maternal RNAs represents a major challenge in understanding the scope, timing and underlying mechanisms regulating ZGA transcription. Recently, metabolic labeling of newly synthesized RNAs using uridine analogs, such as 4-thiol-UTP (4s-UTP)^15,16 and 5-ethynyl uridine (EU)^14,17,18, followed by physical separation of nascent and maternal RNA pools, has provided new tools to interrogate ZGA. We previously demonstrated that compared to conventional RNA-seq, EU-labelled nascent RNA-sequencing (EU-RNA-seq, Figure 1B) enriched biotinylated zygotic transcripts (EU-RNAs) from Xenopus late blastula on streptavidin beads¹⁴, suggesting its potential for detecting newly transcribed RNA. To characterize the nascent transcriptome in embryos at earlier stages, where transcript levels are much lower, we further optimized the EU-RNA-seq protocol (see METHOD DETAILS) and performed it on Xenopus embryos at 1-hour interval from 5–9 hours post-fertilization (hpf), corresponding to NF stages 7–9, covering the period of pre-ZGA to widespread ZGA. In addition to sequencing the nascent transcripts (on ‘Bead’), we sequenced the flowthrough after separation (‘Flowthrough’, presumable maternal RNA) and the total RNA (‘All’) for comparison. We observed that from 5–9 hpf, nascent transcripts of an increasing number of genes enriched in the ‘Bead’ dataset compared to ‘Flowthrough’ (Figure 1C; Figure S1A and S1B), suggesting that EU-RNA-seq captures and enriches nascent transcripts. Although we were able to separate zygotic from maternal transcripts, we also noticed some maternal transcripts bound non-specifically to beads. Therefore, we chose to calculate nascent transcription based on the net increase of reads, treating those at 5 hpf as background. To characterize which genes were activated, we filtered the nascent transcriptome data (see METHOD DETAILS), generating a list of 2577 genes (Figure S1C; Table S1).

Noticeably, at the time of ZGA widespread onset (7 hpf), over 44% genes were more highly detected using EU-RNA-seq than total RNA-seq (Figure 1D); a ~ 2–16-fold enrichment (Figure 1E), consistent with previous observation¹⁴. More strikingly, activation of 240 genes was uniquely detected by EU-RNA-seq (Figure 1F; Table S2). Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) from early gastrula stage (NF stage 10) revealed that the promoter regions in most of these genes uniquely detected by EU-RNA-seq were accessible (Figure S1M and S1N), suggesting these genes are likely actively transcribed. To further validate the sensitivity of EU-RNA-seq, we compared the levels of the most highly transcribed genes identified by Session et al.¹⁹ and by Yanai et al.²⁰ in our nascent versus total transcriptome data. Both comparisons showed a ~3–4-fold enrichment in the EU-RNA-seq (Figure S1D–S1F). This improved sensitivity also revealed earlier expression of a subset of genes in the zygotic transcriptome (Figure S1G). Together, the EU-RNA-seq enabled us to detect nascent transcription in early Xenopus embryos with unprecedented sensitivity and specificity.

Next, we wondered what fraction of the transcriptional output of large-scale ZGA is comprised of transcripts from MZ and Z genes. In traditional gene profiling, it is a challenge to characterize the activation of MZ genes - the presence of high maternal RNA levels can mask the onset of nascent transcription. Based on the presence of transcripts in the egg, we categorized the nascent list of 2577 genes into Z genes that do not contain reads in the egg (≤ 5 reads) and MZ genes that contain transcripts in the egg (> 5 reads) (Figure S1C). We found that among the genes activated from 5–9 hpf MZ genes accounted for ~ 70% total reads and Z genes only ~ 10%, and both increased over time (Figure S1H–S1L). The ratio of MZ:Z genes was ~ 4:1 and the ratio of reads for MZ:Z genes was ~ 8:1, which was relatively constant over time (Figure 1G–1I). These data suggest that compositionally, ZGA transcriptional output is dominated by MZ gene expression, consistent with previous observations²¹, although zygotic-only transcripts are essential for development ^22–24. Gene ontology (GO) analysis revealed that MZ genes are mainly involved in RNA processing, splicing and transport (Figure 1J), whereas Z genes are responsible for patterning, gastrulation, cell fate commitment and germ layer specification (Figure 1K). Thus, onset of widespread ZGA is dominated by a handoff from maternal-to-zygotic control of core regulatory genes, whereas zygotic-only factors that pattern later development represent a smaller portion of the transcriptional output. In summary, the highly sensitive EU-RNA-seq methodology reveals a greater depth to the composition and dynamics of ZGA, offering essential new insights on genome regulation in early embryo development.

EU-RNA-seq on segmented embryos uncovers spatial patterns of single gene activation

We previously performed nascent EU-RNA imaging in single cells of wholemount Xenopus early embryos and observed a stereotypical spatiotemporal pattern of large-scale ZGA which initiates first in small cells at the animal pole (AP) and is delayed in large cells at the vegetal pole (VP), dependent on cells reaching a threshold size^13,14 (Figure 2A). However, an open question was whether the global pattern of ZGA held at the single-gene level for most genes. To address this question, we performed EU-RNA-seq on the dissected regions of AP and VP from embryos at 5–9 hpf (Figure 2A). We chose these regions because they display a striking phase shift in ZGA timing of ~ 90 minutes¹⁴, and they represent the presumptive ectoderm (the AP) and endoderm (the VP). Due to the lower quantity of material, we refined the AP-VP nascent transcriptome by selecting genes that undergo consistent continuous activation in the AP or VP regions at 5–9 hpf, which resulted in a list of 882 genes for further characterization of their spatial activation patterns (Figure S2A). To determine the onset time for each gene at the AP and the VP, respectively, we adapted a previously described method to fit the mean-normalized reads at 5–9 hpf with a smooth spline²⁵ (Figure S2B). By setting thresholds of expression (see METHOD DETAILS), we eventually classified 476 genes into five categories of activation patterns based on their spatial expression profiles: (Figure 2B; Figure S2C; Table S3). We verified the activation patterns of a subset of transcripts using reverse transcription polymerase chain reaction (RT-PCR) and quantitative real-time PCR (Figure S2D and S2E). The existence of various patterns of gene activation (Figure S2F–S2I) is consistent with the view that the embryo is patterned during early embryogenesis²³, and potentially distinct mechanisms regulate ZGA. As expected, the ‘VP regional’ category is replete with genes involved in endoderm-related development (Figure S2G), including the bix1, mixer, sox17 and nodal family genes (Table S3), consistent with the VP as the physical location of the presumptive endoderm cells^26,27. Genes uniquely activated or activated early in VP may be regulated by region-specific maternal determinants, such as VegT^28–30, that prepattern early embryos.

Intriguingly, among the five spatiotemporal patterns, the predominant category is the ‘AP Early, VP Delay’ (~ 56% genes) (Figure 2B and 2C; Figure S2C), paralleling the global spatiotemporal patterning of ZGA from EU-imaging, suggesting that these genes could be regulated by a cell-size or DNA:cytoplasm ratio dependent mechanism¹⁴. GO analysis of this category revealed functional enrichment of ectoderm-related development such as epithelial tube morphogenesis and eye development (Figure S2F), consistent with the role of the AP as the physical location of the presumptive ectoderm. Separately, ~ 13% of genes show an activation pattern similar in space and time (Figure 2B and 2C; Figure S2C; Figure S2I), potentially consistent with a timer mechanism. The observation of diverse patterns is also consistent with previous studies suggesting that ZGA includes expression of distinct subsets of genes regulated by distinct mechanisms^6,7.

In summary, our regional nascent transcriptome analysis revealed patterning that is consistent with a classic developmental control view but also previously underappreciated major spatial and temporal pattern of gene activation, consistent with cell size or DNA:cytoplasm ratio dependent ZGA regulation. The results reveal that distinct modes of regulation likely control distinct subsets of gene expression patterns at the canonical onset of widespread ZGA and that more than half follow a pattern tied to the cell size gradient in the Xenopus blastula. Future studies are warranted to dissect the respective mechanisms underlying these distinct expression patterns.

Reconciling mechanisms that regulate ZGA timing

Multiple mechanisms have been suggested to regulate ZGA timing in various species^1–5, including a timer^{6,17,21,31,32}, DNA:cytoplasm ratio^{7,14,25,33–38}, and cell cycle elongation^{8–10,15,18,39–41}. However, these mechanisms may work in concert or be interrelated and their relative contribution to ZGA onset timing in embryogenesis is debated^6–10. We reasoned that by manipulating the regulatory parameters at various phases of development and measuring nascent transcription would allow us to distinguish the contributions of distinct mechanisms. To this end, we treated EU-injected embryos at various pre-ZGA stages with cycloheximide (CHX), an inhibitor of translation that has been widely used for arresting Xenopus embryos in interphase by administration to the culture medium^42–47, and analyzed its effect on nascent transcription by EU-RNA imaging (Figure 3A). Interestingly, we observed that CHX impacts nascent transcription in a stage-dependent manner – no transcription for 3–7.5 hpf arrest, modest transcription for 4–7.5 hpf arrest, and abundant transcription for 5–7.5 hpf arrest (Figure 3B). This result suggest that a developmental window is essential for the embryo gaining transcriptional competence before ZGA, consistent with a recent finding in zebrafish⁴², potentially by accumulating maternal translation of transcription activators such as the pioneering pluripotency factors^21,32 and impacting chromatin remodeling^17,48. Notably, the highly transcribing embryos arrested from 5–7.5 hpf only contain ~ 500 cells (versus ~ 5,000 cells in control embryos at 7.5 hpf; 3–4 cell cycles behind the control), in which DNA synthesis is arrested⁴² (Figure S3A) and the DNA:cytoplasm ratio is far below the threshold for ZGA onset¹⁴. This suggests that once the embryo gains transcriptional competence, prolonged arrest in interphase may enable nascent transcript accumulation, although the possibility of cell-cycle-independent activities, such as translational inhibition of potential transcriptional inhibitors, cannot be excluded.

Figure 3. — (A) Experimental design to probe effect of cycloheximide (CHX) treatment on ZGA regulation. Embryos were microinjected with EU at 1-cell stage, treated with CHX at 3 hpf, 4 hpf and 5 hpf, respectively, and analyzed for nascent transcription by confocal imaging or EU-RNA-seq at 7.5 hpf. The red line indicates empirical ZGA timing in normal embryos. Estimated cell numbers at each hpf are shown for normal embryos.

(B) Imaging nascent transcript (EU-RNA) accumulation in blastomere nuclei of wholemount embryos. Maximum intensity Z-projections of several slices from individual embryo stacks are shown. Left, representative confocal imaging of control embryos, prior to ZGA (5.5 hpf) and at the canonical onset of widespread ZGA (7.5 hpf). Right, representative confocal imaging of embryos treated with CHX from 3–7.5 hpf, 4–7.5 hpf and 5–7.5 hpf, respectively. DNA channel illustrates number and size of nuclei. Nuclei in CHX-treated embryos are large and round indicating prolonged arrest in interphase. Nascent transcription is absent from embryos arrested from 3–7.5 hpf. Note: nuclear morphology in 3–7.5 hpf arrested embryos is more heterogenous, but no EU-RNA was observed in nuclei from multiple repeats of the experiment. Nuclei in blastomeres arrested from 5–7.5 hpf show strong levels of nascent transcription, similar to and exceeding control 7.5 hpf embryos. Signal is RNAPII-dependent; inhibited by α-amanitin treatment (bottom). Scale bars, 50 μm.

(C-E) Nascent transcriptome reads for control (wildtype) and embryos CHX-arrested from 5–7.5 hpf. Each dot represents individual genes from the selected 2577 genes with log2 reads averaged from duplicates quantified by DESeq2. The differentially enriched genes in each group are labelled in red and blue, respectively, based on a 2-fold threshold (dashed lines) and adjusted p-value < 0.05. Accumulation of nascent reads from 5–7.5 hpf in control embryos (C) and from 5–7.5 hpf in embryos arrested in CHX from 5–7.5 hpf (D). (E) Comparison of reads during widespread ZGA (7.5 hpf) for control embryos versus embryos CHX-arrested from 5–7.5 hpf.

(F) Embryonic arrest in interphase via CHX treatment from 5–7.5 hpf leads to widespread induction of zygotic genes normally induced during ZGA. Percentage of 2577 genes in nascent 7.5 hpf transcriptome of control that in embryos subject to 5–7.5 hpf CHX-treatment are up-regulated (red), down-regulated genes (blue) and similarly expressed (grey); calculated from Figure 3E.

(G) Gene annotation for subset of the transcriptome that are not induced by arrest with CHX treatment from 5–7.5 hpf. Over 80% do not have assigned gene names in the *Xenopus* genome.

