MicroRNA governs bistable cell differentiation and lineage segregation via a noncanonical feedback
2021; Springer Nature; Volume: 17; Issue: 4 Linguagem: Inglês
10.15252/msb.20209945
ISSN1744-4292
AutoresChung-Jung Li, Ee Shan Liau, Yi‐Han Lee, Yang‐Zhe Huang, Ziyi Liu, Andrew Willems, Victoria C. Garside, Edwina McGlinn, Jun‐An Chen, Tian Hong,
Tópico(s)Extracellular vesicles in disease
ResumoArticle23 April 2021Open Access Transparent process MicroRNA governs bistable cell differentiation and lineage segregation via a noncanonical feedback Chung-Jung Li orcid.org/0000-0002-4114-3628 Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Ee Shan Liau orcid.org/0000-0002-4115-5573 Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Yi-Han Lee orcid.org/0000-0001-7650-2443 Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Yang-Zhe Huang orcid.org/0000-0002-3875-994X Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Ziyi Liu orcid.org/0000-0001-7242-2227 Genome Science and Technology Program, The University of Tennessee, Knoxville, TN, USA Search for more papers by this author Andrew Willems orcid.org/0000-0002-7927-0745 Genome Science and Technology Program, The University of Tennessee, Knoxville, TN, USA Search for more papers by this author Victoria Garside orcid.org/0000-0002-4646-0964 EMBL Australia, Australian Regenerative Medicine Institute, Monash University, Clayton, Vic, Australia Search for more papers by this author Edwina McGlinn orcid.org/0000-0002-1829-986X EMBL Australia, Australian Regenerative Medicine Institute, Monash University, Clayton, Vic, Australia Search for more papers by this author Jun-An Chen Corresponding Author [email protected] orcid.org/0000-0001-9870-3203 Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Neuroscience Program Academia Sinica, Taipei, Taiwan Search for more papers by this author Tian Hong Corresponding Author [email protected] orcid.org/0000-0002-8212-7050 Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, USA National Institute for Mathematical and Biological Synthesis, Knoxville, TN, USA Search for more papers by this author Chung-Jung Li orcid.org/0000-0002-4114-3628 Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Ee Shan Liau orcid.org/0000-0002-4115-5573 Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Yi-Han Lee orcid.org/0000-0001-7650-2443 Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Yang-Zhe Huang orcid.org/0000-0002-3875-994X Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Ziyi Liu orcid.org/0000-0001-7242-2227 Genome Science and Technology Program, The University of Tennessee, Knoxville, TN, USA Search for more papers by this author Andrew Willems orcid.org/0000-0002-7927-0745 Genome Science and Technology Program, The University of Tennessee, Knoxville, TN, USA Search for more papers by this author Victoria Garside orcid.org/0000-0002-4646-0964 EMBL Australia, Australian Regenerative Medicine Institute, Monash University, Clayton, Vic, Australia Search for more papers by this author Edwina McGlinn orcid.org/0000-0002-1829-986X EMBL Australia, Australian Regenerative Medicine Institute, Monash University, Clayton, Vic, Australia Search for more papers by this author Jun-An Chen Corresponding Author [email protected] orcid.org/0000-0001-9870-3203 Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Neuroscience Program Academia Sinica, Taipei, Taiwan Search for more papers by this author Tian Hong Corresponding Author [email protected] orcid.org/0000-0002-8212-7050 Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, USA National Institute for Mathematical and Biological Synthesis, Knoxville, TN, USA Search for more papers by this author Author Information Chung-Jung Li1,2, Ee Shan Liau1,2, Yi-Han Lee2, Yang-Zhe Huang2, Ziyi Liu3, Andrew Willems3, Victoria Garside4, Edwina McGlinn4, Jun-An Chen *,1,2,5 and Tian Hong *,6,7 1Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan 2Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan 3Genome Science and Technology