Cargando…
In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus
Sugarcane bacilliform virus (SCBV) is considered one of the most economically damaging pathogens for sugarcane production worldwide. Three open reading frames (ORFs) are characterized in the circular, ds-DNA genome of the SCBV; these encode for a hypothetical protein (ORF1), a DNA binding protein (O...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8775236/ https://www.ncbi.nlm.nih.gov/pubmed/35051194 http://dx.doi.org/10.1371/journal.pone.0261807 |
_version_ | 1784636536528044032 |
---|---|
author | Ashraf, Muhammad Aleem Feng, Xiaoyan Hu, Xiaowen Ashraf, Fakiha Shen, Linbo Iqbal, Muhammad Shahzad Zhang, Shuzhen |
author_facet | Ashraf, Muhammad Aleem Feng, Xiaoyan Hu, Xiaowen Ashraf, Fakiha Shen, Linbo Iqbal, Muhammad Shahzad Zhang, Shuzhen |
author_sort | Ashraf, Muhammad Aleem |
collection | PubMed |
description | Sugarcane bacilliform virus (SCBV) is considered one of the most economically damaging pathogens for sugarcane production worldwide. Three open reading frames (ORFs) are characterized in the circular, ds-DNA genome of the SCBV; these encode for a hypothetical protein (ORF1), a DNA binding protein (ORF2), and a polyprotein (ORF3). A comprehensive evaluation of sugarcane (Saccharum officinarum L.) miRNAs for the silencing of the SCBV genome using in silico algorithms were carried out in the present study using mature sugarcane miRNAs. miRNAs of sugarcane are retrieved from the miRBase database and assessed in terms of hybridization with the SCBV genome. A total of 14 potential candidate miRNAs from sugarcane were screened out by all used algorithms used for the silencing of SCBV. The consensus of three algorithms predicted the hybridization site of sof-miR159e at common locus 5534. miRNA–mRNA interactions were estimated by computing the free-energy of the miRNA–mRNA duplex using the RNAcofold algorithm. A regulatory network of predicted candidate miRNAs of sugarcane with SCBV—ORFs, generated using Circos—is used to identify novel targets. The predicted data provide useful information for the development of SCBV-resistant sugarcane plants. |
format | Online Article Text |
id | pubmed-8775236 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-87752362022-01-21 In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus Ashraf, Muhammad Aleem Feng, Xiaoyan Hu, Xiaowen Ashraf, Fakiha Shen, Linbo Iqbal, Muhammad Shahzad Zhang, Shuzhen PLoS One Research Article Sugarcane bacilliform virus (SCBV) is considered one of the most economically damaging pathogens for sugarcane production worldwide. Three open reading frames (ORFs) are characterized in the circular, ds-DNA genome of the SCBV; these encode for a hypothetical protein (ORF1), a DNA binding protein (ORF2), and a polyprotein (ORF3). A comprehensive evaluation of sugarcane (Saccharum officinarum L.) miRNAs for the silencing of the SCBV genome using in silico algorithms were carried out in the present study using mature sugarcane miRNAs. miRNAs of sugarcane are retrieved from the miRBase database and assessed in terms of hybridization with the SCBV genome. A total of 14 potential candidate miRNAs from sugarcane were screened out by all used algorithms used for the silencing of SCBV. The consensus of three algorithms predicted the hybridization site of sof-miR159e at common locus 5534. miRNA–mRNA interactions were estimated by computing the free-energy of the miRNA–mRNA duplex using the RNAcofold algorithm. A regulatory network of predicted candidate miRNAs of sugarcane with SCBV—ORFs, generated using Circos—is used to identify novel targets. The predicted data provide useful information for the development of SCBV-resistant sugarcane plants. Public Library of Science 2022-01-20 /pmc/articles/PMC8775236/ /pubmed/35051194 http://dx.doi.org/10.1371/journal.pone.0261807 Text en © 2022 Ashraf et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ashraf, Muhammad Aleem Feng, Xiaoyan Hu, Xiaowen Ashraf, Fakiha Shen, Linbo Iqbal, Muhammad Shahzad Zhang, Shuzhen In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus |
title | In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus |
title_full | In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus |
title_fullStr | In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus |
title_full_unstemmed | In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus |
title_short | In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus |
title_sort | in silico identification of sugarcane (saccharum officinarum l.) genome encoded micrornas targeting sugarcane bacilliform virus |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8775236/ https://www.ncbi.nlm.nih.gov/pubmed/35051194 http://dx.doi.org/10.1371/journal.pone.0261807 |
work_keys_str_mv | AT ashrafmuhammadaleem insilicoidentificationofsugarcanesaccharumofficinarumlgenomeencodedmicrornastargetingsugarcanebacilliformvirus AT fengxiaoyan insilicoidentificationofsugarcanesaccharumofficinarumlgenomeencodedmicrornastargetingsugarcanebacilliformvirus AT huxiaowen insilicoidentificationofsugarcanesaccharumofficinarumlgenomeencodedmicrornastargetingsugarcanebacilliformvirus AT ashraffakiha insilicoidentificationofsugarcanesaccharumofficinarumlgenomeencodedmicrornastargetingsugarcanebacilliformvirus AT shenlinbo insilicoidentificationofsugarcanesaccharumofficinarumlgenomeencodedmicrornastargetingsugarcanebacilliformvirus AT iqbalmuhammadshahzad insilicoidentificationofsugarcanesaccharumofficinarumlgenomeencodedmicrornastargetingsugarcanebacilliformvirus AT zhangshuzhen insilicoidentificationofsugarcanesaccharumofficinarumlgenomeencodedmicrornastargetingsugarcanebacilliformvirus |