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What cells in mosses behave analogously to mammalian embryonic stem cells?

What cells in mosses behave analogously to mammalian embryonic stem cells?



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The Washington Post article Ancient life awakens amid thawing ice caps and permafrost describes the work of Peter Convey, an ecologist with the British Antarctic Survey and his team, and includes a discussion of some mosses that have survived over 1000 years in permafrost, and sprung to life once "revived" in a laboratory setting.

This passage caught my eye:

It's not easy to survive being frozen solid. Jagged ice crystals can shred cell membranes and other vital biological machinery. Many plants and animals simply succumb to the cold at winter's onset, willing their seeds or eggs to spawn a new generation come spring.

Mosses have forged a tougher path. They desiccate when temperatures plummet, sidestepping the potential hazard of ice forming in their tissues. And if parts of the plant do sustain damage, certain cells can divide and differentiate into all the various tissue types that comprise a complete moss, similar to stem cells in human embryos.

What are these cells in moss called, and how similar are their functions to mammalian embryonic stem cells?

Convey and his team announced that they had awakened 1,500-year-old moss that had been buried more than three feet underground in the Antarctic permafrost. (P. Boelen/BAS)


The growing tips of roots and shoots contain regions of rapidly dividing cells in all plants that are called apical meristems. Apical meristems contain the plant's stem cells, which are not fundamentally different from the stem cells of animals.

You can begin learning more about this subject from this Nature scitable article.


Embryonic stem cell models of development

Dr. O'Shea received her Ph.D. from the University of Cambridge, and is currently an Associate Professor at the University of Michigan. Her research interests are in early neural differentiation and the extracellular matrix.

Department of Anatomy and Cell Biology, University of Michigan Medical School, 4815 MS II Bldg., Ann Arbor, MI 48109–0616. Fax: 734–763–1166Search for more papers by this author

Department of Anatomy and Cell Biology, University of Michigan Medical School, 4815 MS II Bldg., Ann Arbor, MI 48109–0616. Fax: 734–763–1166

Dr. O'Shea received her Ph.D. from the University of Cambridge, and is currently an Associate Professor at the University of Michigan. Her research interests are in early neural differentiation and the extracellular matrix.

Department of Anatomy and Cell Biology, University of Michigan Medical School, 4815 MS II Bldg., Ann Arbor, MI 48109–0616. Fax: 734–763–1166Search for more papers by this author


Abstract

Malignant cancer cells engage in a dynamic reciprocity with the tumor microenvironment (TME) that promotes tumor growth, development, and resistance to therapy. Early embryonic blastocyst microenvironments can reverse the tumorigenic phenotype of malignant cancer cells via ameliorating of TME. It is potential to apply embryonic stem cell (ESC) microenvironment to suppress the malignant behaviors of cancer cells. This study aimed to investigate a better method and the mechanism of ESC microenvironment supplied by ESCs on suppressing the malignancy of cutaneous melanoma cells. Cutaneous melanoma cell line A2058 were cultured and divided into four groups: (a) A2058-only (Control) (b) A2058 and ESCs continuously co-cultured (Group One) (c) A2058 co-cultured with daily refreshed ESCs (Group two) (d) Group one with VO-Ohpic, inhibitor of PTEN (VO-Ohpic Group). The results showed that, compared to control group, A2058 cells in group one exhibited decreased cellular proliferation, migration, invasiveness and vasculogenic mimicry concomitant with an increase in cell apoptosis, accompanied by down-regulation of PI3K/AKT pathway. Besides, the above mentioned anti-tumor effects on A2058 cells were significantly enhanced in group two but statistically weakened after administration of VO-Ohpic compared to group one. We demonstrate that ESC microenvironment reduces the malignancy of A2058 by down-regulating PI3K/AKT pathway. Notably, such anti-tumor effects can be enhanced by appropriately increasing the quality and quantity of ESCs in co-culture system. Our results suggest that ESC microenvironment could be an effective and safe approach to treating cancer.


Results

Genetic circuits compete for limited shared resources

We reasoned that competition for finite cellular resources would introduce an indirect coupling in the expression levels of two otherwise independently expressed genes. To test this, we co-transfected HEK293T cells with two constitutively expressed fluorescent proteins mCitrine and mRuby3 driven by EF1α promoters, in molar ratios ranging from 1:4 to 4:1, for a total of 50 ng (low) or 500 ng (high) of encoding plasmid (Fig. 2a). The competition for limited resources is expected to shape gene expression as presented in Fig. 2a, according to the modeling framework that will be introduced in Fig. 4a (model described in Supplementary Note 2). As expected, the total amount of 500 ng of encoding plasmids results in a dramatic drop of encoded-gene expression as compared to 50 ng (Fig. 2a, right). Furthermore, in both experimental conditions mCitrine and mRuby3 fluorescence levels are negatively correlated the higher the amount of expressed mCitrine, the lower that of mRuby3 and vice versa (Fig. 2a, right) this correlation was also more severe for 500 ng of transfected plasmid than for 50 ng.

a Left: As the total plasmid amount increases, the total expression plateaus. Right: Titration of two plasmids expressing the fluorescent proteins mCitrine and mRuby3 from EF1α promoters in ratios from 1:4 to 4:1 (total of 50 ng, top right or 500 ng of DNA, bottom right). N = 3 biological replicates. Source data are provided as a Source Data file. b Two plasmids were co-transfected, one constitutively expressing capacity monitor and tTA from a strong constitutive promoter and the other expressing X-tra from a tTA responsive promoter. Capacity monitor levels counterbalance the increase in X-tra expression. Flow cytometry data are normalized to the expression at maximal Dox. N = 3 biological replicates. Source data are provided as a Source Data file. c mRNA quantification of X-tra and a capacity monitor expressed at different molar ratios. As the X-tra increases, the mRNA levels of the capacity monitor decreases. N = 4 biological replicates. qPCR analysis was performed 48 h post-transfection and data show fold change ± SE. Source data are provided as a Source Data file. d Cells transfected with a plasmid expressing two fluorescent proteins from a bidirectional promoter were sorted according to high, intermediate, or no fluorescence (Supplementary Fig. 4) for mRNA extraction. mRNA levels expressed from endogenous genes decrease in cells with intermediate and high fluorescence. N = 3 biological replicates. Data show fold change ± SE. Individual values are plotted in Supplementary Fig. 28. Source data are provided as a Source Data file. e Capacity monitor levels are higher with an HDV ribozyme rapidly degrading the capacity monitor mRNA than with an inactive mutant, suggesting a sequestration of transcriptional resources. N = 3 biological replicates (N = 2 for HDV−, 1.6 ng/μL DOX). Source data are provided as a Source Data file. f The synthetic intron shows higher X-tra levels compared to a control and leads to reduced capacity monitor levels. N = 4 biological replicates. Source data are provided as a Source Data file. g Repressed X-tra expression leads to increased capacity monitor levels. N = 2 biological replicates for L7Ae and N = 4 for Ms2-cNOT7. Source data are provided as a Source Data file. h When X-tra is downregulated by miR-221 endogenously expressed in HEK293T cells, the capacity monitor levels increase. All flow cytometry data were acquired 48 h post-transfection and are plotted as mean ± SE. SE standard error, r.u. relative units. N = 2 biological replicates. Source data are provided as a Source Data file. Unpaired two-sided T-test. P value: ****<0.0001, ***<0.0005, **<0.005, *<0.05.

We demonstrated that the negative correlation is promoter independent: using a CMV and a PGK promoter 30 that have different expression strength in HEK293T and H1299 (Supplementary Fig. 1a), we observed analogous outcomes (Supplementary Fig. 1b–e). Further, by combining different molar ratios of mCitrine and mRuby3 encoding plasmids driven by two promoters of different strengths (EF1α or EFS) a similar behavior to Fig. 2a was observed (Supplementary Fig. 2). Finally, as many synthetic circuits rely on tunable gene expression, we next tested resource competition on transcriptional inducible systems, by modulating X-tra repression with a Doxycycline (Dox)-repressed promoter (Fig. 2b) at different concentrations of Dox (from 0 to 1 μg/mL) while keeping capacity monitor amounts constant (Fig. 2b, left). Consistent with previous results, we observed that increased repression of X-tra corresponds to increased capacity monitor levels (Fig. 2b, right).

To exclude any bias of fluorescent protein expression on resource competition, we transfected a plasmid encoding a human codon optimized variant of the bacterial σ-factor sigW in increasing amounts with a fixed concentration of the mCitrine capacity monitor plasmid, and demonstrated similar behavior to fluorescent protein expression (Supplementary Fig. 3).

Finally, to avoid any experimental confounds as the source of our observations, we showed that neither cell seeding nor nutrient supply had any apparent effect on the expression levels of the two genes, one of which was titrated whereas the second was held at a constant copy number (Supplementary Fig. 4).

These proof-of-concept experiments demonstrate that (i) gene expression in mammalian synthetic circuits is connected even in the absence of direct regulation and (ii) expression of exogenous genes is limited by cellular resource availability.

Transcriptional and translational resources are limiting

Since several different resource pools could be responsible for the observed effects described above, we set out to characterize the individual contributions of transcriptional and translational resource limitation to cellular burden in HEK293T and H1299 cells (Fig. 2). To evaluate potential limitations in transcriptional resources and the consequent gene competition for mRNA expression, we quantified mRNA levels in cells expressing X-tra/capacity monitor molar ratios from 1:1 to 2.5:1 in H1299 cells for a total of 500 ng of plasmid DNA (corresponding protein data in Supplementary Fig. 8a). We observed that as the X-tra mRNA increased, the capacity monitor mRNA levels decreased (Fig. 2c), supporting the hypothesis that shared transcriptional resources are indeed a limiting factor in mammalian synthetic gene co-expression.

