This document discusses global plant virus management strategies including diagnostics, surveillance, and modeling. It summarizes several key points: 1) There is a need for improved diagnostics and surveillance of major crop diseases to better understand epidemiology and identify new pathogens. 2) Next generation sequencing and bioinformatics approaches are enabling high-throughput virus identification from large sample sets. 3) Models are being used to predict virus occurrence and inform surveillance. 4) Field-deployable diagnostics like LAMP are supporting real-time monitoring. Case studies on cassava, potato and maize viruses in various regions demonstrate applications of these approaches. Harmonization of databases and standards is needed to improve disease prediction and preparedness.

1. Global Plant Virus Management:
Diagnostics, Surveillance, and Modelling
Jan Kreuze, Wilmer J. Cuellar, Lava Kumar, B.M. Prasanna,
Aman Omondi

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Plant Health Management under Changing Climates
Need for a Revitalized Strategy and Innovative Approaches
Plant Health and Rapid
Response to Protect Food
Security and Livelihoods

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Better understanding of the major diseases contributing to crop
losses, their epidemiology and cheap rapid accurate diagnostics
Next generation diagnostics
Predicting risk
Characterization & surveillance of
(new & emerging) pathogens
disease management
germplasm exchange
crop improvement
Appropriate diagnostics technologies

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Surveillance/monitoring using HTS
Cytoplasm
Nucleus
siRNA
Initiation
Rnase
RdRp
Maintenance,
amplification
Complementary ssRNA or dsRNA
Targeted RNA
degradation
DNA-virus
(e.g. geminivirus)
overlapping ssRNAs
Systemic signal
RISC
nuclease complex
”Degradative
PCR”
dsRNA (RNA virus)
Replicating RNA virus
1. Illumina
sequencing
2. assembly into
contigs
3. search for
similarity in
databases
using BLAST
1. Rolling circle
amplification
2. Nanopore
sequencing

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The African sweetpotato virome
~2000 samples and bioinformatics pipeline for virus identification using siRNA assembly
and genome subtraction

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virus database &
reference-guided
assembly
sRNA reads
Alignment to reference
t
Contigs
virus Undetermined contigs
virus
Concatenateand remove redundancy
Non-redundant contigs
Alignment of sRNA readsbackto
contigs & correct baseerrors
Non-redundant contigs
(base error corrected)
BLASTN against virus nucleotide
database
Contigs without hit
BLASTX againstvirus protein
database
t
siRNAsize profile
Alignment to hos
optional sequences
Host-derived sRNA Unmapped
reads sRNA reads
Host-derived contigs
De novo assembly
Contigs
Alignment to hos
sequences
Unmapped contigs
Small RNA sequence analysis pipeline:
VirusDetect

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http://bioinfo.bti.cornell.edu/virome/index

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New viruses:
Potyviruses: 3
Ampeloviruses: 3
Mitoviruses: many
Nepovirus
Soymovirus
Emaravirus
Begomoviruses
Alphasatellites
Results: the viruses
3193viruses from 1168samples

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Are all viruses relevant?
https://doi.org/10.3389/fpls.2020.00313 https://doi.org/10.1094/PDIS-06-19-1196-RE

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Can we make predictions about likely
occurance beyond evaluated areas?
Variable Value
Unique Locations 98
TrainingAUC 0.997
Equal training sensitivity and
specificity logistic threshold
0.23
Cultivation intensity 33.67
Biomes ecoregion 24.16
TemperatureSeasonality 18.96
WWF ecoregion 12.07
Latitude 3.73
Totaltop 5 92.54
Example for begomoviruses: Niche analysis using
MaxEnt modeling and bioclimatic-ecosystem
variables
Gamarra et al. 2020:
https://doi.org/10.1016/j.virusres.2020.198107
https://doi.org/10.1016/j.virusres.2020.198109
Example for PYVV: phenology modeling
for surveillance

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For simple sap
extraction
Portable
and battery
operated
BioRanger
Easy to interpret
Lyophilized reagents
Field LAMP kit
Njere et al., 2018: https://doi.org/10.1007/s00705-018-3706-0
Wanjala et al., 2020: https://doi.org/10.1094/PDIS-06-19-1196-RE

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BBTV surveillance system
Real-time
• Reporting
• Mapping
• Diagnosis
• Control

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Remote Sensing for Disease (risk) Mapping
Selveraj et al., 2020: https://doi.org/10.1016/j.isprsjprs.2020.08.025

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CMD in SEA: dev. standard protocols
https://pestdisplace.org
Cuellar et al., 2021 https://hdl.handle.net/20.500.11766/67115
RCA and sequencing of circular
DNA: Only 1 (out of 12) species of
cassava-infecting begomovirus is
present in SEA: Sri Lankan cassava
mosaic virus (SLCMV)
Leiva et al., 2020. https://doi.org/10.1128/MRA.01274-19
Field Lab

