This document discusses the classification of phytoplasmas, which are wall-less bacteria that infect plants and are transmitted by insect vectors. It begins by describing early observations of phytoplasmas and their causal role in plant diseases. It then discusses various classification systems used over time, beginning with symptom-based classification and moving to methods based on serology, DNA hybridization, RFLP analysis of 16S rDNA, and sequencing of 16S rDNA and ribosomal protein genes. The latest classification system divides phytoplasmas into 18 major 16Sr groups and over 40 subgroups, providing an overview of the molecular tools that enabled increasingly precise classification of these obligate plant pathogens.
Plant viruses are transmitted from plant to plant in a number of ways.
Transmission of viruses by vegetative propagation.
Mechanical transmission of viruses through sap.
Transmission of viruses by seed.
Transmission of viruses by Pollen.
Transmission of viruses by dodder.
Transmission by vectors.
This ppt illustrates and describes the two bacterial diseases included in the BSc Hons Program Syllabys Core Course III or DSC 3- Citrus canker and angular leaf spot of cotton
INTRODUCTION
OCCURENCE AND IMPORTANCE
DIFFERENT TYPES OF WHEAT RUST
BLACK RUST
BROWN RUST
YELLOW RUST
COMPARISION OF ALL THREE RUST
SYMPTOMS
SIGNIFICANCE
HISTORY
RUST CYCLE
STAGES OF PATHOGEN
EPIDEMIOLOGY
RUST CYCLE IN INDIA
UG99
Genetic Improvement of Indica Group Rice Through Wide HybridizationDr. Md. Nashir Uddin
Indica Group Rice Cultivar IR64 was recurrently crossed with Japonica Group Rice (New Plant Type originated from Indonesia) and developed the Introgression lines (INLs). These INLs showed 30 to 40% increment of Yield than IR64. This wide hybridization method is very useful for crop improvement including rice.
Plant viruses are transmitted from plant to plant in a number of ways.
Transmission of viruses by vegetative propagation.
Mechanical transmission of viruses through sap.
Transmission of viruses by seed.
Transmission of viruses by Pollen.
Transmission of viruses by dodder.
Transmission by vectors.
This ppt illustrates and describes the two bacterial diseases included in the BSc Hons Program Syllabys Core Course III or DSC 3- Citrus canker and angular leaf spot of cotton
INTRODUCTION
OCCURENCE AND IMPORTANCE
DIFFERENT TYPES OF WHEAT RUST
BLACK RUST
BROWN RUST
YELLOW RUST
COMPARISION OF ALL THREE RUST
SYMPTOMS
SIGNIFICANCE
HISTORY
RUST CYCLE
STAGES OF PATHOGEN
EPIDEMIOLOGY
RUST CYCLE IN INDIA
UG99
Genetic Improvement of Indica Group Rice Through Wide HybridizationDr. Md. Nashir Uddin
Indica Group Rice Cultivar IR64 was recurrently crossed with Japonica Group Rice (New Plant Type originated from Indonesia) and developed the Introgression lines (INLs). These INLs showed 30 to 40% increment of Yield than IR64. This wide hybridization method is very useful for crop improvement including rice.
This master's seminar presentation speaks about the role of bacteriophage in the management of different plant diseases.
It deals with the history and discovery of bacteriophages up to current research studies and usage.