(H) Motifs strongly enriched in the promoter regions of genes that do not induce in response to CHX arrest and prolonged interphase.

(I) Experimental design test whether CHX treatment induces premature ZGA. Embryos were microinjected with EU at 1-cell stage, treated with CHX from 5 hpf and imaged for nascent transcription at 6 hpf, 6.5 hpf and 7 hpf, respectively. The red line indicates ZGA timing in normal embryos at 22 °C. Estimated cell numbers at each hpf are shown for control embryos.

(J) Imaging of nascent transcription (EU-RNA) in nuclei of blastomeres on animal side of wholemount control and CHX-arrested embryos. Maximum intensity Z-projections from individual embryo substacks are shown. Observation of premature ZGA - nascent transcription - in CHX-arrested embryos treated from 5–6 hpf, 5–6.5 hpf and 5–7 hpf. Nascent transcription is largely absent in control embryos at 6 and 6.5 hpf and beginning widespread accumulation in control embryos at 7 hpf. Scale bars, 50 μm.

(K) Quantification of nascent transcription in control and CHX-arrested embryos in (J). The nuclear EU-RNA amount in blastomere nuclei was calculated by multiplying the integrated nuclear EU-RNA signal after cytoplasmic background subtraction, by the nucleus volume. A total of 80–160 cells from at least three embryos were analyzed for each group. Data are represented as mean + SD and statistical significance was determined by one-way ANOVA (Fisher’s LSD test). **, p < 0.01; ****, p < 0.0001.

(L) Schematic summary of precocious ZGA initiated by CHX treatment. Left, CHX induces early ZGA from 5 hpf, after embryos had gained transcriptional competence. Right, CHX treatment promotes nascent transcript accumulation to a higher level and at earlier times in individual nuclei. The color gradient indicates no or low (grey) to high (red) transcription in the nucleus. The red dashed vertical line indicates empirical widespread ZGA onset timing for control embryos.

See also Figure S3.

We next wondered whether nascent transcription in CHX-arrested embryos represents bona fide ZGA. To test this, first we co-microinjected EU with α-amanitin, an inhibitor of RNA polymerase II (RNAPII), and found a majority of EU-RNA signal was abolished, suggesting RNAPII-dependent transcription in CHX-arrested embryos (Figure 3B). We then performed EU-RNA-seq to compare the nascent transcriptome between 7.5 hpf control and the CHX-arrested embryos from 5–7.5 hpf. Surprisingly, 94% of the genes were similarly transcribed and only 4.8% and 1.2% of the genes were downregulated and upregulated by CHX, respectively (Figure 3C–3F; Figure S3B–S3D). A majority of the CHX-downregulated genes are unnamed and unannotated (Figure 3G; Figure S3E–S3G), although they seem to be involved developmental regulation (Figure 3H); in contrast, the majority of the CHX-upregulated genes are named and annotated (Figure S3F) and are enriched in the ectoderm-related development (Figure S4E and S4F). These data suggest that CHX-arrest induces nearly full ZGA, despite its differential impact on subsets of the genome. Notably, this comparable level of transcription at 7.5 hpf was reached by the CHX-arrested embryo (~ 500 cells) that contains ~ 10 times fewer nuclei and DNA template than the control embryo (~ 5,000 cells), suggesting higher and possibly earlier transcriptional output per nucleus in CHX-arrested embryos.

To determine whether CHX-treatment induces early ZGA, we treated embryos with CHX starting from 5 hpf, at which embryos had gained transcriptional competence, and examined nascent transcription at timepoints before the canonical onset of widespread ZGA (Figure 3I). Remarkably, nascent transcription readily occurred upon CHX treatment from 5–6 or 5–6.5 hpf, when transcription is not detectable in control embryos even though they contain many fewer nuclei due to arrest (Figure 3J and 3K; Figure S3H). Most strikingly, upon CHX treatment for from 5–7 hpf transcriptional output per nucleus is 14.8-fold higher (Figure 3K), in embryos ~ 3 divisions behind control embryos (inferred from cell volume, Figure S3H). Furthermore, individual zygotic genes could be detected earlier and higher levels in embryos arrested with CHX starting at 5 hpf compared to control embryos by RT-PCR (Figure S3I). Together, these data suggest that CHX induces precocious ZGA onset by increased transcriptional output in individual nuclei (Figure 3L). These findings agree with an interpretation that short cell cycle can repress nascent transcription and cell cycle elongation can promote large-scale ZGA after an embryo gains transcriptional competence, although it cannot be excluded that separate translational inhibition by CHX may also contribute to transcription^10,42. Noticeably, our finding is consistent with recent observations made in other embryonic systems, including zebrafish¹⁷ and Drosophila^7,8, that cell cycle arrest increases zygotic transcription. Our study is limited by the inability of using similar regimens of specific cell cycle inhibitors such as Cdk inhibitors which did not rapidly block embryo cleavages (Figure S3J).

Timing of germ layer initiation is linked to spatially graded onset of ZGA

Many germ layer-specific genes are transcribed in the blastula embryo during ZGA. In Xenopus, we previously found that cells of the AP - the presumptive ectoderm – initiate large-scale ZGA ~ 90 minutes earlier than cells of the VP – the presumptive endoderm¹⁴, which is linked to AP cells reaching a threshold cell size for ZGA more quickly. We wondered whether the spatiotemporal patterning of ZGA might contribute to a temporal ordering of germ layer-specific gene activation, the chronology of which is debated in different model embryos^11,12. To determine whether a temporal order of germ layer-specific gene activation exists in Xenopus embryos, we focused on a list of marker genes that had been experimentally defined and validated in gastrula embryos for the ectoderm²⁶ and the endoderm²⁷, respectively. We discovered that on average, the ectodermal genes are more highly transcribed, and a majority are activated earlier, than the endodermal genes from 5–9 hpf (Figure 4A–4C; Figure S4A–S4D). Chromatin immunoprecipitation sequencing (ChIP-seq) analysis for RNAPII and H3K4me3¹⁹, marks for active transcription, revealed higher transcriptional activity in the ectodermal genes than the endodermal genes that persists in early gastrula (Figure 4G; Figure S4I). However, it should be noted that several endodermal genes are activated early (Figure 4B), consistent with previous findings that these genes are transcribed early in development, potentially regulated by the maternal T-box factor VegT^49,50. These data, together with those from the AP/VP spatial patterns¹⁴ (Figure 2; Figure S2), suggest that the timing of germ layer initiation is largely correlated with the regional timing of ZGA onset and that distinct mechanisms may regulate the germ layer-specific activation at the single-gene level.

Figure 4. — (A) Average transcript levels for ectoderm and endoderm genes in blastula embryos. Nascent reads averaged from replicate embryos. Red, set of ectoderm genes (N = 111 genes); Blue, set of endoderm genes (N = 172 genes) at 5–9 hpf. Mean ± SE.

(B) Distribution of time-of-onset for transcriptional activation of individual genes within ectoderm (red) and endoderm (blue) sets.

(C) Cumulative density of time-of-onset for transcriptional activation for ectoderm (red) and endoderm (blue) genes.

(D and E) Heatmaps showing z-scores for induction of ectoderm (D) and endoderm (E) genes in control embryos at 7.5 hpf versus embryos treated with CHX from 5–7.5 hpf. Data are selected genes that have detectable transcriptional induction in control embryos from 5–7.5 hpf. Pie chart shows fraction of genes similar (grey) or up- (red) or down-regulated (blue), comparing CHX-treated versus control embryos; threshold 1.5-fold difference.

(F) Cumulative density showing induction relative to control of germ layer genes in CHX-treated embryos at 7.5 hpf. Red, ectoderm; blue, endoderm.

(G) Profile plots for ChIP-seq peaks of RNA Pol II for ectoderm and endoderm genes, respectively, in embryos at stage 10.5. Data from Session et al.¹⁹ The regions of transcription start site (TSS) +/− 2kb from two replicates are shown. The level of RNA Pol II binding in the indicated regions is higher in ectoderm genes than endoderm genes.

(H) Composite model and pathway describing regulated ZGA onset. Widespread ZGA is inhibited by rapid cell cycles in cleavage-stage embryos. As cell size reduces, due to cell division without cell growth, a DNA:cytoplasm ratio threshold is reached, promoting cell cycle elongation and allowing for accumulation of nascent zygotic transcripts. Cell cycle elongation is sufficient to promote large-scale ZGA in embryos that have achieved transcriptional competence. Translation of maternal TFs and histone acetyltransferases are necessary to generate transcriptionally competent embryos. *Xenopus* blastula embryos contain a gradient of cell sizes which achieves widespread ZGA onset at different times. A majority of the nascent transcriptome during ZGA displays early activation in smaller cells at the animal pole (AP) – the presumptive ectoderm - and later activation in larger cells at the vegetal pole (VP) – the presumptive endoderm. Chronological sequence of germ layer induction may be linked to temporally graded onset of widespread ZGA in the blastula. Red, ectoderm genes; blue, endoderm genes. For clarity, the schematic of embryo is shown rotated 90° counterclockwise along the AV axis from the normal orientation of the embryo. Dotted arrow indicates that N/C ratio may affect ZGA transcription in a cell cycle independent manner.

See also Figure S4.

Analysis of CHX-upregulated genes revealed enrichment in ectoderm-related development (Figure S4E and S4F). We wondered whether the ectodermal genes could be upregulated by CHX treatment from 5–7.5 hpf. Most of the ectodermal genes (68.3%) were hyperactivated and only 15% genes were downregulated in CHX-arrested embryos (Figure 4D; Figure S4A); in contrast, most of the endodermal genes (62%) were downregulated and only 16.3% genes were upregulated in CHX-arrested embryos (Figure 4E; Figure S4B). The striking difference in the impact of CHX on expression germ layer genes (Figure 4D–4F; Figure S4A–S4D) suggests that the ectoderm genes might be primed for activation and thus more susceptible to transcription via cell cycle arrest. We did not observe a correlation between CHX-induced expression and time-of-onset (Figure S4G and S4H). In summary, our data suggest that the timing of germ layer-specific gene activation may be linked to the timing of ZGA in different regions of the embryo. However, our study demonstrates only a correlative relationship between spatially patterned ZGA and germ layer-specific gene activation and future studies are required to directly probe the causal link between these two, ideally by manipulating cell size and ZGA onset gradient and measuring the regional timing of transcriptional initiation for germ layer markers.

Composite Model for ZGA.

The combination of nascent imaging and transcriptome profiling coupled to embryo arrest at different times provided tools to link and parse regulatory mechanisms controlling ZGA timing. Our data are consistent with a model in which ZGA timing is regulated by the cell cycle elongation once an embryo gains transcriptional competence and cells achieve a threshold size or DNA:cytoplasm ratio (Figure 4H; Figure S4J and S4K). In multiple species, including Drosophila and zebrafish, early rapid cell cycles block zygotic transcription, leading to abortive or short transcripts^15,18. We interpret the inhibitory effect of short cell cycles to explain why a normal Xenopus embryo does not initiate widespread zygotic transcription at 5 hpf, even though it is likely transcriptional competent, based on our CHX arrest data (from 5–7.5 hpf). Importantly, artificial or natural lengthening of cell cycle promotes zygotic transcription^7,8,17. For vertebrates, maturation and lengthening of the early cell cycle is linked to cells reaching a threshold size^38,51, and the DNA:cytoplasm ratio and histone levels may also regulate the timing of the cell cycle lengthening^{7,10,33,52,53}. This logic helps explain how cells arrested at too low of DNA:cytoplasm ratio, in the CHX-arrested embryos from 5–7.5 hpf, nonetheless initiate ZGA concomitant with a longer time spent arrested in interphase. Linking these concepts, we propose a composite model in which Xenopus embryos must first achieve transcriptional competence, a necessary step, and then wait to initiate widespread ZGA until cell cycle elongation occurs, coupled to when blastomeres achieve a threshold cell size and DNA:cytoplasm ratio.

Overall, our work demonstrates that EU-RNA-seq is a highly sensitive method to characterize ZGA and determine the dynamics of transcription, useful for dissecting regulatory mechanisms underlying genome activation. Using this strategy, we identify distinct spatiotemporal gene expression patterns from segmented embryos, suggesting multiple modes of ZGA regulation in Xenopus, and unveil a potential link between ZGA patterning and germ layer initiation in early development. The methodology is applicable to other embryonic systems, compatible with other high-throughput multi-omics technologies at the single cell level, which will catalyze new insights into genome regulation and cell fating in development.

STAR METHODS

RESOURCE AVAIABLITY

Lead Contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Matthew Good (mattgood@pennmedicine.upenn.edu).

Materials Availability

This study did not generate new unique reagents.