To investigate whether the expression of endogenous genes is also affected by heterologous genetic payloads, we transfected H1299 cells with a plasmid encoding for EGFP and mKate under the control of a bidirectional promoter. We then sorted transfected cells according to high and intermediate levels of fluorescent markers as well as non-transfected cells (absence of fluorescence) (Supplementary Fig. 5). We then quantified the mRNA levels of three endogenous genes (CyCA2, eIF4E, GAPDH, Fig. 2d, Supplementary Fig. 28). Notably, in transfected cells that express high and intermediate levels of EGFP and mKate, the expression of CyCA2, eIF4E, and GAPDH decreases when compared to the non-transfected population. We also measured the mRNA levels of CyCA2, eIF4E, and GAPDH in cells transfected with X-tra/capacity monitor molar ratios from 1:1 to 2:1 and observed a progressive, albeit not dramatic decrease with higher amounts of X-tra when compared to the 1:1 ratio (Supplementary Fig. 6). Of note, in the latter experiment cells were not sorted before mRNA extraction.

To provide further support to the observations on transcriptional burden on exogenous genes (Fig. 2c), we implemented a genetic circuit that can selectively overload the transcriptional resource pool without sequestering translational resources. The system is based on the self-cleaving hepatitis delta virus (HDV) ribozyme, which ensures that most of the transcribed mRNA is cleaved and thus destabilized (Fig. 2e, left). The circuit is composed of a single plasmid with two transcriptional units (TUs). One TU contains a tTA transcription factor co-expressed with the mRuby3 (capacity monitor) via the P2A peptide, driven by a constitutive promoter. The second TU includes the HDV-X-tra expression regulated by the TRE promoter. In this setup, Dox can be used to modulate the amount of burden imposed, similar to what was already shown in Fig. 2b.

We compared this circuit to a catalytically inactive mutant of the HDV ribozyme in HEK293T cells. As expected, we observed that when the HDV ribozyme is inactive, X-tra protein levels increase with decreasing amounts of Dox (Supplementary Fig. 7, top pale pink bar), whereas those of the capacity monitor decrease (Fig. 2e, bottom pale blue bar). In contrast, when the HDV ribozyme is active, X-tra expression is strongly reduced and only minorly increasing with lower Dox concentrations (Supplementary Fig. 7, top dark purple bar). Here, the capacity monitor levels decrease to a smaller extent than in the previous condition, supporting the observations in Fig. 2c that transcriptional resources are limited to a certain extent (Fig. 2e, dark blue bar). Interestingly, the expression levels of the capacity monitor with active HDV ribozyme are higher compared to the inactive mutant (Supplementary Fig. 7, bottom dark blue bar). We suggest that, assuming that the X-tra mRNA with an active HDV ribozyme is decapped and rapidly degraded, it is likely to sequester fewer translational resources, which should result in higher expression of the capacity monitor.

Transcriptional resource pool sharing is therefore at least partially responsible for the described gene expression trade-offs, and translational resources may represent an additional bottleneck to the overall expression of synthetic genes. We confirmed this hypothesis by adding a synthetic intron 31 in the 5′ untranslated region (UTR) of the X-tra fluorescent protein (Fig. 2f, top). The synthetic intron enhances translation by augmenting mRNA export from the nucleus to the cytoplasm 31 and therefore imposes specific translational load. Indeed, we observed higher expression of X-tra in HEK293T (Fig. 2f) and H1299 (Supplementary Fig. 8b) cell lines in the presence of a synthetic intron, accompanied by lower capacity monitor levels, confirming that resources employed for translational regulation are also limiting. Thus our data collectively indicate that exogenous genes compete for resources both at the transcriptional and translational levels, overall imposing a gene expression burden on mammalian cells.

Since one of the goals in synthetic biology is output predictability, reproducibility, and robustness, gene expression burden is a key issue to address. We reasoned that post-transcriptional and translational regulators, such as RBPs and miRNAs, may free up cellular resources 32 by repressing target mRNA translation or inducing its degradation. If true, they could be exploited in more robust circuit topologies to reduce gene expression load, resulting in improved performance and predictability of engineered circuits. Therefore, we tested two RBPs, L7Ae and Ms2-cNOT7 (refs. 33,34 ), as well as endogenous miRNAs, miR-221 and miR-31, in HEK293T (Fig. 2g, h) and H1299 (Supplementary Fig. 8c, d) respectively. For each system, a fluorescent protein encoding mRNA targeted by either RBPs or miRNAs (X-tra) was co-expressed with a second, constitutively expressed fluorescent readout (capacity monitor). L7Ae binds the 5′UTR of the X-tra mRNA inhibiting its translation, whereas Ms2 binds target sites (TS) in the 3′UTR of the X-tra transcript, allowing cNOT7 to cut the polyA tail to destabilize the target mRNA 33 . We consistently observed in both cell lines that X-tra downregulation by RBPs results in increased levels of the capacity monitor (Fig. 2g, Supplementary Fig. 8c).

miRNAs operate by either translation inhibition or mRNA degradation, according to complete 35 or partial 36 complementarity to the mRNA target. To evaluate the effect of miRNA regulation on cellular resource reallocation, we placed three perfect complementary TS in the 3′UTR of X-tra, which respond to the endogenous miR-221 and miR-31 highly expressed in HEK293T and H1299 cells. The capacity monitor expression levels increased when the X-tra mRNA was downregulated by miRNAs, as compared to controls lacking miRNA TS (Fig. 2h, Supplementary Fig. 8d).

To further demonstrate that the burden imposed by synthetic circuits is cell-type independent, we performed the same set of experiments of Supplementary Fig. 1d and Fig. 2f–h in U2OS, HeLa, and CHO-K1 cells, obtaining similar results (Supplementary Figs. 9–11). Interestingly, even CHO-K1 cells, which are the workhorses of the biopharmaceutical industry due to their high productive capability 37 show cellular burden. Redistribution of resources was also observed by the RBPs L7Ae and MS2-cNot7 and the highly expressed endogenous miR-221 and miR-21 in U2OS and HeLa/CHO-K1 cells, respectively.

These results confirm that post-transcriptional regulators can redistribute intracellular resources and, importantly, that this phenomenon is cell-context independent. The extent of negative correlation between X-tra and capacity monitor expression, as well as the amount of repression by post-transcriptional regulators, differs across cell lines this could be the consequence of several factors, such as the relative abundance of transcriptional, post-transcriptional, and translational resources.

A major advantage of miRNAs over RBPs is that they are endogenously expressed and cell line specific. Thus, their expression does not impose an additional burden, and since several thousand endogenous miRNAs with different TS are naturally present in mammalian cells 38 , the design space is rather large, giving rise to a tremendous number of circuits that can be easily tailored to the cell/tissue of interest. Based on the results presented here, we envision that genetic circuits that mitigate resource competition via miRNAs may be designed for any mammalian cell line with a very broad set of potential applications.

Characterizing the effect of miRNAs on resource distribution

We sought to characterize the correlation between miRNA-mediated downregulation and resource redistribution by building a library of miRNA sensors for miR-31, which is endogenously expressed in H1299 lung cancer cells 39 . The miRNA sensor is composed of the fluorescent reporter mKate with or without miR-31 TS, encoded along with the capacity monitor (EGFP) on a single plasmid with a bidirectional promoter (Fig. 3a). The library includes 0, 1, or 3 fully complementary miR-TS in the 3′ or 5′UTR of mKate.

a Schematics of experimental design to infer miRNA-mediated cellular resources redistribution. EGFP (capacity monitor) and mKate (miRNA sensor) are encoded on the same bidirectional CMV promoter plasmid. One or 3 TS for miR-31 (TS) are added either in the 3′ or 5′UTR of mKate. Control: no miR-31 TS. Hypothesis: in the absence of miR-31 regulation, capacity monitor and miRNA sensor are expressed to a certain level (top). In the presence of miR-31, lower miRNA sensor levels correlate with higher capacity monitor expression (middle). This condition is reversed by an miR-31 inhibitor (bottom). b Fold change of miRNA sensor and capacity monitor protein levels compared to control (set to 1). EGFP increases up to fivefold with the strongest downregulation of mKate (3 TS 5′UTR). Flow cytometry data were acquired 48 h post-transfection and are plotted as mean ± SE. SE standard error, r.u. relative units. N = 6 biological replicates. Source data are provided as a Source Data file. Unpaired two-sided T-test. P value: ****<0.0001, **<0.005, *<0.05. c When miR-31 activity was impaired by a miR-31 inhibitor, the rescue of mKate expression corresponds to reduced EGFP levels, whereas both fluorescent proteins do not vary in the control. The heatmaps represent the fold change derived by flow cytometry data, calculated as the ratio between the geometric mean of six biological replicates and the corresponding geometric mean in the control condition. Source data are provided as a Source Data file. Bar plots and statistical analysis are reported in Supplementary Fig. 12.

Similar to what was previously observed (Supplementary Fig. 8d), when the miRNA sensor’s levels decrease as a consequence of miR-31 regulation, the expression of the capacity monitor increases. The strongest repression was achieved with 3 TS in the 5′UTR and was accompanied by corresponding higher capacity monitor levels (Fig. 3b). Conversely, when we rescued mKate expression by a miR-31 inhibitor (Fig. 3c, left and Supplementary Fig. 12, red bars), the capacity monitor levels decreased (Fig. 3c, right and Supplementary Fig. 12, dark blue bars) demonstrating that miRNA sensor and capacity monitor levels are linked. Interestingly, the effect of the miRNA inhibitor was more pronounced with TS placed in the 3′UTR. Synthetic miRNA inhibitors bind to endogenous miRNAs in an irreversible manner 40 , but differences in their action (e.g. when TS are placed in the 3′ versus 5′UTR), as well as mechanistic insights into these differences, are still missing.