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
DNA Fingerprinting and Phylogenetic Relationship of the Genus Chlorophytum Ke...YogeshIJTSRD
Chlorophytum Ker Gawl, is a medicinally important plant genus employed since ancient time as a key component in Ayurvedic and Unani medicine. Genus represented with more than 217 species out of which 17 species have been reported from India. The main objective of this study is to evaluate molecular phylogeny of Chlorophytum species. In this study phylogenetic analysis of Chlorophytum species was carried out using AFLP marker. Total 16 selective primer combinations were scored as presence and absence of alleles for all the 17 species, resulting in total 938 allele, out of which 291 allele were found to be polymorphic. The percentage of polymorphism ranged from 18.3 in the combination E2M3 to 42 in the combination E1M1 .The phylogenetic tree is divided into two clade, each clade contains species with similar morphological characters. The extent of variations within species is discussed. Kale KA | Pohekar PV | Malode UA | Lakhe M. B "DNA Fingerprinting and Phylogenetic Relationship of the Genus Chlorophytum Ker -Gawl, from India using AFLP" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39932.pdf Paper URL: https://www.ijtsrd.com/biological-science/microbiology/39932/dna-fingerprinting-and-phylogenetic-relationship-of-the-genus-chlorophytum-ker-gawl-from-india-using-aflp/kale-ka
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Yellow rust seminar by Priyanka (Phd Scholar Genetics and Plant Breeding CSK ...Priyanka Guleria
This seminar explains about the yellow rust disease of wheat: Its genetics and prevention methods as well as molecular techniques to combat yellow rust
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.
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.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
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.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
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.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.
phytoplasma.ppt
1.
2. Phytoplasma : An Introduction and
Classification with Recent
Molecular Tools
by
SUJATA DANDALE
3. Phytoplasmas: historical
Pleomorphic cells observed in ultra-thin section
of leaves of mulberry infected with dwarf disease
(Doi et al.,1967)
These bodies disappear after tetracycline
treatment when seen in EM (Ishiie et al.,1967)
Called as MLOs (Mycoplasma Like Organisms)
Causal agents of yellow diseases in plants
Trival name “Phytoplasma” adopted in 10th
congress of International Organization of
Mycoplasmology (1994)
Lee et al. (2000) Annu. Rev. Microbiol.
4. Single celled, wall-less prokaryotes, resembling mycoplasmas
in morphology (Doi et al.,1967)
Obligate parasites, can’t be grown in in-vitro cell free culture
(Lee et al., 1986)
Transmitted by phloem feeding insects (leafhoppers, plant
hoppers, psyllids) and Cuscuta (Tsai et al., 1979)
Sensitive to tetracycline but resistant to penicillin (Ishii et al.
1967)
Descended from low G+C gram positive bacterium (Woese et al.,
1987)
Lee et al. (2000) Annu. Rev. Microbiol
5. Round to filamentous (Kirkpatrick,1982)
Size 200-800 nm
Phytos- plant + plasma- thing moulded (Greek)
In sieve elements of Plants
Cont..
6. Symptoms due to Phytoplasmas:
Virescence
Phyllody
Little leaf
Sterility of flowers
Witches’ broom
Slender shoots
Stunting, leaf curling
Generalized decline, Bunchy growth etc.
Virescence on horseradish
Lee et al. (2000) Ann. Rev. Microbiol.
Diseased control
7. lethal yellowingSesame phyllody Little leaf of brinjalCarrot yellow
Witches’ broom lime Palm wilt Brassica phyllody Grape vine yellow
8. First reports of phytoplasma diseases:
Clover phyllody (Merrett,1866)
Peach yellow (Smith,1888)
Aster yellow on China aster (1902)
First Etiology : Mulberry dwarf (1967)
Important plant diseases world wide:
Aster yellows in Carrot & Onion
Apple proliferation
Coconut lethal yellowing
Peach X disease
Elm yellow
More than
300 plant
Diseases
in hundreds
of
plant genera
Lee et al. (2000) Ann. Rev. Microbiol.