Data and Code Availability

The RNA-seq data generated in this study have been deposited at Gene Expression Omnibus (GEO) and are publicly available as of the date of publication. Accession numbers are listed in the key resources table.
The paper does not report original code.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Reagents
RNeasy Mini Kit	Qiagen	Cat# 74104
DNase I recombinant, RNase-free	Roche	Cat# 04716728001
SuperScript^® III First-Strand Synthesis System for RT-PCR	Invitrogen	Cat# 18080-051
DreamTaq Green PCR Master Mix (2×)	Thermo Fisher Scientific	Cat# K1081
PowerUP™ SYBR™ Green Master Mix (2×)	Thermo Fisher Scientific	Cat# A25742
Click-iT™ Nascent RNA Capture Kit	Thermo Fisher Scientific	Cat# C10365
RNaseOUT™ Recombinant Ribonuclease Inhibitor	Thermo Fisher Scientific	Cat# 10777019
Universal RNA-seq with NuQuant^®	NuGEN	Cat# 0364
Agilent High Sensitivity DNA Kit	Agilent	Cat# 5067-4626
SPRIselect Reagent	Beckman Coulter	Cat# B23317
NEBNext® Library Quant Kit for Illumina®	NEB	Cat# E7630
NSQ 500/550 Hi Output KT v2.5 (75 CYS)	Illumina	Cat# 20024906
Biological Samples
Xenopus laevis embryos	This paper	N/A
Chemicals, Peptides, and Recombinant Proteins
5-Ethynyl Uridine (5-EU)	Thermo Fisher Scientific	Cat# E10345
Cycloheximide	Sigma	Cat# C1988
Tetramethylrhodamine (TAMRA)-azide	Abcam	Cat# ab146486
Disulfide Biotin Azide	Click Chemistry Tools	Cat# 1168-5
THPTA	Click Chemistry Tools	Cat# 1010-100
CuSO₄	Sigma	Cat# 61230
Ascorbic acid	Sigma	Cat# A7506
TO-PRO-3	Thermo Fisher Scientific	Cat# T3605
Paraformaldehyde	EMS	Cat# 15710-S
Ficoll® 400	Sigma	Cat# F4375
Hydrogen peroxide	Sigma	Cat# H1009
Formamide	Thermo Fisher Scientific	Cat# AC181090010
Benzyl alcohol	Sigma	Cat# 305197
Benzyl benzoate	ACROS Organics	Cat# 105860010
RO-3306	Sigma	Cat# SML0569
JNJ-7706621	Selleck Chemicals	Cat# S1249
AZD5438	Selleck Chemicals	Cat# S2621
BMS-265246	Selleck Chemicals	Cat# S2014
SKPin C1	Selleck Chemicals	Cat# S8652
Experimental Models: Organisms/Strains
Xenopus laevis embryos	This paper	N/A
Oligonucleotides/Primers
has1.S Forward: 5’-GTGGCATTCCAGCCTATTGT-3’	This paper	https://www.idtdna.com/
has1.S Reverse: 5’-TCAGGAATCTCCATTGTTTCTGC-3’	This paper	https://www.idtdna.com/
foxi1.L Forward: 5’-TGAAGATGATCCAGGCAAGGG-3’	This paper	https://www.idtdna.com/
foxi1.L Reverse: 5’-TAGGGCTCTCACTTAGCGGG-3’	This paper	https://www.idtdna.com/
grhl3.S Forward: 5’-CAGACTTAGCCAAGGCACCA-3’	This paper	https://www.idtdna.com/
grhl3.S Reverse: 5’-GGTCTGTAGCTGTTAATTCTGTCAA-3’	This paper	https://www.idtdna.com/
rgcc.L Forward: 5’-GTGGATACCCCTCATAAAGCAAG-3’	This paper	https://www.idtdna.com/
rgcc.L Reverse: 5’-TCGGTGTCACAGCATATCACT-3’	This paper	https://www.idtdna.com/
crx.L (Figure S2) Forward: 5’-AGTTGAGCACAAAGTACCATCCT-3’	This paper	https://www.idtdna.com/
crx.L (Figure S2) Reverse: 5’-TCAGTGACCAAGTATCAAGGGAC-3’	This paper	https://www.idtdna.com/
ventx1.2.L Forward: 5’-AAGCCTTCCTCAGCAGTGTT-3’	This paper	https://www.idtdna.com/
ventx1.2.L Reverse: 5’-GGGGGTGAATGCTGTTCTCA-3’	This paper	https://www.idtdna.com/
odc.S Forward: 5’-TTCATTCAGGCAGTCGTCGC-3’	This paper	https://www.idtdna.com/
odc.S Reverse: 5’-GCGCTGTTCTGCTGTTTGTA-3’	This paper	https://www.idtdna.com/
crx.L (Figure S3) Forward: 5’-CAGTTGAGCACAAAGTACCATCC-3’	This paper	https://www.idtdna.com/
crx.L (Figure S3) Reverse: 5’-CAGTGACCAAGTATCAAGGGACT-3’	This paper	https://www.idtdna.com/
dlc.L Forward: 5’-CGGACATGCGAATGGTCTCA-3’	This paper	https://www.idtdna.com/
dlc.L Reverse: 5’-GCTGGATACACCAGCGGCA-3’	This paper	https://www.idtdna.com/
pcdh18.L Forward: 5’-TGATTTGGGCAGAGATTCGC-3’	This paper	https://www.idtdna.com/
pcdh18.L Reverse: 5’-GTGCAGAGCCTCATAGCTGAA-3’	This paper	https://www.idtdna.com/
Deposited Data
Superseries of all RNA-seq datasets from this study	This paper	GEO#: GSE201874 https://www.ncbi.nlm.nih.gov/geo/
Total and nascent transcriptome of Xenopus laevis embryos at 5–9 hpf (stage 7–9)	This paper	GEO#: GSE201835 https://www.ncbi.nlm.nih.gov/geo/
Nascent transcriptome of the animal pole (AP) and the vegetal pole (VP) from Xenopus laevis embryos at 5–9 hpf (stage 7–9)	This paper	GEO#: GSE201833 https://www.ncbi.nlm.nih.gov/geo/
Nascent transcriptome of cycloheximide (CHX)-arrested Xenopus laevis embryos at 5–7.5 hpf	This paper	GEO#: GSE201834 https://www.ncbi.nlm.nih.gov/geo/
Software and Algorithms
salmon (v0.12.0)	Patro et al.⁵⁴	https://github.com/COMBINE-lab/salmon
DESeq2 (bioconductor v3.8)	Love et al.⁵⁵	https://bioconductor.org/packages/release/bioc/html/DESeq2.html
STAR (v 2.7.0)	Dobin et al.⁵⁶	https://github.com/alexdobin/STAR
IGV (v2.8.0)	Robinson et al.⁵⁷	https://software.broadinstitute.org/software/igv
clusterProfiler (v4.2.0)	Wu et al.⁵⁸	https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html
HOMER (v4.11)	Duttke et al.⁵⁹	http://homer.ucsd.edu/homer
Cutadapt (v3.7)	Martin⁶⁰	https://cutadapt.readthedocs.io
Bowtie 2 (v2.3.4.1)	Langmead et al.⁶¹	http://bowtie-bio.sourceforge.net/bowtie2
Samtools (v1.1)	Li et al.⁶²	https://www.htslib.org
MACS2 (v2.2.7.1)	Zhang et al.⁶³	https://github.com/macs3-project/MACS
deepTools (v3.5.1)	Ramírez et al.⁶⁴	https://deeptools.readthedocs.io
Fiji	NIH	https://imagej.net/software/fiji
GraphPad Prism 9	GraphPad	https://www.graphpad.com
Excel 2021	Microsoft	https://www.microsoft.com/EN-US/microsoft-365
RStudio	RStudio Inc	https://www.rstudio.com
Adobe Illustrator CC 2021	Adobe	https://www.adobe.com/products/illustrator.html

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EXPERIMENTAL MODEL AND SUBJECT DETAILS

Experiments in this study were performed using the African clawed frog Xenopus laevis according to the Animal Use Protocol approved by the Institutional Animal Care and Use Committee (IACUC) at the University of Pennsylvania. Mature Xenopus laevis females and males were purchased from Nasco, and they were maintained at 20°C in tanks of a recirculating aquatic system. The females were used for procuring eggs and males were used for preparing sperms, the procedures of which have been described previously¹⁴. Briefly, to induce ovulation, 100 U of pregnant mare serum gonadotropin (PMSG) and 500 U of human chorionic gonadotropin (HCG) were sequentially injected into the dorsal lymph sac of female adult frogs at 3–7 days and 14–15 h, respectively, before experiment. Before egg collection, the females were temporarily kept at 16 °C in 1× Marc’s Modified Ringer’s (1× MMR was diluted from the stock 20× MMR that includes 100 mM HEPES pH 7.8, 2 mM EDTA, 2 M NaCl, 40 mM KCl, 20 mM MgCl₂, and 40 mM CaCl₂). Eggs were obtained by gently squeezing the female frogs and collected in glass dishes. After egg procurement, the females were quarantined in high marine salt for at least one week before returning to the recirculating aquatic system. The ovulated females were not used until they rested for at least 3 months. To prepare sperms, adult males were euthanized with 0.2% benzocaine for at least 20 min before dissection for isolating the testes. The isolated testes were kept in L-15 medium on ice and used within one week. Sperm slurry was prepared by crushing 1/2 of a testis using a plastic pestle in 1 ml of deionized water inside a 1.5-ml microfuge tube.

METHOD DETAILS

In vitro fertilization (IVF)

All IVF in this study were performed at room temperature (22 °C ± 0.5 °C). The procedures for IVF have been described previously¹⁴. Briefly, 1 ml of sperm slurry was evenly added onto a monolayer of eggs inside a glass dish collected as described above. The sperms and eggs were mixed by gently sliding a plastic pestle on the surface of the glass dish. Five minutes after adding sperms, the glass dish was flooded with ~20 ml of 0.1× MMR, with all eggs submerged. At ~30 minutes post-fertilization (mpf), the fertilized eggs were incubated with 20 ml of 2% L-cysteine in 0.1× MMR for 2–5 min and the jelly coats were removed by washing with 0.1× MMR for multiple times. The embryos were sorted and kept in 0.1× MMR for further use.

Microinjection, cycloheximide treatment and collection of embryos

The procedures for microinjection have been described previously^13,14. Briefly, embryos at 1-cell stage (~35–40 mpf) were transferred into a microinjection chamber containing 3% Ficoll^® in 0.5 × MMR. Embryos were microinjected with 10 nl of 50 mM 5-ethynyl uridine (EU) using a PLI-100 picoliter microinjector (Medical Systems Corp., Greenvale, NY). The final concentration of EU inside embryos is ~ 0.5 mM, as detailed previously¹³. After microinjection, the embryos were transferred into a glass dish containing 3% Ficoll^® in 0.5 × MMR for 1–2 h before being transferred in 0.1× MMR to continue embryo development.

To prepare embryos for characterizing the nascent transcriptome in whole embryos (Figure 1; Figure S1), embryos from two clutches (i.e., two different frogs) were microinjected with EU as described above. EU-microinjected embryos developed to 5, 6, 7, 8 and 9 hours post-fertilization (hpf), respectively, were collected in microcentrifuge tubes (N = 20 embryos each), followed by complete removal of residual medium before snap-freezing in liquid nitrogen. The biological replicates of samples were stored at −80 °C before use. To distinguish maternal-zygotic (MZ) genes and zygotic-only (Z) genes during ZGA (Figure 1G–1K; Figure S1H–S1L) based on the presence of their transcripts in eggs (see below), eggs from two clutches were collected and the egg transcriptome was directly compared to the nascent transcriptome from embryos at 5–9 hpf. The nascent transcriptome at 5–9 hpf from both experiments (a total of four replicates) were used for analysis (see below).

To prepare embryos to spatially characterize the nascent transcriptome in the animal pole (AP) and vegetal pole (VP) regions (Figure 2; Figure S2), embryos from a total of four clutches (i.e., four frogs) were microinjected with EU as described above. Embryos were segmented via dissection using a hair knife to collect the animal poles (AP, ~ top 1/3 region of embryo) and the vegetal poles (VP, ~ bottom 1/3 region of embryo) For one of the four clutches, EU-microinjected embryos developed to 6, 7, 8 and 9 hpf, respectively, then dissected and collected in in microcentrifuge tubes (N = 30 segments each), followed by complete removal of residual medium before snap-freezing in liquid nitrogen. Two technical replicates of samples were stored at −80 °C before use. For the remainder of the three clutches, developing EU-microinjected embryos were collected at 5, 6, 7, 8 and 9 hpf and dissected in the same manner as above; AP and VP regions were collected (N = 50 segments each). A total of five replicates (four biological replicates with one technical replicate) were used for the spatial nascent transcriptome analysis.

