Deciphering the Molecular Mechanisms of Autonomic Nervous System Neuron Induction through Integrative Bioinformatics Analysis
<p>Derivation of sympathetic and parasympathetic neurons from human PSCs. (<b>A</b>) Schematic representation of autonomic induction of human PSCs. (<b>B</b>) mRNA expression levels on day 13 (n = 3; error bars represent standard deviations (SDs); two-sided Student’s unpaired t-test. * <span class="html-italic">p</span> < 0.01). (<b>C</b>) Phase-contrast and fluorescent images of induced neurons on day 50, labelled with a Synapsin-1-GFP reporter to visualize neuronal networks. (<b>D</b>) Immunofluorescent staining of induced neurons for sympathetic neuron marker DBH and peripheral neuron marker PRPH on day 68. (<b>E</b>) Immunofluorescent staining of the induced neurons for PHOX2B and parasympathetic neuron marker CHAT on day 52. (<b>F</b>) Representative electrical signals of induced neurons cultured on microelectrode array (MEA) substrates on day 81. Signals from two selected electrodes before and after nicotine application are shown. Scale bar; 100 µm. EBs: embryoid bodies.</p> "> Figure 2
<p>Transcriptome analysis of DEGs on day 13 compared to day 0. (<b>A</b>) Global mRNA expression patterns between day 13 and day 0. The MA plot shows upregulated (red color) and downregulated (green color) DEGs on day 13. (<b>B</b>) Bar graph representations of the top three enriched GO_BP terms (grey bars), KEGG pathways (orange bars), Reactome pathways (green bars), and Wikipathways (blue bars) for the upregulated DEGs according to adjusted <span class="html-italic">p</span>-values. (<b>C</b>) Bar graph representations of the top three enriched GO_BP terms (grey bars), KEGG pathways (orange bars), Reactome pathways (green bars), and Wikipathways (blue bars) for the downregulated DEGs according to adjusted <span class="html-italic">p</span>-values. (<b>D</b>) GSEA demonstrates significant enrichment for autonomic development-associated genes on day 13. The heatmap displays the expression changes of genes in the enrichment plot.</p> "> Figure 3
<p>GRN-based analysis identifying the major gene clusters, hub genes, and enriched pathways on day 7 compared to day 0. (<b>A</b>) The MA plot shows upregulated (red color) DEGs on day 7. (<b>B</b>–<b>E</b>) The top four gene clusters in the PPI network of DEGs, as identified by the MCODE plugin. Highlighted nodes indicate the five hub genes in each gene cluster, as identified by the cytohubba plugin. Tables show the representative enriched pathways in each gene cluster.</p> "> Figure 4
<p>GRN-based analysis identifying major gene clusters, hub genes, and enriched pathways on day 13 compared to day 7. (<b>A</b>) The MA plot shows upregulated (red color) DEGs on day 13. (<b>B</b>–<b>E</b>) The top four gene clusters in the PPI network of DEGs, as identified by the MCODE plugin. Highlighted nodes indicate the five hub genes in each gene cluster, as identified by the cytohubba plugin. Tables show the representative enriched pathways in each gene cluster.</p> "> Figure 5
