Post-transcriptional regulation in early cell fate commitment of germ layers

doi:10.21203/rs.3.rs-4908750/v1

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Post-transcriptional regulation in early cell fate commitment of germ layers

https://doi.org/10.21203/rs.3.rs-4908750/v1

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Background: Cell differentiation during development is orchestrated by precisely coordinated gene expression programs. While mechanisms such as the maintenance of pluripotent states are well-understood, others like lineage choice and cell-fate decisions remain poorly comprehended. Given that gene expression is influenced not only by transcriptional control but also by post-transcriptional events, we employed monolayer differentiation protocols to delineate early transcriptional and post-transcriptional events in human embryonic stem cell specification. This involved obtaining representative populations of the three germ layers, followed by sequencing of polysome-bound and total RNAs.

Results: We observed a consistent similar distribution of gene upregulation and downregulation when comparing the transcriptome and translatome during the differentiation of all three germ layers. Notably, certain differentially expressed genes were exclusively detected in the polysome fractions, suggesting active post-transcriptional regulation. Upregulated genes in the translatome more accurately reflected the differentiation process. Additionally, genes such as DLX3, DHFR2, and UNC13D were identified as differentially expressed solely in the polysome fraction, indicating their post-transcriptional regulation during ectoderm commitment. Recruitment of these genes to polysomes was also confirmed.

Conclusions: Substantial post-transcriptional modulation was found during germ layer commitment, emphasizing the translatome reliability in capturing nuanced gene expression regulation. These findings highlight the post-transcriptional regulation's critical role in early embryonic development, offering new insights into the molecular mechanisms of cell differentiation.

post-transcriptional regulation

polysome profiling

embryology

germ layer

human development

Since the beginning of the 20th century, Experimental Embryology has tried to describe how an organism is built up in terms of cellular changes, interactions, and communication. In the last decades, Genetics and Molecular Biology allowed the modeling of embryonic development revealing the molecular mechanisms involved in cell fate and tissue designation and composition [1]. Due to ethical issues, the usage of human early embryos in studies for elucidation of developmental processes is made difficult. Instead, human pluripotent stem cells, like embryonic stem cells (hESC) lineages have been shown to be an efficient tool to study early development events [2]. hESC can be induced to differentiate in vitro into cells of all three embryonic germ layers mimicking human development and allowing the assessment of the key mechanisms implicated in early cell fate commitment [3].

Lineage choice and cell-fate decisions are modulated by highly coordinated gene expression programs which allow the expression of lineage-specific genes while repressing unsuitable genes for that lineage [4]. Robust gene expression analysis, therefore, enables the elucidation of transcriptional mechanisms and epigenetic events involved in the phenomena of cell fate commitment. It’s known that the modulation of a complex and dynamic pluripotency circuit driven by a series of specific transcription factors and epigenetic regulators which are sufficient to hold the cells in a pluripotent state [5]. However, how the cells leave pluripotency and which mechanisms are involved in the lineage choice is not clear and researchers are trying to elucidate the phenomenon of early lineage commitment by different approaches and methodologies [6–8]. Most of the applied strategies primarily emphasize transcriptional analysis, however, it is widely recognized that gene expression regulation extends beyond transcriptional control and is also modulated by post-transcriptional events. Protein expression is significantly influenced by these translational events [9].

Few studies explored the early lineage commitment process at the post-transcriptional level [10–12]. In a previous study from our group, a strong post-transcriptional regulation (PTR) was found to occur during the mesoderm commitment of hESC [13]. These results aroused the interest to understand whether this phenomenon of PTR is restricted to mesodermal differentiation or common to the process of lineage choice across all three embryonic germ layers.

Here, to delineate early transcriptional and post-transcriptional events during hESC specification, monolayer differentiation protocols were employed to obtain representative populations of the three germ layers. Subsequently, sequencing of polysome-bound and total RNAs was conducted. Our results show a significant presence of post-transcriptional modulation during germ layers commitment. Furthermore, we highlighted the reliability of the translatome in capturing nuances, revealing a unique pattern in gene expression regulation. These findings reinforce the crucial role of PTR in early embryonic development, opening new perspectives to understand the molecular mechanisms of cell differentiation.