To prepare embryos to characterize the effect of cell cycle lengthening on nascent zygotic transcription (Figure 3; Figure S3), embryos from one clutch (e.g., one frog) were microinjected with EU as described above. Two replicates of EU-microinjected embryos developed to 3, 4 and 5 hpf, respectively, were incubated in 0.1× MMR (control) or 0.1× MMR containing 0.2 mg/ml of cycloheximide (CHX) to block embryonic divisions and maintain the cells in the interphase. Control embryos and CHX-treated embryos developed to 7.5 hpf were collected as described above and stored at −80 °C before use. For confocal imaging of nascent zygotic transcription in single cells, control embryos and CHX-treated embryos at 7.5 hpf were fixed in 4% paraformaldehyde / 1× MEM (100 mM MOPS pH 7.4, 2 mM EGTA, and 1 mM MgSO₄) solution in 2-ml scintillation vials by rotating for 2 hours at room temperature. Embryos were completely dehydrated with methanol before being stored at −20 °C. To test whether CHX regulated nascent transcription is RNA polymerase II dependent, 0.1 ng of α-amanitin was co-microinjected with 5-EU into embryos at 1-cell stage and the embryos were treated with CHX and fixed as described above.

Cell cycle inhibitor incubation

To assess the effect of specific cell cycle inhibitors on arresting blastula cell cycles, normal embryos were incubated with 100 μM of Cdk inhibitors, including RO-3306 (Sigma, Cat# SML0569), JNJ-7706621 (Selleck Chemicals, Cat# S1249), AZD5438 (Selleck Chemicals, Cat# S2621), BMS-265246 (Selleck Chemicals, Cat# S2014), and SKPin C1 (Selleck Chemicals, Cat# S8652), respectively, from 5 hpf to 7.5 hpf. Untreated and DMSO-treated embryos were used as negative controls, and CHX (0.2 mg/ml) treated embryos were used as positive controls. Live embryos at 7.5 hpf were imaged under a stereomicroscope using Leica Application Suite X (LAS X) (Leica Microsystems, Germany).

RNA isolation, biotinylation and purification

Total RNAs were isolated using the RNeasy Mini Kit (Qiagen), following the instructions provided by the manufacturer. Briefly, eggs or EU-microinjected whole embryos or segmented AP and VP regions were added with 700 μl of Buffer RLT and homogenized by gentle pipetting the samples up and down for multiple times until all embryos were completely dissolved. The homogenates were added with 700 μl of 70% ethanol and the mixtures were transferred into the columns used for binding RNA. The columns were span at 13,000 rpm for 1 min. The columns were washed with 700 μl of Buffer RW1 and incubated with 80 μl of DNase I for 15 min at room temperature. After DNase I incubation, the columns were added with 600 μl of Buffer RW1 and centrifuged at 13,000 rpm for 1 min. The columns were washed twice with Buffer RPE and completely dried by centrifugation at 13,000 rpm for 2 min. The total RNAs were finally eluted in 20 μl of RNase-free water.

To biotinylate RNA, 2.5–10 μg of total RNAs were incubated with a 20 μl reaction that contains 2 mM disulfide biotin azide, 50 mM Hepes (pH 7.5), 1.25 mM CuSO₄/THPTA mix and 10 mM ascorbic acid for 1 h at room temperature. The reaction was stopped by adding 1 μl of 50 mM EDTA. To precipitate the RNA, the reaction was added with 1μl of glycogen, 1 volume of 5 M ammonium acetate and 700 μl of chilled ethanol, incubated at −80 °C for overnight, and centrifuged at 13, 000×g for 20 min at 4 °C. The supernatant was removed, and the pellet was washed twice with 700 μl of chilled 75% ethanol by centrifugation at 13, 000×g for 5 min at 4 °C. The pellet was air dried and resuspended in 10 μl RNase-free H₂O.

The nascent EU-RNA was purified using streptavidin beads following the instructions provided by the Click-iT™ Nascent RNA Capture Kit (Thermo Fisher Scientific, Cat# C10365), with minor modifications⁵⁴. The 10 μl biotinylated RNA from above was added with a 15-μl reaction mix that contains 12.5 μl of Click-iT^® RNA binding buffer, 0.2 μl of RNaseOUT™ Recombinant Ribonuclease Inhibitor and 2.3 μl of RNase-free water. The reaction was incubated at 69 °C for 5 min and added with 5 μl of Dynabeads^® MyOne™ Streptavidin T1 that were pre-washed with Click-iT^® reaction wash buffer 2 for three times. The reaction was incubated for 30 min at room temperature. The beads were concentrated using a magnetic separator (Permagen) and sequentially washed with 50 μl of Click-iT^® reaction wash buffer 1 for five times and wash buffer 2 for five times. The beads were resuspended in 5 μl of for Click-iT^® reaction wash buffer 2 and used directly for first-strand cDNA synthesis and subsequent library prep (see below).

EU-RNA-seq and analysis

To perform RNA-seq, cDNA libraries were prepared for total RNA isolated from embryos (‘All’), purified nascent EU-RNA (‘Bead’) and the flowthrough that contains maternal, non-nascent RNA (‘Flowthrough’). cDNA libraries were prepared using the Universal RNA-seq with NuQuant^® kit (NuGEN, Cat# 0364), following the manual provided by the manufacturer. Ribosomal RNAs were depleted using the custom designed AnyDeplete Probe Mix for Xenopus laevis provided by the kit. The cDNA libraries were further analyzed following the instructions specified in respective kits below. The quality of cDNA libraries was analyzed using the Agilent High Sensitivity DNA Kit (Agilent, Cat# 5067–4626) in the Agilent 2100 Bioanalyzer System (Agilent Technologies, CA). The cDNA libraries were subjected to size selection using SPRIselect beads (Beckman Coulter, Cat# B23317). The concentrations of cDNA libraries were quantified using the NEBNext® Library Quant Kit (NEB, Cat# E7630). The individual cDNA libraries were pooled at equal molar ratios and the pooled cDNA libraries were sequenced using the NSQ 500/550 Hi Output KT v2.5 (75 CYS) (Illumina, Cat# 20024906) in the NextSeq 500 sequencer (Illumina, CA). To quantify transcripts, raw sequence data (fastq files) were aligned to Xenopus laevis genome build 9.2 using salmon v0.12.0⁵⁵. Data were normalized for sequencing depth using DESeq2 (bioconductor v3.8)⁵⁶. To map the transcripts to the genome, the STAR (v 2.7.0) aligner⁵⁷ was used and the peaks were visualized in the Integrative Genomics Viewer (IGV, v2.8.0)⁵⁸.

To circumvent the issue of potential nonspecific binding of maternal transcripts to beads, we decided to use the net increase of reads at each blastula timepoint, subtracting as background the 5 hpf reads. When defining the list of nascent transcribing genes from 5–9 hpf, which we termed the nascent list, the nascent transcriptome data from whole embryos was filtered using the following criteria for each replicate: (1) the gene is continuously transcribed from 5 hpf to 9 hpf, and (2) at least with an increase of 50 reads and 1.5-fold induction from 5 hpf to 9 hpf (using average reads of 8–9 hpf vs. average reads of 5–6 hpf). The final list was determined for the genes meeting these criteria in all replicates, which included 2577 genes (used in Figure 1D–1F; Figure S1D; Figure 3C–3G). To categorize the nascent list into subgroups of MZ vs Z genes (Figure 1G–1K; Figure S1H–S1L), the presence of transcripts detected in the egg was used to determine their identities: Z genes were those with ≤ 5 reads in the eggs and MZ genes were those with > 5 reads. The maternal genes in the rest of all genes were those with the presence of transcripts > 100 reads in the egg but with no transcription in the Bead. To select the genes most highly transcribed at the MBT from previous studies, the transcriptome data from Session et al.¹⁹ (Figure S1E) and Yanai et al.²⁰ (Figure S1F), respectively, were filtered using the following criteria: at least with an increase of 20 reads and 1.5-fold induction from Stage 8 (st08) to Stage 10 (st10).

To select genes for categorizing their spatial patterns of activation (Figure 2; Figure S2), the AP-VP nascent transcriptome data was filtered using the following criteria for each replicate: (1) the gene is continuously transcribed from 5–9 hpf at either the AP or the VP, and (2) at least with an increase of 10 reads from 5 or 6 hpf to 9 hpf (using average reads of 8–9 hpf vs. average reads of 5–6 hpf). The final list was determined for the genes meeting these criteria in at least three out of five replicates, which included 882 genes. To determine the activation patterns for each gene, the reads at both AP and VP from 5 hpf to 9 hpf were first normalized to their mean reads and then categorized using the following criteria for each pattern, respectively: (1) AP regional: both the total reads and the reads at 8–9 hpf were at least 10-fold higher in the AP than the VP; (2) AP early, VP delay: both the total reads and the reads at 8–9 hpf were 1.5–10-fold higher in the AP than the VP; (3) VP regional: both the total reads and the reads at 8–9 hpf were at least 10-fold higher in the VP than the AP; (4) VP early, AP delay: both the total reads and the reads at 8–9 hpf were 1.5–10-fold higher in the VP than the AP; (5) Similar: both the total reads and the reads at 8–9 hpf were within 1.5-fold difference between the VP and the AP. The resulting categorized lists of genes were further manually inspected to remove a small portion of genes with inconsistent profiles between replicates (at least three out of five replicates) or to correct their categorization based on expression profiles. To determine the activation onset time for each gene (Figure 2B; Figure S2B), we adapted the method described in Jukam et al.²⁵ by fitting the mean-normalized reads with a smooth spline function and used the time reaching 20% of the maximum reads (the maximum of AP and VP combined) as the onset time. The fittings were manually inspected and corrected for some genes by fitting with an exponential or sigmoidal function optimal for them. To determine the average activation onset time for each pattern (Figure S2C), the same fitting of a smooth spline function was performed except for using the average of the mean-normalized reads for the AP and VP in each pattern.

Lists of ectoderm and endoderm genes (Figure 4) were defined from previous studies. For ectoderm genes, we used the data from Blitz et al.²⁶ by selecting the animally enriched genes in gastrula of Xenopus tropcalis and matching their names in Xenopus laevis, which generated a list of 111 genes. For endoderm genes, we used the data from Sinner et al.²⁷ and matched their names in Affymetrix microarray with the ones in Xenopus laevis genome build 9.2 at Xenbase, which generated a list of 172 genes. Because many germ-layer specific genes are not expressed or very lowly expressed in blastula embryos, to characterize the effect of CHX on germ layer expression by 7.5 hpf (Figure 4D–4F; Figure S4A–S4D), the ectoderm and endoderm genes were filtered for those genes with an increase of at least 10 reads from 5 hpf to 7.5 hpf in the control embryos.

Functional enrichment analysis

Gene ontology (GO) analysis for genes with functional enrichment in biological processes was performed using clusterProfiler (v4.2.0)⁵⁹. The top 10 of the most significantly enriched GO terms were selected and the −log10(p.adjust) was used as the proxy of enrichment. The enrichment of motifs at the promoter regions of CHX down-regulated genes or up-regulated endoderm genes were performed using HOMER (v4.11)⁶⁰. The names of transcription factors that bind the enriched motifs were manually inspected and confirmed their presence in Xenopus laevis.

ATAC-seq and ChIP-seq analysis

To validate the chromatin accessibility of nascent transcripts uniquely detected by EU-RNA-seq (Figure S2M and S2N), the public ATAC-seq data (GEO accession number: GSE138905) from the animal caps of Xenopus laevis embryos at stages 10 and 12 were analyzed⁶¹. Three replicates of each stage were included in the analysis. The Illumina Nextera adapter sequences were trimmed using Cutadapt (v3.7)⁶² before the ATAC-seq sequences were aligned to the Xenopus laevis genome build 9.2 using Bowtie 2 (v2.3.4.1)⁶³ and BAM files were generated using Samtools (v1.1)⁶⁴. The peak calling was made using MACS2 (v2.2.7.1)⁶⁵, and the heatmap and profile plots for the ATAC-seq peaks were generated using deepTools (v3.5.1)⁶⁶.

To compare the RNA Pol II binding and H3K4me3 mark between ectoderm and endoderm genes (Figure 4G and Figure S4I), the public ChIP-seq data (GEO accession number: GSE76059) from stage 10.5 Xenopus laevis embryos were analyzed¹⁹. The ChIP-seq sequences were similarly processed to ATAC-seq sequences as described above, except for without adaptor removal.