Program, The University of Tennessee, Knoxville, TN, USA 4EMBL Australia, Australian Regenerative Medicine Institute, Monash University, Clayton, Vic, Australia 5Neuroscience Program Academia Sinica, Taipei, Taiwan 6Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, USA 7National Institute for Mathematical and Biological Synthesis, Knoxville, TN, USA *Corresponding author. Tel: +886 2 2788 0460; Fax: +886 2 27826085; E-mail: [email protected] *Corresponding author. Tel: +1 865 974 3089; Fax: +1 949 974 6306; E-mail: [email protected] Mol Syst Biol (2021)17:e9945https://doi.org/10.15252/msb.20209945 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Positive feedback driven by transcriptional regulation has long been considered a key mechanism underlying cell lineage segregation during embryogenesis. Using the developing spinal cord as a paradigm, we found that canonical, transcription-driven feedback cannot explain robust lineage segregation of motor neuron subtypes marked by two cardinal factors, Hoxa5 and Hoxc8. We propose a feedback mechanism involving elementary microRNA–mRNA reaction circuits that differ from known feedback loop-like structures. Strikingly, we show that a wide range of biologically plausible post-transcriptional regulatory parameters are sufficient to generate bistable switches, a hallmark of positive feedback. Through mathematical analysis, we explain intuitively the hidden source of this feedback. Using embryonic stem cell differentiation and mouse genetics, we corroborate that microRNA–mRNA circuits govern tissue boundaries and hysteresis upon motor neuron differentiation with respect to transient morphogen signals. Our findings reveal a previously underappreciated feedback mechanism that may have widespread functions in cell fate decisions and tissue patterning. SYNOPSIS Robust cell fate decision and precise tissue boundary formation are critical for development. This study reports a feedback mechanism involving mRNA-microRNA interactions during cell lineage segregation in mouse spinal cord development. Robust lineage segregation of mouse Hoxa5+ and Hoxc8+ motor neurons does not require canonical transcriptional feedback loops. Mathematical modeling derives a wide range of biologically plausible parameters that allow bistability to arise from post-transcriptional networks. An intuitive interpretation of the mathematical analysis reveals a hidden feedback mechanism involving mRNA-microRNA interactions. In vitro and in vivo experiments validate the critical roles of two microRNAs in lineage segregation and tissue boundary formation. Introduction Systems-level positive feedback serves as a crucial mechanism for cell cycle progression and cell differentiation by generating switch-like behaviors (Xiong & Ferrell, 2003; Novak et al, 2007; Yao et al, 2008). These switches are often bistable with respect to external signal, and they give rise to cellular memory or irreversibility of cell fate decisions. For example, during embryonic development, bistability arising from feedback loops in gene regulatory networks generates cellular memory with respect to transient differentiation signals such as morphogens (MacArthur et al, 2009; Zernicka-Goetz et al, 2009). Whereas several important feedback loops that are responsible for irreversible cell cycle progression involve interactions among proteins (Xiong & Ferrell, 2003; Novak et al, 2007; Yao et al, 2008), the switches in most currently known synthetic and developmental systems are governed by feedback loops mediated by transcriptional activation or inhibition (Gardner et al, 2000; Höfer et al, 2002; MacArthur et al, 2009; Zernicka-Goetz et al, 2009; Balaskas et al, 2012; Tyson & Novak, 2020). One critical developmental process that requires robust cell fate decisions is boundary formation between adjacent tissues that ultimately perform distinct biological functions. Differentiating cells near the tissue boundary use positive feedback to make robust cell fate decisions in