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Transfer RNA detection by small RNA deep sequencing and disease association with myelodysplastic syndromes.

Guo Y, Bosompem A, Mohan S, Erdogan B, Ye F, Vickers KC, Sheng Q, Zhao S, Li CI, Su PF, Jagasia M, Strickland SA, Griffiths EA, Kim AS - BMC Genomics (2015)

Bottom Line: Finally, 14 SNVs within the tDRs were found in at least 20 % of the MDS samples and were not observed in any of the control specimens.The potential etiologies of these species, both technical and biologic, are discussed as well as important challenges in the interpretation of tDR data.Our analysis results suggest that tRNA fragments can be accurately detected through miRNA sequencing data and that the expression of these species may be useful in the diagnosis of MDS and the prediction of response to therapy.

View Article: PubMed Central - PubMed

Affiliation: Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, TN, USA. Yan.guo@vanderbilt.edu.

ABSTRACT

Background: Although advances in sequencing technologies have popularized the use of microRNA (miRNA) sequencing (miRNA-seq) for the quantification of miRNA expression, questions remain concerning the optimal methodologies for analysis and utilization of the data. The construction of a miRNA sequencing library selects RNA by length rather than type. However, as we have previously described, miRNAs represent only a subset of the species obtained by size selection. Consequently, the libraries obtained for miRNA sequencing also contain a variety of additional species of small RNAs. This study looks at the prevalence of these other species obtained from bone marrow aspirate specimens and explores the predictive value of these small RNAs in the determination of response to therapy in myelodysplastic syndromes (MDS).

Methods: Paired pre and post treatment bone marrow aspirate specimens were obtained from patients with MDS who were treated with either azacytidine or decitabine (24 pre-treatment specimens, 23 post-treatment specimens) with 22 additional non-MDS control specimens. Total RNA was extracted from these specimens and submitted for next generation sequencing after an additional size exclusion step to enrich for small RNAs. The species of small RNAs were enumerated, single nucleotide variants (SNVs) identified, and finally the differential expression of tRNA-derived species (tDRs) in the specimens correlated with diseasestatus and response to therapy.

Results: Using miRNA sequencing data generated from bone marrow aspirate samples of patients with known MDS (N = 47) and controls (N = 23), we demonstrated that transfer RNA (tRNA) fragments (specifically tRNA halves, tRHs) are one of the most common species of small RNA isolated from size selection. Using tRNA expression values extracted from miRNA sequencing data, we identified six tRNA fragments that are differentially expressed between MDS and normal samples. Using the elastic net method, we identified four tRNAs-derived small RNAs (tDRs) that together can explain 67 % of the variation in treatment response for MDS patients. Similar analysis of specifically mitochondrial tDRs (mt-tDRs) identified 13 mt-tDRs which distinguished disease status in the samples and a single mt-tDR which predited response. Finally, 14 SNVs within the tDRs were found in at least 20 % of the MDS samples and were not observed in any of the control specimens.

Discussion: This study highlights the prevalence of tDRs in RNA-seq studies focused on small RNAs. The potential etiologies of these species, both technical and biologic, are discussed as well as important challenges in the interpretation of tDR data.

Conclusions: Our analysis results suggest that tRNA fragments can be accurately detected through miRNA sequencing data and that the expression of these species may be useful in the diagnosis of MDS and the prediction of response to therapy.

No MeSH data available.


Related in: MedlinePlus

a. Cluster analysis and heatmap using tRNA expression of all samples. Three phenotype bars are drawn below the dendrogram: pre-treatment, post-treatment and normal controls. Two clusters are visible (light green and light red). These two clusters do not separate pre- and post-treatment, but distinguish MDS and normal samples reasonably well. b. The six differentially expressed tRNA between disease and normal
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Fig2: a. Cluster analysis and heatmap using tRNA expression of all samples. Three phenotype bars are drawn below the dendrogram: pre-treatment, post-treatment and normal controls. Two clusters are visible (light green and light red). These two clusters do not separate pre- and post-treatment, but distinguish MDS and normal samples reasonably well. b. The six differentially expressed tRNA between disease and normal

Mentions: Using MultiRankSeq, we identified six tDRs that are significantly differentially expressed between MDS samples and control samples (Table 1, Fig. 2). Three tDRs demonstrated increased expression in MDS (chrM.tRNA10.TC, chr12.tRNA8.AlaTGC, and chr16.tRNA4.ProAGG) while three were decreased (chr1.tRNA58-LeuCAA, chr19.tRNA8-SeC(e)TCA (SeC(e)TCA), and chr19.tRNA4-ThrAGT). The complete results can be viewed in Additional file 1: Table S3. Unsupervised cluster analysis showed that tRNA profiling was able to distinguish between MDS and control samples (χ2p < 0.0001). No tDRs were significantly differentially expressed between pre- and post-treatment samples (Additional file 1: Table S4, Fig. 2).Table 1


Transfer RNA detection by small RNA deep sequencing and disease association with myelodysplastic syndromes.