<p>GRN and bioinformatics-based analysis identifying regulatory candidate genes and activity during autonomic induction. (<b>A</b>) Identification of the core gene network involved in autonomic induction processes. In total, 101 autonomic development-associated genes, including the 7 input autonomic marker genes, are listed. Nodes with over two degrees are visualized. (<b>B</b>) Regulatory activity of autonomic and sensory TFs during autonomic induction. Changes in gene expression of TFs and their target genes are displayed. Genes marked in red indicate experimentally identified interactions with TFs. (<b>C</b>) Calcium signal heatmap of 20 induced neurons exposed to L-glutamate and nicotine. The upper panel displays overlapped (grey) and averaged (red) traces of calcium transients at day 33. (<b>D</b>) Calcium signal heatmap of 20 induced neurons exposed to capsaicin and menthol. The upper panel displays overlapped (grey) and averaged (red) traces of calcium transients at day 33.</p> ">
Abstract
:1. Introduction
2. Results
2.1. Stepwise Chemical Treatment Leads to Autonomic Development from Human PSCs
2.2. Global Transcriptome Changes Were Revealed during Autonomic Induction
2.3. Gene Regulatory Network Analysis Identifies Key Gene Clusters and Hub Genes
2.4. Core Gene Network Analysis and Functional Evaluation of Autonomic Induction
3. Discussion
4. Materials and Methods
4.1. Cell Culture
4.2. Immunochemical Analysis
4.3. Real-Time Quantitative PCR
4.4. Viral Infection
4.5. Microelectrode Array (MEA) Recording
4.6. RNA-seq
4.7. Enrichment Analysis
4.8. PPI Network Construction and Module Analysis
4.9. Construction of ANS-Associated Network
4.10. TF Activity Analysis
4.11. Calcium Imaging
4.12. Statistical Analysis
4.13. Data Availability
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ANS | Autonomic nervous system |
BMP | Bone morphogenetic protein |
CHIR | CHIR99021 |
DEG | Differentially expressed gene |
DM | Dorsomorphin |
EB | Embryoid body |
ESC | Embryonic stem cell |
FPKM | Fragments per kilobase of exon per million mapped sequence reads |
GO_BP | Gene ontology biological process |
GRN | Gene regulatory network |
GSEA | Gene set enrichment analysis |
iPSC | induced pluripotent stem cells |
KEGG | Kyoto Encyclopedia of Genes and Genomes |
KSR | Knockout serum replacement |
lncRNA | long non-coding RNA |
MCODE | Molecular complex detection |
MEA | Microelectrode array |
MPC | 2-methacryloyloxyethyl phosphorylcholine |
miRNA | microRNA |
NC | Neural crest |
nAchR | nicotinic acetylcholine receptor |
PPI | Protein–protein interaction |
PSC | Pluripotent stem cell |
RNA-seq | RNA-sequencing |
SB | SB431542 |
SHH | Sonic hedgehog |
STRING | Search tool for the retrieval of interacting genes |
TF | Transcription factor |
References
- Norcliffe-Kaufmann, L.; Slaugehaupt, S.A.; Kaufmann, H. Familial dysautonomia: History, genotype, phenotype and translational research. Prog. Neubiol. 2017, 152, 131–148. [Google Scholar] [CrossRef] [PubMed]
- Vinik, A.I.; Maser, R.E.; Mitchell, B.D.; Freeman, R. Diabetic autonomic neuropathy. Diabetes Care 2003, 26, 1553–1579. [Google Scholar] [CrossRef] [PubMed]
- Jain, S. Multi-organ autonomic dysfunction in Parkinson disease. Park. Relat. Disord. 2011, 17, 77–83. [Google Scholar] [CrossRef] [PubMed]
- Chu, C.C.; Tranel, D.; Damasio, A.R.; Van Hoesen, G.W. The autonomic-related cortex: Pathology in Alzheimer’s disease. Cereb. Cortex 1997, 7, 86–95. [Google Scholar] [CrossRef]
- Rio, R.D.; Marcus, N.J.; Inestrosa, N.C. Potential role of autonomic dysfunction in COVID-19 morbidity and mortality. Front. Physiol. 2020, 11, 561749. [Google Scholar] [CrossRef]
