This document discusses the use of pathogenomics in understanding plant pathogens. It begins with an introduction to genomics and pathogenomics. It then discusses techniques used in pathogenomics like sequencing and comparative genomics. Several examples are provided of pathogens that have been studied using pathogenomics, including fungi like Fusarium graminearum and bacteria like Xanthomonas oryzae. The document discusses how pathogenomics can be used to identify virulence factors, understand pathogenesis, and discover new sources of host resistance. It concludes by discussing challenges in pathogenomics like regulatory elements that are difficult to annotate and the need for more plant genome sequences.
The study of pathogenomics attempts to utilize genomic and metagenomics data gathered from high through-put technologies to understand microbe diversity and interaction as well as host-microbe interactions involved in causing the disease. Pathogenomics researchers are generating and analyzing genome sequences of diverse bacterial, oomycete, fungal and viral pathogens to identify genetic sources of virulence, understand differences observed among related pathogens, guide the development of diagnosis tools capable of discriminating among specific strains, reveal sources of host resistance and understand the dynamics of host-microbe interactions and the diseases they cause .
I would like to share this presentation file.
Some basics information regarding to molecular plant breeding, hope this help the beginner who start working in this field.
Thanks for many original source of information (mainly from slideshare.net, IRRI, CIMMYT and any paper received from professor and some over the internet)
The study of pathogenomics attempts to utilize genomic and metagenomics data gathered from high through-put technologies to understand microbe diversity and interaction as well as host-microbe interactions involved in causing the disease. Pathogenomics researchers are generating and analyzing genome sequences of diverse bacterial, oomycete, fungal and viral pathogens to identify genetic sources of virulence, understand differences observed among related pathogens, guide the development of diagnosis tools capable of discriminating among specific strains, reveal sources of host resistance and understand the dynamics of host-microbe interactions and the diseases they cause .
I would like to share this presentation file.
Some basics information regarding to molecular plant breeding, hope this help the beginner who start working in this field.
Thanks for many original source of information (mainly from slideshare.net, IRRI, CIMMYT and any paper received from professor and some over the internet)
Banoth Madhu: Map based gene cloning in plant. In the process of map-based cloning, one starts with a mutant and eventually identifies the gene responsible for the altered phenotype, allowing the plant to tell you what genes are important in the physiological process of interest and using the genetic relationship between a gene and a marker as the basis for beginning a search for a gene
A concise and well fabricated presentation the current techniques used for plant genome editing including CRISPER/cas9 system, TALENS, TELES, ZINC FINGER NUCLEASES(ZFN), HEJ (homologous endjoing) and many other high throughout techniques along references.
Role of biotechnology - gene silencing in plant disease controlAshajyothi Mushineni
An overview of role of biotechnology especially gene silencing approach in plant disease control and success achieved so far and way forward and it's importance in developing countries
Marker Assisted Gene Pyramiding for Disease Resistance in RiceIndrapratap1
Why marker assisted gene pyramiding?
For traits that are simply inherited, but that are difficult or expensive to measure phenotypically, and/or that do not have a consistent phenotypic expression under specific selection conditions, marker-based selection is more effective than phenotypic selection.
Traits which are traditionally regarded as quantitative and not targeted by gene pyramiding program can be improved using gene pyramiding if major genes affecting the traits are identified.
Genes with very similar phenotypic effects, which are impossible or difficult to combine in single genotype using phenotypic selection, can be pyramided through marker assisted selection.
Markers provides a more effective option to control linkage drag and make the use of genes contained in unadapted resources easier.
Pyramiding is possible through conventional breeding but is extremely difficult or impossible at early generations..
DNA markers may facilitate selection because DNA marker assays are non destructive and markers for multiple specific genes/QTLs can be tested using a single DNA sample without phenotyping.
CONCLUSION:
• Molecular marker offer great scope for improving the efficiency of conventional plant breeding.
• Gene pyramiding may not be the most suitable strategy when many QTL with small effects control the trait and other methods such as marker-assisted recurrent selection should be considered.
