Content uploaded by Muhammad Babar Malook
Author content
All content in this area was uploaded by Muhammad Babar Malook on Oct 05, 2024
Content may be subject to copyright.
62
This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
TRENDS IN ANIMAL AND PLANT SCIENCES
https://doi.org/10.62324/TAPS/2023.018
www.trendsaps.com; editor@trendsaps.com
REVIEW ARTICLE
Plant Pathology in Genome Era New Insight into Disease Resistance
Muhammad Babar Malook1, Sania Aslam2 and Ali Ammar3
1Department of Plant Pathology, Institute of Plant Protection, Muhammad Nawaz Sharif University Multan, Pakistan
2Department of Zoology, Riphah International University, Faisalabad Campus, Pakistan
3Department of Plant Breeding and Genetics, University of Agriculture Faisalabad, Pakistan
*Corresponding author: aliammar225@gmail.com
Article History: 23-018
Received:02-Aug-2023
Revised: 11-Sep-2023
Accepted: 29-Sep-2023
ABSTRACT
The field of plant pathology has undergone a transformative evolution, transitioning from traditional, labor-
intensive methods to the genomic era marked by significant advancements in molecular biology and
computational sciences. This shift has revolutionized our understanding of plant diseases and disease resistance.
Genomics, particularly Next-Generation Sequencing (NGS) and CRISPR-Cas systems, has played a central role in
this transformation. NGS has allowed for comprehensive genome and transcriptome analysis, facilitating the
identification of disease resistance genes and the study of gene expression during pathogen attacks. CRISPR-Cas
systems have enabled precise genome editing, contributing to our understanding of disease resistance
mechanisms and the development of disease-resistant plant varieties. While these advancements offer exciting
prospects, they also come with challenges, including data analysis complexity, off-target effects, and ethical
considerations. Nevertheless, the genomic era of plant pathology promises to reshape agriculture and disease
management, offering sustainable solutions to crop losses and food security challenges. The integration of
genomics in plant pathology has revolutionized our understanding of plant-pathogen interactions and disease
resistance mechanisms. This article highlights the significance of genomics in various aspects of plant pathology,
from the study of microbial communities through metagenomics to the identification and manipulation of disease
resistance genes. The use of technologies like Next-Generation Sequencing (NGS) and CRISPR-Cas systems has
enabled precise genome analysis and editing, facilitating the development of disease-resistant crop varieties.
However, challenges such as regulatory approval, genetic erosion, climate change, and ethical considerations
must be addressed. Despite these challenges, genomics offers promising opportunities to enhance crop disease
resistance and ensure global food security in the face of evolving pathogens and changing environments.
Collaboration between researchers, breeders, policymakers, and capacity building in developing countries will be
essential to fully leverage the potential of genomics in agriculture.
Key words: Genomics, Next-Generation Sequencing (NGS), CRISPR-Cas Systems, Disease Resistance, Plant-
Pathogen Interactions.
INTRODUCTION
Plant pathology has always been an integral part of
our endeavor to understand the natural world and our
place in it, especially in the context of agriculture (Alam
et al., 2021). The field itself is dedicated to the scientific
study of diseases in plants and has its roots set deep
within the annals of history, with mankind's earliest
attempts at cultivation and agriculture (Agrios, 2005;
Smith, 2010). It has progressed from simple observation
of disease symptoms to understanding the complex
mechanisms that underpin plant health and disease
(Agrios, 2005; Jones & Dangl, 2006).
Despite the rich history and a myriad of insights that
have come forth through the ages, traditional methods
in plant pathology have always been marked by certain
limitations (Alam et al., 2021). They often involve
intensive, laborious processes, such as the manual
identification of pathogens, symptomatic analysis, and
disease management practices that, while efficient,
Cite This Article as: Malook MB, Aslam S and Ammar A, 2023. Plant pathology in genome era new insight into disease
resistance. Trends in Animal and Plant Sciences 2: 62-70. https://doi.org/10.62324/TAPS/2023.018
Trends Anim Plant Sci, 2023, 2: 62-70.
63
have often lacked precision and comprehensiveness
(Agrios, 2005; Scholthof et al., 2011). The ability to
decipher the complex mechanisms of disease resistance
and susceptibility at a molecular level remained a
challenging endeavor with these techniques (Jones &
Dangl, 2006; Dangl et al., 2013).
This changed dramatically with the advent of the
'genome era', a transformative period that began in the
latter part of the 20th century. This era was driven by
significant advancements in molecular biology and
computational sciences (Green, 2001; Metzker, 2010). It
was a time when researchers gained the ability to
sequence and analyze the complete genomes of various
organisms, opening up a previously unimaginable depth
of insight into the intricate processes that drive life
(Green, 2001; Venter et al., 2001).
The implications of the genome era on plant
pathology have been profound. It has transformed the
way we approach the study of plant diseases and their
management (Mcdowell & Simon, 2006; Dodds &
Rathjen, 2010). Genomics, the study of the entire
genome of an organism, has played an instrumental role
in this transformation (Khalid et al., 2021). With the
complete sequencing of genomes from numerous plant
species and their associated pathogens, our
understanding of plant diseases has shifted from a
purely symptomatic perspective to a much more
nuanced understanding of molecular and genetic
interactions (Mcdowell & Simon, 2006; Dangl et al.,
2013).
Genomics has given us detailed insights into the
mechanisms that underpin disease resistance in plants.
This understanding has been invaluable in the
development of genetically modified plants with
enhanced disease resistance (Jones & Dangl, 2006;
Nicaise, 2014). Furthermore, these advancements
promise a future where we can mitigate crop loss due to
disease, potentially leading to sustainable farming
practices that are environmentally friendly and
economically feasible (Godfray et al., 2010).