- Magnon, C.; Hall, S.J.; Lin, J.; Xue, X.; Gerber, L.; Freedland, S.J.; Frenette, P.S. Autonomic nerve development contributes to prostate cancer progression. Science 2013, 341, 1236361. [Google Scholar] [CrossRef]
- Borden, P.; Houtz, J.; Leach, S.D.; Kuruvilla, R. Sympathetic innervation during development is necessary for pancreatic islet architecture and functional maturation. Cell Rep. 2013, 4, 287–301. [Google Scholar] [CrossRef]
- Takahashi, K.; Tanabe, K.; Ohnuki, M.; Narita, M.; Ichisaka, T.; Tomoda, K.; Yamanaka, S. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 2007, 131, 861–872. [Google Scholar] [CrossRef]
- Sharma, A.; Sances, S.; Workman, M.J.; Svendsen, C.N. Multi-lineage human iPSC-derived platforms for disease modeling and drug discovery. Cell Stem Cell 2020, 26, 309–329. [Google Scholar] [CrossRef]
- Oh, Y.; Cho, G.-S.; Li, Z.; Hong, I.; Zhu, R.; Kim, M.-J.; Kim, Y.J.; Tampakakis, E.; Tung, L.; Huganir, R.; et al. Functional coupling with cardiac muscle promotes maturation of hPSC-derived sympathetic neurons. Cell Stem Cell 2016, 19, 95–106. [Google Scholar] [CrossRef]
- Frith, T.J.R.; Granata, I.; Wind, M.; Stout, E.; Thompson, O.; Neumann, K.; Stavish, D.; Heath, P.R.; Ortmann, D.; Hackland, J.O.S.; et al. Human axial progenitors generate trunk neural crest cells in vitro. eLife 2018, 7, e35786. [Google Scholar] [CrossRef]
- Kirino, K.; Nakahata, T.; Taguchi, T.; Saito, M.K. Efficient derivation of sympathetic neurons from human pluripotent stem cells with a defined condition. Sci. Rep. 2018, 8, 12865. [Google Scholar] [CrossRef]
- Takayama, Y.; Kushige, H.; Akagi, Y.; Suzuki, Y.; Kumagai, Y.; Kida, Y.S. Selective induction of human autonomic neurons enables precise control of cardiomyocyte beating. Sci. Rep. 2020, 10, 9464. [Google Scholar] [CrossRef]
- Hirsch, M.-R.; Tiverron, M.-C.; Guillemot, F.; Brunet, J.-F.; Goridis, C. Control of noradrenergic differentiation and Phox2a expression by MASH1 in the central and peripheral nervous system. Development 1998, 125, 599–608. [Google Scholar] [CrossRef]
- Pattyn, A.; Goridis, C.; Brunet, J.-F. Specification of the central noradrenergic phenotype by the homeobox gene Phox2b. Mol. Cell. Neurosci. 2000, 15, 235–243. [Google Scholar] [CrossRef]
- Ernsberger, U.; Roher, H. Sympathetic tales: Subdivisions of the autonomic nervous system and the impact of development studies. Neural Dev. 2018, 13, 20. [Google Scholar] [CrossRef]
- Witwer, K.W.; Halushka, M.K. Toward the promise of microRNAs—Enhancing reproducibility and rigor in microRNA research. RNA Biol. 2016, 13, 1103–1116. [Google Scholar] [CrossRef]
- Ilott, N.E.; Ponting, C.P. Predicting long non-coding RNAs using RNA sequencing. Methods 2013, 63, 50–59. [Google Scholar] [CrossRef]
- McGettigan, P.A. Transcriptomics in the RNA-seq era. Curr. Opin. Chem. Biol. 2013, 17, 4–11. [Google Scholar] [CrossRef]
- Lin, M.; Pedrosa, E.; Hrabovsky, A.; Chen, J.; Puliafito, B.R.; Gilbert, S.R.; Zheng, D.; Lachman, H.M. Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion. BMC Syst. Biol. 2016, 10, 105. [Google Scholar] [CrossRef]
- Yu, Z.-Y.; Wu, L.; Zhao, F.-K.; Peng, C.; Wang, C.-X.; Qu, B. RNA-seq reveals transcriptome changes of the embryonic lens cells in Prox1 tissue specific knockout mice. Eur. Rev. Med. Pharmacol. Sci. 2019, 23, 7740–7748. [Google Scholar] [CrossRef] [PubMed]
- Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017, 45, D353–D361. [Google Scholar] [CrossRef] [PubMed]
- Gillespie, M.; Jassal, B.; Stephan, R.; Milacic, M.; Rothfels, K.; Senff-Ribeiro, A.; Griss, J.; Sevilla, C.; Matthews, L.; Gong, C.; et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 2022, 50, D687–D692. [Google Scholar] [CrossRef] [PubMed]
- Pico, A.R.; Kelder, T.; van Lersel, M.P.; Hanspers, K.; Conklin, B.R.; Evelo, C. WikiPathways: Pathway editing for the people. PLoS Biol. 2008, 6, e184. [Google Scholar] [CrossRef]
- Soldatov, R.; Kaucka, M.; Kastriti, M.E.; Petersen, J.; Chontorotzea, T.; Englmaier, L.; Akkuratova, N.; Yang, Y.; Häring, M.; Dyachuk, V.; et al. Spatiotemporal structure of cell fate decision in murine neural crest. Science 2019, 364, eaas9536. [Google Scholar] [CrossRef]
- Menendez, L.; Yatskievych, T.A.; Antin, P.B.; Dalton, S. Wnt signaling and a Smad pathway blockade direct the differentiation of human pluripotent stem cells to multipotent neural crest cells. Proc. Natl. Acad. Sci. USA 2011, 108, 19240–19245. [Google Scholar] [CrossRef]
- Mica, Y.; Lee, G.; Chambers, S.M.; Tomishima, M.J.; Studer, L. Modeling neural crest induction, melanocyte specification, and disease-related pigmentation defects in hESCs and patient-specific iPSCs. Cell Rep. 2013, 3, 1140–1152. [Google Scholar] [CrossRef]
- Ichihashi, Y.; Aguilar-Martinez, J.A.; Farhi, M.; Chitwood, D.H.; Kumar, R.; Millon, L.V.; Peng, J.; Maloof, J.N.; Sinha, N.R. Evolutionary developmental transcriptomics reveals a gene network module regulating interspecific diversity in plant leaf shape. Proc. Natl. Acad. Sci. USA 2014, 111, E2616–E2621. [Google Scholar] [CrossRef]
- Xiang, Z.; Li, J.; Song, S.; Wang, J.; Cai, W.; Hu, W.; Ji, J.; Zhu, Z.; Zang, L.; Yan, R.; et al. A positive feedback between IDO1 metabolite and COL12A1 via MAPK pathway to promote gastric cancer metastasis. J. Exp. Clin. Cancer Res. 2019, 38, 314. [Google Scholar] [CrossRef]
- Fraser, S.E.; Bronner-Fraser, M. Migrating neural crest cells in the trunk of the avian embryo are multipotent. Development 1991, 112, 913–920. [Google Scholar] [CrossRef]
- LeDouarin, N.M.; Kalcheim, C. The Neural Crest, 2nd ed.; Cambridge UP: Cambridge, UK, 1999. [Google Scholar]
- Espinosa-Medina, I.; Outin, E.; Picard, C.A.; Chettouh, Z.; Dymecki, S.; Consalez, G.G.; Coppola, E.; Brunet, J.-F. Parasympathetic ganglia derive from Schwann cell precursors. Science 2014, 345, 87–90. [Google Scholar] [CrossRef]
- Zhao, Q.; Liu, H.; Yao, C.; Shuai, J.; Sun, X. Effect of dynamic interaction between microRNA and transcription factor on gene expression. Biomed Res. Int. 2016, 2016, 2676282. [Google Scholar] [CrossRef]
- Chiarella, E.; Aloisio, A.; Scicchitano, S.; Bond, H.M.; Mesuraca, M. Regulatory role of microRNAs targeting the transcription co-factor ZNF521 in normal tissues and cancers. Int. J. Mol. Sci. 2021, 22, 8461. [Google Scholar] [CrossRef]
- Ng, S.-Y.; Johnson, R.; Stanton, L.W. Human long non-coding RNAs promote pluripotency and neuronal differentiation by association with chromatin modifiers and transcription factors. EMBO J. 2012, 31, 522–533. [Google Scholar] [CrossRef]
- Long, Y.; Wang, X.; Youmans, D.T.; Cech, T.R. How so lncRNAs regulate transcription? Sci. Adv. 2017, 3, eaao2110. [Google Scholar] [CrossRef]