• With MAS based gene pyramiding, it is now possible for breeder to conduct many rounds of selections in a year.
• Gene pyramiding with marker technology can integrate into existing plant breeding program all over the world to allow researchers to access, transfer and combine genes at a rate and with precision not previously possible.
• This will help breeders get around problems related to larger breeding populations, replications in diverse environments, and speed up the development of advance lines.
For further queries please contact at isag2010@gmail.com
An insight into the reverse genetics in fisheries research. it includes a brief history about the reverse genetics, background, techniques applied, recovery of virus and zebrafish research
Applications of genomics and proteomics pptIbad khan
Applications of genomics and proteomics ppt
genomics and proteomics ppt
in the field of health genomics and proteomics ppt
oncology ppt
biomedical application of genomics and proteomics ppt
agriculture application of genomics and proteomics ppt
proteomics in agriculture ppt
diagnosis of infectious disease ppt
personalized medicine ppt
QTL is a gene or the chromosomal region that affects a quantitative trait, which should be polymorphic (have allelic variation) to have an effect in a population, must be linked to a polymorphic marker allele to be detected. The QTL mapping consists of 4 steps, like the development of mapping population, generation of polymorphic marker data set among the parents, construction of linkage map, and finally the QTL analysis
All the above steps are described in these slides very briefly along with two case studies.
Degradome sequencing and small rna targetsAswinChilakala
small RNA have numerous roles in plant developmental biology their discovery is one of the important things to take advantage of the plant system moreover the gene target of small RNA identification helps us engineer the development of the plant in a better way one such method is Degradome sequencing.
Banoth Madhu: Map based gene cloning in plant. In the process of map-based cloning, one starts with a mutant and eventually identifies the gene responsible for the altered phenotype, allowing the plant to tell you what genes are important in the physiological process of interest and using the genetic relationship between a gene and a marker as the basis for beginning a search for a gene
A concise and well fabricated presentation the current techniques used for plant genome editing including CRISPER/cas9 system, TALENS, TELES, ZINC FINGER NUCLEASES(ZFN), HEJ (homologous endjoing) and many other high throughout techniques along references.
Role of biotechnology - gene silencing in plant disease controlAshajyothi Mushineni
An overview of role of biotechnology especially gene silencing approach in plant disease control and success achieved so far and way forward and it's importance in developing countries
Marker Assisted Gene Pyramiding for Disease Resistance in RiceIndrapratap1
Why marker assisted gene pyramiding?
For traits that are simply inherited, but that are difficult or expensive to measure phenotypically, and/or that do not have a consistent phenotypic expression under specific selection conditions, marker-based selection is more effective than phenotypic selection.
Traits which are traditionally regarded as quantitative and not targeted by gene pyramiding program can be improved using gene pyramiding if major genes affecting the traits are identified.
Genes with very similar phenotypic effects, which are impossible or difficult to combine in single genotype using phenotypic selection, can be pyramided through marker assisted selection.
Markers provides a more effective option to control linkage drag and make the use of genes contained in unadapted resources easier.
Pyramiding is possible through conventional breeding but is extremely difficult or impossible at early generations..
DNA markers may facilitate selection because DNA marker assays are non destructive and markers for multiple specific genes/QTLs can be tested using a single DNA sample without phenotyping.
CONCLUSION:
• Molecular marker offer great scope for improving the efficiency of conventional plant breeding.
• Gene pyramiding may not be the most suitable strategy when many QTL with small effects control the trait and other methods such as marker-assisted recurrent selection should be considered.
• With MAS based gene pyramiding, it is now possible for breeder to conduct many rounds of selections in a year.
• Gene pyramiding with marker technology can integrate into existing plant breeding program all over the world to allow researchers to access, transfer and combine genes at a rate and with precision not previously possible.
• This will help breeders get around problems related to larger breeding populations, replications in diverse environments, and speed up the development of advance lines.
