Upload
others
View
5
Download
0
Embed Size (px)
Citation preview
Towards metagenomics-based diagnostics to detect and prevent plant pathogens
Davide Spadaro, Edoardo Piombo
DISAFA and AGROINNOVAUniversità di Torino, Italy
Global trade...
https://research.rabobank.com/far/en/sectors/regional-food-agri/world_fruit_map_2018.html?qsl_reqcnt=1
Global trade... and pests
(2018)
Trade of plant material(seeds, propagation material, wood, food, feed)Global movement of pests and diseases
Plant Health Authorities (EPPO, NPPOs, USDA-APHIS)Safe trade
Rapid diagnosis
Export certification Import inspection Disease surveillance and monitoring Containment and eradication programmes
Disease diagnosis: cross-cutting issue (IPPC, 2016)Diagnosis should be rapid
A range of methods
Untargeted methodsCulturing (in vitro/in vivo)Microscopy (optical MS, electron MS)
Targeted methodsImmunological (ELISA)Molecular (PCR, qPCR)
Classical techniques are labor-intensive, slow andgreatly dependent on the expertise of the operator.
Pros• Faster• More sensitive• More specific• More accurate• Can identify non-culturable pathogens• Can differentiate closely related pathogens• Allows for pathogen quantification (qPCR)
Cons• Limited number of targets in each analysis
Molecular techniques
Direct quantification (droplet digital PCR (ddPCR) Field diagnostics (LAMP)
Multiplexing (microarray) Online application (Integration with IT, LOC)
Directions of molecular techniques
Temperature Day inoculated Day testedday after
inoculum
disease
index
average
Tp (h:min:s)-Crude
extraction method
20°C
16/08/2017 18/08/2017 3 0 00:11:31±00:01:55
16/08/2017 21/08/2017 5 0 00:10:03±00:03:02
16/08/2017 23/08/2017 8 0.67 00:10:56±00:00:10
16/08/2017 25/08/2017 10 1 00:07:06±00:04:25
16/08/2017 28/08/2017 13 4 00:09:02±00:01:32
16/08/2017 30/08/2017 15
16/08/2017 01/09/2017 18
4°C
16/08/2017 18/08/2017 3 0 00:18:52±00:14:48
16/08/2017 21/08/2017 5 0 00:12:13±00:08:21
16/08/2017 23/08/2017 8 0 00:12:11±00:01:25
16/08/2017 25/08/2017 10 1 00:10:28±00:00:23
16/08/2017 28/08/2017 13 0 00:19:50±00:08:54
16/08/2017 30/08/2017 15 1.33 00:10:23±00:02:00
16/08/2017 01/09/2017 18
16/08/2017 03/09/2017 20
Test with nectarines inoculated with M. laxa and M. fructicola
LAMP for Monilinia fructicola and M. laxa
Positive
Negative
LAMP assay
SpecieIsolate
nameTp (min:s) Anneal (°C)
Venturia inaequalis 2.16 B9 29:28±01:40 89.72±0.13
V. inaequalis 2.16 B1 26:67±01:30 89.38±0.03
V. inaequalis 2.16 B6 24:04±00:49 89.08±0.03
V. inaequalis 2.16 B7 26:37±00:52 89.06±0.15
V. inaequalis 2.16 B5 23:48±00:58 89.36±0.39
Validated with:• artificially inoculated apple leaves;• asymptomatic-naturally infected apple leaves.
LAMP for Venturia inaequalis
High-throughput sequencing
Sanger sequencing
NGS: 454-pyrosequencing (early 2000s)Early 2010s: Illumina and IonTorrent (greater throughput, lowercost)
TGS: PacBio (2011) and Oxford Nanopore (2105) longer DNA sequences
NGS and TGS: HTS
Metagenomics or metagenome sequencing
shotgun metagenomics: HTS of all DNAshotgun metatranscriptomics: HTS of all RNA
metabarcoding: amplicon-based HTS
Sampling
DNA extractionPCR
Sequencing
Data filtering and denoising
Sequenceclustering (OTU)
Taxonomicassignment
Interpretation
Sequencing
Cleaning
Assembling
Binning
Assembly-free taxonomicprofiling
Shotgun metagenomicsMetabarcoding
Metagenome sequencing
Gene predictionand annotation
Salter et al. (2015) sequenced with different kitprogressively more diluted cultures ofSalmonella bongori.