the presence of competing and/or fluctuating positional signals. The feedback mechanisms often involve a pair of mutually inhibiting transcription factors, which form a double-negative feedback loop (one type of positive feedback loop) (Edgar et al, 1989; Cotterell & Sharpe, 2010; Jaeger, 2011; Balaskas et al, 2012; Zagorski et al, 2017). However, not all known tissue boundary systems have regulatory networks of this type, and it is unclear whether or how robust fate decisions can be made in systems without such canonical transcriptional feedback mechanisms. One such system is the boundary between two types of motor neurons (MNs) along the rostrocaudal (RC; head-to-tail) axis of the spinal cord in bilateral animals. During embryogenesis, antiparallel gradients of retinoic acid (RA) and fibroblast growth factor members (FGFs) along the RC axis determine the expression patterns of Hoxa5 and Hoxc8 paralogs that, in turn, establish the distinct MN identity and synaptic connectivity of rostral limb-innervating lateral motor column (LMC) neurons and caudal LMC neurons at the brachial segments (Liu et al, 2001; Dasen et al, 2003). These neurons form a boundary characterized by mutually exclusive expression of Hoxa5 and Hoxc8 transcription factors, respectively (Dasen et al, 2005; Dasen & Jessell, 2009). However, the possible existence of a double-negative feedback loop between Hoxa5 and Hoxc8 was challenged by observations that Hoxa5 does not inhibit Hoxc8 in chicken embryos (Dasen et al, 2005; Philippidou & Dasen, 2013; Li et al, 2017). Consequently, lack of a known feedback mechanism at the transcriptional level of this system makes it difficult to conceive the mechanism underlying boundary formation between the two MN subtypes. Previous studies have identified two microRNAs, miR-27 and miR-196, that inhibit Hoxa5 and Hoxc8, respectively (Wong et al, 2015; Li et al, 2017), but their specific roles in cell fate decisions at the MN-type boundary remain unclear. Here, we used Hox boundary formation in MN subtypes as a paradigm to systematically examine principles of lineage segregation during embryonic development. We found that Hoxa5 and Hoxc8 do not form a feedback loop and their mRNAs exhibit significant cellular overlap, yet they manifested a mutually exclusive boundary at the protein level. To reveal the feedback mechanism underlying lineage segregation, we constructed a series of mathematical models describing elementary biochemical interactions between mRNA and miRNA, which revealed a broad range of biologically plausible parameters that enable bistability through these mRNA–miRNA interactions. Strikingly, these reaction circuits do not exhibit the loop-like characteristics of most known gene regulatory networks. We derived an intuitive conclusion from mathematical analysis, representing a previously underappreciated feedback mechanism arising from a pair of stoichiometric inhibitors with differential degradation rate constants upon formation of multimeric complexes. This feedback loop between miRNA and mRNA molecules does not require transcriptional control or any other canonical feedback mechanism. We estimate that more than 104 distinct instances of this network topology exist in human cells. Using spatiotemporal modeling, embryonic stem cell differentiation, and mouse genetics, we corroborate that (i) miR-27 is crucial to maintain stable Hoxa5 expression under transient MN differentiation signaling and (ii) miR-27 and miR-196 null embryos exhibit MN boundary disruption. Our study uncovered a family of positive feedback loops with widespread molecular interactions that were not previously known to govern bistable switches, and it reveals an unexpected yet potentially general role for miRNA–mRNA interactions in cell differentiation and development. Results Lineage segregation of Hoxa5 and Hoxc8 in developing spinal motor neurons To investigate how Hox proteins interpret and respond to gradients of RA and FGF, we first examined distributional dynamics