For further queries please contact at isag2010@gmail.com
An insight into the reverse genetics in fisheries research. it includes a brief history about the reverse genetics, background, techniques applied, recovery of virus and zebrafish research
Applications of genomics and proteomics pptIbad khan
Applications of genomics and proteomics ppt
genomics and proteomics ppt
in the field of health genomics and proteomics ppt
oncology ppt
biomedical application of genomics and proteomics ppt
agriculture application of genomics and proteomics ppt
proteomics in agriculture ppt
diagnosis of infectious disease ppt
personalized medicine ppt
QTL is a gene or the chromosomal region that affects a quantitative trait, which should be polymorphic (have allelic variation) to have an effect in a population, must be linked to a polymorphic marker allele to be detected. The QTL mapping consists of 4 steps, like the development of mapping population, generation of polymorphic marker data set among the parents, construction of linkage map, and finally the QTL analysis
All the above steps are described in these slides very briefly along with two case studies.
Degradome sequencing and small rna targetsAswinChilakala
small RNA have numerous roles in plant developmental biology their discovery is one of the important things to take advantage of the plant system moreover the gene target of small RNA identification helps us engineer the development of the plant in a better way one such method is Degradome sequencing.
microRNA in Plant Defence and Pathogen Counter-defenceMahtab Rashid
The presentation is about the role of microRNA in plant defence and the pathogen counter-defences which they adopt to escape or evade the plant defence mechanism.
this is a presentation on molecular markers that include what is molecular marker, it's types, biochemical markets (alloenzyme), it's classification, data analysis and it's applications
Genome editing in crop improvement one of the desirable biotechnology concept. It is useful for the production of new varieties against resistance to diseases and insect pests
A plant genome project aims to discover all genes and their function in a particular plant species.
The main objective of genomic research in any species is to sequence the whole genome and functions of all the different coding and non-coding sequences.
These techniques helped in preparation of molecular maps of many plant genomes.
Plant genome projects initially focused on a few model organisms that are characterized by small genomes or their amenability to genetic studies
Since sequencing technologies have moved on, sequencing cost have dropped and bioinformatics tools advanced, the genomes of many plant species including the enormous genome of bread wheat have been assembled
Genome sequencing projects have been carried out on all three plant genomes: the nuclear, chloroplast and mitochondrial genomes
This opened venues for advanced molecular breeding and manipulation of plant species, but also have accelerated phylogenetics studies amongst species
Several excellent curated plant genome databases, besides the general nucleotide data base archives, allow public access of plant genomes
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
PRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATION
Pathogenomics: Challenges and Opportunities
1. B. SANGEETHA (Ph. D Scholar)
Department of Plant Pathology,
M. SAMYUKTHA (Ph. D Scholar)
Department of Plant Breeding and Genetics,
Tamil Nadu Agricultural University,
Coimbatore- 641003
6. Structural genomics
•Structure of every protein encoded by the genome.
• Identify novel protein folds and 3- D structures.
Mutational Genomics
•Also referred as gene function determination
•Aim to determine function of gene or anonymous sequence
Comparative Genomics
•To determine the function of each genome studying
genes in model organism.