Nevertheless, we must recognize that the journey
into the genome era of plant pathology is only
beginning. As genomics technologies advance at a
breathtaking pace, we are amassing a wealth of data
that presents as much of a challenge as it does an
opportunity (Green, 2001; Metzker, 2010). With each
passing day, new areas of exploration are opening up,
presenting an ever-increasing array of possibilities for
understanding plant-pathogen interactions and disease
resistance at a molecular level. These developments will
undoubtedly revolutionize our approach to plant
pathology and disease resistance, potentially paving the
way for novel and more effective disease management
strategies (Dodds & Rathjen, 2010; Dangl et al., 2013).
The arrival of genomics in plant pathology signifies
a new age in our ongoing journey to understand and
manage plant diseases (Mcdowell & Simon, 2006). As
we continue to chart this unexplored territory, it's clear
that genomics will play an increasingly central role in
shaping the future of plant pathology (Metzker, 2010;
Nicaise, 2014). As we progress, we need to be aware of
the potential challenges and ethical considerations that
come with genetic modifications and the development
of disease-resistant crops (Godfray et al., 2010). The
genome era of plant pathology offers us a powerful tool,
and it is up to us to use it wisely and responsibly.
Overview of Genomic Technologies and Techniques in
Plant Pathology
Next-Generation Sequencing (NGS)
The advent of genomic technologies, particularly
Next-Generation Sequencing (NGS), has brought about
a paradigm shift in plant pathology, leading to profound
advancements in our understanding of plant diseases
and host-pathogen interactions.
Next-Generation Sequencing (NGS), also referred to
as high-throughput sequencing, has revolutionized
genomic research by providing massive parallel
sequencing of DNA or RNA, generating several
gigabases of nucleotide sequence data in a single run
(Schuster, 2008). This technology facilitates a holistic
view of the genome, transcriptome, or epigenome,
thereby providing valuable insights into the functional
aspects of the genome, including gene expression
patterns, regulatory elements, and genomic alterations
(Khalid et al., 2021).
NGS technologies have been employed in plant
pathology to identify, map, and characterize disease
resistance (R) genes, paving the way for more effective
disease management strategies (Jones, J.D.G., & Dangl,
J.L., 2006). R-genes encode proteins that can recognize
specific pathogen-derived molecules, triggering defense
responses. NGS allows for rapid, comprehensive, and
cost-effective identification of R-genes, which is
especially important given the complexity and diversity
of plant genomes.
For example, the application of NGS to genotyping-
by-sequencing (GBS) has been successful in high-
resolution mapping of R-genes in several crops, aiding in
the development of disease-resistant varieties (Poland
et al., 2012). Similarly, RNA sequencing (RNA-seq),
another application of NGS, is used for genome-wide
expression profiling in plants. RNA-seq studies have
helped in identifying differentially expressed genes
during pathogen attack, and the regulatory networks
governing plant defense responses (Baxter et al., 2012).
Moreover, NGS has revolutionized the field of
metagenomics, facilitating the study of microbial
communities associated with plants, including
pathogens, without the need for cultivation (Weinert et
al., 2011). By sequencing the collective genomes of these
communities, researchers have gained insights into
microbial diversity, pathogen abundance, and the
dynamics of host-pathogen interactions.
However, NGS data can be complex and large-scale,
requiring specialized bioinformatic tools for data
processing, alignment, and variant calling (Pabinger et
al., 2014). Additionally, there are technical challenges
Trends Anim Plant Sci, 2023, 2: 62-70.
64
associated with NGS, such as sequencing errors, bias in
the representation of sequences, and difficulties in
assembling short reads, particularly in genomes with
high levels of repetitive sequences.
Despite these challenges, the advent of NGS and its
applications in plant pathology has provided
unprecedented opportunities to investigate complex
genomic landscapes and host-pathogen dynamics. As
the technology continues to evolve, it will undoubtedly
unlock novel avenues for enhancing plant disease
resistance and ensuring food security in the face of
increasing global population and climate change.
CRISPR-Cas systems
The advent of gene-editing technologies,
specifically Clustered Regularly Interspaced Short
Palindromic Repeats (CRISPR) and CRISPR-associated
(Cas) systems, has been transformative for plant
pathology. These powerful tools have provided
researchers with an unprecedented capacity to modify
genomes with high precision and efficiency, leading to
significant advancements in our understanding of plant
disease resistance (Bhutta et al., 2023; Khan et al., 2023).
Originally discovered as a part of the adaptive
immune system in bacteria, the CRISPR-Cas system has
been harnessed as a versatile tool for genome editing in
various organisms, including plants (Barrangou et al.,
2007). The system relies on a guide RNA (gRNA) that
directs the Cas nuclease to a specific DNA sequence,
where it induces double-strand breaks. The cell's repair
machinery then fixes these breaks, often introducing
insertions or deletions that can disrupt the target gene
(Jinek et al., 2012).
CRISPR-Cas systems have been used extensively in
plant pathology for functional genomics studies,
elucidating the roles of specific genes in disease
resistance. For instance, through targeted mutagenesis,
genes suspected of involvement in disease resistance
can be disrupted, allowing researchers to observe the
resulting phenotype and ascertain the gene's function.
This approach has helped validate the role of several
disease resistance (R) genes and unravel the molecular
mechanisms underlying host-pathogen interactions
(Zaidi et al., 2018).