- Cao, D.-D.; Li, L.; Chan, W.-Y. MicroRNAs: Key regulators in the central nervous system and their implication in neurological diseases. Int. J. Mol. Sci. 2016, 17, 842. [Google Scholar] [CrossRef]
- Zhou, Z.; Qi, D.; Gan, Q.; Wang, F.; Qin, B.; Li, J.; Wang, H.; Wang, D. Studies on the regulatory roles and related mechanisms of lncRNAs in the nervous system. Oxid. Med. Cell. Longev. 2021, 2021, 6657944. [Google Scholar] [CrossRef]
- Qing, L.; Chen, H.; Tang, J.; Jia, X. Exosomes and their microRNA cargo: New players in peripheral nerve regeneration. Neurorehabilit. Neural Repair 2018, 32, 765–776. [Google Scholar] [CrossRef]
- Liu, M.; Li, P.; Jia, Y.; Cui, Q.; Zhang, K.; Jiang, J. Role of non-coding RNAs in axon regeneration after peripheral nerve injury. Int. J. Biol. Sci. 2022, 18, 3435–3446. [Google Scholar] [CrossRef]
- Zhou, Z.; Kohda, K.; Ibata, K.; Kohyama, J.; Akamatsu, W.; Yuzaki, M.; Okano, H.J.; Sasaki, E.; Okano, H. Reprogramming non-human primate somatic cells into functional neuronal cells by defined factors. Mol. Brain 2014, 7, 24. [Google Scholar] [CrossRef]
- Liberzon, A.; Subramanian, A.; Pinchback, R.; Thorvaldsdóttir, H.; Tamayo, P.; Mesirov, J.P. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011, 27, 1739–1740. [Google Scholar] [CrossRef] [PubMed]
- Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P.; et al. The STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021, 49, D605–D612. [Google Scholar] [CrossRef] [PubMed]
- Chin, C.-H.; Chen, S.-H.; Wu, H.-H.; Ho, C.-W.; Ko, M.-T.; Lin, C.-Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014, 8 (Suppl. 4), S11. [Google Scholar] [CrossRef] [PubMed]
- Zhou, G.; Soufan, O.; Ewald, J.; Hancock, R.E.W.; Basu, N.; Xia, J. NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019, 47, W234–W241. [Google Scholar] [CrossRef]
- Liska, O.; Bohár, B.; Hidas, A.; Korcsmáros, T.; Papp, B.; Fazekas, D.; Ari, E. TFLink: An integrated gateway to access transcription factor-target gene interactions for multiple species. Database 2022, 2022, baac083. [Google Scholar] [CrossRef]
- Janky, R.; Verfaillie, A.; Imrichová, H.; Van de Sande, B.; Standaert, L.; Christiaens, V.; Hulselmans, G.; Herten, K.; Sanchez, M.N.; Potier, D.; et al. iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections. PLoS Comput. Biol. 2014, 10, e1003731. [Google Scholar] [CrossRef]
- Schneider, C.A.; Rasband, W.S.; Eliceiri, K.W. NIH image to ImageJ: 25 years of image analysis. Nat. Methods 2012, 9, 671–675. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Takayama, Y.; Akagi, Y.; Kida, Y.S. Deciphering the Molecular Mechanisms of Autonomic Nervous System Neuron Induction through Integrative Bioinformatics Analysis. Int. J. Mol. Sci. 2023, 24, 9053. https://doi.org/10.3390/ijms24109053
Takayama Y, Akagi Y, Kida YS. Deciphering the Molecular Mechanisms of Autonomic Nervous System Neuron Induction through Integrative Bioinformatics Analysis. International Journal of Molecular Sciences. 2023; 24(10):9053. https://doi.org/10.3390/ijms24109053
Chicago/Turabian StyleTakayama, Yuzo, Yuka Akagi, and Yasuyuki S. Kida. 2023. "Deciphering the Molecular Mechanisms of Autonomic Nervous System Neuron Induction through Integrative Bioinformatics Analysis" International Journal of Molecular Sciences 24, no. 10: 9053. https://doi.org/10.3390/ijms24109053