De Novo Transcriptome Sequencing and Analysis of Differential Gene Expression among Various Stages of Tail Regeneration in Hemidactylus flaviviridis

• Published in: Journal of developmental biology Vol. 10; no. 2; p. 24
• Main Authors: Patel, Sonam, Ranadive, Isha, Buch, Pranav, Khaire, Kashmira, Balakrishnan, Suresh
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 14.06.2022; MDPI
• Subjects: Animals; Apoptosis; BMP; Cell cycle; Cell division; Cell migration; Cell proliferation; FGF; Gene expression; Generalized linear models; Hemidactylus; house gecko; Proteins; Shh; Tails; transcriptome; Transcriptomes; Values; Variance analysis; Wnt; Wnt protein
• ISSN: 2221-3759; 2221-3759
• DOI: 10.3390/jdb10020024

Across the animal kingdom, lizards are the only amniotes capable of regenerating their lost tail through epimorphosis. Of the many reptiles, the northern house gecko, Hemidactylus flaviviridis, is an excellent model system that is used for understanding the mechanism of epimorphic regeneration. A stage-specific transcriptome profile was generated in the current study following an autotomized tail with the HiSeq2500 platform. The reads obtained from de novo sequencing were filtered and high-quality reads were considered for gene ontology (GO) annotation and pathway analysis. Millions of reads were recorded for each stage upon de novo assembly. Up and down-regulated transcripts were categorized for early blastema (EBL), blastema (BL) and differentiation (DF) stages compared to the normal tail (NT) by differential gene expression analysis. The transcripts from developmentally significant pathways such as FGF, Wnt, Shh and TGF-β/BMP were present during tail regeneration. Additionally, differential expression of transcripts was recorded from biological processes, namely inflammation, cell proliferation, apoptosis and cell migration. Overall, the study reveals the stage-wise transcriptome analysis in conjunction with cellular processes as well as molecular signaling pathways during lizard tail regeneration. The knowledge obtained from the data can be extrapolated to configure regenerative responses in other amniotes, including humans, upon loss of a complex organ.