Moreover, the system can be used to engineer
disease-resistant plant varieties, a strategy that holds
immense promise for sustainable agriculture. By
targeting susceptibility (S) genes, which are often
exploited by pathogens to cause disease, CRISPR-Cas
systems can render plants resistant to various diseases
(Langner et al., 2018). For example, the rice gene
OsSWEET14, which is hijacked by bacterial blight
pathogens, has been successfully edited to confer
resistance to this devastating disease (Zhou et al., 2015).
The precision of CRISPR-Cas systems also enables
precise allele replacement, where an undesirable allele
can be replaced with a more favorable one. This can be
achieved using homology-directed repair (HDR), in
which a repair template carrying the desired sequence is
provided along with the CRISPR-Cas components
(Puchta, 2017). Although HDR efficiency in plants is
generally low, advancements in the delivery methods
and optimization of the repair template are making this
a feasible approach (Baltes et al., 2014).
Despite its potential, the application of CRISPR-Cas
systems in plant pathology does pose several
challenges. Off-target effects, where unintended
regions of the genome are modified, can potentially lead
to undesirable phenotypes. However, the development
of more precise Cas variants and better gRNA design
strategies are mitigating these effects (Zhang et al.,
2019).
Furthermore, regulatory and ethical issues
surrounding the use of gene-editing technologies in
agriculture need to be addressed. Clear guidelines and
regulations that balance the benefits of these
technologies with potential ecological and health risks
are necessary (Wolt et al., 2016).
The integration of CRISPR-Cas systems into plant
pathology research has opened up exciting possibilities
for understanding and combating plant diseases. As the
technology continues to evolve, it will undoubtedly
continue to shape the future of disease resistance in the
genomic era.
Metagenomics
Metagenomics has emerged as a powerful tool in
plant pathology, enabling the study of microbial
communities associated with plants and providing
valuable insights into the complex interactions between
plants and their pathogens.
In traditional plant pathology, identifying and
characterizing microbial pathogens often relied on
isolating and culturing individual organisms. However,
many microorganisms, including pathogens, are
challenging to culture in the laboratory, leading to a
significant knowledge gap regarding the full diversity of
plant-associated microbes. Metagenomics overcomes
this limitation by directly analyzing the genetic material
(DNA or RNA) extracted from environmental samples,
bypassing the need for cultivation (Tringe & Rubin,
2005).
Metagenomics in plant pathology allows
researchers to explore the composition and dynamics of
microbial communities in different environments, such
as phyllosphere (leaf surface), rhizosphere (root zone),
and soil. High-throughput sequencing technologies,
including Next-Generation Sequencing (NGS), have
revolutionized metagenomic studies by enabling the
generation of vast amounts of sequence data from
diverse environmental samples (Quince et al., 2017).
One of the significant applications of metagenomics
in plant pathology is the identification of pathogenic
microorganisms associated with plant diseases. By
sequencing the DNA or RNA in a sample, researchers can
identify the presence of known plant pathogens and
even discover novel pathogens. This approach has
proven particularly useful in studying emerging
Trends Anim Plant Sci, 2023, 2: 62-70.
65
infectious diseases, where the causative agents may be
previously unknown (Nagy et al., 2018).
Furthermore, metagenomics allows for the
simultaneous analysis of multiple plant-associated
microbes in a single sample. This comprehensive analysis
enables researchers to investigate the intricate network
of interactions between pathogens, beneficial
microbes, and the host plant's immune system
(Hacquard, 2016). Understanding these complex
relationships can provide insights into the factors that
influence disease development and plant health.
In addition to pathogen discovery, metagenomics
has the potential to identify beneficial microorganisms
that contribute to plant health and disease resistance.
Many microbes play critical roles in promoting plant
growth, nutrient uptake, and disease suppression
(Berendsen et al., 2012). Metagenomic studies can help
identify and characterize these beneficial
microorganisms, which can then be harnessed for
sustainable agriculture through biocontrol strategies or
probiotic applications.
However, metagenomic data analysis presents
several challenges. The vast amount of sequencing data
requires advanced bioinformatics tools and
computational resources for accurate analysis and
interpretation (Escudero et al., 2018). Additionally,
distinguishing between pathogenic and non-pathogenic
strains of closely related microorganisms can be
challenging and requires complementary techniques
such as PCR and qPCR for validation.
Despite these challenges, metagenomics continues
to advance our understanding of plant-pathogen
interactions and microbial diversity in the plant
environment. As this technology evolves, it holds
immense potential for developing tailored and
sustainable disease management strategies in agriculture.
Metagenomics represents a revolutionary approach
in plant pathology, providing a comprehensive
understanding of the microbial world that surrounds
plants. By revealing the hidden complexity of plant-
microbe interactions, metagenomics offers
opportunities to develop innovative disease
management strategies and promote sustainable
agriculture in the genomic era.
New Insights into Plant Disease Resistance in the
Genome Era
Genomic Understanding of Plant Immune System
In the genome era, significant strides have been
made in unraveling the complexities of the plant
immune system, providing new insights into plant
disease resistance (Mansoor et al., 2003). The plant
immune system is a sophisticated defense network that
enables plants to recognize and respond to invading
pathogens, ultimately leading to disease resistance.
Genomic technologies and techniques have played a
pivotal role in advancing our understanding of the plant
immune system and its dynamic interactions with
pathogens.
One of the key components of the plant immune
system is the recognition of pathogen-derived
molecules, known as pathogen-associated molecular
patterns (PAMPs), by pattern recognition receptors
(PRRs) present on the plant cell surface. This initial
recognition triggers PAMP-triggered immunity (PTI), a
frontline defense response that restricts pathogen
growth and entry (Jones & Dangl, 2006). Genomic
studies have identified a diverse array of PRRs,
highlighting the repertoire of strategies employed by
plants to detect and respond to different pathogens.
