The document discusses small RNAs (sRNAs) and their roles in post-transcriptional gene regulation, focusing specifically on microRNAs (miRNAs) and their targets. It explores the principles and applications of degradome sequencing as a method to identify these targets, presenting case studies on the influence of miRNAs in various plant responses. The findings highlight the complexity of miRNA interactions and their potential to enhance plant traits such as stress tolerance.

1. Degradome sequencing and small RNA
targets:
What information it can offer?
By
Aswin Reddy Chilakala

2. Content
• Introduction to small RNA and its importance.
• Various methods available for studying miRNA targets.
• Degradome sequencing: principles, procedures.
• Applications: Case studies I
Case Study II

3. Introduction
• Small RNAs (sRNAs) are non-coding RNA
(ncRNA) fragments - Post transcriptional
silencing of target genes
• Mode of Action: a)transcript cleavage or by
b) translational inhibition (Axtell, 2013).
• The three distinct types of small RNAs
• small interfering RNAs,
• microRNAs
• Piwi-interacting RNAs.
(Großhans and Filipowicz, 2008)

4. Continued..
• Over recent decades, many miRNA families have been discovered in plants, and have been
shown to regulate more aspects of plant biology than siRNAs.
• Understanding the miRNA functions completely, we can take advantage of using them to
improve plant features like stress tolerance (Ding et al., 2013).
Database for miRNA
http://www.mirbase.org/

5. Importance of miRNA
• Noncoding RNAs (ncRNAs) have been found to
have roles in a great variety of processes,
including transcriptional regulation, chromosome
replication, RNA processing and modification,
messenger RNA stability and translation, and
even protein degradation and translocation.
• miRNAs and ta-siRNAs constitute two important
classes of endogenous small RNAs in plants,
which play important roles in plant growth and
developmental processes like embryogenesis,
organ formation and patterning, shoot and root
growth, and reproductive development (Singh et
al., 2018).
(Lima et al., 2012)

6. Techniques utilized for Identification of Targets
1. Target specific validation methods: qRT-PCR, Western blot, 5’- Rapid amplification of
cDNA ends (RACE) analyses.
2. Computational methods: Prediction programs serve to identify potential targets eg:
psRNATarget and TAPIR are such tools.
3. Degradome sequencing: also Known as Parallel analysis of RNA Ends (PARE) OR Genome
Wide Mapping of Uncapped Transcripts (GMUCT).

7. Degradome Sequencing: Principle
(Henderson and Jacobsen, 2008)
(a) An intact mRNA possesses
a 5 cap (5mG, 5 7-methylguanosine) structure and a 3
-poly(A) tail. The presence of an miRNA (loaded
into an ARGONAUTE protein) with complementarity
to the mRNA can lead to endonucleolytic cleavage,
yielding a downstream fragment with a 5′
monophosphate end. Molecules with a 5′
monophosphate are ligated and used for high-
throughput sequencing. AGO, ARGONAUTE.
(b) Endonucleolytic cleavage directed by the miRNA is
evident as a peak in the sequenced mRNA fragments.

8. Procedure
General workflow for degradome sequencing

9. Sample Preparation
Total RNA is extracted from tissue of interest using TRIzol reagent.
RNA purity determined by Nanodrop Spectrophotometer (OD260/280 1.8-2.0) and
Quantification done by Qubit Fluorometer.
RNA profile determined by Bioanalyzer 2100 and desired RNA with RNA integrity Number (RIN >7.0) will be
used for further study.

10. Library Construction
(German et al., 2002)

11. Identification of miRNA targets from the PARE data.
(German et al., 2002)

12. Identification of miRNA targets from the PARE data.
Experimental studies have shown that small RNA-guided, AGO mediated cleavage of mRNA
targets occurs exactly between the 10th and 11th nucleotide of complementarity relative to the
small RNA 5’-end. The resulting upstream fragment of the cleaved target rapidly degrades,
while the downstream fragment is stable in vivo.

13. CleaveLand4 from Axtell Lab (first general pipeline)
SoMART- a web server for sRNA analysis resources and tools.
PAREsnip from the UEA small RNA workbench,
Software Used for degradome data analysis

14. CleaveLand Pipeline
(Addo-Quaye et al., 2008)

15. Target plots (t plots)
They are plotted to determine if targets of
miRNA could be identified based on the
distribution of the signatures along their
transcripts.
A high abundance signature is evident at the
miRNA cleavage site.
T-plots of miRNA target candidates used as
filters prior to 5’ RACE validation.
(German et al., 2002)

16. Output files Obtained
Mapping and visualization of degradome reads using the CLC Genomics
Workbench interface
(Lin et al., 2019)

17. Applications
S.no Crop or
material used
Trait
influenced
Conserved
miRNA
identified
Novel miRNA
discovered
Reference
1 Wild tomato Heat response 138 13 Zhou et al.,
2016
2 Orchard grass Drought
response
328 19 Ji et al., 2018
3 Blueberry Ripening
influence
41 7 Hou et al.,
2017
4 Tea Secondary
metabolite
(catechin)
26 5 Sun et al.,
2017

18. CASE STUDY I
OBJECTIVES: Identification of Se – related miRNAs and their putative targets in Astragalus
chrysochlorus.