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Cassava Mosaic Disease in SEA
>30,000 observations
CMD in:
Thailand
Cambodia
Vietnam
Lao PDR
China
Graziosi et al., 2016. https://doi.org/10.1002/ps.4250 Siriwan et al., 2020. https://doi.org/10.1016/j.virusres.2020.197959

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Tracking SLCMV genome evolution
EA
SEA
https://nextstrain.org/community/pestdisplace/CMDASIA1?c=virus&r=location
Cuellar et al., 2021

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EA
SEA
These extra 7 amino acids
are involved in virulence
Siriwan et al., 2020​. https://doi.org/10.1016/j.virusres.2020.197959
Wang et al., 2020. https://doi.org/10.1094/MPMI-06-19-0163-FI

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Field
Plant
Leaves
• Sample collection:
994 geo-referenced potato leaf
samples
Drying Weighting
The Peruvian potato virome

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PVY
(40.3%)
PVV
(23.8%)
PVA
PVS
(16.9%)
PLRV
(3.7%)
PYV
(2.6%)
APLV (2.2%)
APMMV (2.7%)
APMoV (9.6%)
PMTV (3.8%)
PVB (20.6%)
PBRSV (0.2%)
PVT (0.3%)
Nepo (6.7%)
Como (7.4%)
AVB (1.2%)
Badna (6.0%)
Faba(1.9%)
Torrado(4.8%)
Ophio (1.5%) Poty (1.2%) Carla (1.0%) Enamo Tobra Potex Tepo Polero Pomo
Chera
Tymo
545
175
341
60
45
192
134
124
34
26
177
74
68 60 48
28
38
72
15
26
37
General results
PVX
(72.4%)
Fuentes et al., 2021:
https://doi.org/10.3390/v13040644
Fuentes et al., 2020:
https://doi.org/10.1094/PHYTO-08-20-0354-FI
Fuentes et al., 2022: https://doi.org/10.1094/PDIS-09-21-1897-RE
(15.1%)
Fuentes et al. 2020: https://doi.org/10.1094/PHYTO-
08-20-0354-FI
Silvestre et al., 2020:
https://doi.org/10.1016/j.virusres.
2020.197944

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• Observed since 1990 in Arequipa, Moquegua and Tacna and studied as SB26 and SB29
• Isometric virus, two dsRNA sizes, transmitted by South American potato psyllid (Russelliana solanicola)
and generated losses of between 35-85% depending on the variety
• Farmers changed their planting dates to manage the disease or quit potato al together and it is no
longer considered a problem
• It was never determined what type of virus it was
Rugose stunting disease of potato: the
history of the emergence and decline of a
viral disease

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PVX
(72.4%)
PVY
(40.3%)
PVV
(23.8%)
PVA
(15.1%)
PVS
(16.9%)
PLRV
(3.7%)
PYV
(2.6%)
APLV (2.2%)
APMMV (2.7%)
APMoV (9.6%)
PMTV (3.8%)
PVB (20.6%)
PBRSV (0.2%)
PVT (0.3%)
Nepo (6.7%)
Como (7.4%)
AVB (1.2%)
Badna (6.0%)
Torrado(4.8%)
Faba(1.9%) Ophio (1.5%) Poty (1.2%) Carla (1.0%) Enamo Tobra Potex Tepo Polero Pomo
Chera
Tymo
545
175
341
60
45 192
134
124
34
26
177
74
68 60 48
28
38
72
15
26
37
Are any of the new viruses of relevance?

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Potato rugose stunting virus is a torradovirus
and present in low frequencies in potatoes
throughout Peru

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Maize Lethal Necrosis (MLN)in Africa

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MLN Diagnostics and Surveillance
 MLN Diagnostics and
Surveillance: Coordinated by
CIMMYT and implemented
by NPPOs in five countries in
eastern Africa (Ethiopia,
Kenya, Uganda, Rwanda, and
Tanzania) and three countries
in southern Africa (Malawi,
Zambia, and Zimbabwe).
 MLN Toolbox in partnership
with NPPOs and Aarhus
University.

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Tracking MLN Dynamics over the Years
Prasanna et al. (2020): https://doi.org/10.1016/j.virusres.2020.197943

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MLN Management in Africa
Success through I
ntegration of Various Components
No scope for complacency as MLN causing viruses are
still prevalent in eastern Africa and are not eradicated..

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The way forward
Many different databases that
are unlinked, sometimes even for
same diseases
More data=more power to
predict & prepare
Need to make databases
interoperable and FAIR
(Findable, Accessible,
Interoperable and Reusable)
Requires development of
Application Program Interface &
specifications

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CGIAR Germplasm Health Program
•Global network committed to ensuring phytosanitary compliance and safe distribution of pest-free
germplasm
• Prevention of transboundary spread of pathogens through germplasm
AU-IAPSC
Kumar et al., 2021; Plants 10(2), 328; https://www.mdpi.com/2223-7747/10/2/328
Tools, technologies, strategies and policies for pest-free
distribution of plant genetic resources

29. Thank you!