Molecules derived from tRNA and snoRNA: Entering the degradome pool

Światowy, Witold Józef; Jagodzińśki, Paweł P.

doi:10.1016/j.biopha.2018.09.017

Cited by 16 publications

(16 citation statements)

References 51 publications

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“…Recent genomic studies using parallel high throughput technologies like Next-generation sequencing (NGS) and microarrays have revealed that the molecular heterogeneity of CLL is further complicated by alterations in gene expression patterns and epigenetic regulatory events; and abundance of long noncoding RNAs (lncRNA) and small noncoding RNAs (sncRNAs) such as microRNAs (miRs), tRNA, piRNA, snoRNA, etc 4,5 . A plethora of studies on transcriptional profiling of miRs have identified a variety of differentially expressed miRs (DEMs) in CLL [6][7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

RNA-Seq profiling of deregulated miRs in CLL and their impact on clinical outcome

et al. 2020

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Abnormal expression patterns of regulatory small non-coding RNA (sncRNA) molecules such as microRNAs (miRs), piwi-interacting RNAs (piRNAs), and small nucleolar RNAs (snoRNAs) play an important role in the development and progression of cancer. Identification of clinically relevant sncRNA signatures could, therefore, be of tremendous translational value. In the present study, genome-wide small RNA sequencing identified a unique pattern of differential regulation of eight miRs in Chronic Lymphocytic Leukemia (CLL). Among these, three were up-regulated (miR-1295a, miR-155, miR-4524a) and five were down-regulated (miR-30a, miR-423, miR-486*, let-7e, and miR-744) in CLL. Altered expression of all these eight differentially expressed miRs (DEMs) was validated by RQ-PCR. Besides, seven novel sequences identified to have elevated expression levels in CLL turned out to be transfer RNA (tRNA)/piRNAs (piRNA-30799, piRNA-36225)/snoRNA (SNORD43) related. Multivariate analysis showed that miR-4524a (HR: 1.916, 95% CI: 1.080-3.4, p value: 0.026) and miR-744 (HR: 0.415, 95% CI: 0.224-0.769, p value: 0.005) were significantly associated with risk and time to first treatment. Further investigations could help establish the scope of integration of these DEM markers into risk stratification designs and prognostication approaches for CLL.

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Section: Introductionmentioning

confidence: 99%

RNA-Seq profiling of deregulated miRs in CLL and their impact on clinical outcome

et al. 2020

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“…Some snoRNAs were differentially expressed between the various immunized groups (Table S5). Despite the variety in chicken breeds and differences in maintenance conditions, some of these differences may be caused by the recognition of snoRNA derivatives by the Transcript Clusters optimized to detect snoRNA molecules [37,38]. Moreover, Transcript Clusters optimized for detection of certain mRNA can detect also regulatory RNA made from pseudogenes [39].…”

Section: Discussionmentioning

confidence: 99%

Response to a DNA vaccine against the H5N1 virus depending on the chicken line and number of doses

Kalenik

Góra-Sochacka

Stachyra

et al. 2020

Virol J

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Background: Avian influenza virus infections cause significant economic losses on poultry farms and pose the threat of a possible pandemic outbreak. Routine vaccination of poultry against avian influenza is not recommended in Europe, however it has been ordered in some other countries, and more countries are considering use of the avian influenza vaccine as a component of their control strategy. Although a variety of such vaccines have been tested, most research has concentrated on specific antibodies and challenge experiments. Methods: We monitored the transcriptomic response to a DNA vaccine encoding hemagglutinin from the highly pathogenic H5N1 avian influenza virus in the spleens of broiler and layer chickens. Moreover, in layer chickens the response to one and two doses of the vaccine was compared. Results: All groups of birds immunized with two doses of the vaccine responded at the humoral level by producing specific anti-hemagglutinin antibodies. A response to the vaccine was also detected in the spleen transcriptomes. Differential expression of many genes encoding noncoding RNA and proteins functionally connected to the neuroendocrine-immune system was observed in different immunized groups. Conclusion: Broiler chickens showed a higher number and wider range of fold-changes in the transcriptional response than laying hens.

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“…In this sense, it has been shown that snoRNAs can be a target of RNase P in some species during snoRNA maturation (Coughlin et al, 2008;Marvin et al, 2011). We know now that snoRNAs molecules can be cleaved into snoRNA fragments (snoRFs) but the enzyme(s) responsible for their cleavage remains poorly studied (Światowy and Jagodzińśki, 2018). snoRFs are aberrantly present in several pathologies such as cancer and neurodegeneration diseases (Falaleeva and Stamm, 2013;Patterson et al, 2017;Romano et al, 2017;Światowy and Jagodzińśki, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…We know now that snoRNAs molecules can be cleaved into snoRNA fragments (snoRFs) but the enzyme(s) responsible for their cleavage remains poorly studied (Światowy and Jagodzińśki, 2018). snoRFs are aberrantly present in several pathologies such as cancer and neurodegeneration diseases (Falaleeva and Stamm, 2013;Patterson et al, 2017;Romano et al, 2017;Światowy and Jagodzińśki, 2018). Thus, RNase P could somehow participate in limiting snoRNA fragmentation to preserve homeostasis.…”