of Hoxa5 and Hoxc8 within spinal MNs along the RC axis of mouse embryos from embryonic days 9.5 to 12.5 (E9.5~E12.5) (Figs 1A and EV1). At E9.5, Hoxa5 was expressed in a subset of cervical segments (prevertebral C4 -> C6), whereas Hoxc8 was absent at this stage. Motor neurons underwent a dynamic boundary formation process from E10.5 to E11.5, with Hoxc8 beginning to be expressed and a small number of Hoxa5on/Hoxc8on double-positive MNs being observed at the Hoxa5-Hoxc8 boundary. At E12.5, Hoxa5on/Hoxc8off and Hoxa5off/Hoxc8on MNs were sharply segregated into rostrocervical (C4 -> C7) or caudal-cervical (C8 -> T1) segments, and virtually no mixed Hoxa5on/Hoxc8on hybrid cells were observed (Fig 1B, N ≥ 3 embryos). Thus, Hoxa5 and Hoxc8 manifested dynamic yet sharp and robust segregation during spinal MN development in a timely manner, making it a suitable system for interrogating how a gene regulatory network (GRN) can control lineage decisions at a developing tissue boundary (Fig 1C). Figure 1. Lineage segregation of Hoxa5/Hoxc8-expressing cells in the developing spinal cord Expression patterns of Hoxa5 and Hoxc8 in MNs (demarcated by white dashed lines) along the rostrocaudal axis of the spinal cord (cervical C4 to thoracic T1) from mouse embryos from E9.5 to E12.5. Pink scale bars: 12.5 μm for E9.5; 12.5 μm for E10.5; 25 μm for E11.5; and 50 μm for E12.5. Quantification of Hoxon cells across MN domains. Data represent mean ± SD from N ≥ 3 embryos. Summary of the intrasegmental expression profiles of Hoxa5 and Hoxc8 in cervical MNs. Yellow squares represent the Hoxa5onHoxc8 on cells. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Expression of Hoxa5 and Hoxc8 along the rostrocaudal axis of the spinal cord Schematic illustration of the juxtaposed positioning of cervical segments in Figs 1 and EV1B. Red and green boxes represent a region of Hoxa5on cells and a region of Hoxc8on cells, respectively. Immunostainings of Hoxa5 and Hoxc8 reveal the lineage segregation and boundary sharpening process between E11.5 and E12.5. Longitudinal sections of the spinal cord in which dorsal root ganglia were preserved were used to determine positions of cervical segments. Arrowhead indicates co-expression of Hoxa5 and Hoxc8 proteins at E11.5. Scale bar represents 100 μm. Download figure Download PowerPoint Segregation of Hoxa5- and Hoxc8-expressing spinal motor neurons is not achieved by mutual inhibition Lineage segregation of cells at several known tissue boundaries, including the cardinal dorsoventral (DV) progenitors along the neural tube, relies on cross-repressive interactions of transcription factors (TFs). Therefore, one of the most conceivable mechanisms potentially underlying the Hoxa5/Hoxc8 lineage decision at the cervical boundary is a canonical feedback loop formed by Hoxa5 and Hoxc8 mutual inhibition (transcriptional cross-repression or T-CR, Fig EV2A) (Cotterell & Sharpe, 2010; Jaeger, 2011; Balaskas et al, 2012; Zagorski et al, 2017). Nevertheless, the T-CR assumption contradicted the observation from chicken embryos that Hoxc8 unilaterally inhibits Hoxa5 (Dasen et al, 2005). To test whether Hoxc8 and Hoxa5 exert mutual or unilateral inhibition in a mammalian context, we generated “Tet-ON” inducible Hoxa5::V5 and Hoxc8::V5 tagged mouse embryonic stem cell (ESC) lines (Fig 2A and B) (Li et al, 2017). Under conditions of Hoxa5on MN differentiations (Fig 2A), doxycycline treatment on day 4 of differentiation resulted in efficient induction of exogenous Hoxc8::V5 and concomitant suppression of endogenous murine Hoxa5 expression (Fig 2C, quantification in Fig 2D, N = 3 from three independent experiments) (see Materials & Methods for details). Similar to the finding from an avian context (Dasen et al, 2005), induction of exogenous Hoxa5::V5 in Hoxc8on differentiated MNs did not repress endogenous Hoxc8 expression (Fig 2E, quantification in Fig 2F, N ≥ 9 embryoid bodies (EBs) from three independent experiments). These results exclude the possibility of