Pathogens, on the other hand, deploy effector
molecules to suppress PTI and establish successful
infection. However, in response, plants have evolved
another layer of defense known as effector-triggered
immunity (ETI). ETI relies on the specific recognition of
pathogen effectors by intracellular resistance (R)
proteins, leading to a potent defense response (Dodds
& Rathjen, 2010). Genomic studies have been
instrumental in identifying and characterizing a wide
array of R-genes, which play a crucial role in determining
the outcome of host-pathogen interactions.
With the advent of NGS and bioinformatics tools,
researchers have been able to conduct large-scale
genomic analyses to identify and classify R-genes. These
analyses have provided valuable insights into the
diversity and evolution of R-genes in plant genomes.
Additionally, comparative genomics approaches have
revealed the presence of conserved domains and motifs
within R-genes, contributing to our understanding of
the molecular mechanisms underlying plant immune
responses (Mcdowell & Simon, 2006).
Moreover, genomic studies have shed light on the
co-evolutionary arms race between plants and
pathogens. As plants evolve new R-genes for disease
resistance, pathogens, in turn, undergo genetic changes
to overcome host defenses. This evolutionary dance is
reflected in the intricate genomic variations observed in
both plants and pathogens. Such insights have led to the
development of the "zig-zag model," which describes
the molecular dialogue between plants and pathogens
during host-pathogen interactions (Jones & Dangl,
2006).
In addition to R-genes, genomic studies have
elucidated the roles of regulatory genes and small RNAs
in modulating plant immune responses. Small RNAs,
particularly microRNAs (miRNAs) and small interfering
RNAs (siRNAs), have emerged as crucial players in post-
transcriptional gene regulation during plant immunity
(Ding & Voinnet, 2007). The identification of these
regulatory components has enhanced our
understanding of the complex gene regulatory
networks that shape plant immune responses.
Furthermore, the genomic era has facilitated the
study of plant immunity in diverse plant species.
Comparative genomics has enabled researchers to
identify conserved immune components across
different plant families, highlighting the common
defense strategies employed by plants. Such cross-
Trends Anim Plant Sci, 2023, 2: 62-70.
66
species comparisons have provided valuable
information for translational research, allowing the
transfer of knowledge from model plants to
economically important crops (Nicaise, 2014).
Despite the substantial progress made in genomic
understanding of the plant immune system, challenges
persist. Genome-wide association studies (GWAS) have
identified numerous candidate genes associated with
disease resistance, but functional validation remains a
bottleneck. CRISPR-Cas systems offer a promising
solution, enabling targeted mutagenesis to validate
gene function and decipher their roles in disease
resistance (Zaidi et al., 2018).
The genomic era has brought about a deeper
understanding of the plant immune system, unraveling
the molecular intricacies of plant-pathogen interactions.
The identification and characterization of PRRs, R-
genes, regulatory elements, and small RNAs have shed
light on the complexities of plant immunity. By
integrating genomic technologies and techniques,
researchers are paving the way for the development of
innovative strategies to enhance disease resistance in
crops and ensure global food security.
Plant-Pathogen Interactions at the Genomic Level
Plant-pathogen interactions at the genomic level
represent a dynamic interplay between plants and
pathogens, driven by evolutionary adaptations and
genetic variations. The genome era has provided
unprecedented opportunities to delve into the
intricacies of these interactions, uncovering new
insights into plant disease resistance mechanisms.
At the heart of plant-pathogen interactions lie the
recognition events between the host plant and the
invading pathogen. As mentioned earlier, plants have
evolved pattern recognition receptors (PRRs) to detect
conserved pathogen-associated molecular patterns
(PAMPs), leading to PAMP-triggered immunity (PTI)
(Jones & Dangl, 2006). PTI acts as a general defense
mechanism, providing a rapid and broad-spectrum
response to diverse pathogens. Genomic studies have
significantly contributed to the identification and
characterization of PRRs, shedding light on their roles in
plant immunity (Boutrot & Zipfel, 2017).
In response to PTI, pathogens have developed
effector molecules that can suppress or evade host
immunity, promoting disease establishment. However,
plants have evolved resistance (R) genes to recognize
specific pathogen effectors and trigger effector-
triggered immunity (ETI) (Dodds & Rathjen, 2010). This
recognition specificity forms the basis of gene-for-gene
resistance, where each R-gene confers resistance to a
specific pathogen effector. Genomic studies have been
pivotal in deciphering the genetic basis of gene-for-gene
resistance and the co-evolutionary arms race between
plants and pathogens (Mcdowell & Simon, 2006).
Advancements in genomic technologies,
particularly NGS, have enabled researchers to conduct
genome-wide analyses to identify and characterize R-
genes. These studies have not only provided insights
into the diversity of R-genes but also revealed evidence
of positive selection acting on these genes (Zaidi et al.,
2018). Comparative genomics has further demonstrated
the presence of R-gene clusters, indicating the
importance of gene duplication events in the evolution
of plant immune systems (Boller & Felix, 2009).
The genomic era has also facilitated the
identification of pathogen effectors and their functional
characterization. By sequencing the genomes of various
pathogens, researchers have gained a comprehensive
catalog of effectors, allowing them to predict potential
targets in host plants (Kamoun, 2006). Additionally,
transcriptomic analyses have been employed to study
the expression profiles of pathogens during infection,
providing insights into the temporal regulation of
effector delivery and the strategies employed by
pathogens to manipulate host immune responses
(Hacquard, 2016).