To confirm that miRNA-mediated resource redistribution is independent of experimental setting and plasmid design, we encoded the miRNA sensor and capacity monitor on two separate plasmids. Similar to previous results, miRNA sensor and capacity monitor were negatively correlated (Supplementary Fig. 13a), suggesting that cellular burden and miRNA-dependent resource reallocation are a common challenge and solution respectively. Downregulation of the miRNA sensor was also confirmed by qPCR (Supplementary Fig. 13b). Finally, when the miR-31 sensor was transfected in low miR-31 cell lines such as U2OS and HEK293T, neither the miRNA sensor nor the capacity monitor levels varied (Supplementary Fig. 14), further confirming the miRNA-dependent resource reallocation.

We showed in Fig. 2h and Supplementary Figs. 8d, 9d, 10d and 11d that miRNA-dependent resource reallocation is observed across different cell lines, by expressing cell-specific miRNA sensors which include 3 TS in the 3′UTR. We then built a library of sensors with different numbers and locations of TS for miRNA-221 and -21 which are highly expressed in U2OS and HeLa cells, respectively. We also confirmed here that miRNA sensor and capacity monitor are inversely correlated, consistent with our observations in H1299 cells (Supplementary Figs. 15 and 16).

Overall these data show that miRNAs can be used to develop resource-aware plasmid-designs harboring burden-mitigating circuit topologies, and that the number and location of TS can be tuned to achieve desired protein expression levels.

A resource-aware model framework

In order to provide a better understanding of our results, we developed a general resource-aware model, which offers a simple and convenient framework for extending existing models of biochemical reactions allowing them to incorporate the effects of shared limited resources.

Figure 4a illustrates an overview of the framework. The main idea is to replace the rates of reactions that involve a shared resource with an effective reaction rate that captures the reduced availability of that resource due to the presence of competing genes. To create a distinction between regular reactions and resource-limited ones, we use double-headed reaction arrows to denote resource-limited reactions as illustrated at the bottom of Fig. 4a. This double-headed arrow summarizes the set of intermediate interactions shown in more detail at the top left of Fig. 4a. Here, the substrate Ai binds resource R with rate k + i to form the complex Ci. This reaction is also assumed to be reversible with rate ki. With a rate k cat i the complex gives rise to the product Bi, while also freeing up both the substrate Ai and the resource R. We assume that the total amount of resource, R total , is conserved and remains constant at the time scale of the considered reactions. Considering all possible substrates that require resource R and assuming that Ci is in quasi-steady state, the rate for resource-limited production can be expressed as k eff i, shown in the top right corner of Fig. 4a. A more detailed derivation can be found in Supplementary Note 1. k eff i is a function of the total amount of resources and the current concentration of all substrates competing for this resource. This expression can be readily used to substitute all reaction rates that involve shared and limited resources.

a General framework for transforming molecular interaction network models. Existing models of molecular interaction networks can be transformed to include shared limiting resources by substituting ki, the reaction rate of a resource-limited production, with k eff i. Shown above an exemplary resource-limited production are the detailed interactions between the substrate and the shared resource. b Limited shared resources reproduce non-monotonous dose response in open-loop and incoherent feedforward circuit topologies. On the left, a graphical representation of a model for both the open-loop (OLP) and incoherent feedforward (IFF) topologies from Lillacci et al. 24 . Transcriptional activation is modeled by a Hill-type function. The solid arrows denote reactions assumed to follow the law of mass action. The model incorporates resources as introduced in panel a. These reactions are depicted as double-headed arrows. The model was fit to data obtained by transiently transfecting HEK293T cells with increasing amounts of plasmid encoding tTA-Cerulean. The data and the fit are shown on the right. c Limited shared resources reproduce non-monotonous dose-response in feedback and hybrid circuit topologies. The model shown on the left is the same as in panel b with an additional negative feedback from miR-FF4 to tTA-mRNA. These topologies correspond to the feedback (FBK) and hybrid (HYB) topologies from Lillacci et al. 24 The activation of gene expression by tTA-Cerulean is modeled by a Hill-type function as shown in the center. Reactions with double-headed arrows denote resource-limited production reactions as introduced in panel a. Solid arrows are assumed to follow the law of mass action. The model was fit to experimental data obtained from transient transfections with increasing amounts of plasmid encoding tTA-Cerulean. A description of the models can be found in Supplementary Note 4 and the parameter values obtained by fitting are summarized in Supplementary Table 7. Data were obtained 48 h after transfection and are plotted as mean ± SE. SE standard error. N = 3 biological replicates. Source data are provided as a Source Data file.

To demonstrate the effectiveness of our modeling framework, we extend the models of different circuit topologies introduced in Lillacci et al. 24 to include limited resources and show that the resulting extended models recapitulate the previously unexplained non-intuitive experimental observations.

The four topologies considered in Lillacci et al. 24 were split into two groups based on the presence of negative feedback from the fluorescent protein DsRed to the transcriptional activator (tTA). The first group consisted of the open-loop (OLP) and incoherent feedforward (IFF) topologies. In both these circuits, the constitutively expressed transcriptional transactivator, fused to the fluorescent protein Cerulean (tTA-Cer), activates the expression of the fluorescent protein DsRed. Furthermore, the gene of DsRed intronically encodes the synthetic miRNA FF4 (miR-FF4). In the IFF topology, the matched target of this miRNA is present in the 3′UTR of the DsRed gene. This target is replaced by a mismatched target for the miRNA FF5 in the OLP. These detailed interactions are depicted here in Fig. 4b, left side. To observe potential shifts in the allocation of resources, we generated dose–response curves by increasing the amount of transfected tTA-Cer plasmid, while the other two plasmids, containing DsRed and the constitutively expressed fluorescent transfection reporter mCitrine, were held constant. As can be seen from the model fit, plotted as a solid line in the data graph, the extended model reproduces the non-monotonic behavior of the dose responses (Fig. 4b, right).

The second group of topologies considered by Lillacci et al. 24 consisted of the feedback (FBK) and the FBK + IFF hybrid (HYB) topologies. In addition to all the interactions described for the OLP and IFF circuits, the FBK and the HYB circuits possess miR-FF4 targets in the 3′UTR of the tTA-Cer gene, which introduces negative feedback. Furthermore, the FBK and HYB differ from each other by the presence of a matched target for miR-FF4 in the HYB topology, which introduces incoherent feedforward and is replaced by a mismatched FF5 target in the FBK circuit. All the interactions are illustrated in detail in Fig. 4c, left. The dose–response curves for the two circuits were obtained as described above. Again, the fit of the extended model to the data captures its rather unexpected behavior (Fig. 4c, right).

Lastly, we also apply our framework to model the gene expression systems presented in Figs. 2b, e, g and 3b. The resulting model fits are shown in Supplementary Fig. 17. The models are described in Supplementary Note 6 and the parameter values obtained by fitting are summarized in Supplementary Tables 13–16.

Our simple framework adapts existing models of gene expression to include pools of shared and limited resources. We show that it can be used to provide an explanation for unintuitive dose responses in tTA-based circuits. With this framework as a tool, we believe that performance issues attributed to gene expression burden can be addressed head-on in the design phase of circuit-building, thereby reducing the need for costly subsequent build-test-learn iterations.

Mitigating burden with iFFL circuits

We implemented a strategy that exploits miRNA to reduce the indirect coupling between co-expressed genes. In particular, we took advantage of the fact that miRNA production also requires (pre-translational) cellular resources, therefore acting as a sensor for resource availability. Because of this, it is possible to reduce the coupling between genes co-expressed via a common resource pool by introducing miRNA-mediated repression of those genes (as long as the miRNA itself is also affected by the same resource pool). Since both the miRNA and the miRNA-repressed gene are affected by the availability of resources, miRNA-mediated repression implements an iFFL similar to previously published circuits 24,29,41 (Fig. 5a). Interestingly, this iFFL-based circuit constitutes a biological implementation of the miRNA circuit proposed by Zechner et al. 42 . In this setting, the miRNA can be interpreted as an estimator of its cellular context (e.g. amount of free resources) and acts to filter out this context, thereby minimizing its impact on the output of interest.

a The microRNA-based incoherent feedforward loop (iFFL) motif. b Mitigation system based on endogenous microRNA. At high copy number of the X-tra, resources are drawn away from the production of the GOI and miR-31. By sensing the resource availability and repressing the GOI less when there are fewer resources, the miRNA reduces the effect of limited resources. c Two plasmids were co-transfected into H1299 cells which respectively express the X-tra and GOI genes (EGFP and mKate respectively (b)), and the molar ratio of the X-tra:GOI plasmid was progressively increased. The presence of miR-31 TS in mKate 5′UTR mitigates effects due to resource sharing. The parameter values obtained by fitting are summarized in Supplementary Table 8. N = 3 biological replicates. d Mitigation system based on synthetic miRNA. In the presence of many copies of the X-tra gene, resources are drawn away from the production of both the GOIs and the miR-FF4. Due to lower production of miR-FF4 the GOIs are less repressed. This compensates for the reduced availability of resources. e A plasmid encoding both the fluorescent protein mCitrine and an intronic microRNA expressed from the mRuby3 gene (GOI1, GOI2 and miR-FF4 (d)) was co-transfected into HEK293T cells with increasing amounts of a plasmid expressing the X-tra gene (miRFP670 (d)). The impact of resource limitation on both GOIs was reduced when they contained three miR-FF4 targets in their 3′UTRs compared to when they contained three mismatched miR-FF5 targets. The parameter values obtained by fitting are summarized in Supplementary Table 9. N = 3 biological replicates. Source data are provided as a Source Data file. A description of the models can be found in Supplementary Note 5. Flow cytometry data were acquired 48 h post-transfection and are plotted as mean ± SE. SE standard error, r.u. relative units.