9. Important diseases in India
Disease Host Area First Report of etiology
Little leaf Brinjal
Periwinkle
All India
Lukhnow
Varma et al. (1969)
Rao et al. (1983)
X disease Peach NE region Ahlawat & Chenulu (1979)
Bushy Stunt Brinjal New Delhi Mitra & Chakraborty (1988)
Phyllody Bottle gourd &
other gourd
Black pepper
Sesame
Banglore
Banglore
Karela
All India
Sastry & Singh (1981)
Bhat et al. (2006)
Sahambi (1970)
Witches’ broom Acid Lime
Winged bean
Sunhemp
MH, AP Ghosh et al. (1999)
Singh (1991)
Sharma et al. (1990)
Rubbery wood Citrus Darjeeling Ahlawat & Chenula (1985)
Root wilt Coconut Kerala Solomon et al. (1983)
Sandal spike Sandal Kerala,Kr Varma et al. (1969)
GSD Sugarcane All India Rishi et al. (1973)
White leaf Bermuda grass UP Rishi (1978) Rao et al. (2007)
Yellow dwarf Rice All India Raychaudhri et al. (1967)
10. “After a time the growth of and accumulation of
specimens or phenomena forces people to try to
classify” - Pirie (1995)
Hurdles to definite description and classification:
Obligately parasitic habit
Structural fragility
Presence in low numbers in infected plants
Intimate association with host tissues
Firraro et al. (2005) J. Pl. Pathol
11. Based on biological properties
(1970s)
Based on serological properties
(1980s)
Based on molecular properties
(1990s onward)
Working Team on
Phytoplasmas of International
Research Program of
Comparative Mycoplasmology
(IRPCM)
International Committee on Systematic Bacteriology (ICSB)-
Subcommittee on the Taxonomy of Mollicutes
Development of classification systems in Phytoplasmas
Bergeys’ manual of systematic bacteriology: Vol. III , class- Mollicutes
12. First attempts:
Symptomatology
Host Range (eg. Aster yellow, Clover phyllody)
Transmission by insect vectors (vector species)
Groups based on Symptoms
Kirkpatrick et al. (1992)
Decline agents
Proliferation agents
Virescence agents
Kirkpatrick et al. (1992)
Aster yellows
Stolbur
Witches’ broom
Decline
Phyllody
Grunewald et al. (1977)
13. Chyokowski & Sinha (1989)
Phytoplasmas
Mutually exclusive
floral
symptoms
Reduced flower size
and colour with other
symptoms
On experimental host Periwinkle
Catharanthus roseus
Classification based on symptoms is not reliable
14. Serology mostly used for detection/identification
Phytoplasma enriched extract used for production of :
Monoclonal antibodies (Chen et al., 1988)
Polyclonal antibodies (Kirkpatrick et al., 1992)
Monoclonal antibodies suited for differentiation of closely
related strains (Lee et al., 1993a )
Limitations:
Difficult to obtain pure phytoplasmas
Low concentration
Non-specific reaction
Lee et al. (1993a)
15. Molecular Era
Year Work Scientists
1987 Improvement in phytoplasma extraction from
infected hosts
Kirkpatrick et al.
1989 First estimate of phytoplasma DNA composition -
Reported low G+C value: 23-26 mol%
Kollar et al.
1989 Plant pathogenic MLOs different from
Mycoplasmas by 16S rDNA sequence analysis
Lims & Sears
1991 Suggested primer pair for 16S rRNA gene
amplification for wider array of MLO identification
Deng & Hiruki
1992 Cloned DNA fragments as probes in dot blot
hybridization to identify phytoplasma
Lee et al.
1992 Suggested RFLP analysis of amplified 16S rDNA Ahrens & Seemuller
1993 by pulse field gel electrophoresis showed genome
size of phytoplasma
Neimark et al.
1993 Used oligo-nuceotide primers that amplify 16S
DNA of AY-MLO Cluster
Davis & Lee
16. Table Cont..
Year Work Scientists
1994 Cloned DNA fragments used in southern
hybridization to identify phytoplasmas from hosts
Schneider et al.
1994 combined RFLP (16S rDNA) & ribosomal protein
gene sequence for classification
Gundersen et al.
1997 characterization & classification by using enzymes
and sequence analysis
Schneider et al.
1998 Based on RFLP of 16S rRNA gene & rp gene
classified phytoplasmas to 14 groups
Lee et al.