Cell culture and induction of germ layers differentiation

Human embryonic stem cell, H1 line, was obtained from WiCell Research Institute (Madison, WI, USA) under a Materials Transfer Agreement (No.18-W0416) with Carlos Chagas Institute. H1 hESC line is a male and presents normal karyotypes. Cells were cultured on Matrigel-coated dishes using mTeSR-1 medium (Stem Cell Technologies). The medium was replaced daily until cells reach 70–80% confluence. Cells were maintained in a 37°C, 5% CO2 incubator.

For endoderm differentiation, the cells were subjected to a protocol adapted from Caruso and coworkers [14]. The cells were seeded at 5 x 10⁴ cells/cm² in a 24-wells plate pre-coated with Geltrex hESC-qualified (Thermo Fisher Scientific) in StemFlex medium (Thermo Fisher Scientific) with 10 µM of Y27632 (Med Chem Express). After 24 hours, the medium was replaced to remove the ROCK inhibitor. On the following day (D0), the medium was change to RPMI 1640 (Gibco) supplemented with 1% l-glutamine, 1% non-essential amino acids and 2% B-27 minus insulin (Gibco), the induction was performed by adding 3 µM of CHIR99021 (TOCRIS) and 50 ng/ml of Activin A (B&D systems) during the medium replacement. After 2 days (D2), the medium was replaced to the base medium containing only 50 ng/ml Activin A (B&D systems). On day 4 (D4), cells were collected to RNA isolation.

For ectodermal differentiation, the cells were subjected to a protocol adapted from Yan and coworkers [15]. The cells were seeded at 2,5 x 10⁴ cells/cm² in a 6-wells plate pre-coated with Geltrex hESC-qualified (Thermo Fisher Scientific) in StemFlex medium (Thermo Fisher Scientific) with 10 µM of Y27632 (Med Chem Express). The next day (D0), the medium was changed to Neural Induction Medium (Thermo Fisher Scientific) containing 2% of Neural Induction Supplement (Thermo Fischer Scientific). Following the manufacturer, the medium was changed on days 3, 5, and 7. On D8, cells were collected to RNA isolation.

For mesoderm differentiation, the cells were subjected to a protocol adapted from Lian and coworkers [16]. The cells were seeded at 10⁵ cells/cm² in a Geltrex-precoated 24-well plate in StemFlex medium (Thermo Fisher Scientific) with 10 µM of Y27632 (Med Chem Express). In the next two days the medium was replaced until the culture reached 100% confluence. The monolayer was induced to mesoderm differentiation in RPMI 1640 (Gibco) supplemented with 2% B27 minus insulin (Gibco) and 12 µM of CHIR99021 (TOCRIS). After 24 hours of induction, cells were collected to RNA isolation.

Polysome profiling

Polysome profiling was performed as previously described [17]. Cells at undifferentiated and differentiated stages were treated with 0.1 mg/mL of cycloheximide for 10 min or 10 mg/mL of puromycin for 1 hour at 37ºC. After incubation, cells were detached and lysed in polysome lysis buffer (15 mM Tris HCl, pH 7.4, 15 mM MgCl₂, 0.3 M NaCl, 1% Triton X-100, 40 U/µL RNAse Out, 50 U/µL DNAse). Lysis was performed for 10 min on ice, then centrifuged at 12.000 x g for 10 min at 4ºC. For both cycloheximide and puromycin treatments, supernatants were loaded on 10–50% sucrose gradients (BioComp model 108 Gradient Master v.5.3) and centrifuged (SW40 rotor, HIMAC CP80WX HITACHI) at 270.000 x g for 120 minutes at 4º C.

Sucrose gradient fractions were isolated using the ISCO gradient fractionation system (ISCO Model 160 Gradient Former Foxy Jr. Fraction Collector) coupled to a UV light for RNA detection, which recorded the polysome profiling at 254 nm. Fractions were identified based on chart by RNA detection, then polysome fraction were pooled and added an equal volume of TRIreagent (Thermo Fisher Scientific). Total RNA and polysome fractions RNA were isolated by Direct-zol™ RNA kit (Zymo® Research), following the manufacturer's instructions.