RT-PCR and real-time PCR

Total RNAs were isolated as described above. cDNA was generated by using the SuperScript^® III First-Strand Synthesis System (Invitrogen, Cat# 18080–051), following the instructions provided by the product. Briefly, for each sample 2.5 μg of total RNA was mixed with 1 μl of 50 ng/μl random hexamers and 1 μl of 10 mM dNTP mix to make a reaction of 10 μl, which was incubated at 65 °C for 5 min and placed on ice for at least 1 min. Each reaction was added with 10 μl of cDNA Synthesis Mix, which was composed of 2 μl of 10× RT buffer, 4 μl of 25 mM MgCl₂, 2 μl of 0.1 M DTT, 1 μl of RNaseOUT™ (40 U/μl) and 1 μl of SuperScript^® III RT (200 U/μl), and it was incubated at 25 °C for 10 min followed by at 50 °C for 50 min. The reaction was terminated by incubation at 85 °C for 5 min. To eliminate RNA contamination, 1 μl of RNase H was added to each reaction by incubation at 37 °C for 20 min.

RT-PCR was performed by mixing 100 ng of cDNA with 10 μl of the 2× DreamTaq™ Green PCR Master Mix (Thermo Fisher Scientific, Cat# K1081) and 0.25 μM of each gene-specific Forward and Reverse primers (see Key Resources Table) to make a total reaction of 20 μl, followed by performing PCR in a Bio-Rad C1000 Touch™ thermal cycler: 95 °C for 3 min; 25–35 cycles of 95 °C for 30 s, 51–53 °C for 30 s (note that the choose of annealing temperature is primer dependent) and 72 °C for 30 s; and 72 °C for 10 min. The PCR products were subjected to electrophoresis in 2% agarose gel containing 0.5 μg/ml of ethidium bromide and the gene-specific bands were visualized under ultraviolet light in a Bio-Rad Gel Doc™ EZ Imager.

Real-time PCR was performed by mixing 100 ng of cDNA with 10 μl of the 2× PowerUP™ SYBR™ Green Master Mix (Thermo Fisher Scientific, Cat# A25742) and 0.5 μM of each gene-specific Forward and Reverse primers (see Key Resources Table) to make a total reaction of 20 μl (in triplicates), followed by performing PCR in a QuantStudio™ 3 Real-Time PCR System (Applied Biosystems) with the standard cycling mode: 50 °C for 2 min; 95 °C for 2 min; 40 cycles of 95 °C for 15 s, 56 °C for 15 s and 72 °C for 1 min. The melt curve stage was performed by the following conditions: 95 °C for 15 s (1.6 °C /s), 60 °C for 1 min (1.6 °C /s) and 95 °C for 15 s (0.15 °C /s). The fold difference in expression level between AP and VP was calculated by 2^{−ΔCt(AP-VP)}, where ΔCt(AP-VP) was the average Ct(AP) of triplicates – average Ct(VP) of triplicates.

Confocal imaging nascent transcripts in wholemount embryos ad image analysis

Confocal imaging of nascent transcripts in wholemount embryos have been described previously^13,14. Briefly, the fixed EU-microinjected control and CHX-treated embryos were sequentially rehydrated with 75%, 50% and 25% methanol in 0.5× SSC (75 mM NaCl and 7.5 mM sodium citrate) for 10 min each, followed by washing with 0.5× SSC for three times. Embryos were bleached in the solution of 5% formamide/2% H₂O₂/0.5× SSC for 6 h under light. Embryos were briefly rinsed with 0.5× SSC for three times, followed by washing with 1× TBST (containing 0.1% vol/vol Triton X-100) for 30 min each of six times and with 1× TBS for 10 min each of three times. Embryos were incubated with 25 μM TAMRA-azide, 100 mM Tris-HCl pH 8.5, 1 mM CuSO₄, and 100 mM ascorbic acid for 12 h at room temperature. Embryos were extensively washed with 1× TBST at room temperature for 1 day by changing buffer every 2 h. Embryos were incubated with TO-PRO-3 (1:500 dilution) overnight at 4°C, followed by washing with 1× TBST for 1 day at room temperature by changing buffer every 2 h. Embryos were completely dehydrated in anhydrous methanol by changing it for several times. Embryos were cleared in a mixture of 1 part of benzyl alcohol and 2 parts of benzyl benzoate (BABB) for 24 h before confocal imaging. Confocal imaging was performed with the ZEN software on a Zeiss LSM710 confocal microscope. EU-RNA and TO-PRO-3 were imaged with a frame size of 1,024 pixels × 1,024 pixels using lasers 561 nm (0.15% power) and 633 nm (10% power), respectively, without saturating signals. Z-stacks were collected at an interval of 2 μm using the Plan-Apochromat 25× / 0.8 immersion oil objective. Images were processed in Fiji (NIH) and presented as Z-projections with maximum intensity for several selected slices.

To assess the effect of CHX on DNA synthesis (Figure S3A), DNA-integrated TO-PRO-3 signal in the nucleus was quantified using confocal image stacks collected using the 25× objective. A total number of 50–70 cells from five embryos were analyzed for each group. The boundaries of nuclei and cell of individual blastomeres were manually demarcated in Fiji (NIH) using the slice with the highest signal of the TO-PRO-3 channel by specific DNA signal and non-specific background signal, respectively. TO-PRO-3 signal in the nucleus as well as in the cell was measured and the net nuclear TO-PRO-3 signal was calculated by subtracting the cytoplasmic background signal from the nucleus signal. The nuclear TO-PRO-3 amount was calculated by multiplying the net nuclear TO-PRO-3 signal with the nucleus volume assuming a spherical shape of the nucleus.

To quantify the nascent EU-RNA intensity (Figure 3K) and cell size (Figure S3H) of single blastomeres, confocal image stacks collected using the 25× objective were used. For proper comparison between groups, only blastomeres in the animal pole regions within 100 μm depth of the image stacks were analyzed. For each group, a total number of 80–160 cells from at least three embryos were analyzed. The nucleus and cell boundaries of individual blastomeres were manually demarcated in Fiji (NIH) using the slice with the highest signal of the TO-PRO-3 channel by specific DNA signal and non-specific background signal, respectively. EU-RNA signal in the nucleus as well as in the cell was measured using the EU-RNA channel and the net nuclear EU-RNA signal was calculated by subtracting the cytoplasmic background signal from the nucleus signal. The nuclear EU-RNA amount was calculated by multiplying the net nuclear EU-RNA signal with the nucleus volume assuming a spherical shape of the nucleus. Cell size, represented as cell diameter, was also calculated by assuming a spherical shape of blastomeres.

QUANTIFICATION AND STATISTICAL ANALYSIS

All statistical parameters, including sample numbers, mean and standard deviation or error, were included in Figures and Figure legends. In Figure 2B, the statistical significance was determined by student t-test to compare the activation onset time between AP and VP. In Figure 3K and S3A, the statistical significance was determined by one-way ANOVA (Fisher’s LSD test) to compare the nuclear EU-RNA level and cell size between control and CHX treatment at various developmental stages. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001; ns, not significant. In other plots where p-values or adjusted p-values (p.adjust) were used, e.g. for functional enrichment and motif enrichment, the p-values were calculated and provided by individual packages in R.

Supplementary Material

NIHMS1829064-supplement-1.pdf^{(19.4MB, pdf)}

Table S1. List of genes with nascent transcription from 5–9 hpf in Xenopus laevis embryos. Related to Figure 1. DESeq2 normalized reads and calculated net nascent reads are shown. Net nascent reads are calculated by subtracting DESeq2 normalized reads at 5 hpf from those at 6/7/8/9 hpf and negative values are replaced with 0 (meaning no increase). Gene category is determined by zygotic (Z, reads in egg ≤ 5) and maternal-zygotic (MZ, reads in egg > 5).

NIHMS1829064-supplement-3.xlsx^{(643.8KB, xlsx)}

Table S2. List of genes with nascent transcription uniquely detected by EU-RNA-seq from 5–9 hpf in Xenopus laevis embryos. Related to Figure 1. DESeq2 normalized reads and calculated net nascent reads are shown. Net nascent reads are calculated by subtracting DESeq2 normalized reads at 5 hpf from those at 6/7/8/9 hpf and negative values are replaced with 0 (meaning no increase).

NIHMS1829064-supplement-4.xlsx^{(60.8KB, xlsx)}

Table S3. Categorization of ZGA Patterns from 5–9 hpf in the AP and the VP of Xenopus laevis embryos. Related to Figure 2. DESeq2 normalized reads and calculated net nascent reads are shown. Net nascent reads are calculated by subtracting DESeq2 normalized reads at 5 hpf from those at 6/7/8/9 hpf and negative values are replaced with 0 (meaning no increase). Time-of-onset (Tonset) is set to 9.1 when beyond determination between 5 and 9 hpf (meaning no activation during this period).

NIHMS1829064-supplement-5.xlsx^{(109.3KB, xlsx)}

Highlights.

Whole-embryo and regional EU-RNA-seq determines timing and spatial patterns of ZGA
Maternal-zygotic genes dominate transcriptional output during ZGA
Manipulation of translation and cell division reconciles regulatory mechanisms of ZGA
Timing of germ layer-specific expression appears sequential in the blastula

ACKNOWLEDGEMENTS

We would like to thank the members of the Good, Klein, Mullins, Zaret, Berger, Bonasio, Lampson, Black, Greenberg and Grishchuk labs at the University of Pennsylvania for helpful discussion and providing feedback. We particularly thank Dr. Peter Klein, Lily Einstein, Wenchao Qian, Boao Xia, Jorge Dabdoub, Haidar Ahmed and Rachel Wells for providing help on frogs; Dr. Katherine Palozola (Ken Zaret Lab) for providing and discussing protocols for EU-RNA-seq and Dr. Jamie Kwasnieski (David Bartel Lab) for providing an alternative protocol for biotinylating EU-RNA; Dr. Dario Nicetto (Ken Zaret Lab) and Dr. Lihong Sheng (Roberto Bonasio Lab) for providing technical help on RNA-seq; and Dr. John Tobias (Penn Genomic Analysis Core) for providing help on RNA-seq data analyses. We also thank the Epigenetics Institute for training and providing instrument, the Cell and Developmental Biology Microscopy Core for imaging support and National Xenopus Resource (NXR) for training and guidance on frogs. This work was supported in part by Burroughs Wellcome Fund, Charles E. Kaufman Foundation, the March of Dimes and the National Institute of General Medical Sciences (R35GM128748) (M.C.G.), and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R03HD105802) (H.C.).

Footnotes

DECLARATION OF INTERESTS

The authors declare no competing interests.