An essential aspect of plant-pathogen interactions
is the recognition of pathogen effectors by R-genes,
leading to ETI. Genomic studies have revealed the co-
evolutionary dynamics between effectors and their
corresponding R-genes, indicating the role of gene gain
and loss events in shaping pathogen virulence and host
resistance (Jones & Dangl, 2006). Such genomic insights
have provided a conceptual framework to understand
the durability of R-gene-mediated resistance in the face
of rapidly evolving pathogens (Mcdowell & Simon,
2006).
Apart from the direct recognition of effectors, some
R-genes may act indirectly by monitoring the
perturbations caused by effectors in the host cell. This
mode of indirect recognition, known as "guard model"
or "decoy model," involves R-genes that resemble the
targets of pathogen effectors (Jones & Dangl, 2006).
Genomic studies have led to the identification of several
decoy R-genes that play crucial roles in ETI (Zaidi et al.,
2018).
Furthermore, the application of metagenomics has
expanded our understanding of plant-pathogen
interactions by exploring the broader microbial
communities associated with plants. Metagenomic
studies have unraveled the complexity of the
phyllosphere, rhizosphere, and soil microbiomes,
shedding light on how diverse microbial communities
influence plant health and disease resistance
(Berendsen et al., 2012).
The genomic era has revolutionized our
understanding of plant-pathogen interactions at the
molecular level. From the identification and
characterization of PRRs and R-genes to the exploration
of pathogen effectors and their co-evolution with hosts,
genomics has provided invaluable insights into the
intricate world of plant disease resistance. These
discoveries hold great promise for developing
innovative strategies to enhance crop resistance and
sustain global food security in the face of evolving
pathogens and changing environments.
Trends Anim Plant Sci, 2023, 2: 62-70.
67
Harnessing Genomic Knowledge for Disease Resistant
Crop Development
Harnessing genomic knowledge for disease-
resistant crop development has emerged as a promising
avenue in agriculture, leveraging the advances in
genomics to breed crops with enhanced resistance to
pathogens. The insights gained from studying plant-
pathogen interactions at the genomic level have
provided valuable information to devise targeted
strategies for developing disease-resistant crops (Zafar,
Mustafa, et al., 2022; Zafar et al., 2020).
One of the key applications of genomic knowledge
in crop development is the identification and
characterization of disease resistance (R) genes.
Through genome-wide association studies (GWAS) and
linkage mapping, researchers can pinpoint genomic
regions associated with disease resistance in diverse
germplasm collections (Poland et al., 2019). These
studies have enabled the discovery of novel R-genes and
the utilization of existing natural genetic variation to
enhance crop resistance (Zaidi et al., 2018).
Genome editing technologies, such as CRISPR-Cas
systems, have revolutionized crop improvement by
providing precise and targeted gene modifications. By
harnessing genomic knowledge of R-genes, researchers
can use CRISPR-Cas to engineer disease-resistant crop
varieties with increased precision and efficiency
(Steinert et al., 2020). This approach allows for the rapid
development of crops with desirable traits, bypassing
the lengthy traditional breeding processes.
In addition to engineering individual R-genes,
genomic information has also facilitated the stacking of
multiple R-genes or defense-related genes to create
crops with broad-spectrum resistance. This approach,
known as "pyramiding," offers enhanced durability and
efficacy against diverse pathogen strains, reducing the
risk of pathogen adaptation (Poland et al., 2019).
Genomic tools enable researchers to predict and
validate the synergistic effects of gene combinations,
accelerating the development of robust disease-
resistant crops.
Moreover, the use of genomic knowledge in
understanding the intricacies of plant immunity has
paved the way for innovative strategies, such as priming
and primed defenses. Priming involves pre-exposing
plants to low levels of certain elicitors or pathogen-
derived molecules, which subsequently enhances their
defense response upon pathogen attack (Martinez-
Medina et al., 2016). By understanding the genetic and
molecular basis of primed defenses, researchers can
develop crop varieties that exhibit stronger and faster
immune responses, bolstering their resistance to
pathogens.
Another approach to harness genomic knowledge is
through the use of molecular markers associated with
disease resistance. These markers, derived from R-genes
or other defense-related genes, serve as signatures to
track disease resistance traits during crop breeding
(Cobb et al., 2019). Genomic selection, a data-driven
breeding approach that uses genome-wide markers, has
emerged as a powerful tool to predict and select
disease-resistant individuals in breeding populations,
reducing the time and cost of conventional breeding
(Crossa et al., 2017).
Furthermore, metagenomics has contributed to the
development of sustainable disease management
strategies by exploring the potential of beneficial
microbes as biocontrol agents. By analyzing the plant-
associated microbiome, researchers have identified
microbial species that can antagonize pathogens or
enhance plant immunity (Berendsen et al., 2012).
Harnessing this genomic information has led to the
development of biopesticides and biofertilizers,
providing environmentally friendly alternatives to
chemical pesticides and synthetic fertilizers.
Despite the promising prospects, the successful
translation of genomic knowledge into disease-resistant
crop development is not without challenges. Intellectual
property rights and access to genomic data can create
disparities between public and private research efforts,
limiting the equitable distribution of advancements
(Brooks et al., 2018). Addressing these concerns is
crucial to ensuring that genomic benefits are accessible
to all stakeholders, particularly small-scale farmers in
developing countries.
Furthermore, the deployment of genetically
resistant crop varieties can exert selective pressures on
pathogen populations, potentially leading to the
emergence of new virulent strains. Continuous
monitoring and surveillance are essential to assess the
durability and long-term efficacy of disease resistance in
crops (Poland et al., 2019).