a Hoxa5/Hoxc8 cross-repression circuit (T-CR model). Click here to expand this figure. Figure EV2. Transcriptional cross-repression (T-CR) and transcriptional unilateral repression (T-UR) models Schematic of the transcriptional cross-repression model (T-CR model). Presumptive simulated time-course of RA and FGF signaling at multiple locations along the rostrocaudal (RC) axis. A.U.: arbitrary unit. Simulation of the T-CR model. A grid of 10 × 40 cells was used to represent a segment of developing spinal cord where progenitor cells are influenced by competing FGF and RA concentrations. Heatmaps reflect final distributions of denoted molecules in the tissue domain. Bottom panel shows the distribution of the ratios between Hoxa5 and Hoxc8 protein levels. Steady-state levels of Hoxa5 and Hoxc8 proteins across RC domains. Error bar indicates 95% confidence interval (obtained with bootstrapping of 10 replicates) for each position receiving the same amount of morphogen. Transition width is defined as number of positions where one or more cells have equivocal lineage decision. Network diagram of the T-UR model. Unlike the T-CR model, regulation in this model is supported by experimental data. The T-UR model was simulated in the same way as for the T-CR model (panel A), including an assumed time-course of RA and FGF signaling at multiple locations along the rostrocaudal axis. Simulation of the T-UR model. A grid of 10 × 40 cells was used to represent a segment of developing spinal cord where progenitor cells are influenced by competing FGF and RA concentrations. Heatmaps show final distributions of denoted molecules in the tissue domain. Bottom panel shows the distribution of ratios between Hoxa5 and Hoxc8 protein levels. Steady-state levels of Hoxa5 and Hoxc8 proteins across RC domains. Error bar indicates 95% confidence interval (obtained with bootstrapping of 10 replicates) for each position receiving the same amount of morphogen. Bifurcation analysis with position as the control parameter. Solid curves denote stable steady states. Black line represents the steady state of the T-UR model. Dashed curves denote unstable steady states. Other lines represent the steady states of the T-CR model. Red: Hoxa5onHoxc8off state and green: Hoxa5offHoxc8on state. The identities of these states were determined by protein levels, which are equivalent to free mRNA levels. Distributions of transition widths from simulations with 10,000 parameter sets for each of the T-CR and T-UR models. Parameter sets with transition widths < 10 are shown. Network diagram of a system incorporating hypothetical positive feedbacks upstream of Hoxa5 and Hoxc8 via transcriptional control. Dashed lines represent hypothetical (unvalidated) regulations that may improve lineage segregation performance. Hoxa5 and Hoxc8 self-activation is also considered one type of hypothetical positive feedback. Download figure Download PowerPoint Figure 2. Cross-repressive loop is not applicable in the Hoxc8-Hoxa5 lineage segregation in spinal motor neurons A–F. (A, B) Schematic illustrations of the design of inducible “Tet-On” ESC lines expressing Hoxa5 or Hoxc8 under the doxycycline (Dox)-regulated promoter. In the presence of Dox, the reverse tTA (rtTA) activator is recruited to the TRE (tetracycline response element), thereby initiating transcription of the downstream gene. (C, E) Immunostaining reveals expression of Hoxa5 and Hoxc8 upon induction of exogenous Hoxc8:V5 (iHoxc8:V5) or Hoxa5:V5 (iHoxa5:V5), respectively. (D, F) Quantification of data from (C) and (E) (mean ± SD, N ≥ 3 EBs from three independent experiments, *P < 0.01, Student’s t-tests). Scale bars in (C) and (E) represent 50 μm. Download figure Download PowerPoint Next, we built two ordinary differential equation (ODE) models based on the T-CR network and a transcriptional unilateral repression (T-UR) network, respectively (Fig EV2). Transcriptional regulation and cooperativities were described by Hill functions (Appendix Supplementary