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REFERENCES

1.Vastenhouw NL, Cao WX, and Lipshitz HD (2019). The maternal-to-zygotic transition revisited. Development 146. 10.1242/dev.161471. [DOI] [PubMed] [Google Scholar]
2.Jukam D, Shariati SAM, and Skotheim JM (2017). Zygotic Genome Activation in Vertebrates. Developmental cell 42, 316–332. 10.1016/j.devcel.2017.07.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Lee MT, Bonneau AR, and Giraldez AJ (2014). Zygotic genome activation during the maternal-to-zygotic transition. Annual review of cell and developmental biology 30, 581–613. 10.1146/annurev-cellbio-100913-013027. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Schulz KN, and Harrison MM (2019). Mechanisms regulating zygotic genome activation. Nat Rev Genet 20, 221–234. 10.1038/s41576-018-0087-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Palfy M, Joseph SR, and Vastenhouw NL (2017). The timing of zygotic genome activation. Curr Opin Genet Dev 43, 53–60. 10.1016/j.gde.2016.12.001. [DOI] [PubMed] [Google Scholar]
6.Lu X, Li JM, Elemento O, Tavazoie S, and Wieschaus EF (2009). Coupling of zygotic transcription to mitotic control at the Drosophila mid-blastula transition. Development 136, 2101–2110. 10.1242/dev.034421. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Syed S, Wilky H, Raimundo J, Lim B, and Amodeo AA (2021). The nuclear to cytoplasmic ratio directly regulates zygotic transcription in Drosophila through multiple modalities. Proceedings of the National Academy of Sciences of the United States of America 118. 10.1073/pnas.2010210118. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Strong IJT, Lei X, Chen F, Yuan K, and O’Farrell PH (2020). Interphase-arrested Drosophila embryos activate zygotic gene expression and initiate mid-blastula transition events at a low nuclear-cytoplasmic ratio. PLoS biology 18, e3000891. 10.1371/journal.pbio.3000891. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Edgar BA, Kiehle CP, and Schubiger G (1986). Cell cycle control by the nucleocytoplasmic ratio in early Drosophila development. Cell 44, 365–372. [DOI] [PubMed] [Google Scholar]
10.Collart C, Allen GE, Bradshaw CR, Smith JC, and Zegerman P (2013). Titration of four replication factors is essential for the Xenopus laevis midblastula transition. Science 341, 893–896. 10.1126/science.1241530. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Argelaguet R, Clark SJ, Mohammed H, Stapel LC, Krueger C, Kapourani CA, Imaz-Rosshandler I, Lohoff T, Xiang Y, Hanna CW, et al. (2019). Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature 576, 487–491. 10.1038/s41586-019-1825-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Hashimshony T, Feder M, Levin M, Hall BK, and Yanai I (2015). Spatiotemporal transcriptomics reveals the evolutionary history of the endoderm germ layer. Nature 519, 219–+. 10.1038/nature13996. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Chen H, and Good MC (2020). Imaging nascent transcription in wholemount vertebrate embryos to characterize zygotic genome activation. Methods in enzymology 638, 139–165. 10.1016/bs.mie.2020.03.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Chen H, Einstein LC, Little SC, and Good MC (2019). Spatiotemporal Patterning of Zygotic Genome Activation in a Model Vertebrate Embryo. Developmental cell 49, 852–866 e857. 10.1016/j.devcel.2019.05.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Heyn P, Kircher M, Dahl A, Kelso J, Tomancak P, Kalinka AT, and Neugebauer KM (2014). The earliest transcribed zygotic genes are short, newly evolved, and different across species. Cell reports 6, 285–292. 10.1016/j.celrep.2013.12.030. [DOI] [PubMed] [Google Scholar]
16.Holler K, Neuschulz A, Drewe-Boss P, Mintcheva J, Spanjaard B, Arsie R, Ohler U, Landthaler M, and Junker JP (2021). Spatio-temporal mRNA tracking in the early zebrafish embryo. Nature communications 12, 3358. 10.1038/s41467-021-23834-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Chan SH, Tang Y, Miao L, Darwich-Codore H, Vejnar CE, Beaudoin JD, Musaev D, Fernandez JP, Benitez MDJ, Bazzini AA, et al. (2019). Brd4 and P300 Confer Transcriptional Competency during Zygotic Genome Activation. Developmental cell 49, 867–881 e868. 10.1016/j.devcel.2019.05.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Kwasnieski JC, Orr-Weaver TL, and Bartel DP (2019). Early genome activation in Drosophila is extensive with an initial tendency for aborted transcripts and retained introns. Genome research 29, 1188–1197. 10.1101/gr.242164.118. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Session AM, Uno Y, Kwon T, Chapman JA, Toyoda A, Takahashi S, Fukui A, Hikosaka A, Suzuki A, Kondo M, et al. (2016). Genome evolution in the allotetraploid frog Xenopus laevis. Nature 538, 336–343. 10.1038/nature19840. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Yanai I, Peshkin L, Jorgensen P, and Kirschner MW (2011). Mapping gene expression in two Xenopus species: evolutionary constraints and developmental flexibility. Developmental cell 20, 483–496. 10.1016/j.devcel.2011.03.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Lee MT, Bonneau AR, Takacs CM, Bazzini AA, DiVito KR, Fleming ES, and Giraldez AJ (2013). Nanog, Pou5f1 and SoxB1 activate zygotic gene expression during the maternal-to-zygotic transition. Nature 503, 360–364. 10.1038/nature12632. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Zernicka-Goetz M, Morris SA, and Bruce AW (2009). Making a firm decision: multifaceted regulation of cell fate in the early mouse embryo. Nat Rev Genet 10, 467–477. 10.1038/nrg2564. [DOI] [PubMed] [Google Scholar]
23.Heasman J (2006). Patterning the early Xenopus embryo. Development 133, 1205–1217. 10.1242/dev.02304. [DOI] [PubMed] [Google Scholar]
24.Langdon YG, and Mullins MC (2011). Maternal and zygotic control of zebrafish dorsoventral axial patterning. Annu Rev Genet 45, 357–377. 10.1146/annurev-genet-110410-132517. [DOI] [PubMed] [Google Scholar]
25.Jukam D, Kapoor RR, Straight AF, and Skotheim JM (2021). The DNA-to-cytoplasm ratio broadly activates zygotic gene expression in Xenopus. Current biology : CB 31, 4269–4281 e4268. 10.1016/j.cub.2021.07.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Blitz IL, Paraiso KD, Patrushev I, Chiu WTY, Cho KWY, and Gilchrist MJ (2017). A catalog of Xenopus tropicalis transcription factors and their regional expression in the early gastrula stage embryo. Developmental biology 426, 409–417. 10.1016/j.ydbio.2016.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Sinner D, Kirilenko P, Rankin S, Wei E, Howard L, Kofron M, Heasman J, Woodland HR, and Zorn AM (2006). Global analysis of the transcriptional network controlling Xenopus endoderm formation. Development 133, 1955–1966. 10.1242/dev.02358. [DOI] [PubMed] [Google Scholar]
28.Zhang J, Houston DW, King ML, Payne C, Wylie C, and Heasman J (1998). The role of maternal VegT in establishing the primary germ layers in Xenopus embryos. Cell 94, 515–524. 10.1016/s0092-8674(00)81592-5. [DOI] [PubMed] [Google Scholar]
29.Xanthos JB, Kofron M, Wylie C, and Heasman J (2001). Maternal VegT is the initiator of a molecular network specifying endoderm in Xenopus laevis. Development 128, 167–180. 10.1242/dev.128.2.167. [DOI] [PubMed] [Google Scholar]
30.Zhang J, and King ML (1996). Xenopus VegT RNA is localized to the vegetal cortex during oogenesis and encodes a novel T-box transcription factor involved in mesodermal patterning. Development 122, 4119–4129. [DOI] [PubMed] [Google Scholar]
31.Blythe SA, and Wieschaus EF (2016). Establishment and maintenance of heritable chromatin structure during early Drosophila embryogenesis. Elife 5. 10.7554/eLife.20148. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Leichsenring M, Maes J, Mossner R, Driever W, and Onichtchouk D (2013). Pou5f1 transcription factor controls zygotic gene activation in vertebrates. Science 341, 1005–1009. 10.1126/science.1242527. [DOI] [PubMed] [Google Scholar]
33.Amodeo AA, Jukam D, Straight AF, and Skotheim JM (2015). Histone titration against the genome sets the DNA-to-cytoplasm threshold for the Xenopus midblastula transition. Proceedings of the National Academy of Sciences of the United States of America 112, E1086–1095. 10.1073/pnas.1413990112. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Newport J, and Kirschner M (1982). A Major Developmental Transition in Early Xenopus Embryos II: Control of the Onset of Transcription. Cell 687–696. [DOI] [PubMed] [Google Scholar]
35.Newport J, and Kirschner M (1982). A Major Developmental Transition in Early Xenopus Embryos I: Characterization and Timing of Cellular Changes At the Midblastula Stage. Cell, 675–686. [DOI] [PubMed] [Google Scholar]
36.Dekens MP, Pelegri FJ, Maischein HM, and Nusslein-Volhard C (2003). The maternal-effect gene futile cycle is essential for pronuclear congression and mitotic spindle assembly in the zebrafish zygote. Development 130, 3907–3916. [DOI] [PubMed] [Google Scholar]
37.Prioleau MN, Huet J, Sentenac A, and Mechali M (1994). Competition between chromatin and transcription complex assembly regulates gene expression during early development. Cell 77, 439–449. [DOI] [PubMed] [Google Scholar]
38.Kane DA, and Kimmel CB (1993). The zebrafish midblastula transition. Development 119, 447–456. [DOI] [PubMed] [Google Scholar]
39.Zhang M, Skirkanich J, Lampson MA, and Klein PS (2017). Cell Cycle Remodeling and Zygotic Gene Activation at the Midblastula Transition. Adv Exp Med Biol 953, 441–487. 10.1007/978-3-319-46095-6_9. [DOI] [PubMed] [Google Scholar]
40.Shermoen AW, and O’Farrell PH (1991). Progression of the cell cycle through mitosis leads to abortion of nascent transcripts. Cell 67, 303–310. 10.1016/0092-8674(91)90182-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Rothe M, Pehl M, Taubert H, and Jackle H (1992). Loss of gene function through rapid mitotic cycles in the Drosophila embryo. Nature 359, 156–159. 10.1038/359156a0. [DOI] [PubMed] [Google Scholar]
42.Kimelman D, Kirschner M, and Scherson T (1987). The events of the midblastula transition in Xenopus are regulated by changes in the cell cycle. Cell 48, 399–407. [DOI] [PubMed] [Google Scholar]
43.Gard DL, Hafezi S, Zhang T, and Doxsey SJ (1990). Centrosome duplication continues in cycloheximide-treated Xenopus blastulae in the absence of a detectable cell cycle. The Journal of cell biology 110, 2033–2042. 10.1083/jcb.110.6.2033. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Gerhart J, Wu M, and Kirschner M (1984). Cell cycle dynamics of an M-phase-specific cytoplasmic factor in Xenopus laevis oocytes and eggs. The Journal of cell biology 98, 1247–1255. 10.1083/jcb.98.4.1247. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Miake-Lye R, Newport J, and Kirschner M (1983). Maturation-promoting factor induces nuclear envelope breakdown in cycloheximide-arrested embryos of Xenopus laevis. The Journal of cell biology 97, 81–91. 10.1083/jcb.97.1.81. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Newport JW, and Kirschner MW (1984). Regulation of the cell cycle during early Xenopus development. Cell 37, 731–742. 10.1016/0092-8674(84)90409-4. [DOI] [PubMed] [Google Scholar]
47.Levy DL, and Heald R (2010). Nuclear size is regulated by importin alpha and Ntf2 in Xenopus. Cell 143, 288–298. 10.1016/j.cell.2010.09.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Hontelez S, van Kruijsbergen I, Georgiou G, van Heeringen SJ, Bogdanovic O, Lister R, and Veenstra GJC (2015). Embryonic transcription is controlled by maternally defined chromatin state. Nature communications 6, 10148. 10.1038/ncomms10148. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Skirkanich J, Luxardi G, Yang J, Kodjabachian L, and Klein PS (2011). An essential role for transcription before the MBT in Xenopus laevis. Developmental biology 357, 478–491. 10.1016/j.ydbio.2011.06.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Yang J, Tan C, Darken RS, Wilson PA, and Klein PS (2002). Beta-catenin/Tcf-regulated transcription prior to the midblastula transition. Development 129, 5743–5752. [DOI] [PubMed] [Google Scholar]
51.Wang P, Hayden S, and Masui Y (2000). Transition of the blastomere cell cycle from cell size-independent to size-dependent control at the midblastula stage in Xenopus laevis. J Exp Zool 287, 128–144. [DOI] [PubMed] [Google Scholar]
52.Chari S, Wilky H, Govindan J, and Amodeo AA (2019). Histone concentration regulates the cell cycle and transcription in early development. Development 146. 10.1242/dev.177402. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Shindo Y, and Amodeo AA (2021). Excess histone H3 is a competitive Chk1 inhibitor that controls cell-cycle remodeling in the early Drosophila embryo. Current biology : CB 31, 2633–2642 e2636. 10.1016/j.cub.2021.03.035. [DOI] [PubMed] [Google Scholar]
54.Palozola KC, Donahue G, and Zaret KS (2021). EU-RNA-seq for in vivo labeling and high throughput sequencing of nascent transcripts. STAR Protoc 2, 100651. 10.1016/j.xpro.2021.100651. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Patro R, Duggal G, Love MI, Irizarry RA, and Kingsford C (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14, 417–419. 10.1038/nmeth.4197. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Love MI, Huber W, and Anders S (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550. 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, and Gingeras TR (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21. 10.1093/bioinformatics/bts635. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, and Mesirov JP (2011). Integrative genomics viewer. Nature biotechnology 29, 24–26. 10.1038/nbt.1754. [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, et al. (2021). clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (N Y) 2, 100141. 10.1016/j.xinn.2021.100141. [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Duttke SH, Chang MW, Heinz S, and Benner C (2019). Identification and dynamic quantification of regulatory elements using total RNA. Genome research 29, 1836–1846. 10.1101/gr.253492.119. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Esmaeili M, Blythe SA, Tobias JW, Zhang K, Yang J, and Klein PS (2020). Chromatin accessibility and histone acetylation in the regulation of competence in early development. Dev Biol 462, 20–35. 10.1016/j.ydbio.2020.02.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Martin M (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 3. 10.14806/ej.17.1.200. [DOI] [Google Scholar]
63.Langmead B, and Salzberg SL (2012). Fast gapped-read alignment with Bowtie 2. Nature methods 9, 357–359. 10.1038/nmeth.1923. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, and Genome Project Data Processing, S. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079. 10.1093/bioinformatics/btp352. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, and Liu XS (2008). Model-based analysis of ChIP-Seq (MACS). Genome Biol 9, R137. 10.1186/gb-2008-9-9-r137. [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Ramirez F, Ryan DP, Gruning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Dundar F, and Manke T (2016). deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 44, W160–165. 10.1093/nar/gkw257. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS1829064-supplement-1.pdf^{(19.4MB, pdf)}