RNA-seq and data analysis

Total and polysome RNA-seq was performed using H1 hESC cell line in undifferentiated and germ layer differentiated states. Quality assessment and trimming of reads was done with FastQC and Trim Galore (v.0.4.0) [18]. Reads were mapped to hg38/GRCh38 with HISAT2 (v.2.1.0) [19] and counted with HTSeq 174 (v.0.11.1) [20]. Significantly differentially expressed genes were calculated with DESeq2 175 (v.1.24.0) [21] with an adjusted p-value cutoff of 0.01 and log₂FoldChange cutoff of 2. Gene Ontology (GO) analysis was performed using enrichR [22]. Venn diagram was performing using FunRich (v.3.1.3) [23]. RNA-seq data are available at NCBI Sequence Read Archive, accessing number PRJNA1144790.

cDNA synthesis and qPCR

cDNA synthesis was performed using Improm-II Reverse Transcription System (Promega), following the manufacturer's instructions. For polysomal samples, a GFP spike-in (5 pg) was included in each reaction. Quantitative PCR reactions were performed with GoTaq® Master Mix (Promega). Analyses were carried out in QuantStudio™ 5 Real-Time PCR System. GFP detection was used for normalization in these samples. The primers are listed in Supplementary Table 1.

Data and statistical analysis

Graphed data are expressed as mean ± SD. Statistical analysis was performed with Prism 8.0 software (GraphPad Software). For comparisons between two mean values the student’s unpaired t-test was performed. A p-value lower than 0.05 was considered statistically significant.

Supplementary Table 1. List of primers used in qPCR reaction

Transcriptome and translatome: Similar distribution with different gene profiles

To evaluate PTR during germ layer differentiation, we induced a human embryonic stem cell (hESC) line to independently differentiate into endoderm, mesoderm or ectoderm. Polysome profiling was performed to access transcripts under translation process, and polysome-bound RNAs were collected. In parallel, total RNA was isolated from the same samples. Both polysome-bound and total RNA were submitted to RNA-sequencing (Fig. 1A). Polysome-profiling analysis did not detect changes in the polysome fraction between the germ layers, except for a slight decrease of RNA in endoderm samples. We also observed an increase of RNA in the monosome fraction for the ectoderm samples compared to the other two differentiated cell types (Figure S1A). Initially, we evaluated the similarities between polysome-bound (translatome) and total RNA (transcriptome) sets of transcripts. Principal component analysis revealed distinct clustering of each germ layer in both the transcriptome and translatome, indicating a unique set of expressed genes for each differentiation pathway (Figure S1 A-B). When the transcriptome was compared to the translatome within the same germ layer, we observed distinctly clustered for transcriptome and translatome samples, except for one sample in the endoderm where the difference is more subtle (Figure S2 C). This data suggests that the transcriptome and the translatome in this experimental model have distinct sets of representative genes.

Next, we wanted to identify the genes that were regulated during the three distinct differentiation protocols in each translatome and transcriptome data sets. Upon analyzing the differentially expressed genes (DEG), a gene was considered differentially expressed if it exhibited a fold change (log2) ≥ 2 or ≤ -2 and a p-value ≤ 0.01. We observed a similarity of DEGs numbers between transcriptome and translatome for the three groups when compared to the pluripotent state, with slightly fewer DEGs in mesoderm samples (Fig. 1B). In both total RNA and polysome samples, we assessed gene expression profiles after germ layer commitment (Fig. 1C). For total RNA, we identified 4,341 DEGs in ectoderm, being almost 39% upregulated and 61% downregulated. In mesoderm, we found 1,623 DEGs, with 35% upregulated and 65% downregulated. Lastly, for endoderm, we found 3,571 DEGs, with 51% upregulated and 49% downregulated. In parallel, polysome fraction analysis revealed 4,644 DEGs in ectoderm, with a similar proportion to total RNA showing 39% upregulated and almost 61% downregulated genes. The same pattern was observed for the other two embryonic germ layer differentiations. For mesoderm and endoderm, we found 1,674 and 4,204 DEGs, respectively, in the polysome fraction. In polysome samples of mesoderm, 43% of the DEGs were upregulated and 57% were downregulated, while in endoderm, 53% were upregulated and 47% were downregulated. The proportion of DEGs being up- and down-regulated was mostly similar when comparing total RNA and polysome data except for a slightly higher number of up-regulated DEGs in mesoderm. Collectively, our results shows that transcriptome and translatome has a distinct set of DEGs, beside the similar distribution of up- and down-regulated in these both datasets.