We explored this strategy for an endogenously expressed miRNA (Fig. 5b, c) and a synthetic miRNA encoded on a plasmid (Fig. 5d, e). More specifically, Fig. 5b describes a strategy that exploits endogenous miRNAs to reduce the coupling of a gene of interest (GOI) to the expression level of other genes, introduced by the limitation in resources. Implementation of this strategy only requires adding the TS of an endogenous miRNA to the 5′UTR of the gene of interest (mKate). In our experimental setup, when the copy number of a second gene (X-tra) is increased, resources are drawn away from the expression of mKate and allocated to the expression of X-tra. The shift in resource allocation is expected to also affect miR-31, which acts as a capacity monitor. This leads to a reduction in the repression of mKate, effectively compensating for the burden imposed by the co-expression of the X-tra gene.

To demonstrate this mitigation approach experimentally, we co-transfected H1299 cells with increasing amounts of EGFP (X-tra), along with a constant amount of mKate (GOI) that either includes (for mitigation) or omits (no mitigation) three miR-31-TS in the 5′UTR. As expected, the expression level of X-tra approached saturation as the plasmid copy number increased, both for the targeted and non-targeted GOI variants (Fig. 5c). In agreement with previous results, the expression of the non-targeted GOI strongly decreased with increased expression of X-tra. Conversely, the decrease in expression of the targeted GOI was only about a third of that of the non-targeted variant, indicating improved adaptation to changes in resource availability (Fig. 5c and Supplementary Fig. 18). This observation was also captured well by a model of the system that explicitly considered resources, as described in the previous section. It should be noted that while the relative dynamic output range of X-tra is slightly reduced (fold change of 1.94× with mitigation versus 2.18× without mitigation (Fig. 5c), our data show that the absolute levels of X-tra increases about 2× in the presence of miR-31-based iFFL, de facto benefiting from this network topology (Supplementary Fig. 19). Analogously, miR-221-iFFL circuits specific for U2OS and HEK293T cells 43 (Supplementary Fig. 21) show improved robustness to burden imposed by increasing exogenous gene load (Supplementary Figs. 20 and 22). Models used for fitting and the resulting parameter values are summarized in Supplementary Note 5 and Supplementary Tables 10 and 11.

Importantly, the delivery of genetic payloads also affects the expression of endogenous genes (CyCA2, elF4E, and GAPDH), as shown in Fig. 2d. We then sought to compare the expression of the same endogenous genes in the presence or absence of miR-31 sensor in H1299 cells. After 48 h from transfection of EGFP and mKate on a bidirectional plasmid, with mKate either including (miRNA sensor) or not (noTS) TS for miR-31, we sorted cells according to high, intermediate, or absence of fluorescence expression (Supplementary Fig. 23a) and performed qPCR. Curiously, we observed that in cells transfected with miR-31 sensor, the decrease in the expression of the endogenous genes was much lower than in its absence (Supplementary Fig. 23c). Furthermore, the expression of endogenous genes was inversely proportional to the levels of fluorescent proteins (Supplementary Fig. 22b). Thus, the lower expression of endogenous genes due to the burden imposed by exogenous payloads is counteracted by the miR-31-sensor. To investigate whether the use of endogenous miRNAs may impair the regulation of native targets, we measured the expression of SATB2 mRNA, a natural target of miR-31 (ref. 44 ) in cells transfected with miR-31-sensor versus the noTS control, and observed no difference between the two conditions (Supplementary Fig. 24).

Motivated by our desire to achieve portability across cell lines and multiple-output regulation, we implemented and tested a synthetic miRNA-iFFL circuit that tunes two GOIs (Fig. 5d). Similar to the endogenous case, the genes of interest, mCitrine (GOI1) and mRuby3 (GOI2), encode TS for the miRNA-FF4 in their 3′UTRs. In contrast to endogenous miRNA expression, however, here the miRNA is expressed intronically from GOI2. In this way, the circuit forms a self-contained unit that can be easily transferred between cell types.

We co-transfected HEK293T cells with a plasmid encoding constitutively expressed miRFP670 (X-tra) and a plasmid composed of two TUs, each expressed under the constitutive promoter EF1α (Fig. 5d). The first TU encodes mCitrine, whereas the second drives mRuby3. Furthermore, the 3′UTR of mCitrine and mRuby3 contained either three TS for the synthetic miRNA-FF4 or three mismatched miR-FF5 TS (negative control). The miRNA-FF4 was intronically encoded in the mRuby3 gene. Identically to the endogenous case, the amount of X-tra plasmid was increased while keeping the GOIs plasmid constant. Again, expression of X-tra increased and approached saturation with increasing molar amounts and consequently, the non-targeted variants of the GOIs decreased (TFF5 in Fig. 5e). Conversely, the expression of the targeted variants (TFF4 in Fig. 5e) decreased to a lesser extent than the non-targeted ones, analogously to what was observed for endogenous miRNAs, albeit with lower efficiency. Finally, to demonstrate the portability of the device we tested the approach in mouse embryonic stem cells (Supplementary Fig. 25). Here, adaptation to shifts in resource availability was similar to the endogenous miRNA-based regulation (Fig. 5c). The model used for fitting and the resulting parameter values are summarized in Supplementary Note 5 and Supplementary Table 12. Thus, we showed that also in entirely synthetic systems, adaptation to shifts in resource availability was achieved. To ensure that the observed mitigation was not caused by a higher tolerance to changes in availability at lower expression levels, we showed analytically using the described modeling framework that the normalized expression at lower levels was more sensitive to burden (Supplementary Note 3).

Indeed, mitigation comes at the cost of the maximal achievable expression levels for the capacity monitor. Moreover, tuning the iFFL circuit to become even less sensitive to changes in available resources will necessarily further limit the maximal expression. This trade-off is intrinsic to the iFFL mitigation strategy. Nevertheless, these results suggest that our approach can be used to mitigate resource-mediated coupling of gene expression despite cell-to-cell variability, demonstrating the portability and broad applicability of our findings. Our results demonstrate that iFFL circuits can mitigate burden from transgene expression in mammalian cells. Importantly, by using miRNAs one can either opt for endogenous miRNAs to specifically tailor a circuit to a desired cell line or create a portable circuit by using a synthetic miRNA such as miR-FF4.


Introduction

The mammalian neocortex is the most evolutionarily recent structure of the central nervous system and is responsible for processing sensory information, controlling motor output, and mediating higher-order cognitive functions [1]. Excitatory neocortical neurons are generated from neural precursor cells (NPCs) between embryonic (E) days 10.5 and E17.5 in mice and gestational week 7–27 in humans, followed by astrocytes and oligodendrocytes [1]. Production of the correct number and subtypes of neurons during this critical developmental window is crucial for the formation of functional neural circuitry, and defects in this process contribute to neurodevelopmental and neurological disorders including microcephaly, autism spectrum disorder, epilepsy, and schizophrenia [2]. In this review, we provide an overview of embryonic and adult ventricular-subventricular zone (V-SVZ) neurogenesis and the emerging role of posttranscriptional control. We also discuss the concept of transcriptional priming in the context of posttranscriptional mechanisms. Finally, we integrate these findings into a model of posttranscriptional control of neurogenesis.


Results

Characterization of mouse ESCs that lack functional Pax7

To directly test the role of Pax7 in the myogenic differentiation of pluripotent stem cells, we derived mouse ESCs that lack a functional Pax7 transcription factor. To this point, we used previously generated Pax7 +/− mice [29] on a C57Bl6N background and crossed them with 129Sv mice, that is, a strain of mice permissive for the establishment of ESC lines [65]. We used F1 (CB76BlN ×129Sv) animals with a Pax7 +/− genotype to generate blastocysts. The resulting wild-type and mutant ESC lines were genotyped (Fig. 1A) and karyotyped. Finally, based on the chromosome count, two wild-type (Pax7wt1 and Pax7wt2) and two mutant (Pax7ko1 and Pax7ko2) cell lines were chosen for further analyses. These ESCs were chosen based on the highest proportion of 2n metaphase plates (40 chromosomes), which was 97% for Pax7wt1, 94.6% for Pax7wt2, 87.5% for Pax7ko1, and 80% for Pax7ko2 ESCs. These four selected cell lines were carefully analyzed to document their pluripotent characteristics. Their in vitro characteristics were compared with analyses of the commercially available D3 ESC line, which was previously shown to be pluripotent (eg, [66,67]).