19. Results
Distribution of miRNAs between Control
and Selenium.
Treatment. (a) Conserved miRNAs (b)
Novel miRNAs.
a)
b)
Degradome sequence analysis:
In total, 1339 predicted sites were identified.
The predicted sites were determined to be cleaved by
499 miRNAs. The total predicted sites were in 1256
genes with 2027 cleavage events.
The target genes were annotated and classified as
transcription factors, enzyme coding genes, resistance and
many other structural and functional proteins.
Small RNA library sequencing
analysis:

20. Conclusion
Detected 151 novel and 418 known miRNAs.
In silico analysis showed that some miRNAs are found to
be involved in targeting genes leading to Se metabolism.
miR1507a, miR3633a-3p, miR4244, miR5049-3p,
miR5485, miR7767-3p and miR9748 were expressed
significantly by Se exposure.
The results we obtained from our study show that
miRNAs involved in plant–pathogen interaction pathway
may decrease the plant’s hypersensitive response and
increase the tolerance.

21. CASE STUDY II
OBJECTIVE:
Application value of degradome-seq data in tracking the miRNA processing intermediates.
Materials used:
miRNA information from miRbase.
Degradome sequences of 15 species were retrieved from GEO, SRA and Next gen seq databases.

22. Averaged read count of the potential slicing signals (Signal) : The averaged read count (RPM) of
the degradome signatures with their 5’ ends mapped to the potential slicing site.
Averaged read count of the surrounding signals (Noise) is defined as the averaged read count
(RPM) of the degradome signatures mapped onto the miRNA precursor, except for those mapped
to the potential slicing site.
Signal/noise ratio ≥ 5.
Supporting Ratio: percentage of the miRNA with degradome supported processing signals.
Terminologies used:

23. Pre-treatment of Degradome-Seq and sRNA-Seq Data the parameter
signal/noise ratio (perfectly mapped signatures retained)
Degradome signatures were mapped onto the miRNA precursors
Checked for mapping extracted degradome signals with 5’ ends (or) the 3’ ends + 1 nt of the
miRBase-registered mature miRNAs on their precursors.
Tracking miRNA Processing Signals by Degradome-Seq Data:

24. Numbers of the miRNA precursors and
the mature miRNAs whose processing
was supported by degradome-seq.
Ath, Arabidopsis thaliana;
Bdi, Brachypodium distachyon;
Cel, Caenorhabditis elegans;
Dme, Drosophila melanogaster;
Gma, Glycine max;
Hsa, Homo sapiens;
Mtr, Medicago truncatula;
Mmu, Mus musculus;
Osa, Oryza sativa;
Ppt, Physcomitrella patens;
Ppe, Prunus persica;
Sly, Solanum lycopersicum;
Stu, Solanum tuberosum;
Vvi, Vitis vinifera;
Zma, Zea mays.

25. Conclusion
The degradome supporting ratios of the miRBase-registered miRNA precursors range from
2.23% (Solanum tuberosum) to 57.56% (Zea mays) (treating the number of the miRBase-
registered miRNA precursors as the denominator.
Relatively low supporting ratios were observed for human (10.90%) and mouse (7.29%). One
of the possible reasons may attribute to the different regulatory mechanisms related to
miRNA processing and/or stability between plants and mammals, which results in ineffective
detection of processing intermediates by degradome-seq.

26. KEY REFERENCES
• Borges, F., and R. A. Martienssen. 2015. The expanding world of small RNAs in plants Nat Rev Mol
Cell Biol 16 (12):727-741.
• Ding, J., S. Zhou, and J. Guan. 2012. Finding microRNA targets in plants: current status and
perspectives Genomics Proteomics Bioinformatics 10 (5):264-275.
• German, M. A., M. Pillay, D. H. Jeong, A. Hetawal, S. Luo, P. Janardhanan, V. Kannan, L. A.
Rymarquis, K. Nobuta, R. German, E. De Paoli, C. Lu, G. Schroth, B. C. Meyers, and P. J. Green.
2008. Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends Nat
Biotechnol 26 (8):941-946.
• Gregory, B. D., R. C. O'Malley, R. Lister, M. A. Urich, J. Tonti-Filippini, H. Chen, A. H. Millar, and J. R.
Ecker. 2008. A link between RNA metabolism and silencing affecting Arabidopsis development Dev
Cell 14 (6):854-866.
• Lin, S. S., Y. Chen, and M. J. Lu. 2019. Degradome Sequencing in Plants Methods Mol Biol
1932:197-213.
• Yu, D., M. Xu, H. Ito, W. Shao, X. Ma, H. Wang, and Y. Meng. 2018. Tracking microRNA Processing
Signals by Degradome Sequencing Data Analysis Front Genet 9:546.

27. Continued…
• Zhou, R., Q. Wang, F. Jiang, X. Cao, M. Sun, M. Liu, and Z. Wu. 2016. Identification of miRNAs and
their targets in wild tomato at moderately and acutely elevated temperatures by high-throughput
sequencing and degradome analysis Sci Rep 6:33777.
• Ji, Y., P. Chen, J. Chen, K. K. Pennerman, X. Liang, H. Yan, S. Zhou, G. Feng, C. Wang, G. Yin, X. Zhang, Y.
Hu, and L. Huang. 2018. Combinations of Small RNA, RNA, and Degradome Sequencing Uncovers the
Expression Pattern of microRNA(-)mRNA Pairs Adapting to Drought Stress in Leaf and Root of
Dactylis glomerata L Int J Mol Sci 19 (10)
• Hou, Y., L. Zhai, X. Li, Y. Xue, J. Wang, P. Yang, C. Cao, H. Li, Y. Cui, and S. Bian. 2017. Comparative
Analysis of Fruit Ripening-Related miRNAs and Their Targets in Blueberry Using Small RNA and
Degradome Sequencing Int J Mol Sci 18 (12)
• Sun, P., C. Cheng, Y. Lin, Q. Zhu, J. Lin, and Z. Lai. 2017. Combined small RNA and degradome
sequencing reveals complex microRNA regulation of catechin biosynthesis in tea (Camellia sinensis)
PLoS One 12 (2):e0171173.