Section: Discussionmentioning

confidence: 99%

“…In Drosophila, snoRNAs >120nt are box H/ACA and play a role in Pseudouridylation whereas snoRNAs <120nt are box C/D snoRNA and play a role in 2'-O-methylation (Huang et al, 2005;Angrisani et al, 2015;Falaleeva et al, 2017). Since RNase P has been shown to participate in snoRNAs maturation in some species (Coughlin et al, 2008) and since snoRNAs molecules can be cleaved to form snoRNA fragments (snoRFs) by enzymes that remains to be elucidated (Falaleeva and Stamm, 2013;Światowy and Jagodzińśki, 2018), we studied a potential role of RNase P in snoRFs biogenesis (Sup. Fig.4).…”

Section: Trna Processing Defects Lead To Trfs Accumulationmentioning

confidence: 99%

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tRNA fragments (tRFs) populations analysis in mutants affecting tRNAs processing and tRNA methylation

Mollà-Herman¹,

Angelova²,

Carré³

et al. 2019

Preprint

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tRNA fragments (tRFs) are a class of small non-coding RNAs (sncRNAs) derived from tRNAs. tRFs are highly abundant in many cell types including stem cells and cancer cells, and are found in all domains of life. Beyond translation control, tRFs have several functions ranging from transposon silencing to cell proliferation control. However, the analysis of tRFs presents specific challenges and their biogenesis is not well understood. They are very heterogeneous and highly modified by numerous post-transcriptional modifications. Here we describe a bioinformatic pipeline to study tRFs populations and shed light onto tRNA fragments biogenesis. Indeed, we used small RNAs Illumina sequencing datasets extracted from wild type and mutant Drosophila ovaries affecting two different highly conserved steps of tRNA biogenesis: 5'pre-tRNA processing (RNase-P subunit Rpp30) and tRNA 2'-O-methylation (CG7009 and CG5220). Using our pipeline, we show how defects in tRNA biogenesis affect nuclear and mitochondrial tRFs populations and other small non-coding RNAs biogenesis, such as small nucleolar RNAs (snoRNAs). This tRF analysis workflow will advance the current understanding of tRFs biogenesis, which is crucial to better comprehend tRFs roles and their implication in human pathology. MATERIALS AND METHODS Fly stocksFly stocks are described in (Molla-Herman et al., 2015;M. Angelova, 2019). RNA extraction from ovariesRNA was extracted from Drosophila ovaries following standard methods detailed in (Molla-Herman et al., 2015;M. Angelova, 2019). Small RNA sequencingRNA samples of 3-5 µg were used for High-throughput sequencing using Illumina HiSeq, 10% single-reads lane 1×50 bp. (Fasteris). 15-29 nt RNAs sequences excluding rRNA (riboZero) were sequenced. All the analyses were performed with Galaxy tools https://mississippi.snv.jussieu.fr. Data set deposition is described in (Molla-Herman et al., 2015;M. Angelova, 2019). European Nucleotide Archive (ENA) of the EMBL-EBI (http://www.ebi.ac.uk/ena), accession numbers: PRJEB10569 (Rpp30 mutants), PRJEB35301 and PRJEB35713 (Nm mutants). Clipping and concatenationRaw data were used for clipping the adaptors [Clip adapter (Galaxy-Version 2.3.0, owner: artbio)] and FASTQ quality control was performed (FastQC Read Quality reports (Galaxy-Version 0.72)). Since replicates were homogeneous in quality and analysis (replicates for CG7009* heterozygous and homozygous, triplicates for CG7009*-CG5220* double mutants) we merged them [Concatenate multiple datasets tailto-head (Galaxy-Version 1.4.1, owner: artbio) to have single fasta files. CG7009*/Def9487 was used to obtain normalization numbers but is not shown in the figures. Data normalization using DeSeq miRNA countsData were normalized with bank Size Factors (SF) obtained by using [DESeq geometrical normalization (Galaxy-Version 1.0.1, owner: artbio)] with miRNA counts obtained using [miRcounts (Galaxy-Version 1.3.2)], allowing 0 mismatch (MM). Then, 1/SF values were used in Galaxy small RNA maps (Sup. Fig. 5). Data normalization with DeSeq using ...

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Molecules derived from tRNA and snoRNA: Entering the degradome pool

Cited by 16 publications

References 51 publications

RNA-Seq profiling of deregulated miRs in CLL and their impact on clinical outcome

RNA-Seq profiling of deregulated miRs in CLL and their impact on clinical outcome

Response to a DNA vaccine against the H5N1 virus depending on the chicken line and number of doses

tRNA fragments (tRFs) populations analysis in mutants affecting tRNAs processing and tRNA methylation

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