FIG. 1. Characteristics of Pax7wt, Pax7ko, and D3 embryonic stem cells (ESCs). (A) Genotyping of Pax7wt and Pax7ko ESCs. Representative agarose gel images with PCR products ∼200 bp representing the wild-type allele and ∼600 bp representing the mutant allele. (B) qRT-PCR analysis of Nanog and Sox2 mRNA transcript levels in Pax7wt, Pax7ko, and D3 ESCs. (C) Localization of Nanog (green) and nuclei (red) in undifferentiated Pax7wt, Pax7ko, and D3 ESC colonies growing on a mouse embryonic fibroblast (MEF) feeder layer (out of focus). (D) Localization of Oct-4 (green) and nuclei (red) in undifferentiated Pax7wt, Pax7ko, and D3 ESCs that formed colonies grown on an MEF feeder layer (out of focus) bar, 50 μm. (E) Proportion of Nanog-expressing cells detected by immunolocalization. (F) Proportion of Oct-4-expressing cells detected by immunolocalization. (G) Proportion of SSEA-1-expressing cells detected by flow cytometry. (E–G) The bottom and top of the box represent the first and third quartiles, band inside the box or band alone represents the second quartile (the median), and the ends of the whiskers represent the minimum and maximum of all of the data. (H) Histological analysis of teratomas derived from Pax7wt and Pax7ko ESCs (a–d), ectodermal squamous epithelium (red stars) (e–h), endodermal epithelia and (i–l), mesodermal cartilage (yellow star). Gomori's trichrome staining was used in (a–e, k–l) Masson's trichrome staining was used in (g–h) and hematoxylin and eosin staining was used in (f, i, j) bar, 100 μm. The mRNA transcript levels measured by qPCR as CT values were normalized to the CT value of actin data are represented as a percentage of expression observed in a mouse embryo at day 13.5 of development. Color images available online at www.liebertpub.com/scd

Pax7wt1, Pax7wt2, Pax7ko1, Pax7ko2, and D3 ESCs were cultured under conditions that supported pluripotency and self-renewal, that is, in a medium containing LIF, and then were processed for mRNA analysis or immunolocalization of pluripotency markers. All analyzed ESCs expressed Sox2 and Nanog, as shown by qPCR (Fig. 1B) and immunodetection (Fig. 1C). In all tested cell lines, Nanog was present in the nuclei of ESCs (Fig. 1C). Analyses of cell colonies revealed that in the D3 line, ∼86% of cells expressed Nanog, compared with 56.2% in Pax7wt1, 53.8% in Pax7wt2, 59% in Pax7ko1, and 80.7% in Pax7ko2 (Fig. 1E). Similarly, Oct-4 was detectable in the nuclei of all of cell lines analyzed, as 92% of D3 cells, 88% of Pax7wt1 cells, 85% of Pax7wt2 cells, 90% of Pax7ko1 cells, and 92% of Pax7ko2 cells expressed this factor (Fig. 1D, F). Flow cytometry analysis showed that ESCs also expressed SSEA-1 antigen (D3, 68% Pax7wt1, 81.9% Pax7wt2, 53.8% Pax7ko1, 80.7% and Pax7ko2, 59% Fig. 1G).

Next, we compared the ability of Pax7wt and Pax7ko ESC lines to differentiate into tissues that originated from the three germ layers. To do so, we tested whether they could differentiate in vivo (ie, form teratomas) or in vitro (ie, in EBs). In both settings, spatiotemporal interactions between differentiating cells allowed for the generation of ectodermal, endodermal, and mesodermal derivatives (reviewed in Grabowska et al. [17]). In vivo assays showed that all cell lines analyzed—Pax7wt1, Pax7wt2, Pax7ko1, and Pax7ko2 cells—could form complex teratomas. Histological analyses revealed that many tissues could be generated, including ectodermal squamous epithelium (Fig. 1H, a–d), endodermal ciliated epithelia (Fig. 1H, h) or secretory epithelium (Fig. 1H, e–g), as well as tissues of mesodermal origin, such as connective tissues, including cartilage (Fig. 1H, i–l).

In vitro differentiation of ESCs could be induced by the formation of EBs (Fig. 2A). In brief, for the first 2 days, all EBs were cultured in media supplemented with 15% FBS. Starting from 13 day of culture, EBs were cultured in media in which the concentration of FBS was reduced. The choice of such conditions was based on previously published observations that suggested that deprivation of certain factors present in FBS could prevent proliferation and enhance differentiation (reviewed in Salani et al. [68]). Under such conditions, Pax7wt1, Pax7wt2, Pax7ko1, Pax7ko2, and D3 ESCs expressed the ectodermal marker Pax6, the endodermal marker Foxa2, and the mesodermal marker Brachyury (T), as indicated by qPCR analyses (Fig. 2B). The ability of the ESCs that we tested to differentiate into cardiomyocytes was confirmed based on a protocol designed by Wobus et al. (Fig. 2C). All ESC lines that we tested synthesized cardiac troponin T, as shown by immunolocalization (Fig. 2D). Thus, we established that the ESCs that we derived, like D3 cells, are pluripotent, as they could differentiate in vitro and in vivo into cells of ectodermal, endodermal, and mesodermal origin.

FIG. 2. Analysis of the differentiation of Pax7wt, Pax7ko, and D3 ESCs. (A) This schematic diagram shows the protocol used to induce the differentiation of ESCs. (B) qRT-PCR analysis of Pax6, Foxa2, and T (Brachyury) mRNA transcript levels in undifferentiated ESCs at day 0, embryoid bodies (EBs) at days 2 and 7, and EB outgrowths at day 21 of in vitro culture. (C) This schematic diagram shows the protocol used to induce the differentiation of ESCs into cardiomyocytes. (D) Localization of cardiac troponin T (green) and nuclei (red) in Pax7wt, Pax7ko, and D3 ESCs at 12 days of in vitro culture bar 50 μm. Data obtained by qPCR analysis of mRNA transcript levels are presented as CT values normalized against those of actin data represent the percentage of expression observed in mouse embryos at day 13.5 of development. Color images available online at www.liebertpub.com/scd

Myogenic differentiation of Pax7wt and Pax7ko ESCs

Subsequent analyses of Pax7wt and Pax7ko ESCs focused on the ability of these cells to differentiate into skeletal myoblasts. To induce myogenic differentiation in vitro, ESCs were subjected to a protocol involving the generation of EBs and EB outgrowths (Fig. 3A). According to this experimental scheme, starting from 13 day of culture, EBs were placed in media characterized by a reduced concentration of FBS [68]. From days 2 to 5, to support myogenic differentiation, the culture medium was supplemented with RA and a combination of insulin, transferrin, and selenium (ITS) [56,58,69].

FIG. 3. Analysis of the expression of pluripotency- and differentiation-associated genes in Pax7wt and Pax7ko ESCs. (A) This schematic diagram shows the protocol used to induce the myogenic differentiation of ESCs. (B) qRT-PCR analysis of Pax3, MyoD, and Myog mRNA transcript levels in undifferentiated ESCs cultured under pluripotency supporting conditions. (C) Semi-quantitative RT-PCR analysis of Pax7 expression in undifferentiated Pax7wt1 ESCs at day 0, in differentiating EBs at days 2, 4, 6, and 7, and in EB outgrowths at day 14 of culture. Representative agarose gel images with PCR products ∼466 bp representing Pax7 and ∼540 bp representing GAPDH. (D) qRT-PCR analysis of T (Brachyury) expression in undifferentiated ESCs at day 0, in EBs at days 2 and 7, and in EB outgrowths at day 21 of in vitro culture. (E) The Venn graphs show the number of transcripts for which expression is common or different for Pax7wt1 and Pax7ko1 ESCs analyzed at various time points of differentiation. Colors: green, undifferentiated ESCs (day 0) blue, EBs (day 7) pink, EB outgrowths (day 21). Significantly differentially expressed genes were identified using analysis of variance (ANOVA). (F) Analysis of the expression levels of transcripts that encoded pluripotency- and differentiation-associated proteins each group included triplicate measurements. Blue color indicates low expression and red color indicates high expression levels of transcripts. All genes shown were selected from the complete list presented in Supplementary Tables S1–S6. (G) qPCR analysis of Sox2, Nanog, Pdgfrα, Fgfr1, and Gata6 mRNA transcript levels. Data obtained using qPCR to assess mRNA transcript levels are shown as CT values, which were normalized against those of actin data are represented as the percentage of expression observed in mouse embryos at day 13.5 of development. Color images available online at www.liebertpub.com/scd

In preliminary experiments, we confirmed that ESCs cultured under conditions that supported pluripotency did not express significant levels of transcripts that encoded myogenic markers, such as Pax3, or MRFs, such as MyoD1 or Myog. The levels of mRNA transcripts that encoded the abovementioned factors were much lower than those detected in day 13.5 mouse embryos, which served as a positive control (Fig. 3B). Using sqRT-PCR, we also showed that Pax7wt ESCs could induce Pax7, which became detectable at days 7 and 14 of differentiation (Fig 3C).

Next, we analyzed the expression of genes that regulate pluripotency and differentiation by the microarray technique. For these analyses, we used Pax7wt1 and Pax7ko1 ESCs. We chose these two cell lines because they induced high levels of the mesodermal marker T (Fig. 3D) and were characterized by a high efficiency of myoblast formation (based on MyHC and MyoD expression, see Fig. 4), compared with two other cell lines differentiated in the presence of RA and ITS (Fig. 3A). For each genotype and time point (days 0, 7, and 21), we analyzed three independent samples. ANOVA allowed us to create lists of similarly expressed genes and also significantly up- and downregulated genes that differed between Pax7wt1 and Pax7ko1 ESCs at day 0 (undifferentiated) and at days 7 and 21 of differentiation (Supplementary Tables S1–S6 Supplementary Data are available online at www.liebertpub.com/scd). The transcriptomes of undifferentiated ESCs showed 442 up- or downregulated genes that differed between Pax7wt and Pax7ko cells. Those differences in gene expression become more pronounced as ESC differentiation progressed, with 805 differentially expressed genes at day 7 and 1988 differentially expressed genes at day 21 (Fig. 3E and Supplementary Tables S1–S6).

