Differential alterations in metabolic pattern of the spliceosomal uridylic acid‐rich small nuclear RNAs (UsnRNAs) during malignant transformation of 20‐methylcholanthrene‐induced mouse CNCI‐PM‐20 embryonic fibroblasts

Abstract

Differential alterations of the spliceosomal Uridylic acid rich small nuclear RNAs (UsnRNAs) (U1, U2, U4, U5, and U6) are reported to be associated with cellular proliferation and development, but definitive information is scarce and also elusive. An attempt is made in this study to analyze the metabolic patterns of major spliceosomal UsnRNAs, during tumor development, in an in vitro carcinogenesis model of 20-methylcholanthrene (MCA)-transformed Swiss Mouse Embryonic Fibroblast (MEF), designated as CNCI-PM-20. MEF cells, after treatment with 20-MCA, progressed through a sequence of passages with distinct and heritable changes, finally becoming neoplastic at passage-42 (P42). A differential expression pattern of major UsnRNAs was observed during this process. The abundance of U1 was 20% below control (P1) at passage-20 (P20), followed by a gradual increase up until P42 (approximately 12% above the P1 value). The abundance of U2 was more or less constant during the cellular transformation. U4 showed a trend of increase, with above 30% abundance than control at P20, followed by a significant increase at P36 and P42 (1.5- and 2-fold, respectively, P-value <0.01). U5 also followed an identical pattern, with an increase of 70% compared to control (P-value <0.05) at P42. Interestingly, U6 gradually decreased from P20 onwards up until P42, with 22% at P20 and 67% at P42 (P-value <0.01). An overall significant quantitative alteration in abundance of U4, U5, and U6, observed in our study, contributes to the understanding of the fact that, the metabolism of major spliceosomal UsnRNAs is differentially regulated during the process of neoplastic transformation.

Competitive endogenous RNA network reveals that miR-1343 play key regulatory roles in longissimus dorsi muscle between Saba and Yorkshire pigs

“…For Ribo-Zero RNA sequencing data, filtered paired-end reads were mapped across the pig genome (Sscrofa11.1) using Bowtie2.0 and HISAT2.0 packages with default parameters [28,32], and the mapped reads were assembled using StringTie1.3.1 software [32]. The novel lncRNAs were identified according to the following: (1) transcripts with length shorter than 200 nt and less than 2 exons were excluded; (2) transcripts were discarded by overlapping with annotated exon region; (3) transcripts with FPKM (fragments per kilobase of transcript sequence per millions base pairs sequenced)< 0.5 were abandoned; (4) transcripts predicted with coding potential were filtered using the CNCI, CPC, Pfam-Scan and PhyloCSF tools [33][34][35]. Putative circRNAs were predicted using find_circ1.1 and CIRI2.1.2 packages with default parameters [36,37], and the expressions of circRNAs were quantified using TPM.…”

Pesticide application has little influence on coding and non-coding gene expressions in rice

“…Five steps were adopted to identify bona fide lncRNAs as previously described [47]: (1) transcripts should be with length ≥ 200 bp and detected in more than 3 samples; (2) transcripts derived from rRNA and tRNA were removed (cutoff E-value0.001); (3) transcripts encoding proteins and protein-coding domains were removed by searched against the Swiss-Prot and Pfam databases (cutoff E-value 0.001); (4) OrfPredictor was applied to predict ORFs and transcripts that encode more than 100 amino acid was removed [48]. (5) transcripts were removed that did not pass the protein-coding-score test using Coding-Non-Coding Index (CNCI) [49] and Coding Potential Calculator (CPC) software [50].…”

Pesticide application has little influence on coding and non-coding gene expressions in rice

“…Five steps were adopted to identify bona fide lncRNAs as previously described [42]: (1) transcripts should be with length ≥200 bp and detected in more than 3 samples; (2) transcripts derived from rRNA and tRNA were removed (cutoff E-value0.001); 3transcripts encoding proteins and protein-coding domains were removed by searched against the Swiss-Prot and Pfam databases (cutoff E-value 0.001); (4) OrfPredictor was applied to predict ORFs and transcripts that encode more than 100 amino acid was removed [43]. (5) transcripts were removed that did not pass the protein-coding-score test using Coding-Non-Coding Index (CNCI) [44] and Coding Potential Calculator (CPC) software [45].…”