Methods). We found that the T-CR model exhibited desirable performance in lineage segregation in the presence of noisy morphogen signals described by white noise terms in the ODEs (Fig EV2, EV3-EV5, Movie EV1), whereas the T-UR model produced fluctuating cell lineages and a blurred tissue boundary under the same condition despite assuming very high cooperativity of gene regulation (Fig EV2, EV3-EV5 and Movie EV2). The poor lineage decision performance of the T-UR model relative to our T-CR model was because the former lacks a positive feedback loop to endow robustness on cell differentiation. In contrast, the mutual inhibition circuit in the T-CR model serves as a canonical form of positive feedback loop to generate bistability and enable hysteresis (Fig EV2H) (Balaskas et al, 2012). The difference between the two models was robust with respect to the changes of kinetic rates in the models (Fig EV2I). Combined, these in vitro and in silico analyses indicate that the segregation of Hoxa5on and Hoxc8on MNs in the face of known inherently noisy morphogen signals (Sosnik et al, 2016) may be mediated by a mechanism that differs from the canonical design principle involving mutual inhibition of lineage-determining TFs. Click here to expand this figure. Figure EV3. Two alternative models of miRNA-mediated regulation Schematic of the model representing transcriptional unilateral repression with miRNA-mediated regulation (Tmi-UR model). Simulation of the Tmi-UR model. A grid of 10 × 40 cells was used to represent a segment of developing spinal cord where progenitor cells are influenced by competing FGF and RA concentrations. Heatmaps show the final distributions of denoted molecules in the tissue domain. Bottom panel shows the distribution of ratios between Hoxa5 and Hoxc8 protein levels. Steady-state levels of Hoxa5 and Hoxc8 mRNA (top) and proteins (bottom) across RC domains under Tmi-UR model. Error bar indicates 95% confidence interval for each position receiving the same amount of morphogen. Schematic of the model representing transcriptional unilateral repression with miRNA-mediated feedback (Tmi-FB model). Hypothetical mRNA–miRNA feedback involving transcriptional repression of miRNA by target mRNA is assumed. Simulation of the Tmi-FB model. A grid of 10 × 40 cells was used to represent a segment of developing spinal cord where progenitor cells are influenced by competing FGF and RA concentrations. Heatmaps show final distributions of denoted molecules in the tissue domain. Bottom panel shows the distribution of ratios between Hoxa5 and Hoxc8 protein levels. Color scales are the same as those in (B). Steady-state levels of Hoxa5 and Hoxc8 mRNA (top) and proteins (bottom) across RC domains under Tmi-FB model. Error bar indicates 95% confidence interval (obtained with bootstrapping of 10 replicates) for each position receiving the same amount of morphogen. Performance of 3000 top performing parameter sets from randomly generated values for each model (Appendix Table S4). y-coordinates are mRNA-to-protein ratios in terms of gradient steepness along the RC axis (segregation index, see Appendix Supplementary Methods for details). Red square indicates selected 190 sets (100% from Tmi-FB) for further analysis. Quantifications of steady-state miR-196 levels for models selected from (G). Caudal boundary level is compared to maximum level across the RC domain. Summary of lineage decision performance and consistency with experimental data for four models. Download figure Download PowerPoint Click here to expand this figure. Figure EV4. Luciferase assay with 3′ UTR and Hoxa5 expression in response to RA in ESC differentiation Predicted targeting sites for miR-196 in the Hoxc8 3′ UTR (left panel) and for miR-27 in the Hoxa5 3′ UTR (right panel), based on TargetScan. Left panel: Luciferase reporters were constructed with either a control Hoxc8 3′ UTR or the 3′ UTR sequence in which the individual or multiple potential target sites of miR-196 were mutated (red). Right