NIHMS1829064-supplement-3.xlsx^{(643.8KB, xlsx)}

NIHMS1829064-supplement-4.xlsx^{(60.8KB, xlsx)}

NIHMS1829064-supplement-5.xlsx^{(109.3KB, xlsx)}

Data Availability Statement

The RNA-seq data generated in this study have been deposited at Gene Expression Omnibus (GEO) and are publicly available as of the date of publication. Accession numbers are listed in the key resources table.
The paper does not report original code.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Reagents
RNeasy Mini Kit	Qiagen	Cat# 74104
DNase I recombinant, RNase-free	Roche	Cat# 04716728001
SuperScript^® III First-Strand Synthesis System for RT-PCR	Invitrogen	Cat# 18080-051
DreamTaq Green PCR Master Mix (2×)	Thermo Fisher Scientific	Cat# K1081
PowerUP™ SYBR™ Green Master Mix (2×)	Thermo Fisher Scientific	Cat# A25742
Click-iT™ Nascent RNA Capture Kit	Thermo Fisher Scientific	Cat# C10365
RNaseOUT™ Recombinant Ribonuclease Inhibitor	Thermo Fisher Scientific	Cat# 10777019
Universal RNA-seq with NuQuant^®	NuGEN	Cat# 0364
Agilent High Sensitivity DNA Kit	Agilent	Cat# 5067-4626
SPRIselect Reagent	Beckman Coulter	Cat# B23317
NEBNext® Library Quant Kit for Illumina®	NEB	Cat# E7630
NSQ 500/550 Hi Output KT v2.5 (75 CYS)	Illumina	Cat# 20024906
Biological Samples
Xenopus laevis embryos	This paper	N/A
Chemicals, Peptides, and Recombinant Proteins
5-Ethynyl Uridine (5-EU)	Thermo Fisher Scientific	Cat# E10345
Cycloheximide	Sigma	Cat# C1988
Tetramethylrhodamine (TAMRA)-azide	Abcam	Cat# ab146486
Disulfide Biotin Azide	Click Chemistry Tools	Cat# 1168-5
THPTA	Click Chemistry Tools	Cat# 1010-100
CuSO₄	Sigma	Cat# 61230
Ascorbic acid	Sigma	Cat# A7506
TO-PRO-3	Thermo Fisher Scientific	Cat# T3605
Paraformaldehyde	EMS	Cat# 15710-S
Ficoll® 400	Sigma	Cat# F4375
Hydrogen peroxide	Sigma	Cat# H1009
Formamide	Thermo Fisher Scientific	Cat# AC181090010
Benzyl alcohol	Sigma	Cat# 305197
Benzyl benzoate	ACROS Organics	Cat# 105860010
RO-3306	Sigma	Cat# SML0569
JNJ-7706621	Selleck Chemicals	Cat# S1249
AZD5438	Selleck Chemicals	Cat# S2621
BMS-265246	Selleck Chemicals	Cat# S2014
SKPin C1	Selleck Chemicals	Cat# S8652
Experimental Models: Organisms/Strains
Xenopus laevis embryos	This paper	N/A
Oligonucleotides/Primers
has1.S Forward: 5’-GTGGCATTCCAGCCTATTGT-3’	This paper	https://www.idtdna.com/
has1.S Reverse: 5’-TCAGGAATCTCCATTGTTTCTGC-3’	This paper	https://www.idtdna.com/
foxi1.L Forward: 5’-TGAAGATGATCCAGGCAAGGG-3’	This paper	https://www.idtdna.com/
foxi1.L Reverse: 5’-TAGGGCTCTCACTTAGCGGG-3’	This paper	https://www.idtdna.com/
grhl3.S Forward: 5’-CAGACTTAGCCAAGGCACCA-3’	This paper	https://www.idtdna.com/
grhl3.S Reverse: 5’-GGTCTGTAGCTGTTAATTCTGTCAA-3’	This paper	https://www.idtdna.com/
rgcc.L Forward: 5’-GTGGATACCCCTCATAAAGCAAG-3’	This paper	https://www.idtdna.com/
rgcc.L Reverse: 5’-TCGGTGTCACAGCATATCACT-3’	This paper	https://www.idtdna.com/
crx.L (Figure S2) Forward: 5’-AGTTGAGCACAAAGTACCATCCT-3’	This paper	https://www.idtdna.com/
crx.L (Figure S2) Reverse: 5’-TCAGTGACCAAGTATCAAGGGAC-3’	This paper	https://www.idtdna.com/
ventx1.2.L Forward: 5’-AAGCCTTCCTCAGCAGTGTT-3’	This paper	https://www.idtdna.com/
ventx1.2.L Reverse: 5’-GGGGGTGAATGCTGTTCTCA-3’	This paper	https://www.idtdna.com/
odc.S Forward: 5’-TTCATTCAGGCAGTCGTCGC-3’	This paper	https://www.idtdna.com/
odc.S Reverse: 5’-GCGCTGTTCTGCTGTTTGTA-3’	This paper	https://www.idtdna.com/
crx.L (Figure S3) Forward: 5’-CAGTTGAGCACAAAGTACCATCC-3’	This paper	https://www.idtdna.com/
crx.L (Figure S3) Reverse: 5’-CAGTGACCAAGTATCAAGGGACT-3’	This paper	https://www.idtdna.com/
dlc.L Forward: 5’-CGGACATGCGAATGGTCTCA-3’	This paper	https://www.idtdna.com/
dlc.L Reverse: 5’-GCTGGATACACCAGCGGCA-3’	This paper	https://www.idtdna.com/
pcdh18.L Forward: 5’-TGATTTGGGCAGAGATTCGC-3’	This paper	https://www.idtdna.com/
pcdh18.L Reverse: 5’-GTGCAGAGCCTCATAGCTGAA-3’	This paper	https://www.idtdna.com/
Deposited Data
Superseries of all RNA-seq datasets from this study	This paper	GEO#: GSE201874 https://www.ncbi.nlm.nih.gov/geo/
Total and nascent transcriptome of Xenopus laevis embryos at 5–9 hpf (stage 7–9)	This paper	GEO#: GSE201835 https://www.ncbi.nlm.nih.gov/geo/
Nascent transcriptome of the animal pole (AP) and the vegetal pole (VP) from Xenopus laevis embryos at 5–9 hpf (stage 7–9)	This paper	GEO#: GSE201833 https://www.ncbi.nlm.nih.gov/geo/
Nascent transcriptome of cycloheximide (CHX)-arrested Xenopus laevis embryos at 5–7.5 hpf	This paper	GEO#: GSE201834 https://www.ncbi.nlm.nih.gov/geo/
Software and Algorithms
salmon (v0.12.0)	Patro et al.⁵⁴	https://github.com/COMBINE-lab/salmon
DESeq2 (bioconductor v3.8)	Love et al.⁵⁵	https://bioconductor.org/packages/release/bioc/html/DESeq2.html
STAR (v 2.7.0)	Dobin et al.⁵⁶	https://github.com/alexdobin/STAR
IGV (v2.8.0)	Robinson et al.⁵⁷	https://software.broadinstitute.org/software/igv
clusterProfiler (v4.2.0)	Wu et al.⁵⁸	https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html
HOMER (v4.11)	Duttke et al.⁵⁹	http://homer.ucsd.edu/homer
Cutadapt (v3.7)	Martin⁶⁰	https://cutadapt.readthedocs.io
Bowtie 2 (v2.3.4.1)	Langmead et al.⁶¹	http://bowtie-bio.sourceforge.net/bowtie2
Samtools (v1.1)	Li et al.⁶²	https://www.htslib.org
MACS2 (v2.2.7.1)	Zhang et al.⁶³	https://github.com/macs3-project/MACS
deepTools (v3.5.1)	Ramírez et al.⁶⁴	https://deeptools.readthedocs.io
Fiji	NIH	https://imagej.net/software/fiji
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[R1] 1.Vastenhouw NL, Cao WX, and Lipshitz HD (2019). The maternal-to-zygotic transition revisited. Development 146. 10.1242/dev.161471. [DOI] [PubMed] [Google Scholar]

[R2] 2.Jukam D, Shariati SAM, and Skotheim JM (2017). Zygotic Genome Activation in Vertebrates. Developmental cell 42, 316–332. 10.1016/j.devcel.2017.07.026. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R3] 3.Lee MT, Bonneau AR, and Giraldez AJ (2014). Zygotic genome activation during the maternal-to-zygotic transition. Annual review of cell and developmental biology 30, 581–613. 10.1146/annurev-cellbio-100913-013027. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Schulz KN, and Harrison MM (2019). Mechanisms regulating zygotic genome activation. Nat Rev Genet 20, 221–234. 10.1038/s41576-018-0087-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R5] 5.Palfy M, Joseph SR, and Vastenhouw NL (2017). The timing of zygotic genome activation. Curr Opin Genet Dev 43, 53–60. 10.1016/j.gde.2016.12.001. [DOI] [PubMed] [Google Scholar]

[R6] 6.Lu X, Li JM, Elemento O, Tavazoie S, and Wieschaus EF (2009). Coupling of zygotic transcription to mitotic control at the Drosophila mid-blastula transition. Development 136, 2101–2110. 10.1242/dev.034421. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Syed S, Wilky H, Raimundo J, Lim B, and Amodeo AA (2021). The nuclear to cytoplasmic ratio directly regulates zygotic transcription in Drosophila through multiple modalities. Proceedings of the National Academy of Sciences of the United States of America 118. 10.1073/pnas.2010210118. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R8] 8.Strong IJT, Lei X, Chen F, Yuan K, and O’Farrell PH (2020). Interphase-arrested Drosophila embryos activate zygotic gene expression and initiate mid-blastula transition events at a low nuclear-cytoplasmic ratio. PLoS biology 18, e3000891. 10.1371/journal.pbio.3000891. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Edgar BA, Kiehle CP, and Schubiger G (1986). Cell cycle control by the nucleocytoplasmic ratio in early Drosophila development. Cell 44, 365–372. [DOI] [PubMed] [Google Scholar]

[R10] 10.Collart C, Allen GE, Bradshaw CR, Smith JC, and Zegerman P (2013). Titration of four replication factors is essential for the Xenopus laevis midblastula transition. Science 341, 893–896. 10.1126/science.1241530. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Argelaguet R, Clark SJ, Mohammed H, Stapel LC, Krueger C, Kapourani CA, Imaz-Rosshandler I, Lohoff T, Xiang Y, Hanna CW, et al. (2019). Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature 576, 487–491. 10.1038/s41586-019-1825-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Hashimshony T, Feder M, Levin M, Hall BK, and Yanai I (2015). Spatiotemporal transcriptomics reveals the evolutionary history of the endoderm germ layer. Nature 519, 219–+. 10.1038/nature13996. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Chen H, and Good MC (2020). Imaging nascent transcription in wholemount vertebrate embryos to characterize zygotic genome activation. Methods in enzymology 638, 139–165. 10.1016/bs.mie.2020.03.002. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Chen H, Einstein LC, Little SC, and Good MC (2019). Spatiotemporal Patterning of Zygotic Genome Activation in a Model Vertebrate Embryo. Developmental cell 49, 852–866 e857. 10.1016/j.devcel.2019.05.036. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Heyn P, Kircher M, Dahl A, Kelso J, Tomancak P, Kalinka AT, and Neugebauer KM (2014). The earliest transcribed zygotic genes are short, newly evolved, and different across species. Cell reports 6, 285–292. 10.1016/j.celrep.2013.12.030. [DOI] [PubMed] [Google Scholar]

[R16] 16.Holler K, Neuschulz A, Drewe-Boss P, Mintcheva J, Spanjaard B, Arsie R, Ohler U, Landthaler M, and Junker JP (2021). Spatio-temporal mRNA tracking in the early zebrafish embryo. Nature communications 12, 3358. 10.1038/s41467-021-23834-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.Chan SH, Tang Y, Miao L, Darwich-Codore H, Vejnar CE, Beaudoin JD, Musaev D, Fernandez JP, Benitez MDJ, Bazzini AA, et al. (2019). Brd4 and P300 Confer Transcriptional Competency during Zygotic Genome Activation. Developmental cell 49, 867–881 e868. 10.1016/j.devcel.2019.05.037. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Kwasnieski JC, Orr-Weaver TL, and Bartel DP (2019). Early genome activation in Drosophila is extensive with an initial tendency for aborted transcripts and retained introns. Genome research 29, 1188–1197. 10.1101/gr.242164.118. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Session AM, Uno Y, Kwon T, Chapman JA, Toyoda A, Takahashi S, Fukui A, Hikosaka A, Suzuki A, Kondo M, et al. (2016). Genome evolution in the allotetraploid frog Xenopus laevis. Nature 538, 336–343. 10.1038/nature19840. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Yanai I, Peshkin L, Jorgensen P, and Kirschner MW (2011). Mapping gene expression in two Xenopus species: evolutionary constraints and developmental flexibility. Developmental cell 20, 483–496. 10.1016/j.devcel.2011.03.015. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Lee MT, Bonneau AR, Takacs CM, Bazzini AA, DiVito KR, Fleming ES, and Giraldez AJ (2013). Nanog, Pou5f1 and SoxB1 activate zygotic gene expression during the maternal-to-zygotic transition. Nature 503, 360–364. 10.1038/nature12632. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Zernicka-Goetz M, Morris SA, and Bruce AW (2009). Making a firm decision: multifaceted regulation of cell fate in the early mouse embryo. Nat Rev Genet 10, 467–477. 10.1038/nrg2564. [DOI] [PubMed] [Google Scholar]