2004,2006 Expanded phytoplasma classification to 18 16Sr
groups
Lee et al.
2005 Gave description of 21 Candidatus phytoplasma sp. Firrao et al.
2007 Expanded RFLP based 16Sr classification through
in silico analysis
Wei w. et al.
17. Classification based on DNA hybridization assays
SNo. Strain Cluster Ref.
1 Little leaf disease of
periwinkle-MLO
Davis et al.
(1990)
2 Ash yellows-MLO Davis et al.
(1991)
3 Clover proliferation-MLO Lee et al.
(1992)
4 Aster yellows-MLO Lee et al.
(1992)
5 Canadian peach X disease ,
Western X disease, Clover
yellow edge
Lee et al.
(1992)
6 Italian periwinkle virescence Davis et al.
(1992)
•Cloned DNA
fragments from
known phytoplasma
were used for
hybridization
• Each Strain Cluster
consists of strains
with extensive
sequence homology
Lee & Davis et al. (1992)
18. DNA fragments were cloned only from limited numbers of
Phytoplasmas
Difficulty in obtaining desired concentration of phytoplasma
strains from infected hosts
Standardized DNA probes for general detection & identification
were not available
PCR based Molecular
tools became more
popular
Lee & Davis et al. (1992)
Classification based on DNA hybridization assays:
Difficulties
19. Classification systems based on recent molecular tools
Tools for classification:
Most conserved Less conserved regions
16S rRNA ( primers- P1/P7,
R16F2n/R16R2)
23S rRNA
Rp gene operon (rpl22,rps3)
(primers-rpF1, rpR1)
16S-23S spacer
Tuf elongation factor
Gene Phytoplasma Phytoplasma &
Acholeplasma
16S rRNA 88-99% 87.0-88.5%
Ribosomal protein 60-79% 50-57%
16S rRNA Spacer 23S rRNA
1.5 Kb 0.3 Kb 0.4 Kb
Lee et al. (1998, 2000)
Sequence similarity:
20. DNA extracted from 52 isolates and digested with BclI
30 PCR amplification Cycles (primers- fD1, rp1)
Amplification product (1500bp) digested with BclI
and then with AlulI, RsaI, EcoRI
Amplified DNA cloned and sequenced
Schneider et al. (1992) J. Gen. Microbiol.
Seven Groups determined
22. 16S rRNA gene sequence homology:
52 isolates from 4 symptom groups (Aster yellows, Clover phyllody,
Periwinkle virescence and stolbur) were divided to 7 groups
Isolates from
Same group
Different gp
Schneider et al. (1992)
V IV II III I VI
Fig: AluI Restriction profile Phytoplasma 16S rDNA
Inference
97.8 to 99.5%
89.6 to 92%
23. Total nucleic acid extracted from 40 strains
35 PCR amplification cycles with primer pair R16F2/R2
PCR product digested separately with 15 Restriction enzymes
Gel electrophoresis
Similarity coefficient (F)= 2Nxy
Nx+Ny
Nei & Li (1979)
Lee et al. (1993) Phytopathol.