Amplification of 16S
Shotgun metagenomics
Sequencing of negative controls (buffers) is necessary.
High concentration of target DNACertification of DNA free kit
Contamination riskDNA is ubiquitous in commonly used DNA
extraction kits and other laboratory reagents,
varies greatly in composition between different
kits and kit batches.
This contamination critically impacts results from
samples containing a low microbial biomass.
Cell lysis is influenced by the cell wall composition of present microorganisms.Depending on the extraction protocol, differences in estimated abundance of the 25 most abundant bacterial generaWesolowska-Andersen et al. (2014)
Preliminary studies are necessary to establish the best method to extract DNA from target populations
with minimum possible bias.Internal controls (known amounts of different MO).
Choosing the protocol
Comparison of abundance estimations for bacterial genera with MetaHIT and HMP methods
Sampling
DNA extractionPCR
Sequencing
Data filtering and denoising
Sequenceclustering (OTU)
Taxonomicassignment
Interpretation
Sequencing
Cleaning
Assembling
Binning
Assembly-free taxonomicprofiling
Shotgun metagenomicsMetabarcoding
Metagenome sequencing
Gene predictionand annotation
Illumina amplicon correction withDeblur (Amir et al., 2017), DADA2(Callahan et al., 2016) andUNOISE2 (Edgar, 2016) allows toskip OTU construction, obtaininginstead ESVs (Exact SequenceVariants).
ESV: consistent in different experimentsOTU: experiment-dependent
Species-level identification possible.
Pros:• Not much computational power is needed• Not expensive• Generated data are less complex than in shotgun
metagenomics.
Cons:• OTU construction at 97% similarity threshold
underestimates real biological differences.• Even the best primers only detect roughly 50% of
microbial community (Scibetta et al., 2018).• ITS sequencing does not allow precise species
identification in some taxa.
Metabarcoding
Oligotyping focuses on specifichighly variable sites insequences to identify diversityin specific target taxa.
(Eren et al., 2013)
Pros:• Not much computational power is needed• Not expensive• Generated data are less complex than in shotgun
metagenomics.
Cons:• OTU construction at 97% similarity threshold
underestimates real biological differences.• Even the best primers only detect roughly 50% of
microbial community (Scibetta et al., 2018).• ITS sequencing does not allow precise species
identification in some taxa.
Metabarcoding
Pros:• Not much computational power is needed• Not expensive• Generated data are less complex than in shotgun
metagenomics.
Cons:• OTU construction at 97% similarity threshold can
underestimate real biological differences.• Even the best primers only detect roughly 50% of
microbial community (Scibetta et al., 2018).• ITS sequencing does not allow precise species
identification in some taxa.
Results improved with:- more primers for the same locus- degenerate primers- primers for more loci
Metabarcoding
Pros:• Not much computational power is needed• Not expensive• Generated data are less complex than in shotgun
metagenomics.
Cons:• OTU construction at 97% similarity threshold can
cause the missing of real biological differences.• Even the best primers only detect roughly 50% of
microbial community (Scibetta et al., 2018).• ITS sequencing does not allow precise species
identification in some taxa.
Metabarcoding
Many important plant pathogens are Ascomycetes(Alternaria spp., Fusarium spp., Aspergillus spp., Penicillium spp.)
It does not allow identification beyond genus level formany Ascomycetes
ITS:• High sequence diversity• High number of copies per cell• Conserved primer sites• Numerous sequences in the database
Choosing the primers
(Scibetta et al. 2018)
RPB2:• Second largest subunit of
RNA polymerase 2• Most commonly used in
conjunction with others.• Good results in
Ascomycetes at specieslevel (Liu and Hall, 2004)
TEF1-α:• Translational elongation factor
1-alpha• Good for Fusarium and
Trichoderma.• The primers EF1-1018F/EF1-
1620R had an averageamplification success veryclose to that of ITS.
β-tubulin :• Good for important and
variable genera such asPenicillium and Aspergillus.
• Some commonly usedprimers can preferentiallyamplify the paralog tubC,resulting in incongruentanalysis.
MCM7:• Component of the minichromosome maintenance proteins
complex.• Good for Camarops, Lasiosphaeria, Aspergillius,…• Contrastings results on Penicillium.• Availability of degenerate primers for the amplification of
MCM7 in a wide range of most ascomycetes.