Post-transcriptional regulation occurs during the differentiation process of all three germ layers

To investigate the occurrence of PTR, we then examined genes that exhibited a differential expression pattern between the polysome-bound fraction and total RNA. To categorize the type of regulation, we used the classification described in Pereira et al., 2019 (Table 1). It is considered “Coordinate” regulation when the genes were up- or down-regulated similarly in both polysome fraction and total RNA. RNAs that were up-regulated in polysome fraction but showed no change in total RNA (Up-loaded), or that were non-differentially expressed (non-DEG) in polysome but were down-regulated in total RNA (Down-buffered), we considered, in both cases, as “post-transcriptional positive” regulation. On the contrary, RNAs that were down-regulated in polysome fraction but showed no change in total RNA (Down-loaded), or genes that were non-DEG in polysome but were up-regulated in total RNA (Up-buffered), we considered, in both cases, as “post-transcriptional negative” regulation (Table 1).

Analyzing the distribution of genes within the categories, we observed both positive and negative post-transcriptional modulation across all embryonic germ layer groups (Fig. 2A). We identified genes that, despite being up- or down-regulated in total RNA, were not recruited to the polysome RNA (green dots), indicating buffered regulation during differentiation. Conversely, we also observed cases where genes were non-DEG in total RNA but showed fluctuations in their recruitment to the translation machinery (yellow dots), suggesting a loaded effect. When assessing the number of genes undergoing changes in polysome recruitment, we found that although the majority of genes recruited to the polysome were coordinated with an increase in the transcriptome, a substantial number of genes were regulated post-transcriptionally. In the ectoderm, post-transcriptional positive regulation (PTPR) was observed in 384 genes under an up-loaded effect and 298 genes under a down-buffered effect, while for post-transcriptional negative regulation (PTNR), we identified 427 and 244 genes under down-loaded and up-buffered effects, respectively (Fig. 2B). Similarly, we found both positive and negative regulations in the other two germ layers. For the mesoderm, we identified 181 and 255 genes under up-loaded and down-buffered effects, respectively, in PTPR, while 182 genes were down-loaded and 79 genes were up-buffered during PTNR. The same was observed in the endoderm, where 575 genes were up-loaded and 315 were down-buffered in PTPR, while 495 genes were down-loaded and 322 genes were up-buffered in PTNR.

Analyzing the percentage of PTR across all DEGs identified during germ layer differentiation, we observed that, while both positive and negative regulation were present, positive modulation predominantly occurred in ectoderm and mesoderm. In contrast, in endoderm, negative and positive regulations were similar. Overall, ectoderm exhibited 25.36% and 40.69% for negative and positive modulation respectively; mesoderm showed 28.44% and 53.76% for the same parameters; and endoderm displayed 41.84% and 43.52%, respectively (Fig. 2C). In addition to the difference between negative and positive PTR, these findings suggest that robust PTR is a crucial step in early embryonic differentiation. Notable, at least one-fourth of the identified DEGs during the differentiation are subject to PTR, with this proportion exceeding 50% in the mesoderm.

Translatome is more reliable than transcriptome as a parameter for analyzing the differentiation process

To assess the reliability of transcriptome and translatome data in capturing nuances during differentiation processes, we subjected the gene sets from each group to Gene Ontology (GO) analysis of Biological Processes (Fig. 3). Some differences were observed between the analyses of total and polysome samples. In the ectoderm, for instance, certain terms such as "Nervous System Development" (GO:0007399) were present in both total and polysome tables at same position. However, some terms related to differentiation, like “Generation of Neurons” (GO:0048699), appeared with a higher p-value and increased in rank only in polysome table, while other terms, like “Neuron Differentiation” (GO:0030182), emerged exclusively in the polysome table. Despite the presence of some differentiation-related terms in the total set, the differences were notables. For the mesoderm and endoderm, we chose to rank the terms by a combined score (enrichR) due to the similarity of these two germ layers. In mesoderm, no significant distinctions were observed between the two sets of samples. All genes contributing to terms like "Mesoderm Morphogenesis" (GO:0048332) were present in both genes set, as well as WNT pathway terms (GO:2000096; GO:2000095). In the endoderm group, the term "Endoderm Development" (GO:0007492) increased in rank in the polysome set in relation to total set. We also analyzed down-regulated DEGs (Figure S2A). We observed similar terms in ectoderm comparing transcriptome to translatome. However, polysome set has more gene representing the same process than total RNA set. For others two germ layers we found a distinct list of term for polysome and total set, beside the common terms between them. Next, we compare DEGs up- and down-regulated common for the three germ layers, again, we found similar list of terms but with slight difference in number of genes in polysome set (Figure S2B). While there is overlap with terms found in total RNA, the polysome gene set reveals unique biological process terms not identified in the transcriptome sample. This highlights the enhanced reliability and depth of insights gained by utilizing polysomal RNA for analysis.