The genomic era has ushered in a new era of
disease-resistant crop development, leveraging our
understanding of plant-pathogen interactions at the
genomic level. By harnessing genomic knowledge of R-
genes, utilizing genome editing technologies, and
exploring the potential of beneficial microbes,
researchers are paving the way for innovative strategies
to enhance crop resistance and ensure global food
security. However, addressing ethical and practical
challenges remains imperative to maximize the benefits
of genomics for sustainable and inclusive agriculture.
Future Perspectives and Challenges
The genomic era has revolutionized our
understanding of plant-pathogen interactions and
provided novel insights into disease resistance
mechanisms in plants. As we look to the future, the
integration of genomic knowledge in crop development
offers promising opportunities for sustainable
agriculture. However, several challenges must be
addressed to fully harness the potential of genomics in
enhancing crop disease resistance.
Accelerating Translational Research: While
genomic knowledge has led to exciting discoveries,
translating these findings into practical applications
Trends Anim Plant Sci, 2023, 2: 62-70.
68
remains a crucial step. Researchers must bridge the gap
between fundamental genomics research and applied
crop breeding to ensure that disease-resistant crop
varieties reach farmers' fields. Collaborations between
scientists, breeders, and policymakers are essential to
facilitate the adoption of genomic tools in crop
improvement programs (Cobb et al., 2019).
Deployment of Gene Editing Technologies: The advent
of gene editing technologies, such as CRISPR-Cas
systems, holds immense promise for precise and
targeted crop improvement. However, the regulatory
approval and public acceptance of genetically modified
crops pose challenges (Zafar, Rehman, et al., 2022).
Clear and transparent regulations, along with effective
communication about the safety and benefits of gene-
edited crops, are crucial for their widespread adoption
(Steinert et al., 2020).
Addressing Genetic Erosion: The widespread adoption
of a limited number of high-yielding crop varieties has
led to genetic erosion, reducing the genetic diversity
available for disease resistance breeding. Emphasizing
the utilization of diverse germplasm, landraces, and wild
relatives in breeding programs is essential to enhance
the resilience of crops against evolving pathogens
(Poland et al., 2019).
Climate Change and Emerging Pathogens: Climate
change is altering the distribution and virulence of
pathogens, posing new challenges for disease
management. Genomic approaches can help identify
genes and traits associated with climate resilience and
disease resistance, enabling the development of
climate-smart crop varieties (Kamoun, 2006).
Balancing R-gene Pyramiding and Durability: Stacking
multiple R-genes to achieve broad-spectrum resistance
is a powerful approach. However, overreliance on R-
genes with narrow specificities can lead to rapid
pathogen adaptation. Sustainable resistance
management strategies, such as rotating R-genes or
combining R-genes with other modes of defense, must
be employed to prolong the effectiveness of disease
resistance (Dodds & Rathjen, 2010).
Expanding Knowledge of Non-Host Resistance: Non-
host resistance, the inability of a pathogen to infect a
certain plant species, is an underexplored area in crop
protection. Genomic studies can shed light on the
genetic basis of non-host resistance and offer insights
into enhancing resistance in cultivated crops against a
broader range of pathogens (Mcdowell & Simon, 2006).
Big Data and Bioinformatics Challenges: The wealth of
genomic data generated by high-throughput
sequencing technologies presents bioinformatics
challenges. Developing robust data analysis pipelines,
establishing standardized data repositories, and
enhancing data sharing and collaboration are critical for
maximizing the utility of genomic resources in crop
research (Brooks et al., 2018).
Ethical Considerations: The use of genomic
technologies in crop development raises ethical
considerations, including issues related to intellectual
property rights, equitable access to genomic resources,
and potential unintended consequences of genetically
modified crops. Ethical guidelines and policies that
promote transparency, fairness, and inclusivity are
essential to ensure responsible and sustainable
deployment of genomics in agriculture.
Education and Capacity Building: Building genomics
capacity among scientists and breeders in developing
countries is essential to promote equitable access to
genomic tools and knowledge. Training programs and
collaborative initiatives can empower scientists to
leverage genomics for developing locally adapted
disease-resistant crop varieties (Crossa et al., 2017).
Conclusion
In conclusion, this study has unraveled the
complexities of plant-pathogen interactions, thanks to
genomic technologies and techniques. The integration
of genomics in crop development offers promising
opportunities to breed disease-resistant crops and
secure global food production. As we move forward,
continued research, collaboration, and innovation will
be instrumental in harnessing genomic knowledge for
sustainable agriculture and safeguarding crops against
emerging pathogens and environmental challenges.
Embracing the genomic era holds the potential to
revolutionize the future of plant pathology and propel
us toward a more resilient and productive agricultural
landscape.
REFERENCES
Agrios, G. N. (2005). Plant pathology (5th ed.). Elsevier
Academic Press.
Alam, S., Khalid, M. N., Ijaz, M., Akram, M. S., Irfan, M., &
Hassan, M. (2021). Rearing of Maize Stem Borer, Chilo
partellus (Lepidoptera: Crambidae) under Laboratory
Conditions.
Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976).
A land use and land cover classification system for use
with remote sensor data (Vol. 964). US Government
Printing Office.
Baltes, N. J., Gil-Humanes, J., Cermak, T., Atkins, P. A., & Voytas,
D. F. (2014). DNA replicons for plant genome engineering.
Plant Cell, 26(1), 151–163.
Barrangou, R., Fremaux, C., Deveau, H., Richards, M., Boyaval,
P., Moineau, S., Romero, D. A., & Horvath, P. (2007).
CRISPR provides acquired resistance against viruses in
prokaryotes. Science, 315(5819), 1709–1712.