FIG. 4. Analysis of myogenic differentiation in Pax7wt and Pax7ko ESCs. (A) Analysis of the expression levels of transcripts that encoded myogenesis-associated genes in Pax7wt1 and Pax7ko1 ESCs triplicate measurements were obtained for each group. Blue color indicates low and red color indicates high expression levels of mRNA transcripts. The genes shown were selected from the lists presented in Supplementary Tables S1–S6. (B) Analysis of Pax3, MyoD, and Myog expression by qPCR in undifferentiated ESCs at day 0, in EBs at days 2 and 7, and in EB outgrowths at day 21. (C) EB outgrowths analyzed at day 21 of culture. Localization of MyHC (green) and nuclei (red) bar, 50 μm. (D) The number of MyHC-expressing cells detected by immunolocalization. (E) The number of MyoD-expressing cells detected by immunolocalization. (D, E) The band represents the second quartile (the median) and the ends of the whiskers represent the minimum and maximum of all of the data. (F) EB outgrowths were analyzed at day 21 of culture. Localization of MyoD (green) and nuclei (red) bar, 100 μm. (G) Analysis of Myh7, Nfix, and Eno3 expression by qPCR in undifferentiated ESCs at day 0, in EBs at days 2 and 7, and in EB outgrowths at day 21. Data obtained by qPCR analysis of mRNA transcript expression levels are shown as CT values, which were normalized against those of actin data are represented as the percentage of expression observed in mouse embryos at day 13.5 of development. Color images available online at www.liebertpub.com/scd

In both Pax7wt1 and Pax7ko1 ESCs, the expression of mRNAs that encoded the pluripotency markers Sox2, Nanog, and Utf1 was downregulated by day 21 of differentiation (Fig. 3F). In addition, expression levels of Pdgfrα, a marker of the paraxial mesoderm [70], increased and its levels were only slightly lower in Pax7ko1, compared to Pax7wt1. The mRNA transcript levels of Fgfr1, which encodes a factor known to be involved in mesoderm formation and myogenic differentiation [71], were higher in Pax7ko1 (Fig. 3F). Thus, in both of the cell lines that we analyzed, mesodermal precursors had formed, as was also indicated by analyses of T (Brachyury) expression (Fig. 3D). Next, we found that Pax7wt1 and Pax7ko1 ESCs showed increased expression of differentiation-associated genes, such as neuroectodermal Sox1 [72] or cardiac and skeletal myogenesis-associated Gata4, Gata6 [73], and Myl3 [74] (Fig. 3F). The expression levels of these and other transcripts were higher in Pax7ko1 compared with Pax7wt1 ESCs (Fig. 3F). Microarray results for selected genes, Nanog, Sox2, Pdgfrα, Fgfr1, and Gata6, were verified by qRT-PCR analyses of mRNA isolated from cells of each of the four ESC lines (Pax7wt1, Pax7wt, Pax7ko1, and Pax7ko2) that were induced to undergo differentiation in additional independent experiments (Fig. 3G). For almost all of the genes analyzed, except for Fgfr1, changes in transcript levels reflected the results that we obtained from microarray analyses.

The expression of factors critical for the specification and differentiation of skeletal myoblasts, such as Pax3, MyoD1, Myog, Myf5, Meg3, and Id2, increased during ESC differentiation, but was not significantly different between Pax7wt1 and Pax7ko1 ESCs (Fig. 4A). Levels of Pax3, MyoD, myogenin (Myog) (Fig. 4B), and other myoblast-specific factors, such as M-cadherin (Cdh15, data not shown) and MyHC (Myh7), were verified in the four ESC lines by qPCR (Fig. 4D). The expression levels of these genes were low in undifferentiated ESCs and in EBs at day 2 of differentiation of all analyzed cell lines, but then increased at days 7 or 21. Interestingly, qPCR analysis of Pax3 expression differed from the microarray findings, as it showed that Pax3 was expressed at day 7, whereas microarrays indicated that this gene was induced later, at approximately day 21 of differentiation. This discrepancy most likely resulted from subtle changes in the culture conditions (mRNA for these analyzes was obtained from independent ESC cultures), which in case of some sensitive genes might have altered expression levels. The levels of MyoD1 and Myog were upregulated at day 21 (Fig. 4A, B). In addition to Pax3 and MRFs, levels of transcripts that encoded other regulators of myogenesis, such as BMP4 or Id2, as well as structural and adhesion proteins, such as myosins, laminin α1, and N-CAM, increased during differentiation, but were comparable between Pax7wt1 and Pax7ko1 ESCs (Fig. 4A).

At 21 days of culture, EB outgrowths of the four ESC lines (Pax7wt1, Pax7wt2, Pax7ko1, and Pax7ko2) were similar to those of D3 ESCs (data not shown), which contained cells that synthesized MyHC, a late differentiation marker of myoblasts. In all Pax7wt and Pax7ko cultures that we analyzed, MyHC expression was clearly detectable (Fig. 4C). MyHC-positive outgrowths contained various numbers of myoblasts and multinucleated myotubes, indicating that the progeny of the ESCs that we tested could reach advanced stages of myogenic differentiation. The mean numbers of MyHC-positive cells per outgrowth culture, calculated from at least three independent analyzes, were as follows: Pax7wt1, 87 Pax7wt2, 8 Pax7ko1, 156 and Pax7ko2, 4 (minima, maxima, and median values are shown in Fig. 4D). Next, we analyzed the expression of the transcription factor MyoD in the two ESC lines that were characterized by the highest number of MyHC-expressing myoblasts and myotubes, Pax7wt1 and Pax7ko1 (Fig. 4F). In Pax7wt1 EB outgrowths, we identified ∼171 cells per outgrowth culture that expressed this transcription factor. Interestingly, Pax7ko1 EB outgrowths were characterized by a higher number of MyoD-positive cells, as they contained ∼311 MyoD-positive cells (minima, maxima, and median values are shown in Fig. 4E).

We also assessed whether myoblasts and myotubes formed by the ESCs that we analyzed were primary (embryonic) or secondary (fetal) myoblasts. To verify this, we analyzed the expression of mRNA transcripts that encoded Myh7, Nfix, and Eno3 in undifferentiated ESCs and at days 2, 7, and 21 of differentiation (Fig. 4D). The slow isoform of myosin heavy chain (Myh7) is expressed in embryonic myofibers [75]. The product of the gene Nfix controls the switch between embryonic and fetal myogenesis. Expression of Nfix occurs in fetal myoblasts and induces the expression of genes that are characteristic of fetal myofibers, such as Ckm that encodes creatinine kinase or Eno3 that encodes beta-enolase [76]. Our analysis showed that by day 21 of differentiation, both Pax7wt and Pax7ko ESCs induced Myh7 expression. At day 21 of differentiation, Nfix expression was only higher in Pax7ko1 ESCs compared with the levels observed in the other three cell lines at days 0, 2, and 7. Levels of Eno3 transcripts, which encode a factor characteristic of fetal myoblasts, were comparable in all analyzed samples, regardless of the stage of differentiation or genotype (Fig. 3D). Thus, the myogenic differentiation of ESCs leads to the generation of myotubes with embryonic characteristics, indicated by Myh7 expression, and precursors of fetal ones, indicated by Nfix expression.

Microarray analyses of ESCs also showed that the levels of transcripts that encoded proteins associated with chromatin organization or nuclear trafficking and the regulation of transcription or translation were higher in differentiating Pax7ko1 compared with Pax7wt ESCs. Among the genes characterized by higher expression levels in Pax7ko1 cells were importins, histones, methyltransferases, ribosomal proteins, and cell cycle regulating factors (Supplementary Fig. S1 and Supplementary Tables S1–S6). However, these differences mentioned earlier did not impact the potential of these ESCs to undergo myogenic differentiation.

MicroRNAs in Pax7wt and Pax7ko ESCs

In addition to analyses of mRNA transcript levels, we also assessed the expression levels of microRNAs (miRNAs). We chose to analyze those miRNAs known to be involved in stem cell differentiation or the regulation of myogenesis in two of the ESC lines, Pax7wt1 and Pax7ko1, cultured under myogenic differentiation-inducing conditions (Fig. 3A). For each analysis, we used RNA isolated from cells that were cultured during three independent experiments.

In the developing mouse embryo, Let7 is synthesized in the endoderm and mesoderm, but not the ectoderm [77]. An ESC-specific miRNA, miR294, has been linked to cell cycle regulation and is known to be downregulated throughout differentiation [78,79]. Consistently, we observed an increase in Let7 expression in differentiating ESCs that was correlated with a reduction in the expression levels of miR294 (Fig. 5). Interestingly, at day 14 of differentiation, the EB outgrowth stage, Let7 expression was found to be much higher in Pax7ko1 than in Pax7wt1 ESCs. A similar expression pattern has been reported for miR145a, a miRNA associated with mesodermal differentiation of ESCs [80]. Furthermore, miR181 has been shown to be involved in myogenic differentiation by inhibiting the expression of Hoxa11, which results in blocking MyoD [81]. Similarly, at day 14 of differentiation, the expression of this miRNA was higher in Pax7ko1 than in Pax7wt1 cells.

FIG. 5. Analysis of microRNAs expression in pluripotent and differentiating Pax7wt and Pax7ko ESCs. Levels of Let7a, miR294, miR145a, miR181, mir133a, miR133b, miR1, and miR206 expression were analyzed by qPCR in undifferentiated ESCs at day 0, in EBs at days 2 and 7, and in EB outgrowths at days 14 and 21. Data obtained for the expression levels of microRNAs as CT values were normalized against those of U6 snRNA data are represented as the percentage of expression observed in mouse embryo at day 13.5 of development.

Additional miRNAs, miR1, miR133a, and miR133b, also affect the myogenic differentiation of skeletal muscle and heart tissue. Specifically, miR1 promotes myogenesis by inhibiting the expression of Hdac4, which can block Mef2 [82]. It also impacts expression of Pax7 [24]. The miR133a reduces the transcript levels of cyclin D1 (Ccnd1) and serum responsive factor (Srf), resulting in a reduction in the proliferation capacity of myoblasts [82,83]. The miR133b downregulates Fscn1 expression, which encodes a protein that is involved in the proper organization of actin cytoskeleton [84]. By day 21 of culture, the expression levels of miR1, miR133a, and miR133b were similarly increased in both Pax7wt1 and Pax7ko1 cells. Interestingly, miR206, which has been shown to regulate myogenic differentiation and to downregulate Pax7 mRNA levels [25], was upregulated in Pax7wt1, but not Pax7ko1 cells. Thus, in the absence of a functional Pax7 gene, the expression of miRNAs that regulate mesoderm induction, such as Let7 and miR145a, as well as a miRNA that is involved in the regulation of myogenic differentiation, such as miR181, was clearly increased at 14 days of differentiation.