Some genes are regulated exclusively at the translational level

After that we detected PTR in germ layer differentiation in our analysis and its relevance, we evaluated the expression profile of DEGs presents in translatome from a specific germ layer to the others differentiation. So, we choose to focus in ectoderm to track genes that were up- or down-regulated in translatome, but non-DEGs in the transcriptome. Then, we filtered for those non-DEGs for others germ layers in both transcriptome and translatome for data sets to capture those exclusively regulated in the ectoderm differentiation (Fig. 4A). We found 330 up-regulated and 342 down-regulated DEGs in the ectoderm comparing to mesoderm. Comparing to endoderm, we identified 251 up-regulated DEGs and 260 down-regulated DEGs in the ectoderm. Thus, this analysis showed us a higher number of genes exclusively regulated at post-transcriptional level in ectoderm differentiation.

We choose three genes that showed an interesting pattern of regulation: Distal-less homeobox 3 (DLX3), Unc-13 Homolog D (UNC13D), Dihydrofolate Reductase 2 (DHFR2) (Fig. 4B). DLX3 displayed comparable expression levels in both endoderm and ectoderm, with log₂ fold change (log2FC) of 1.56 and 1.86, respectively to undifferentiated cells in transcriptional data set. Interestingly, a notable shift in expression was observed in the translatome data, revealing log2FC of 0.8 for the endoderm and 2.93 for the ectoderm. In contrast, UNC13D showed a slight decrease in transcriptome samples, with log2FC of -1.29 for the endoderm and − 1.38 for the ectoderm. A more pronounced decrease was observed in the translatome samples, with FC of -1.42 for the endoderm and − 2.33 for the ectoderm, while the log2FC we. Finally, DHFR2 behave similarly to DLX3, showing an increase in total RNA in at least two embryonic layers, but a higher recruitment to the polysome fraction in the ectoderm. Total fraction presented FC of 1.52 and 1.72 for the mesoderm and ectoderm, respectively. In contrary, in the polysome fraction, these values changed to 1.74 and 2.30, respectively. These findings illustrate that certain genes may not exhibit differential expression in a biological process when solely examining the transcriptional regulation; however, distinctions emerge when analyzing genes recruited to the translational machinery.

To confirm that the genes are specifically polysome-bound, we performed polysome profiling after treatment with puromycin to disassemble the polysomes and analyzed the genes by qPCR. We verified the disassembly of polysomes by recording the absorbance at 254 nm (Fig. 5A-B), observing a decrease in RNA detection in the polysome fractions post-puromycin treatment. qPCR analysis confirmed that the genes identified by RNA-seq were associated to the translation machinery during ectoderm differentiation (Fig. 5C). For all three genes, we observed a slight shift of RNA abundance from heavier polysome fractions to lighter fractions upon puromycin treatment, suggesting the release of the transcripts from the polysomes. DLX3 showed the more pronounced shift when compared to the other two genes. These data confirm that the three genes identified by RNA-seq are indeed recruited to the polysome fraction and are associated with multiple ribosomes.

During development, specific genes are expressed in distinct cells, contributing to the establishment of cellular diversity within an organism [24]. The gene expression program adheres to the central dogma of molecular biology, wherein proteins are synthesized based on the genetic information encoded in DNA, which is transcribed into RNA molecules. However, it has been demonstrated that there is a low correlation between the detection of mRNA and the actual presence of corresponding proteins [25–28]. Analyzing mRNA alone may not accurately reflect protein levels, making it less robust than approaches that consider translational regulation alongside gene transcription [29]. Therefore, scientific community has discussed the analysis of the translatome instead of the transcriptome to recapitulate the ultimate gene expression [28–30]. To distinguish transcripts actively being translated into proteins, a viable method is to specifically target the RNA associated with cytoplasmic ribosomes. The polysomal profiling technique offers a robust method to achieve this distinction [17, 31, 32].