[R23] 23.Heasman J (2006). Patterning the early Xenopus embryo. Development 133, 1205–1217. 10.1242/dev.02304. [DOI] [PubMed] [Google Scholar]

[R24] 24.Langdon YG, and Mullins MC (2011). Maternal and zygotic control of zebrafish dorsoventral axial patterning. Annu Rev Genet 45, 357–377. 10.1146/annurev-genet-110410-132517. [DOI] [PubMed] [Google Scholar]

[R25] 25.Jukam D, Kapoor RR, Straight AF, and Skotheim JM (2021). The DNA-to-cytoplasm ratio broadly activates zygotic gene expression in Xenopus. Current biology : CB 31, 4269–4281 e4268. 10.1016/j.cub.2021.07.035. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R26] 26.Blitz IL, Paraiso KD, Patrushev I, Chiu WTY, Cho KWY, and Gilchrist MJ (2017). A catalog of Xenopus tropicalis transcription factors and their regional expression in the early gastrula stage embryo. Developmental biology 426, 409–417. 10.1016/j.ydbio.2016.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Sinner D, Kirilenko P, Rankin S, Wei E, Howard L, Kofron M, Heasman J, Woodland HR, and Zorn AM (2006). Global analysis of the transcriptional network controlling Xenopus endoderm formation. Development 133, 1955–1966. 10.1242/dev.02358. [DOI] [PubMed] [Google Scholar]

[R28] 28.Zhang J, Houston DW, King ML, Payne C, Wylie C, and Heasman J (1998). The role of maternal VegT in establishing the primary germ layers in Xenopus embryos. Cell 94, 515–524. 10.1016/s0092-8674(00)81592-5. [DOI] [PubMed] [Google Scholar]

[R29] 29.Xanthos JB, Kofron M, Wylie C, and Heasman J (2001). Maternal VegT is the initiator of a molecular network specifying endoderm in Xenopus laevis. Development 128, 167–180. 10.1242/dev.128.2.167. [DOI] [PubMed] [Google Scholar]

[R30] 30.Zhang J, and King ML (1996). Xenopus VegT RNA is localized to the vegetal cortex during oogenesis and encodes a novel T-box transcription factor involved in mesodermal patterning. Development 122, 4119–4129. [DOI] [PubMed] [Google Scholar]

[R31] 31.Blythe SA, and Wieschaus EF (2016). Establishment and maintenance of heritable chromatin structure during early Drosophila embryogenesis. Elife 5. 10.7554/eLife.20148. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Leichsenring M, Maes J, Mossner R, Driever W, and Onichtchouk D (2013). Pou5f1 transcription factor controls zygotic gene activation in vertebrates. Science 341, 1005–1009. 10.1126/science.1242527. [DOI] [PubMed] [Google Scholar]

[R33] 33.Amodeo AA, Jukam D, Straight AF, and Skotheim JM (2015). Histone titration against the genome sets the DNA-to-cytoplasm threshold for the Xenopus midblastula transition. Proceedings of the National Academy of Sciences of the United States of America 112, E1086–1095. 10.1073/pnas.1413990112. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Newport J, and Kirschner M (1982). A Major Developmental Transition in Early Xenopus Embryos II: Control of the Onset of Transcription. Cell 687–696. [DOI] [PubMed] [Google Scholar]

[R35] 35.Newport J, and Kirschner M (1982). A Major Developmental Transition in Early Xenopus Embryos I: Characterization and Timing of Cellular Changes At the Midblastula Stage. Cell, 675–686. [DOI] [PubMed] [Google Scholar]

[R36] 36.Dekens MP, Pelegri FJ, Maischein HM, and Nusslein-Volhard C (2003). The maternal-effect gene futile cycle is essential for pronuclear congression and mitotic spindle assembly in the zebrafish zygote. Development 130, 3907–3916. [DOI] [PubMed] [Google Scholar]

[R37] 37.Prioleau MN, Huet J, Sentenac A, and Mechali M (1994). Competition between chromatin and transcription complex assembly regulates gene expression during early development. Cell 77, 439–449. [DOI] [PubMed] [Google Scholar]

[R38] 38.Kane DA, and Kimmel CB (1993). The zebrafish midblastula transition. Development 119, 447–456. [DOI] [PubMed] [Google Scholar]

[R39] 39.Zhang M, Skirkanich J, Lampson MA, and Klein PS (2017). Cell Cycle Remodeling and Zygotic Gene Activation at the Midblastula Transition. Adv Exp Med Biol 953, 441–487. 10.1007/978-3-319-46095-6_9. [DOI] [PubMed] [Google Scholar]

[R40] 40.Shermoen AW, and O’Farrell PH (1991). Progression of the cell cycle through mitosis leads to abortion of nascent transcripts. Cell 67, 303–310. 10.1016/0092-8674(91)90182-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R41] 41.Rothe M, Pehl M, Taubert H, and Jackle H (1992). Loss of gene function through rapid mitotic cycles in the Drosophila embryo. Nature 359, 156–159. 10.1038/359156a0. [DOI] [PubMed] [Google Scholar]

[R42] 42.Kimelman D, Kirschner M, and Scherson T (1987). The events of the midblastula transition in Xenopus are regulated by changes in the cell cycle. Cell 48, 399–407. [DOI] [PubMed] [Google Scholar]

[R43] 43.Gard DL, Hafezi S, Zhang T, and Doxsey SJ (1990). Centrosome duplication continues in cycloheximide-treated Xenopus blastulae in the absence of a detectable cell cycle. The Journal of cell biology 110, 2033–2042. 10.1083/jcb.110.6.2033. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R44] 44.Gerhart J, Wu M, and Kirschner M (1984). Cell cycle dynamics of an M-phase-specific cytoplasmic factor in Xenopus laevis oocytes and eggs. The Journal of cell biology 98, 1247–1255. 10.1083/jcb.98.4.1247. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R45] 45.Miake-Lye R, Newport J, and Kirschner M (1983). Maturation-promoting factor induces nuclear envelope breakdown in cycloheximide-arrested embryos of Xenopus laevis. The Journal of cell biology 97, 81–91. 10.1083/jcb.97.1.81. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R46] 46.Newport JW, and Kirschner MW (1984). Regulation of the cell cycle during early Xenopus development. Cell 37, 731–742. 10.1016/0092-8674(84)90409-4. [DOI] [PubMed] [Google Scholar]

[R47] 47.Levy DL, and Heald R (2010). Nuclear size is regulated by importin alpha and Ntf2 in Xenopus. Cell 143, 288–298. 10.1016/j.cell.2010.09.012. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Hontelez S, van Kruijsbergen I, Georgiou G, van Heeringen SJ, Bogdanovic O, Lister R, and Veenstra GJC (2015). Embryonic transcription is controlled by maternally defined chromatin state. Nature communications 6, 10148. 10.1038/ncomms10148. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R49] 49.Skirkanich J, Luxardi G, Yang J, Kodjabachian L, and Klein PS (2011). An essential role for transcription before the MBT in Xenopus laevis. Developmental biology 357, 478–491. 10.1016/j.ydbio.2011.06.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R50] 50.Yang J, Tan C, Darken RS, Wilson PA, and Klein PS (2002). Beta-catenin/Tcf-regulated transcription prior to the midblastula transition. Development 129, 5743–5752. [DOI] [PubMed] [Google Scholar]

[R51] 51.Wang P, Hayden S, and Masui Y (2000). Transition of the blastomere cell cycle from cell size-independent to size-dependent control at the midblastula stage in Xenopus laevis. J Exp Zool 287, 128–144. [DOI] [PubMed] [Google Scholar]

[R52] 52.Chari S, Wilky H, Govindan J, and Amodeo AA (2019). Histone concentration regulates the cell cycle and transcription in early development. Development 146. 10.1242/dev.177402. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R53] 53.Shindo Y, and Amodeo AA (2021). Excess histone H3 is a competitive Chk1 inhibitor that controls cell-cycle remodeling in the early Drosophila embryo. Current biology : CB 31, 2633–2642 e2636. 10.1016/j.cub.2021.03.035. [DOI] [PubMed] [Google Scholar]

[R54] 54.Palozola KC, Donahue G, and Zaret KS (2021). EU-RNA-seq for in vivo labeling and high throughput sequencing of nascent transcripts. STAR Protoc 2, 100651. 10.1016/j.xpro.2021.100651. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R55] 55.Patro R, Duggal G, Love MI, Irizarry RA, and Kingsford C (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14, 417–419. 10.1038/nmeth.4197. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R56] 56.Love MI, Huber W, and Anders S (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550. 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R57] 57.Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, and Gingeras TR (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21. 10.1093/bioinformatics/bts635. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R58] 58.Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, and Mesirov JP (2011). Integrative genomics viewer. Nature biotechnology 29, 24–26. 10.1038/nbt.1754. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R59] 59.Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, et al. (2021). clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (N Y) 2, 100141. 10.1016/j.xinn.2021.100141. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R60] 60.Duttke SH, Chang MW, Heinz S, and Benner C (2019). Identification and dynamic quantification of regulatory elements using total RNA. Genome research 29, 1836–1846. 10.1101/gr.253492.119. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R61] 61.Esmaeili M, Blythe SA, Tobias JW, Zhang K, Yang J, and Klein PS (2020). Chromatin accessibility and histone acetylation in the regulation of competence in early development. Dev Biol 462, 20–35. 10.1016/j.ydbio.2020.02.013. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R62] 62.Martin M (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 3. 10.14806/ej.17.1.200. [DOI] [Google Scholar]

[R63] 63.Langmead B, and Salzberg SL (2012). Fast gapped-read alignment with Bowtie 2. Nature methods 9, 357–359. 10.1038/nmeth.1923. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R64] 64.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, and Genome Project Data Processing, S. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079. 10.1093/bioinformatics/btp352. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R65] 65.Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, and Liu XS (2008). Model-based analysis of ChIP-Seq (MACS). Genome Biol 9, R137. 10.1186/gb-2008-9-9-r137. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R66] 66.Ramirez F, Ryan DP, Gruning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Dundar F, and Manke T (2016). deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 44, W160–165. 10.1093/nar/gkw257. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Nascent Transcriptome Reveals Orchestration of Zygotic Genome Activation in Early Embryogenesis

Hui Chen

Matthew C Good

SUMMARY

In Brief

RESULTS AND DISCUSSION

Nascent EU-RNA-seq characterizes the composition and dynamics of ZGA with high sensitivity

Figure 1. Nascent EU-RNA-seq to characterize the composition and dynamics of ZGA with high sensitivity.

EU-RNA-seq on segmented embryos uncovers spatial patterns of single gene activation

Figure 2. EU-RNA-seq on segmented embryos uncovers spatial patterns of single gene activation.

Reconciling mechanisms that regulate ZGA timing

Figure 3. Reconciling mechanisms that regulate ZGA timing.

Timing of germ layer initiation is linked to spatially graded onset of ZGA

Figure 4. Timing of germ layer initiation is correlated to spatially graded onset of ZGA.

Composite Model for ZGA.

STAR METHODS

RESOURCE AVAIABLITY

Lead Contact

Materials Availability

Data and Code Availability

KEY RESOURCES TABLE.

EXPERIMENTAL MODEL AND SUBJECT DETAILS

METHOD DETAILS

In vitro fertilization (IVF)

Microinjection, cycloheximide treatment and collection of embryos

Cell cycle inhibitor incubation

RNA isolation, biotinylation and purification

EU-RNA-seq and analysis

Functional enrichment analysis

ATAC-seq and ChIP-seq analysis

RT-PCR and real-time PCR

Confocal imaging nascent transcripts in wholemount embryos ad image analysis

QUANTIFICATION AND STATISTICAL ANALYSIS

Supplementary Material

Highlights.

ACKNOWLEDGEMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

KEY RESOURCES TABLE.

ACTIONS

PERMALINK

RESOURCES

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