Calmodulin:• Good for Aspergillus, Penicillium and
Fusarium.
Choosing the primers
Fusarium spp.: TEF
(2016)
Metabarcoding with 454-pyrosequencing
Penicillium spp.: beta-tubulin
Aspergillus spp.: calmodulin
Databases need to contain enough information for each genus of interest
Databases are often not
complete and/or revised.
In-house databases
can be built.
(time-consuming)
Databases
Initiatives such as UNITE, SILVA and UniEuk have generated databases and reference datasets populated with
filtered and third‐party annotated sequences.
Illlumina, but...Use of PacBio for metabarcoding
1.5-kb long fragment covering parts of SSU, ITS and parts of the large ribosomal subunit (LSU).
(2016)
Choice of the sequencing method
Sampling
DNA extractionPCR
Sequencing
Data filtering and denoising
Sequenceclustering (OTU)
Taxonomicassignment
Interpretation
Sequencing
Cleaning
Assembling
Binning
Assembly-free taxonomicprofiling
Shotgun metagenomicsMetabarcoding
Metagenome sequencing
Gene predictionand annotation
Very good for viruses:1 - small genomes2 - low level of sequenceconservation (no universalprimers).
Pros:• Avoids PCR-derived biases.• Allows strain-level resolution for abundant species (eg: viruses).
Cons:• Expensive.• Complex operations required to interpret the data.• Taxonomical assignment difficult when sequencing complex
communities.• Assembling and binning are problematic when many related
strains are present (risk of chimera).• Risk of mistakenly detect pathogens from conserved sequences
originating from non-pathogenic organisms whose genome isnot available.
Shotgun metagenomics
Afshinnekoo et al., 2015:Yersinia pestis andBacillus anthracison the New York subwayRetraction
Pros:• Best base/cost ratio• High quality (99.9% accuracy)
Cons:• Short reads (150-300 bps)
Sequencing methods: Illumina
Pros:• Higher read length (400-450 bp)• More robust species inference
Cons:• More expensive
Sequencing methods: IonTorrent
Pros:• 30-100 kb reads• higher average contig length.• enhancements in binning and genome reconstruction in
shotgun metagenomics (Frank et al., 2016)• Allows for sequencing of the full ITS region and flanking
rRNA small subunit gene (Schlaeppi et al. 2016).
Cons:• Much more expensive
Source: Rhoads and Au (2015)
Sequencing methods: PacBio
Pros:• All the advantages of PacBio• Very fast (minutes)• Small and portable• Operable by a laptop
Cons:• Low accuracy (95%)
Source: https://phys.org/news/2014-02-oxford-nanopore-unveils-portable-genome.html
Sequencing methods: Oxford Nanopore
Databases do not contain enough information for allthe species
Genbank not
revised.
Databases
WGS lacking,
particularly for non
pathogens.
Discovery of new pathogens
Shotgun metagenomics on plant tissues
New pathogen species or strains- > 100 new viruses through HTS- Identification of Calonectria pseudonaviculata, agent of blight on Sarcococca- Duan et al. 2009: WGS of ‘Candidatus Liberibacter asiaticus’ from the psyllid vector- Adams et al. 2011: identified Xanthomonas in a diseased Hedera (ivy)
Causality: relationship between microorganism presence and diseaseKoch’s postulatesAdams et al. 2018: 10 factors to infer causality(including abundance of OTUs/coverage)
Biological characterizationPest risk analysis (PRA)NPPOs: Risk assessment / Risk management
Shotgun metagenomics to improve targeted diagnostics
qPCR or LAMP
Origin of an outbreak
In human health:FDA: HTS as default tracking tool for microbial foodpoisoning outbreak.
In plant pathology:Wheat yellow rust, obligate parasiteField pathogenomics (shotgun metatanscriptomics)4 different lineages, from 219 wheat samples across UK
soilborne
postharvest
foliarseedborne
Surveillance and monitoring
SoilSeed and propagation materialPlant products: fruit, leaves, woodAir
HTS: untargeted approachBaseline surveys airborne
• Metagenome sequencing has great potential for pest survelliance
• It is difficult to distinguish between new pathogens and already presentbut previously unreported organisms.
• EPPO underlines the importance of baseline surveys (knowledge of a pathogenstatus into a territory needed) for correct metagenomic data interpretation.
• Some countries, like Belgium, began projects to detect any virus present incultivated and wild plants of selected botanical families or genera within theirrespective territories.