F > 0.9 for members of same group
25. Key enzymes for Group and subgroup classification:
MseI & AluI – sufficient
to classify in 16Sr-I
MseI, AluI, HpaII &
HhaI – Further
differentiation
RFLP from 2
Strains (BBSI, HyphI)
16SrI-B, 16SrI-E
(newly classified)
Lee et al. (1993)
AB B C
26. Resulted nine major 16Sr groups and 14 subgroups based on F
Comparison of 16Sr groups with previous strain clusters
Lee et al. (1993)-16Sr groups Lee & Davis (1992)-Strain Clusters
16SrI (5 subgroups) AY-MLO
16SrII Peach X disease -MLO
16SrV EY-MLO
16SrVI CP-MLO
16SrVII Ash Y-MLO
Lee et al. (1993)
16SrI: 5 subgroups (largest)
16SrIII: 2 subgroups
Inference
27. Nested PCR of 34 representative phytoplasma strains (16SrRNA)
RFLP with 17 restriction enzymes and similarity coefficient
Based on 16S rRNA
similarity
coefficient
--14 groups
-- 41 subgroups
By combined RFLP
16S rDNA &
rp gene sequence
--46 subgroups
Similarity coefficient between distinct Groups < 90%
Lee et al.(1998) IJSB
29. 16Sr RNA groups based on phylogeny
Lee et al. (1998)
Fig: RFLP analysis Fig: Phylogenetic Tree
30. Finer subgroup classification
Strain 16S r-rp subgp. Strain 16S r-rp subgp
Tomato big bud BB 16SrI-A (rp-A) Maize bushy stunt MBS 16SrI-B (rp-L)
New Jersey AY NJAY 16SrI-A (rp-A) Clover phyllody CPh 16SrI-C (rp-C)
Periwinkle little leaf CNI 16SrI-A (rp-A) Strawberry green petal SGP 16SrI-C (rp-C)
Oklahoma AY OKAYI 16SrI-A (rp-A) Annulus phyllody RPh 16SrI-C (rp-C)
Maryland aster yellow AYI 16SrI-B (rp-B) Paulownia WB PaWB 16SrI-D (rp-D)
Dwarf aster yellow DAY 16SrI-B(rp-B) Blueberry stunt BBSI 16SrI-E (rp-E)
Hydrangea phyllody HyPH 16SrI-B (rp-K) Grey dogwood WB GDI 16SrI (rp-M)
Ipomoea WB IOB 16SrI-B (rp-F)
Fig: RFLP of rp gene Lee et al. (1998)
31. Approach based on
16S r Groups: 16S rRNA gene sequence
Subgroups: 16S r RNA gene & rp gene cluster
16S rRNA sequence homologies
88-94% : Two distinct 16S rRNA Groups
95-98%: Two subgroups with in a Group
Grouping consistent with strain clusters (identified based on DNA-
DNA homology and serological data)
Lee et al. (1998)
Inference
So valid classification system
32. Case Study 4. Candidatus phytoplasma approach
Taxonomic Notes Reference
Major part of gene to be sequenced (1000bp from 16S rRNA)
for taxonomy
Murray et al.
(1990)
Candidatus (L. Candidatus, a candidate to indicate that
assignment is provisional) and must include:
Sequence (16S rRNA)
Identification of morphotype with probes from
characteristic sequence
Murray &
Schleifer (1994)
Organisms with less than 97% sequence homology of 16S
rRNA will not have more than 60-70% reassociation
Stackebrandt &
Goebel (1994)
Int. J. Syst. Bacteriol. (1994)
33. Candidatus phytoplasma description
Character Description Refrence
Morphology Single unit membrane, pleomorphic Doi et al. (1967)
Habitat Phloem sieve, gut, haemolymph of
sapsucking insects
Tsai et al. (1979)
Antibiotic
sensitivity
Tetracycline Ishii et al. (1967)
DNA base
composition
G+C : 23-29% Kollar & Seemuller
(1989)
Chromosomal Size 530-1350 bp Neimark & Kirkpatrick
(1993)
Codon usage UGA- stop codon, not for tryptophan Lims & Sears (1991)
Sterol in
membrane
Non sterol requiring Lim et al. (1992)
Ribosomal RNA Two rRNA operons & a spacer 16s
-23s rRNA genes
Kuske & Kirkpatrick
(1992)
Phytoplasma/Spiroplasma Working Team IRPCM (2000) IJSEM
34. Rules:
1. Single, unique 16S rRNA gene sequence (>1200bp) from the
‘reference strain’
2. A strain- novel Ca. Phytoplasma sp if sequence <97.5%
similarity to previously defined Ca. Phytoplasma
3. Even if >97.5% similarity but ecologically separated population:
Transmitted by different vectors
Have different natural plant host/ different response on same host
Significant molecular diversity ( DNA probe hybridization,
serology, PCR assay)
Phytoplasma/Spiroplasma working team
IRPCM (2000)