Functional genomics
•Protein functions
•Genes and protein interactions
8. Analyzing of pathogenic genome
Identification of genetic sources of virulence
Guide the development of diagnostic tools capable of
discriminating different strains
Revealing sources of host resistance
Understand the dynamics of host-microbe
interactions
13. Understanding ways of infection
• Factors of virulence and resistance (mobile pathogenic
elements)
• Host and pathogen diversity
• Immune response (defensins)
• Role of surface proteins
Understanding pathogenesis
• Communication between species (biofilm)
• Factors and mechanisms of pathogenicity (secretion systems)
• Host–microbe interaction
• SNP analysis and molecular epidemiology
17. Genes Encoded proteins References
16S rRNA 16S Ribosomal protein subunit (Lee et al., 1997 & Alvarez,
2004)
23S rRNA 23S Ribosomal protein subunit (Maes et al., 1996)
16S-23S
rRNA
Internal transcribed spacer
region between 16S and
23S ribosomal subunit
(Maes et al.,1996 & Song
et al., 2004)
groEL β subunit of RNA polymerase (Yushan et al., 2010)
gyrB Heat-shock protein (Mondal et al., 2012)
rpoD Sigma -70 factor of RNA
polymerase
(Young et al., 2008)
efP Elongation factor P protein (Bui et al., 2010)
glnA Glutamine synthetase I (Takle et al., 2007)
18. (Idnurm et al., 2001)
ACR1, Mpg1 – M. oryzae
pelA &
PelB
F. solani
ABC1 –
M. oryzae
magB – M. oryzae
Tri5 – G. zeae
25. (Islam et al., 2016)
a) Bleached wheat spike
b) Complete bleaching of a
wheat spike
c) Blast sporulation at the base
d) Typical eye-shaped lesion
e) Slightly shriveled wheat seeds
f) Severe blast-affected shriveled
and pale wheat seeds
g) Severely infected rachis with
dark gray blast sporulation
h) Conidia
26. (Islam et al., 2016)
Artificially inoculated in wheat, barley, and goosegrass.
40. (Ryan et al., 2011)
Xanthomonas species and pathovars show host and tissue specificity.
41. • xps-encode a type II secretion system (T2SS) –
pathogenecity factors
• hrp - type III secretion system (T3SS) – extracellular
polysaccharide xanthan.
• Functional genomics- molecular basis of virulence, host
specificity and mode of pathogenesis.
• Example – xopE3 and xopA1- X. citri pv. citri
44. • Approach in identification of disease resistance
genes
• Methods to identify candidates genes- PCR
based methods, data mining
(Pavan Kumar et al., 2017)
46. • Discovery of new resistant and defense related genes.
(Klosterman, 2016).
• Comparative genomics analyses of plant-associated pathogens
and respective hosts - Prediction of their interactions (Van Sulys et
al., 2003).
• Understanding of infectious disease mechanism- prevention and
treatment.
• SNP- Genetic basis of disease.
• Identification of candidate genes.
48. • Plant genome sequences needed to be compared against
databases known pathogen sequences -pairwise sequence
similarity search methods- not sufficient to diagnose new
pathogens.
• Regulatory elements in most genomes remain poorly
annotated and require complex experimental methodologies
for accurate identification (Shen et al., 2012).
Genome refers one complete set of chromosome or DNA in an organism.
Genomics is an interdisciplinary field of science focusing on genomes. A genome is a complete set of DNA within a single cell of an organism, and as such genomics is a branch of molecular biology concerned with the structure, function, evolution, and mapping of genomes.
.
Genomics aims at the collective characterization and quantification of genes, which direct the production of proteins with the assistance of enzymes and messenger molecules.
Pathogenomics researchers are generating and analyzing genome sequences of diverse bacterial, oomycete, fungal, and viral pathogens to identify genetic sources of virulence, understand differences observed among related pathogens, guide the development of diagnosis tools capable of discriminating among specific strains, reveal sources of host resistance, and understand the dynamics of host-microbe interactions and the diseases they cause.
NGS is significantly cheaper, quicker, needs significantly less DNA and is more accurate and reliable than Sanger sequencing
Epidemiology
•Commensal bacterial flora and opportunistic
infections
•Lifestyle of humans
•Routes of transmission
•Spread of pathogens
•Transmission bottlenecks
•Vector analysis
Some of the many projects include surveys of (i) fungal genome sequences to identify conserved patterns associated with genes encoding secondary metabolites and natural products, (ii) viral genome sequences to inform the course of disease spread, and (iii) bacterial genome sequences for comprehensive identification of secreted virulence factors and of commonly regulated genes associated with disease.
4600kb
How genomes are compared ?