Baxter, L., Tripathy, S., Ishaque, N., Boot, N., Cabral, A., Kemen,
E., Thines, M., Ah-Fong, A., Anderson, R., Badejoko, W.,
Bittner-Eddy, P., Boore, J. L., Chibucos, M. C., Coates, M.,
Dehal, P., Delehaunty, K., Dong, S., Downton, P., Dumas,
Trends Anim Plant Sci, 2023, 2: 62-70.
69
B., … Judelson, H. S. (2012). Signatures of adaptation to
obligate biotrophy in the Hyaloperonospora arabidopsidis
genome. Science, 338(6114), 1651–1655.
Berendsen, R. L., Pieterse, C. M. J., & Bakker, P. A. H. M. (2012).
The rhizosphere microbiome and plant health. Trends in
Plant Science, 17(8), 478–486.
Bhutta, M. A., Bibi, A., Ahmad, N. H., Kanwal, S., Amjad, Z.,
Farooq, U., Khalid, M. N., & Nayab, S. F. (2023). Molecular
Mechanisms of Photoinhibition in Plants: A Review.
Sarhad Journal of Agriculture, 39(230).
Boller, T., & Felix, G. (2009). A renaissance of elicitors:
perception of microbe-associated molecular patterns and
danger signals by pattern-recognition receptors. Annual
Review of Plant Biology, 60, 379–406.
Boutrot, F., & Zipfel, C. (2017). Function, discovery, and
exploitation of plant pattern recognition receptors for
broad-spectrum disease resistance. Annual Review of
Phytopathology, 55, 257–286.
Brooks, S., Yan, W., & Jackson, A. (2018). Impact of data
sharing on genomic selection breeding programs. Crop
Science, 58(2), 690–701.
Chapman, N. H., Bowers, J. E., Feltus, F. A., & Paterson, A. H.
(2006). Buffering of crucial functions by paleologous
duplicated genes may contribute cyclicality to
angiosperm genome duplication. Proceedings of the
National Academy of Sciences, 103(8), 2730–2735.
Cobb, J. N., Declerck, G., Greenberg, A., Clark, R., Mccouch, S.,
& Price, A. H. (2019). Genetic architecture of arbuscular
mycorrhizal fungus colonization in rice (Oryza sativa).
Theoretical and Applied Genetics, 132(4), 1131–1150.
Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López,
O., Jarquín, D., de los Campos, G., Burgueño, J., González-
Camacho, J. M., Pérez-Elizalde, S., Beyene, Y.,
Dreisigacker, S., Singh, R., Zhang, X., Gowda, M.,
Roorkiwal, M., Rutkoski, J., Varshney, R. K., & Sorrells, M.
(2017). Genomic selection in plant breeding: methods,
models, and perspectives. Trends in Plant Science, 22(11),
961–975.
Dangl, J. L., Horvath, D. M., & Staskawicz, B. J. (2013). Pivoting
the plant immune system from dissection to deployment.
Science, 341(6147), 746–751.
Ding, S. W., & Voinnet, O. (2007). Antiviral immunity directed
by small RNAs. Cell, 130(3), 413–426.
Dodds, P. N., & Rathjen, J. P. (2010). Plant immunity: towards
an integrated view of plant-pathogen interactions. Nature
Reviews Genetics, 11(8), 539–548.
Escudero, L. V., de León, L. F., & Ferrero, M. (2018). How to
make the most of metagenomics in agriculture: an
overview. Frontiers in Microbiology, 9, 1880.
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L.,
Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas,
S. M., & Toulmin, C. (2010). Food security: the challenge of
feeding 9 billion people. Science, 327(5967), 812–818.
Green, E. D. (2001). Strategies for the systematic sequencing of
complex genomes. Nature Reviews Genetics, 2(8), 573–
583.
Hacquard, S. (2016). Disentangling the factors shaping
microbiota composition across the plant holobiont. New
Phytologist, 209(2), 454–457.
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., &
Charpentier, E. (2012). A programmable dual-RNA–guided
DNA endonuclease in adaptive bacterial immunity.
Science, 337(6096), 816–821.
Jones, J. D. G., & Dangl, J. L. (2006). The plant immune system.
Nature, 444(7117), 323–329.
Kamoun, S. (2006). A catalogue of the effector secretome of
plant pathogenic oomycetes. Annual Review of
Phytopathology, 44, 41–60.
Khalid, M. N., Abdullah, A., Ijaz, Z., Naheed, N., Hamad, A.,
Sheir, M. A., Shabir, F., Parveen, K., & Khan, M. D. (2021).
Application and Potential Use of Advanced Bioinformatics
Techniques in Agriculture and Animal Sciences. Ind. J.
Pure App. Biosci, 9(3), 237-246.
Khan, R., Ghayas, M., Khalid, M., & Amjad, I. (2023). Transgenic
strategies for enhancing cotton disease resistance:
current status and future directions. Agrobiological
Records 13: 82-91. In.
Langner, T., Kamoun, S., & Belhaj, K. (2018). CRISPR crops:
plant genome editing toward disease resistance. Annual
Review of Phytopathology, 56, 479–512.
Mansoor, S., Amin, I., Iram, S., Hussain, M., Zafar, Y., Malik, K.,
& Briddon, R. (2003). Breakdown of resistance in cotton
to cotton leaf curl disease in Pakistan. Plant pathology,
52(6), 784-784.
Martinez-Medina, A., Flors, V., Heil, M., Mauch-Mani, B.,
Pieterse, C. M. J., Pozo, M. J., Ton, J., & Van Dam, N. M.
(2016). Recognizing plant defense priming. Trends in Plant
Science, 21(10), 818–822.
Mcdowell, J. M., & Simon, S. A. (2006). Recent insights into R
gene evolution. Molecular Plant Pathology, 7(5), 437–448.