35. 4. No rank of subspecies
5. Description of new species submitted to IJSEM
6. Reference strain should be made available to scientists
7. Abbreviation of Candidatus is Ca.
Reference sequence alignment available from TreeBase
(accession no. S1048-1788)
Phytoplasma/Spiroplasma working
team, IRPCM (2000)
36. 16S rRNA groups and Ca. Phytoplamas
(IRPCM Phytoplasma/spiroplasma Working Team, 2004)
Phylogenetic Group Candidatus Phytoplasma sp. Reference
Aster yellows (16SrI) Ca. Phytoplasma asteris Lee et al. (2004a)
Peanut witches’-broom
(16SrII)
Ca. Phytoplasma aurantifolia Zreik et al. (1995)
X-disease(16SrIII) Ca. Phytoplasma pruni
Coconut lethal yellowing
(16SrIV)
Ca. Phytoplasma palmae
Ca. Phytoplasma cocostanzaniae
Ca. Phytoplasma castaneae
Elm yellows (16SrV) Ca. Phytoplasma ziziphi
Ca. Phytoplasma vitis
Ca. Phytoplasma ulmi
Jung et al. (2003a)
Lee et al. (2004b)
Clover proliferation
(16SrVI)
Ca. Phytoplasma trifolii Hiruki & Wang (2004)
Ash yellows(16SrVII) Ca. Phytoplasma fraxini Griffith et al. (1999)
Firraro et al. (2005) J. Plant pathol.
37. Table cont…
Phylogenetic Group Candidatus Phytoplasma sp Reference
Loofah witches’-broom
(16SrVIII)
Ca. Phytoplasma luffae
Pigeon pea witches’-broom
(16SrIX)
Ca. Phytoplasma phoenicum Verdin et al. (2003)
Apple proliferation (16SrX) Ca. Phytoplasma mali
Ca. Phytoplasma pyri
Ca. Phytoplasma prunorum
Seemuller & Schneider
(2004)
Rice yellow dwarf (16SrXI) Ca. Phytoplasma oryzae Jung et al. (2003b)
Stolbur (16SrXII) Ca. Phytoplasma australiense
Ca. Phytoplasma japonicum
Davis et al. (1997)
Sawayanagi et al. (1999)
BGWL (16SrXIV) Ca. Phytoplasma cynodontis Marcone et al. (2004)
Hibiscus witches’- broom (XV) Ca. Phytoplasma brabilience
Mexican periwinkle virescence
(16SrXIII)
No name suggested
Firraro et al. (2005)Firraro et al. (2005) J. Plant pathol.
38. Phytoplasma 16S rRNA gene sequences retrieved (NCBI)
Aligned and trimmed to 1.25 Kb (F2nR2) fragment bounded
by two Conserved Nucleotide blocks
In-silico restriction enzyme digestion (17 enzymes)
Virtual gel plotting (3.0% agrose)
Comparison of RFLP pattern and Similarity coefficient
Calculation (51 patterns)
Wei et al. (2007) IJSEM
42. New 16Sr groups based on 90% threshold of similarity
Each strain in new groups F< 0.85 with other Group strains
A total of 28 groups and more than 100 subgroups given
New groups contains 3 previously defined Ca. Phytoplasma Sp.
and 7 Potential sp. to be described
Provided feasible method for future extension of classifation
Wei et al. (2007)
43. Conclusion
Phytoplasma are important plant pathogens causing
economic losses in number of crop plants and tree species
RFLP analysis of PCR-amplified 16S rRNA gene with
restriction enzymes remains a valuable tool for studying
phytoplasma diversity and classification
Till now the most accepted and stable classification is to
describe phytoplasmas in ‘Candidatus phytoplasma species’
rank which combines both molecular (16S rRNA gene
sequence) as well as biological, phytopathological properties.