Chromosome level
No. of Genes
Genome size
Content (Sequence)
Location (Map position)
Gene order
Gene cluster
Translocation
Evolutionary reltnship
Geographical distribution and severity of the wheat blast outbreak in eight southwestern districts of Bangladesh. The map depicts the
intensity of the 2016 wheat blast outbreak across Bangladesh. The percentage of affected area and the total area (hectares) under cultivation are
shown for each district based on the color chart
Germinated conidia, growth of mycelia, infection, andsporu lation of strains used to artificially inoculate wheat, barley, and goosegrass. aA germinated three-celled pyriform conidia (arrow) with hyphal
growth on water agar medium. b, c Culture of isolate BTJP 3-1 on PDA plate; upper (left) and reverse side (right). d Photograph showing a
diamond-shaped, water-soaked lesion (initial stage of infection symptom, upper arrow) on a green wheat seedling leaf five days after conidial
inoculation. e, f Development of an eye-shaped lesion with a gray center (arrows in e and f) on wheat leaves. g, h A gradual progression of
symptoms (arrows) on wheat leaves. i–l Light micrographs showing massive conidia production (red arrow) on aerial conidiophores (black arrow)
on artificially infected leaves of wheat cultivars Prodip (i) and Shatabdi (j), goosegrass (k), and barley (l). Photographs were taken by a camera
attached to a microscope at 100× magnification. Scale bars in j, k, and l indicate 50
The origin of the Bangladesh wheat blast fungus. a Maximum likelihood genealogy inferred from the concatenation of aligned genomic
data at 2193 orthologous groups of predicted transcript sequences. Scale bar represents the mean number of nucleotide substitutions per site. b
Population genomic analyses of transcriptomic single nucleotide polymorphisms among M. oryzae isolates from wheat in Brazil and Bangladesh.
The network was constructed using the Neighbor-Net algorithm. The scale shows the number of
(a) Expression profi les of two representative Phytophthora infestans eff ector genes
in diff erent developmental (mycelia, sporangia and zoospores) and infection (biotrophic and necrotrophic) phases. The life cycle of P. infestans
begins with sporangia that are released and travel by wind to new host plants. Under suitable environmental conditions zoospores are released
from the sporangium, germinate and penetrate the host plant to initiate infection. As a hemibiotroph, P. infestans undergoes a two-stage infection
process, with an initial biotrophic phase followed by a later necrotrophic phase. The data correspond to P. infestans reference strain T30-4 and are
based on [3], with expression during infection phases measured in potato. Gene induction values were normalized against the mycelia sample. epi1
encodes an apoplastic eff ector that functions as protease inhibitor [73] and Avr3a encodes a cytoplasmic RXLR eff ector [74]. Ubiquitin and pisp3 [75]
are included as controls. dpi, days post-inoculation. (b) Crinkler (CRN) gene expression profi les during infection (reproduced from [47]). The heat
map shows the expression pattern of full-length Phytophthora capsici CRN genes following tomato infection. Green is downregulated and red is
upregulated compared with the median of each sample. Samples were collected at 0, 8, 16, 24, 48 and 72 hours post-infection. Gene classes with
distinct expression profi les are indicated on the right.
Ninety four HSPs were selected arbitrarily and their expression patterns were analysed by RT-PCR.
Primers were designed to amplify the full length gene sequence minus the signal peptide-encoding region. Pst 104E137A- genomic DNA, and cDNAs
from germinated spores, isolated haustoria, infected wheat leaves, and uninfected wheat tissue were used as templates. Two expression patterns
were obtained: 1. Expression only during the biotrophic phase (Pstv_3161-1), and 2. Expression during early and late stages of development
(Pstv_7541-1). None of the tested effector candidates were amplified from uninfected wheat leaves. B. Number of HSPs showing digital expression
patterns as shown. Overexpression was evaluated using Baggerley’s
12,282 transcripts were assembled from 454-
pyrosequencing data and used as reference for digital gene expression analysis to compare the germinated uredinospores
and haustoria transcriptomes based on Illumina RNAseq data. More than 400 genes encoding secreted proteins which
constitute candidate effectors were identified from the haustorial transcriptome, with two thirds of these up-regulated in
this tissue compared to germinated spores.
