RNA Sequence Analysis of Growth-Related Genes in Penaeus monodon

Abstract

Penaeus monodon is one of the most economically important shrimp species in Thailand. However, little information is available about the functional genomics related to its growth performance. In this study, Illumina paired-end sequencing was used to analyze transcriptomes related to growth performance in P. monodon muscle. A total of 38.4 million reads were generated. The pooled reads, from 10 libraries, were de novo assembled into 113,991 genes, with an average length of 337 bp. Gene expression was analyzed with the edgeR program, which revealed 705 differentially expressed contigs (p < 0.05) in fast-growth shrimp compared to slow-growth shrimp. The results show the 234 up-regulated contigs in fast-growth shrimp are mostly underlined genes involving the metabolic pathway. Quantitative real-time polymerase chain reaction (qRT-PCR) revealed seven genes involved in the cell cycle that were expressed more in fast-growth shrimp (p < 0.05) than in slow-growth shrimp, and moderately to strongly correlated with shrimp body weight. These genes may be good candidates for growth performance improvement in P. monodon.

Identification of differentially expressed genes and SNPs linked to harvest body weight of genetically improved rohu carp, Labeo rohita

“…Furthermore, improved bioinformatics pipelines made it possible to automate the large-scale genotyping of SNPs ( Slate et al, 2009 ), and their subsequent utilization in high-resolution linkage maps and genome-wide association studies ( Bennet et al, 2010 ; Salem et al, 2012 ; Li et al, 2016 ; Joshi et al, 2018 ; Hillestad et al, 2020 ; Yáñez et al, 2022 ) has been elucidated. Use of differential expression data from transcriptomes for identification of genes linked to improved growth ( Chealoh et al, 2018 ; Duran et al, 2022 ; Shen et al, 2022 ), disease resistance ( Yanez, Houston, and Newman, 2014 ; Tadmor-Levi et al, 2019 ), and low saline adaptation ( Lin et al, 2019 ; Powell et al, 2021 ) has been instrumental in understanding molecular pathways associated with performance traits. In rohu, a number of genomics resources have been generated in the last 1 decade to enable genome-guided data analysis and marker-assisted breeding programs ( Robinson et al, 2012 ; Robinson et al, 2014 ; Das et al, 2020 ; Arick et al, 2022 ).…”