In summary, analyses of the levels of myogenic transcripts and the in vitro differentiation of Pax7wt and Pax7ko ESCs revealed that irrespective of genotype, these cells could undergo myogenic differentiation.


The stem cell niche is an anatomical site that contains a reservoir of multipotent stem cells (SCs) that can maintain normal tissue, or replenish injured or aged cell populations, in response to mechanisms that regulate whether they should remain quiescent, undergo self-renewal, or differentiate. The choice among these hallmark SC behaviors is governed by intricate soluble and “solid phase” signals that are systemic or presented by the local niche cells. In this review, we discuss the progress achieved in understanding the mechanisms and principles that govern microenvironmental regulation of SC behavior, and focus on novel approaches that have been developed to synthesize this basic information to engineer creative strategies for harnessing and controlling SCs ex vivo and in vivo.

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RESULTS

IAP Antiserum Detects the gag Protein Encoded in IAP Elements, Which Is Expressed in Demethylated Fibroblast Cells

Given that IAP repeats are heavily methylated by Dnmt1, we asked whether IAP protein could be detected in DNA demethylation models. The original p73 antiserum also recognizes other proteins containing partial products of IAP coding regions (23) ( Fig. 1 B). For specific recognition of IAP gag protein product, we created a recombinant protein fusing GST to a partial fragment of IAP gag protein (IAP2, amino acids 251�) ( Fig. 1 A). Once purified over IAP2-GST columns, the original serum yielded a clean p73 band via immunoblot in tissue possessing over 95% Dnmt1 −/− cells in the central nervous system (32) ( Fig. 1 B). The purified serum was partially blocked by preadsorption against IAP2 peptide fragment, indicating the specificity of antiserum (data not shown). We used this recombinant IAP2 protein fragment to generate a separate polyclonal antibody for both immunoblotting and immunostaining assays to detect IAP protein expression ( Fig. 1 B).

To examine the onset of IAP protein reactivation after Dnmt1 gene deletion, Dnmt1 2lox/2lox MEFs were infected with retrovirus containing cre recombinase fused to green fluorescent protein (MSCV-Cre-GFP) (33). Significant genomic demethylation was detected after 4 days postinfection via Southern blotting for the IAP repeat probe ( Fig. 2 A). Because DNA methylation levels dropped after the deletion of Dnmt1, we found reactivation of IAP protein translation in infected cells starting 5 days postinfection via immunoblot ( Fig. 2 B). When individual cells were examined for IAP immunoreactivity, IAP protein was detectable in a minority of infected cells as early as 4 days postinfection ( Fig. 2 C). IAP expression was restricted to the Cre-GFP-infected cell population ( Fig. 2 C, arrows). Thus, the reactivation of IAP protein expression is tightly associated with the onset of genomic DNA demethylation caused by the deletion of Dnmt1.

IAP protein reactivation is tightly linked to the onset of DNA demethylation in MEFs. A, Dnmt1 2lox/2lox MEFs were infected with MSCV-Cre, and genomic demethylation was monitored using restriction enzyme digestion with methyl-sensitive HpaII followed be Southern blot analysis of IAP repetitive elements. Genomic demethylation appears 4 days after infection (DIV). B, immunoblotting shows that IAP protein expression appears 5 days postinfection with MSCV-Cre thus, the IAP protein expression occurs after significant genome demethylation. The far right lane is a positive control for IAP proteins detected in the brain of E18.5 Nestin-Cre Dnmt1 conditional knockouts (Brain E18.5 MUT) (29). C, IAP immunochemistry (red) colocalizes with MSCV-Cre-GFP-infected cells (green) once the genome becomes significantly demethylated after DNMT1 deletion. GAPDH, glyceraldehyde-3-phosphate dehydrogenase DAPI, 4′,6-diamidino-2-phenylindole.

IAP Protein Immunostaining Marks Demethylated Cells and Cultured Neuroblastoma Cells at a Single Cell Resolution

We next asked whether IAP protein could be used to detect demethylated cells at a single cell resolution in the developing nervous system after conditional Dnmt1 deletion in neural precursors. Using our anti-IAP2, IAP immunoreactivity was restricted to the zone of Dnmt1 deletion as dictated by the expression pattern of Emx1-Cre and Nestin-Cre ( Fig. 3 , A and B) (9, 32). In the Emx1-Cre-driven deletion of Dnmt1, no immunoreactivity for IAP was seen in control regions of striatum, thalamus, brain stem, or cerebellum, where DNA methylation is maintained (data not shown).

Detection of IAP protein expression in demethylated somatic cells. A, left, immunoblotting using anti-IAP2 shows IAP protein in control and E18.5 Nestin-Cre Dnmt1 conditional mutant brain lysates. Right, IAP immunoreactivity in E18.5 dorsal cortex of E18.5 Nestin-Cre Dnmt1 mutant mice (29). B, left, Western blot analysis of IAP protein expression in control and Emx1-Cre Dnmt1 cortical lysate from neonate P0 to 1.5 years old. Right, IAP immunoreactivity in the dorsal cortex (ctx) of 6-month-old Emx1-Cre Dnmt1 mutant mice (9). C, immunocytochemistry for IAP protein in cultured N2a neuroblastoma cell lines. Western blot showing IAP expression in N2a cells but not in other types of neuroblastoma cells, including B104 and Hs 683 from ATCC. GAPDH, glyceraldehyde-3-phosphate dehydrogenase DAPI, 4′,6-diamidino-2-phenylindole.

The neuroblastoma cell line N2a, which is known to transcribe certain IAP-LTR-containing genes (34, 35), shows strong anti-IAP2 immunoreactivity in the cytoplasm with a characteristic juxtanuclear staining pattern for viral A particles ( Fig. 3 C). Endogenous IAP protein levels are strongly reactivated in N2a cultured cells, which correlates with the known hypomethylation of IAP elements in the N2a genome (35). Thus, IAP immunoreactivity is a very useful tool to locate demethylated cells in a variety of DNA demethylation models.

Demethylated Embryonic Stem Cells Do Not Contain Detectable IAP mRNAs and Proteins yet Exhibit Dramatic IAP Induction upon Differentiation

Dnmt1 −/− mESCs possess less that 22% normal genomic methylation levels and exhibit increased expression of IAP mRNAs (27, 28, 36). Surprisingly, when we performed Western blot analysis to assay IAP proteins, we could not detect IAP proteins in lysate from undifferentiated Dnmt1 −/− mESC cultures ( Fig. 4 A). When we used IAP FISH to detect mRNA signal in the undifferentiated Dnmt −/− mESCs, we did not visualize increased IAP mRNA levels ( Fig. 4 E, Day 0). We then performed co-immunostaining of IAP and Oct3/4 to verify whether individual cells express IAP protein in the undifferentiated state. Only a small minority of IAP-positive cells existed in Dnmt1 −/− cultures (3.6 ± 0.51%, mean ± S.E., n = 825 cells Fig. 4 D, Day 0), but all IAP positive cells were Oct3/4-negative, indicative of differentiation. IAP protein reactivation in this minor population may be below immunoblot detection levels, yet the level of IAP transcripts was elevated sufficiently in this differentiated subset to account for detection by more sensitive quantitative RT-PCR assay in the population of Dnmt1 −/− ESCs.

Expression of IAP protein is suppressed in Dnmt1 −/− mESCs and is only reactivated upon partial differentiation coupled with dramatic increases in IAP mRNAs. A, IAP protein is not expressed in undifferentiated Dnmt1 −/− mESCs, although Dnmt1 −/− cells possess less than 22% of normal genomic DNA methylation levels (28). Scale bar, 25 μm. B, upon induction of differentiation of Dnmt1 −/− (c/c) mESCs in the absence of LIF treatment, IAP mRNA levels increase dramatically over the time course of 6𠄹 days in culture. No induction of IAP expression was observed in wild type control (J1) mESCs. C, IAP protein levels in the heterogeneous lysate of Dnmt1 −/− cells were also detected after 6 days of LIF withdrawal, reaching a detectable threshold by Western blot. The blot on the left is a longer exposure to show detectable IAP expression at Day 6 of LIF withdrawal. D, IAP immunocytochemistry shows that only Oct4-negative cells express IAP protein in Dnmt1 −/− ESCs. IAP-expressing cells were detected in a small population (under 10%) at Day 0𠄴 of differentiation but reached peak levels at Day 6𠄹 upon the induction of differentiation by LIF withdrawal. Scale bar, 50 μm. E, IAP FISH analysis reveals that both wild type (J1) and Dnmt1 −/− (c/c) undifferentiated colonies at Day 0 of LIF withdrawal do not express IAP mRNA. Upon LIF withdrawal to induce differentiation, partially differentiated Dnmt1 −/− cells migrating away from the colony express IAP RNA (arrowheads). Dnmt1 −/− cells with undifferentiated colony morphology (arrow) do not express IAP mRNA. Scale bar, 25 μm. F, Northern blot analysis of the IAP gag coding region reveals the dramatic induction of IAP mRNA as LIF is withdrawn from culture. -Fold induction levels are assessed after normalization of 28 S rRNA bands from wild type (J1) and Dnmt1 −/− (c/c) during the time course of differentiation. GAPDH, glyceraldehyde-3-phosphate dehydrogenase DAPI, 4′,6-diamidino-2-phenylindole. Error bars, S.E.