Comparative ontology analysis of transcripts with statistically significant changes in expression between haustoria and
germinated spores.
Venn diagram of reference transcripts set, showing the
number of transcripts that did not show differential expression
between the two tissues, and those that had statistically significant
changes in expression between germinated spores and haustoria
Of the original transcript set, 30.2% (601) of the 1,989 haustorial-enriched genes and 47% (1,109) of the 2,357 genes upregulated
in germinated spores were annotated with B2G
Metabolic processes in haustoria. Metabolic processes or specific enzymatic activities overrepresented in haustoria identified by B2G
analysis and manual annotation are highlighted in this cartoon. Orange boxes and yellow ovals are metabolic processes or particular enzymes that
showed statiscally significant upregulation in haustoria. Light orange boxes are metabolic processes where some genes showed statiscally significant
upregulation in haustoria, and others showed a tendency to be more expressed in haustoria but were not statistically significant.
Metabolic processes in germinated spores. Metabolic processes or specific enzymatic activities overrepresented in spores identified
by B2G analysis and manual annotation are highlighted in this cartoon. Orange boxes and yellow ovals are metabolic processes or particular enzymes
that showed statistically significant upregulation in germinated spores.
pathogenic mechanisms of quarantined fungus
Overall workflow for de novo genome sequencing, assembly and annotation of T. indica Karnal
isolate for identification of Effector Candidates
Using this draft genome pahto Pathogen survival and successful infectioncan be easoily identifd. These study
Only a small portion of the assembled sequence of
T. indica showed synteny with U. hordei. No synteny was observed with the other two genomes
T. indica shows seq similarity with U. hordei. Whereas s. r seq similarity with
Different bacteria from the genus Xanthomonas attack monocotyledonous and dicotyledonous plants to cause a range of diseases. These pathogens show a high level of host plant specificity and many exhibit tissue specificity, invading either the xylem elements of the vascular system (vascular pathogens) or the intercellular spaces of the mesophyll tissue (mesophyllic pathogens) of the host. Some bacteria such as the cassava pathogen Xanthomonas manihotis produce unusually diverse symptoms and are capable of colonizing both mesophyllic and vascular tissue.
This neighbour-joining tree is based on the DNA gyrase subunit B (gyrB) gene sequence of Xanthomonas spp., Xylella fastidiosa and a Stenotrophomonas sp. Bootstrap values (for 1,000 replicates) are given at the nodes, and branches with <50% bootstrap support were collapsed to better reveal the phylogenetic structure. The scale bar represents 0.1 changes per nucleotide position. In addition to gyrB, analysis of 16S–23S ribosomal RNA intergenic spacer sequences and a combination of molecular markers, such as repetitive element sequence-based PCR (rep-PCR), amplified fragment length polymorphism (AFLP) and other fingerprints, have been used to establish the taxonomic status of the genus. A species can contain pathovars, which are pathogenic variants that infect diverse plant hosts and/or exhibit different patterns of plant colonization. Up to 80 pathovars have been recognized so far.
Subtractive genomics approach for identification
of novel antimicrobial targets in Xanthomonas oryzae pv.
PXO99A, the causative agent of bacterial blight in rice,
has successfully been used. Thus, comparative analyses
of the bacterial genome led to subsequent analyses of 27
essential proteins which were involved in different
metabolic activities essential for its survival and
pathogenicity. Further analyses revealed three essential
non-homologous proteins as novel antimicrobial targets
(Keshri et al., 2014).
It is useful in order to identify specific target genes for the
development of new vaccines and therapeutic approaches .
Its wide-most application is in Pharmaco-genics .
It is used to prevent re-emerging infectious diseases from turning
into an epidemic .
By obtaining the genetic information we can manipulate the gene
expressing the virulence factor to modify the pathogenicity.
The genetic information should not be misused .
Contd