Here, we submitted a hESC cell line to three germ layer differentiation processes and compared transcriptome and translatome data. Our analysis revealed a pervasive presence of post-transcriptional regulation in all three embryonic layer differentiation. Additionally, our findings demonstrate that analyzing translatome samples, rather than transcriptome samples, can unveil previously undetected genes. RNA-seq analysis of both polysome-bound and total RNA provided insights into potential differences between transcriptional and translational regulations. Interestingly, polysome-bound and total RNA datasets showed similar distribution of DEGs. However, comparing the genes between both datasets revealed that a substantial number of genes undergo dynamic regulation during differentiation. Our findings align with previous studies demonstrating dynamic changes in gene recruitment to the translational machinery throughout development models and differentiation process [30, 33]. While specific genes being regulated varied between differentiation protocols, the consistent proportion of regulatory events across all embryonic layers suggests a shared mechanism. This data addresses our question into whether the post-transcriptional regulation previously identified in mesoderm is a unique characteristic of this particular developmental lineage or a broader phenomenon across commitment processes of embryonic germ layers [13].

It is also worth emphasize the significance of the translatome in understanding the buffering mechanisms that maintain the stability of protein levels, ensuring a specific phenotype despite significant fluctuations in mRNA levels. This phenomenon has been extensively studied in recent years and accounts for the phenotypic diversity observed among species, individuals of the same species, and different tissues within the same individual [34]. In our study, we demonstrate that, during the commitment of the three embryonic germ layers, translational regulation is crucial in these processes, similar to what has been shown for other cellular signaling pathways involved in developmental processes [35].

Comparison between transcriptome and translatome datasets has been explored over the years [36–38]. It is known that protein synthesis is a major regulator of gene expression during differentiation processes [39]; therefore, the translatome has been used to identify genes that impact these processes [29]. In our analysis of GO using datasets from the transcriptome and translatome of each differentiation group, we observed that the translatome either includes a higher number of terms related to the specific process or scores higher for those terms. This evidence indicates that the translatome dataset provides greater accuracy in describing the process compared to the transcriptome dataset, as supported by the fact that all genes involved in the process overlap between both the total and polysome RNA fractions. Although in our analysis of the endoderm and mesoderm we used a combined score to group terms, it has been reported that distinguishing between these two layers in the early stages of differentiation is challenging due to the similarity of their cells and gene expression profiles, often referred to as endomesoderm or mesendoderm in the literature [40–42].

While exploring post-transcriptional regulation, particularly in the ectoderm samples, we identified the up-regulated DLX3 and DHFR2, known to participate in or influence neuroectodermal differentiation [43, 44]. Additionally, UNC13D, which was found to be down-regulated in polysome, lacks reported involvement in neuroectodermal development [45]. Our findings highlight that some genes undergo regulation at the post-transcriptional level. This crucial insight would have been missed if we had solely relied on transcriptional data for analysis. We also confirmed the enrichment of genes in the polysome fraction by treating with puromycin to disassemble the ribosome complex [17]. Genes interacting with the translation machinery are expected to shift fractions in the density gradient, as observed with DLX3, UNC13D, and DHFR2 in the ectoderm.

Overall, it is evident that studying the translatome is an essential strategy for effectively and robustly mapping previously unknown gene expression programs involved in development and cellular commitment. Some authors assert that translational regulation is the most crucial step in the processing of genetic information [46].

Our findings reveal a substantial presence of post-transcriptional modulation during germ layer commitment. Our analysis identified genes involved in germ layer differentiation that were not apparent in the transcriptome, emphasizing the robustness of polysome profiling for uncovering post-transcriptional regulation. These data highlight the essential role of post-transcriptional mechanisms in early embryonic development, offering new perspectives on the molecular underpinnings of cell differentiation and supporting future studies on the functions of the genes identified during differentiation.

DEG: Differentially expressed genes

DHFR2: Dihydrofolate Reductase 2

DLX3: Distal-less homeobox 3

DNA: deoxyribonucleic acid

FC: Fold Change

GFP: Green fluorescent protein

GO: Gene Ontology

hESC: human embryonic stem cell

Log2FC: log2 fold change

mRNA: messenger ribonucleic acid

PTR: post-transcriptional regulation

PTNR: post-transcriptional negative regulation

PTPR: post-transcriptional positive regulation

qPCR: quantitative polymerase chain reaction

RNA: ribonucleic acid

SD: standard deviation

UNC13D: Unc-13 Homolog D

Ethical approval and consent to participate
Not applicable.