We next examined the time course of IAP immunoreactivity in demethylated mESCs after in vitro differentiation. Upon LIF withdrawal in the absence of feeder cells, Dnmt1 −/− mESCs undergo a dramatic increase in IAP transcription and translation ( Fig. 4 , B and C). Quantitative RT-PCR reveals a 10-fold increase in IAP transcript levels 5 days after LIF withdrawal in Dnmt1 −/− cells. Moreover, a similar profile of mRNA expression is detected using FISH labeling and Northern blot analysis specific for IAP2 ( Fig. 4 , E and F). After 6 days of LIF withdrawal, IAP protein is detectable in total cell lysate (Day 6 long exposure Fig. 4 C). By Day 9 of the LIF withdrawal time course, we see robust protein expression ( Fig. 4 C) and a corresponding 103 ± 29-fold relative increase of IAP transcription in Dnmt1 −/− cultures (p < 0.0001 Fig. 4 B). Z-stack confocal microscopy through individual colonies revealed IAP immunoreactivity present only in cells with low or no Oct3/4 immunoreactivity throughout the time course ( Fig. 4 D). Over the course of LIF withdrawal, the differentiated population increases dramatically ( Fig. 4 D), and only after a significant increase in this population was the threshold for immunoblot detection reached.

Detection of IAP Protein Expression in Demethylated EpiSCs

To determine if the DNA methylation-independent mechanism in suppression of IAP elements is unique to pluripotent stem cells, such as mouse ESCs, we derived Dnmt1 2lox/2lox EpiSCs and examined the effect of DNA demethylation on IAP expression. EpiSCs are derived from the epiblast of the post-implantation embryo at E5.5, express pluripotent stem cell markers, such as Oct4 and Nanog, and form cells of the three germ layers in vitro and in vivo (25, 26). However, distinct differences in morphology, culture conditions, and gene expression profiles set EpiSCs apart from mESCs. Because EpiSCs exhibit up-regulation of endodermal and ectodermal markers when compared with mESCs, EpiSCs are regarded as more lineage-committed in developmental pathways.

Using lentivirus to deliver cre recombinase, we monitored infected Dnmt1 2lox/2lox EpiSCs for IAP protein reactivation. By 2 days postinfection, IAP protein was dramatically up-regulated in Oct3/4-positive cells ( Fig. 5 ). IAP signal remained high in the infected (GFP + ) EpiSCs and their derivatives in early passages. However, demethylated EpiSCs do not survive passaging 3 and behave more like demethylated MEF cells (33).

Hypomethylated EpiSCs rapidly induce IAP protein reactivation. A, photomicrographs of a representative colony 2 days after lentiviral cre recombinase delivery to Dnmt1 2lox/2lox EpiSCs. IAP immunoreactivity is present in Oct4-positive (blue) infected (GFP-positive, green) EpiSCs. Scale bar, 25 μm. B, 4 days after lentiviral cre recombinase delivery, colonies arising from infected EpiSCs show a homogenous population of Oct4/IAP/GFP-positive cells. Scale bar, 25 μm.

Examine Potential DNA Methylation-independent IAP Repression Mechanism(s) in Demethylated mESCs

Dnmt1 −/− mESCs appear to possess an alternative mechanism unique to embryonic stem cells to silence IAP elements in the absence of DNA methylation. Inhibition of histone deacetylases shifts chromatin to a permissive state for transcription, relieving chromatin repression in mESCs(37). Histone deacetylase inhibitors significantly increased the number of IAP-positive mESC colonies as well as increasing the number of IAP-positive cells per colony ( Fig. 6 ). However, IAP-positive cells did not colocalize with strong Oct3/4 immunoreactivity ( Fig. 6 A) thus, cell differentiation, a known side effect of treatment with HDAC inhibitors, explains the increase in IAP immunoreactivity.

Inhibition of histone deacetylases and proteosome activities does not lead to IAP protein expression in undifferentiated Dnmt1 −/− mESCs. A, HDAC inhibitor treatments of Dnmt1 −/− mESCs in the presence of LIF were performed to question whether blocking histone deacetylation, thus promoting the active chromatin conformation, in Dnmt1 −/− mESCs would lead to IAP protein expression in undifferentiated cells. Confocal images show that HDAC inhibitors do not reactivate IAP protein expression in Dnmt1 −/− mESCs, suggesting that an alternative repressive mechanism is involved. Scale bar, 25 μm. B, cell counts show that HDAC inhibitors increase the number of IAP-positive colonies, which correlates with the known differentiating effect of HDAC inhibitors in cell culture. C, after 8 h of treatment with the proteosome inhibitor MG132, IAP protein was not visualized in Oct3/4-expressing Dnmt1 −/− mESCs (confocal photomicrograph scale bar, 25 μm). Error bars, S.E. DAPI, 4′,6-diamidino-2-phenylindole.

The 20 S proteasome acts as a transcriptional silencer blocking nonspecific transcription initiation at intergenic and intragenic regions in mESCs (38). Because the majority of IAP LTR insertions are intergenic, we investigated whether this mechanism controls unwanted transcription of IAP elements. After treatment with the proteosome inhibitor MG132 for 8 h, no colocalization of Oct3/4 and IAP immunostaining was seen in colonies examined ( Fig. 6 C) nor was a dramatic rise of IAP mRNA present in treated Dnmt1 −/− mESCs (0.58 ± 0.17-fold change, p = 0.08). Because IAP reactivation was not seen in either wild type or demethylated mESCs after MG132 treatment, proteosome-mediated degradation of intergenic gene transcription probably is not the alternative mechanism.

It has been postulated that Dicer protein-mediated RNA interference or microRNA production could be a potential mechanism to block IAP expression. Minor demethylation of IAP elements is detected in Dicer −/− mESCs (39). We therefore generated Dnmt1 −/− Dicer −/− double mutant ESCs using lentiviral cre recombinase and examined by immunocytochemistry for the colocalization of IAP and Oct4. Individual subclones were genotyped via PCR for 2lox versus 1lox detection at both loci, and quantitative RT-PCR verified the reduced expression of both Dnmt1 and Dicer ( Fig. 7 ). Although IAP protein was strongly expressed in spontaneously differentiating cells (Oct3/4-negative), we failed to detect the co-localization of strong IAP signal with Oct3/4-positive cells ( Fig. 7 ). It is of note that two clones showed a few cells (π.2% of the counted colonies) with weak juxtanuclear staining ( Fig. 7 ). Our results argue against the possibility that Dicer-mediated RNA interference or microRNAs would be the alternative mechanism in repressing IAP expression in undifferentiated mESCs.

Clonal analysis of IAP expression in Dicer −/− Dnmt1 −/− mESCs. A, IAP protein reactivation was counted in individual clones after genotyping was performed. The vast majority of IAP-positive cells were Oct3/4-negative, thus indicating that only Dicer/Dnmt1 double mutant mESCs express IAP after spontaneous differentiation. Of note, two clones had a few cells expressing weak levels of IAP protein in Oct3/4-positive cells. The numbers on the x axis denote the clone number for the 3𠄶 individual clones with one of the following genotypes: Dnmt1 2lox Dicer 2lox (control) Dnmt1 1lox Dicer 2lox (Dnmt1 −/− mutant cells) Dnmt1 1lox Dicer 1lox (Dnmt1 −/− Dicer −/− double mutant ESCs)). B, photomicrographs of clone 127, where differential levels of IAP are expressed. Strong IAP immunostaining is seen in differentiating (Oct3/4-negative) cells, whereas a weak perinuclear stain can be occasionally seen in few Oct3/4 + cells (arrow, bottom). Scale bar, 25 μm.


Challenges and prospects for the establishment of embryonic stem cell lines of domesticated ungulates ☆

Embryonic stem (ES) cell lines provide an invaluable research tool for genetic engineering, developmental biology and disease models. These cells can be maintained indefinitely in culture and yet maintain competence to produce all the cells within a fetus. While mouse ES cell lines were first established over two decades ago and primate ES cells in the 1990s, validated ES cell lines have yet to be established in ungulates. Why competent, pluripotent ES cells can be established from certain strains of mice and from primates, and not from cows, sheep, goats or pigs is an on-going topic of interest to animal reproduction scientists. The identification of appropriate stem cell markers, functional cytokine pathways, and key pluripotency-maintaining factors along with the release of more comprehensive bovine and porcine genomes, provide encouragement for establishment of ungulate ES cell lines in the near future.


Maysam Mansouri is a postdoctoral fellow in Prof. Martin Fussenegger's lab at the ETH Zürich. He received his Ph.D. from the Paul Scherrer Institute and the University of Basel in 2016. During his Ph.D., he developed an efficient multigene delivery system for mammalian cells. His current research focus is on mammalian cell engineering for biomedical and biotechnological applications.

Tobias Strittmatter is a Ph.D. student working in Prof. Martin Fussenegger's group at the ETH Zürich. He studied biochemistry at the University of Tübingen and received his diploma in 2014. Since then, he has been working in the field of mammalian synthetic biology with a focus on receptor engineering and synthetic biology-inspired disease treatment.

Martin Fussenegger is Professor of Biotechnology and Bioengineering at the ETH Zürich and the University of Basel. He is a cofounder of the emerging field of Synthetic Biology, a member of the Swiss Academy of Engineering Sciences (SATW), a fellow of the American Institute for Medical and Biological Engineering (AIBME), a member of the European Molecular Biology Organization (EMBO), and a foreign member of the National Academy of Engineering (NAE) of the USA.


Watch the video: Why Cant We Experiment On Human Embryonic Stem Cells? (August 2022).