Monitor background population
Source: Tremblay et al. 2018
Monitoring of pathogens: IonTorrent
Air samplesSoil samplesInsect samples
ESV for surveillance of pathogens in seed lots of forest trees
Source: Franic et al. (2019)
Illumina Miseq
Exact Sequence Variants
• Air microbiome is frequently understudied.• 4-11% of fine particle mass is made up of fungal spores.• Spores can move over 500 km (rusts).• Air is important for transmission of many diseases.• Spore identification is time-consiming.• Forecasting models capable of predicting the probability
of infection of a crop (frequently measure spores).
Airborne pathogens
52 forecasting models for Magnaporthe oryzae.
Brown spot (Cochliobolus miyabeanus), rice blast (Magnaporthe oryzae)
and bakanae (Fusarium fujikuroi) are airborne.
Rice pathogens
Traditional monitoring: quantification of airborne conidia under a microscope from
spore trap samples.
Difficult, especially if insects, dust or other materials are captured by the spore trap.
UNTARGETED APPROACHES
Metabarcoding of the ITS rDNA- (ITS1)-Illumina platform
Ky02F (5’- TAGAGGAAGTAAAAGTCGTAA-3’) and ITS1 Wobble (5’- CWGYGTTCTTCATCGATG-3’)
PE: 2X300 bp
TARGETED APPROACHES
qPCR for the detection of Magnaporthe oryzae
qPCR for the detection of Cochliobolus miyabeanus
Rice paddy
Spore trap: aerial mycobiome
Daily sampling: 73 days (25 June – 12 September)
Cochliobolus
Magnaporthe
Fusarium
Rice aerial mycobiome
Alpha-diversity of rice aerial mycobiome
GenusRelative abundance
(Average over season)
Cladosporium 20.1771
Alternaria 9.209575
Myrothecium 7.312185
Ascomycota 7.026058
Epicoccum 5.786639
Basidiomycota 5.292312
Sporidiobolales 5.154338
Sordariomycetes 4.44805
Davidiella 4.298913
Russulaceae 2.72734
Leptosphaerulina 2.332917
Entylomatales 1.851611
Magnaporthe 1.573834
Auriculibuller 1.515907
Pleosporales 1.356646
Sporobolomyces 0.975467
Tremellaceae 0.880249
Lewia 0.672838
Cochliobolus 0.568411
Uromyces 0.543934
Hyphodontia 0.526904
Fusarium 0.516212
Polyporales 0.501581
Schizophyllum 0.496519
Coriolopsis 0.487519
Aspergillus 0.460448
Sclerotiniaceae 0.450546
The difficult collection of the airborne particulate, the low amount of
organisms collected in the samples and the problematic DNA extraction
are challenging steps for airborne-mycobiota studies.
Method for DNA extraction from sticky tape.
Oligotyping was used to analyze the most interesting OTUs.
Strong correlation between the output of the qPCR and the Magnaporthe oligotypes, which were identified as M. oryzae and M. grisea, demonstrated the reliability of this analysis even with fungi.
qPCR confirmation
M. oryzae –Rice blast symptoms on leaves
Date 19.7.16 26.7.16 2.8.16 10.8.16 17.8.16 23.8.16 30.8.16
Disease index 3.00 3.75 4.00 3.25 6.50 8.25 9.00
The cells of C. miyabeanus were not correlated with any of the oligotypes of Cochliobolus that were identified as different species.
The combination with the TaqMan assay could solve the limit of detection of oligotyping.
Conclusions
High potential of HTS for diagnostics.
Metabarcoding, due to the low cost, has the greatest array of applications in diagnostics. Choice of the best ITS primers able to amplify most Ascomycotes. Choice of primers based on the target genus.
Shotgun metagenomics has important advantages, but it is more expensive and it requires particular conditions to be applicable (low biodiversity, low number of related strains, mostly even organism distribution).
It remains the only HTS-based technique to detect viruses, due to small genomes and low level of sequence conservation.
Both strategies require: extraction protocols planned to not introduce biases, sequencing of negative controls and complete and trustworthy databases.
Acknowledgements
Silvia Valente, Betta Meloni, Simona Prencipe, Edoardo Piombo
Samir DrobyMichael Wisniewski
Leonardo Schena
Neil BoonhamIan Adams
Ilario Ferrocino