panel: Co-expression of a luciferase construct with miR-196a in HeLa cells silences a reporter carrying intact miR-196 target sites, whereas miR-196 fails to fully silence Mut1 (site#1 mutated), Mut2/3 (site#2 and site#3 mutated), and Mut1/2/3 (site#1, site#2, and site#3 mutated) luciferase constructs (N = 3 independent experiments, mean ± SD, *P < 0.05, **P < 0.01, Student’s t-tests). Left panel: Luciferase reporters were constructed with either a control Hoxa5 3′ UTR or the 3′ UTR sequence in which the individual or multiple potential target sites of miR-27 were mutated (red). Right panel: Co-expression of a luciferase construct with miR-27b in ES cells silences a luciferase reporter constructs carrying one or more mutated sites (N = 3 independent experiments, mean ± SD, *P < 0.05, **P < 0.01, Student’s t-tests). Schematic illustration of the experiments in (E) and (F). Immunostainings of Hoxa5 and Hb9 in embryoid bodies with a concentration gradient of RA ranging from 100 nM to 1 µM. Quantification of Hoxa5on cells from the Hb9on population. Data information: Scale bar in (D) represents 50 μm. Data in (F) represent mean ± SD, N ≥ 3 EBs from three independent experiments. Download figure Download PowerPoint Click here to expand this figure. Figure EV5. Overexpression of miR-27 and miR-196 leads to the efficient repression of Hoxa5 and Hoxc8 in spinal MNs A, B. Schematic illustrations of the generation of inducible ESC lines expressing primary miRNA sequences inserted into the GFP 3′ UTR. ESCs were differentiated under conditional MN differentiation conditions with doxycycline treatment on day 4 of differentiation. C–F. Expression of Hoxa5/Hoxc8 and Hb9/Isl1 in EBs from control (iGFP) and iMir-27b- or iMir-196a-overexpressing (OE) cells. Induction of miR-27b on day 4 of differentiation under RA/SAG conditions resulted in reduced Hoxa5 levels (D), whereas induction of miR-196a repressed Hoxc8 expression (F). Both conditions have no discernible effect on MN differentiation, as revealed by Hb9 or Isl1 expression (D and F). Pink scale bar in (C) and (E) represents 50 μm. Data in (D) and (F) represent mean ± SD, N ≥ 4 EBs from three independent experiments, *P < 0.01, Student’s t-tests. Download figure Download PowerPoint Hoxa5- and Hoxc8-expressing cells delineate a sharp tissue boundary without segregation of their respective mRNAs We wondered whether an alternative positive feedback loop mechanism (Delás & Briscoe, 2020) involving other TFs upstream of Hoxa5 and Hoxc8 might function as a potential GRN to explain the boundary formation of Hoxa5on and Hoxc8on MNs (Fig EV2J). In this case, a clear all-or-none pattern of Hoxa5 and Hoxc8 mRNAs near the boundary at E12.5 would be observed. However, unlike the mutually exclusive pattern of Hoxa5 and Hoxc8 protein expression at the boundary, in situ hybridization in the same region revealed largely overlapping Hoxa5 and Hoxc8 mRNAs within the cervical spinal cord (Fig 3A). To confirm this observation quantitatively at the individual cell level, we performed single-cell RNA sequencing (scRNA-seq) of cervical MNs collected from Hb9::GFP embryos at E12.5 by fluorescence-activated cell sorting (FACS) (Fig 3B). We clustered single-cell transcriptomes using a graph-based approach, which identified major MN subtypes in the cervical spinal cord (known as motor columns) according to known markers (Appendix Fig S7) (see Materials and Methods for details) (Chen & Chen, 2019). To focus solely on post-mitotic MNs and to simplify our analysis by excluding Hox genes poorly or not expressed in other cell types (i.e., MN progenitors, interneurons, or other cell types), we focused on LMC MNs for further characterization given their strong expression of Hox genes (Fig 3C) (Dasen et al, 2005). Similar to the in situ hybridization data, we found that Hoxa5/Hoxc8 mRNAs largely overlapped within individual LMC-MN subtypes, whereas Hoxc8-mediated downstream effector genes Etv4 (Pea3
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