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Candidate effectors for leaf rust resistance gene Lr28 identified through transcriptome and in-silico analysis

Puccinia spp. causing rust diseases in wheat and other cereals secrete several specialized effector proteins into host cells. Characterization of these proteins and their interaction with host’s R proteins could greatly help to limit crop losses due to diseases. Prediction of effector proteins by co...

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Autores principales: Prasad, Pramod, Jain, Neelu, Chaudhary, Jyoti, Thakur, Rajni Kant, Savadi, Siddanna, Bhardwaj, Subhash Chander, Gangwar, Om Prakash, Lata, Charu, Adhikari, Sneha, Kumar, Subodh, Balyan, Harindra Singh, Gupta, Pushpendra Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543267/
https://www.ncbi.nlm.nih.gov/pubmed/37789861
http://dx.doi.org/10.3389/fmicb.2023.1143703
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author Prasad, Pramod
Jain, Neelu
Chaudhary, Jyoti
Thakur, Rajni Kant
Savadi, Siddanna
Bhardwaj, Subhash Chander
Gangwar, Om Prakash
Lata, Charu
Adhikari, Sneha
Kumar, Subodh
Balyan, Harindra Singh
Gupta, Pushpendra Kumar
author_facet Prasad, Pramod
Jain, Neelu
Chaudhary, Jyoti
Thakur, Rajni Kant
Savadi, Siddanna
Bhardwaj, Subhash Chander
Gangwar, Om Prakash
Lata, Charu
Adhikari, Sneha
Kumar, Subodh
Balyan, Harindra Singh
Gupta, Pushpendra Kumar
author_sort Prasad, Pramod
collection PubMed
description Puccinia spp. causing rust diseases in wheat and other cereals secrete several specialized effector proteins into host cells. Characterization of these proteins and their interaction with host’s R proteins could greatly help to limit crop losses due to diseases. Prediction of effector proteins by combining the transcriptome analysis and multiple in-silico approaches is gaining importance in revealing the pathogenic mechanism. The present study involved identification of 13 Puccinia triticina (Pt) coding sequences (CDSs), through transcriptome analysis, that were differentially expressed during wheat-leaf rust interaction; and prediction of their effector like features using different in-silico tools. NCBI-BLAST and pathogen-host interaction BLAST (PHI-BLAST) tools were used to annotate and classify these sequences based on their most closely matched counterpart in both the databases. Homology between CDSs and the annotated sequences in the NCBI database ranged from 79 to 94% and with putative effectors of other plant pathogens in PHI-BLAST from 24.46 to 54.35%. Nine of the 13 CDSs had effector-like features according to EffectorP 3.0 (≥0.546 probability of these sequences to be effector). The qRT-PCR expression analysis revealed that the relative expression of all CDSs in compatible interaction (HD2329) was maximum at 11 days post inoculation (dpi) and that in incompatible interactions (HD2329 + Lr28) was maximum at 3 dpi in seven and 9 dpi in five CDSs. These results suggest that six CDSs (>0.8 effector probability as per EffectorP 3.0) could be considered as putative Pt effectors. The molecular docking and MD simulation analysis of these six CDSs suggested that candidate Lr28 protein binds more strongly to candidate effector c14094_g1_i1 to form more stable complex than the remaining five. Further functional characterization of these six candidate effectors should prove useful for a better understanding of wheat-leaf rust interaction. In turn, this should facilitate effector-based leaf rust resistance breeding in wheat.
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spelling pubmed-105432672023-10-03 Candidate effectors for leaf rust resistance gene Lr28 identified through transcriptome and in-silico analysis Prasad, Pramod Jain, Neelu Chaudhary, Jyoti Thakur, Rajni Kant Savadi, Siddanna Bhardwaj, Subhash Chander Gangwar, Om Prakash Lata, Charu Adhikari, Sneha Kumar, Subodh Balyan, Harindra Singh Gupta, Pushpendra Kumar Front Microbiol Microbiology Puccinia spp. causing rust diseases in wheat and other cereals secrete several specialized effector proteins into host cells. Characterization of these proteins and their interaction with host’s R proteins could greatly help to limit crop losses due to diseases. Prediction of effector proteins by combining the transcriptome analysis and multiple in-silico approaches is gaining importance in revealing the pathogenic mechanism. The present study involved identification of 13 Puccinia triticina (Pt) coding sequences (CDSs), through transcriptome analysis, that were differentially expressed during wheat-leaf rust interaction; and prediction of their effector like features using different in-silico tools. NCBI-BLAST and pathogen-host interaction BLAST (PHI-BLAST) tools were used to annotate and classify these sequences based on their most closely matched counterpart in both the databases. Homology between CDSs and the annotated sequences in the NCBI database ranged from 79 to 94% and with putative effectors of other plant pathogens in PHI-BLAST from 24.46 to 54.35%. Nine of the 13 CDSs had effector-like features according to EffectorP 3.0 (≥0.546 probability of these sequences to be effector). The qRT-PCR expression analysis revealed that the relative expression of all CDSs in compatible interaction (HD2329) was maximum at 11 days post inoculation (dpi) and that in incompatible interactions (HD2329 + Lr28) was maximum at 3 dpi in seven and 9 dpi in five CDSs. These results suggest that six CDSs (>0.8 effector probability as per EffectorP 3.0) could be considered as putative Pt effectors. The molecular docking and MD simulation analysis of these six CDSs suggested that candidate Lr28 protein binds more strongly to candidate effector c14094_g1_i1 to form more stable complex than the remaining five. Further functional characterization of these six candidate effectors should prove useful for a better understanding of wheat-leaf rust interaction. In turn, this should facilitate effector-based leaf rust resistance breeding in wheat. Frontiers Media S.A. 2023-09-17 /pmc/articles/PMC10543267/ /pubmed/37789861 http://dx.doi.org/10.3389/fmicb.2023.1143703 Text en Copyright © 2023 Prasad, Jain, Chaudhary, Thakur, Savadi, Bhardwaj, Gangwar, Lata, Adhikari, Kumar, Balyan and Gupta. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Prasad, Pramod
Jain, Neelu
Chaudhary, Jyoti
Thakur, Rajni Kant
Savadi, Siddanna
Bhardwaj, Subhash Chander
Gangwar, Om Prakash
Lata, Charu
Adhikari, Sneha
Kumar, Subodh
Balyan, Harindra Singh
Gupta, Pushpendra Kumar
Candidate effectors for leaf rust resistance gene Lr28 identified through transcriptome and in-silico analysis
title Candidate effectors for leaf rust resistance gene Lr28 identified through transcriptome and in-silico analysis
title_full Candidate effectors for leaf rust resistance gene Lr28 identified through transcriptome and in-silico analysis
title_fullStr Candidate effectors for leaf rust resistance gene Lr28 identified through transcriptome and in-silico analysis
title_full_unstemmed Candidate effectors for leaf rust resistance gene Lr28 identified through transcriptome and in-silico analysis
title_short Candidate effectors for leaf rust resistance gene Lr28 identified through transcriptome and in-silico analysis
title_sort candidate effectors for leaf rust resistance gene lr28 identified through transcriptome and in-silico analysis
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543267/
https://www.ncbi.nlm.nih.gov/pubmed/37789861
http://dx.doi.org/10.3389/fmicb.2023.1143703
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