Funding

This work was financially supported by CNPq PROEP/ICC grant 442353/2019-7, INCT-REGENERA grant 88887.136364/2017-00. R.G.J. and C.D.S.H. received fellowship from FIOCRUZ and I.T.P, A.F.F.H. and A.L.R. from CAPES.

Author Contribution

R.G.J. and C.D.S.H. developed the study concept, experimental design, performed experiments, analyzed the data and wrote the main manuscript text. I.T.P. performed experiments. A.F.F.H. and L.S. performed bioinformatic analysis. A.L.R. performed experiments and wrote the main manuscript text. B.D. developed the study concept, experimental design and supervised the project.

Acknowledgement

We would like to thank all the staff of the Carlos Chagas Institute (FIOCRUZ-PR) for the laboratory and administrative support. We also would like to thanks Program for Technological Development in Tools for Health (RPT-FIOCRUZ) for using the Genomic facility at Instituto Oswaldo Cruz – Fiocruz/RJ and qPCR facility at Instituto Carlos Chagas – Fiocruz/PR.

Data Availability

RNA-seq data have been depositede at NCBI Sequence Read Archive, accessing number PRJNA1144790 and are publicly available as of the data of publication.

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Table 1 is available in the Supplementary Files section.

No competing interests reported.

Table1.pdf
Table 1. Categorization of post-transcriptional regulation based on gene expression. Adapted from Pereira et al., 2019. Red arrows indicate up-regulated genes, blue arrows indicate down-regulated genes, dashes indicate non-DEG.
SupplementaryFigure1.pdf
Figure S1. Principal component analysis for three germ layer commitment. A. Representative polysome profiling chart from endoderm, mesoderm and ectoderm cells. B. Data plot using Total RNA samples (transcriptome). C. Data plot using Polysome-bound RNA samples (translatome). D. Data plot comparing transcriptome and translatome for each sample.
SupplementaryFigure2.pdf
Figure S2. Gene ontology analysis of downregulated and common DEGs. A. Gene ontology analysis using the down-regulated genes (log2FC ≤ 2; p-value ≤ 0.01) comparing polysome and total genes sets for each differentiation. Table indicate the top five terms related to biological process. Rank of term was based at p-value for ectoderm and combined score (enrichR) for mesoderm and endoderm. B. Gene ontology analysis using the up- (log2FC ≥ 2; p-value ≤ 0.01) and down-regulated (log2FC ≤ 2; p-value ≤ 0.01) genes common for the three germ layers, comparing polysome and total samples. Table indicate the top five terms related to biological process. Rank of term was based at p-value.
SupplementaryTable1.pdf
Supplementary Table 1. List of primers used in qPCR reaction

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You are reading this latest preprint version

Post-transcriptional regulation in early cell fate commitment of germ layers

Status:

Version 1

Abstract

Figures

Introduction

Methods

Cell culture and induction of germ layers differentiation

Polysome profiling

RNA-seq and data analysis

cDNA synthesis and qPCR

Data and statistical analysis

Results

Transcriptome and translatome: Similar distribution with different gene profiles

Post-transcriptional regulation occurs during the differentiation process of all three germ layers

Translatome is more reliable than transcriptome as a parameter for analyzing the differentiation process

Some genes are regulated exclusively at the translational level

Discussion

Conclusions

Abbreviations

Declarations

Ethical approval and consent to participate
Not applicable.

Funding

Author Contribution

Acknowledgement

Data Availability

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1

Post-transcriptional regulation in early cell fate commitment of germ layers

Status:

Version 1

Abstract

Figures

Introduction

Methods

Cell culture and induction of germ layers differentiation

Polysome profiling

RNA-seq and data analysis

cDNA synthesis and qPCR

Data and statistical analysis

Results

Transcriptome and translatome: Similar distribution with different gene profiles

Post-transcriptional regulation occurs during the differentiation process of all three germ layers

Translatome is more reliable than transcriptome as a parameter for analyzing the differentiation process

Some genes are regulated exclusively at the translational level

Discussion

Conclusions

Abbreviations

Declarations

Ethical approval and consent to participate Not applicable.

Funding

Author Contribution

Acknowledgement

Data Availability

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1

Ethical approval and consent to participate
Not applicable.