Metzker, M. L. (2010). Sequencing technologies - the next
generation. Nature Reviews Genetics, 11(1), 31–46.
Mumm, R. H., & Dudley, J. W. (1994). A classification of 148 US
maize inbreds: II. Utility of the classification for grouping
parents and predicting heterosis. Crop Science, 34(3),
825–831.
Nagy, T., Price, J., & Joyce, P. (2018). The emergence of
metagenomics as a powerful tool for plant pathology.
Phytopathology, 108(1), 49–60.
Nicaise, V. (2014). Crop immunity against viruses: outcomes
and future challenges. Frontiers in Plant Science, 5, 660.
Nowak, R. M. (1999). Walker's Mammals of the World (Vol. 1).
JHU Press.
Pabinger, S., Dander, A., Fischer, M., Snajder, R., Sperk, M.,
Efremova, M., Krabichler, B., Speicher, M. R., Zschocke, J.,
& Trajanoski, Z. (2014). A survey of tools for variant
analysis of next-generation genome sequencing data.
Briefings in Bioinformatics, 15(2), 256–278.
Poland, J. A., Balint-Kurti, P. J., Wisser, R. J., Pratt, R. C., &
Nelson, R. J. (2009). Shades of gray: the world of
quantitative disease resistance. Trends in Plant Science,
14(1), 21–29.
Poland, J. A., Brown, P. J., Sorrells, M. E., & Jannink, J. L. (2012).
Development of high-density genetic maps for barley and
wheat using a novel two-enzyme genotyping-by-
sequencing approach. PLoS ONE, 7(2), e32253.
Puchta, H. (2017). Applying CRISPR/Cas for genome
engineering in plants: the best is yet to come. Current
Opinion in Plant Biology, 36, 1–8.
Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata,
N. (2017). Shotgun metagenomics, from sampling to
analysis. Nature Biotechnology, 35(9), 833–844.
Scholthof, K. B. G., Adkins, S., Czosnek, H., Palukaitis, P.,
Jacquot, E., Hohn, T., Hohn, B., Saunders, K., Candresse,
T., Ahlquist, P., Hemenway, C., & Foster, G. D. (2011). Top
10 plant viruses in molecular plant pathology. Molecular
Plant Pathology, 12(9), 938–954.
Schuster, S. C. (2008). Next-generation sequencing transforms
today's biology. Nature Methods, 5(1), 16–18.
Trends Anim Plant Sci, 2023, 2: 62-70.
70
Shewry, P. R., & Hey, S. J. (2015). The contribution of wheat to
human diet and health. Food and Energy Security, 4(3),
178–202.
Smith, J. D. (2010). Historical development of plant pathology
and the concept of disease resistance. Phytopathology,
100(3), 220–226.
Steinert, J., Schiml, S., & Puchta, H. (2020). Plant genome
editing in the age of CRISPR/Cas9 – aiming for precision.
Plant Biotechnology Journal, 18(8), 2012–2023.
Stover, P., & Schirch, V. (1993). Serine hydroxymethyl-
transferase catalyzes the hydrolysis of 5,10-
methenyltetrahydrofolate to 5-formyltetrahydrofolate.
Journal of Biological Chemistry, 268(28), 21416–21424.
Tringe, S. G., & Rubin, E. M. (2005). Metagenomics: DNA
sequencing of environmental samples. Nature Reviews
Genetics, 6(11), 805–814.
Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J.,
Sutton, G. G., ... & Kalush, F. (2001). The sequence of the
human genome. science, 291(5507), 1304-1351.
Weinert, L. A., Welch, J. J., Suchard, M. A., Lemey, P., Rambaut,
A., & Fitzgerald, J. R. (2012). Molecular dating of human-
to-bovid host jumps by Staphylococcus aureus reveals an
association with the spread of domestication. Biology
Letters, 8(5), 829–832.
Wolt, J. D., Wang, K., & Yang, B. (2016). The regulatory status
of genome-edited crops. Plant Biotechnology Journal,
14(2), 510–518.
Zafar, M. M., Mustafa, G., Shoukat, F., Idrees, A., Ali, A., Sharif,
F., Shakeel, A., Mo, H., Youlu, Y., & Ali, Q. (2022).
Heterologous expression of cry3Bb1 and cry3 genes for
enhanced resistance against insect pests in cotton.
Scientific Reports, 12(1), 10878.
Zafar, M. M., Razzaq, A., Farooq, M. A., Rehman, A., Firdous,
H., Shakeel, A., Mo, H., & Ren, M. (2020). Insect resistance
management in Bacillus thuringiensis cotton by MGPS
(multiple genes pyramiding and silencing). Journal of
Cotton Research, 3(1), 1-13.
Zafar, M. M., Rehman, A., Razzaq, A., Parvaiz, A., Mustafa, G.,
Sharif, F., Mo, H., Youlu, Y., Shakeel, A., & Ren, M. (2022).
Genome-wide characterization and expression analysis
of Erf gene family in cotton. BMC Plant Biology, 22(1),
134.
Zaidi, S. S. E. A., Mahfouz, M. M., & Mansoor, S. (2018). CRISPR-
Cpf1: a new tool for plant genome editing. Trends in Plant
Science, 23(7), 661–673.
Zhang, Y., Malzahn, A. A., Sretenovic, S., & Qi, Y. (2019). The
emerging and uncultivated potential of CRISPR
technology in plant science. Nature Plants, 5(7), 778–
794.
Zhou, H., Liu, B., Weeks, D. P., Spalding, M. H., & Yang, B.
(2015). Large chromosomal deletions and heritable small
genetic changes induced by CRISPR/Cas9 in rice. Nucleic
Acids Research, 42(17), 10903–1091.