Guo Y, Bosompem A, Mohan S, Erdogan B, Ye F, Vickers KC, Sheng Q, Zhao S, Li CI, Su PF, Jagasia M, Strickland SA, Griffiths EA, Kim AS - BMC Genomics (2015)

a. Cluster analysis and heatmap using tRNA expression of all samples. Three phenotype bars are drawn below the dendrogram: pre-treatment, post-treatment and normal controls. Two clusters are visible (light green and light red). These two clusters do not separate pre- and post-treatment, but distinguish MDS and normal samples reasonably well. b. The six differentially expressed tRNA between disease and normal
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4581457&req=5

Fig2: a. Cluster analysis and heatmap using tRNA expression of all samples. Three phenotype bars are drawn below the dendrogram: pre-treatment, post-treatment and normal controls. Two clusters are visible (light green and light red). These two clusters do not separate pre- and post-treatment, but distinguish MDS and normal samples reasonably well. b. The six differentially expressed tRNA between disease and normal
Mentions: Using MultiRankSeq, we identified six tDRs that are significantly differentially expressed between MDS samples and control samples (Table 1, Fig. 2). Three tDRs demonstrated increased expression in MDS (chrM.tRNA10.TC, chr12.tRNA8.AlaTGC, and chr16.tRNA4.ProAGG) while three were decreased (chr1.tRNA58-LeuCAA, chr19.tRNA8-SeC(e)TCA (SeC(e)TCA), and chr19.tRNA4-ThrAGT). The complete results can be viewed in Additional file 1: Table S3. Unsupervised cluster analysis showed that tRNA profiling was able to distinguish between MDS and control samples (χ2p < 0.0001). No tDRs were significantly differentially expressed between pre- and post-treatment samples (Additional file 1: Table S4, Fig. 2).Table 1

Bottom Line: Finally, 14 SNVs within the tDRs were found in at least 20 % of the MDS samples and were not observed in any of the control specimens.The potential etiologies of these species, both technical and biologic, are discussed as well as important challenges in the interpretation of tDR data.Our analysis results suggest that tRNA fragments can be accurately detected through miRNA sequencing data and that the expression of these species may be useful in the diagnosis of MDS and the prediction of response to therapy.

View Article: PubMed Central - PubMed

Affiliation: Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, TN, USA. Yan.guo@vanderbilt.edu.

ABSTRACT

Background: Although advances in sequencing technologies have popularized the use of microRNA (miRNA) sequencing (miRNA-seq) for the quantification of miRNA expression, questions remain concerning the optimal methodologies for analysis and utilization of the data. The construction of a miRNA sequencing library selects RNA by length rather than type. However, as we have previously described, miRNAs represent only a subset of the species obtained by size selection. Consequently, the libraries obtained for miRNA sequencing also contain a variety of additional species of small RNAs. This study looks at the prevalence of these other species obtained from bone marrow aspirate specimens and explores the predictive value of these small RNAs in the determination of response to therapy in myelodysplastic syndromes (MDS).

Methods: Paired pre and post treatment bone marrow aspirate specimens were obtained from patients with MDS who were treated with either azacytidine or decitabine (24 pre-treatment specimens, 23 post-treatment specimens) with 22 additional non-MDS control specimens. Total RNA was extracted from these specimens and submitted for next generation sequencing after an additional size exclusion step to enrich for small RNAs. The species of small RNAs were enumerated, single nucleotide variants (SNVs) identified, and finally the differential expression of tRNA-derived species (tDRs) in the specimens correlated with diseasestatus and response to therapy.

Results: Using miRNA sequencing data generated from bone marrow aspirate samples of patients with known MDS (N = 47) and controls (N = 23), we demonstrated that transfer RNA (tRNA) fragments (specifically tRNA halves, tRHs) are one of the most common species of small RNA isolated from size selection. Using tRNA expression values extracted from miRNA sequencing data, we identified six tRNA fragments that are differentially expressed between MDS and normal samples. Using the elastic net method, we identified four tRNAs-derived small RNAs (tDRs) that together can explain 67 % of the variation in treatment response for MDS patients. Similar analysis of specifically mitochondrial tDRs (mt-tDRs) identified 13 mt-tDRs which distinguished disease status in the samples and a single mt-tDR which predited response. Finally, 14 SNVs within the tDRs were found in at least 20 % of the MDS samples and were not observed in any of the control specimens.

Discussion: This study highlights the prevalence of tDRs in RNA-seq studies focused on small RNAs. The potential etiologies of these species, both technical and biologic, are discussed as well as important challenges in the interpretation of tDR data.

Conclusions: Our analysis results suggest that tRNA fragments can be accurately detected through miRNA sequencing data and that the expression of these species may be useful in the diagnosis of MDS and the prediction of response to therapy.

No MeSH data available.


Related in: MedlinePlus