Population structure and selection pressure analysis among Sugarcane yellow leaf virus isolates based on P0 and P1 sequences

ABSTRACT We have analyzed the genotypic diversity of Sugarcane yellow leaf virus (SCYLV) collected from North, South, and Central America by fingerprinting assays and selective cDNA cloning and sequencing. One group of isolates from Colombia, designated the C-population, has been identified as residing at the root node between a separable superpopulation structure of SCYLV and other members of the family Luteoviridae, indicating that the progenitor viruses of the North, South, and Central American isolates of the SCYLV superpopulation most likely arose from a C-population structure. From a model of intrafamilial evolution (F. Moonan et al., Virology 269:156–171, 2000), a prediction could be made that within the SCYLV species, the capacity of genomic sequence divergence would range from lowest in the capsid protein open reading frame 3 (ORF 3) to highest in a region spanning across the carboxy-terminal end of the RNA-dependent RNA polymerase ORF. We have demonstrated the validity and applicability of this intrafamilial model for the prediction of intraspecies SCYLV diversity. Analysis of spatial phylogenetic variation (SPV) within the SCYLV isolates could not be assessed by application of a “partial likelihoods assessed through optimization” (PLATO)-derived intraspecies model alone. However, application of a PLATO-derived intrafamilial model with the intraspecies-derived model allowed distinction of three forms of SPV. Two of the SPV forms identified correspond to the extremes in a continuum of sequence evolution displayed in a SCYLV superpopulation structure, and the third form was diagnostic of a C-population structure. The application of these types of models has value in terms of predicting the types of SCYLV intraspecies diversity that may exist worldwide, and in general, may be useful in application for more informed design of transgenes for use in the elicitation of homology-dependent virus resistance mechanisms in transgenic plants.

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Improved detection of Sugarcane yellow leaf virus using a real-time fluorescent (TaqMan) RT-PCR assay

Journal of Virological Methods ◽

10.1016/s0166-0934(01)00406-2 ◽

2002 ◽

Vol 103 (2) ◽

pp. 109-120 ◽

Cited By ~ 75

Author(s):

J Korimbocus ◽

D Coates ◽

I Barker ◽

N Boonham

Keyword(s):

Real Time ◽

Yellow Leaf ◽

Rt Pcr ◽

Pcr Assay ◽

Sugarcane Yellow Leaf Virus ◽

Leaf Virus

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Incidence of sugarcane yellow leaf virus in clones maintained in the world collection of sugarcane and related grasses at the United States national repository in Miami, Florida

Sugar Tech ◽

10.1007/bf02956805 ◽

2001 ◽

Vol 3 (4) ◽

pp. 128-133 ◽

Cited By ~ 19

Author(s):

J. C. Comstock ◽

J. D. Miller ◽

R. J. Schnell

Keyword(s):

United States ◽

The United States ◽

Yellow Leaf ◽

Sugarcane Yellow Leaf Virus ◽

World Collection ◽

The World ◽

Leaf Virus

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Detecting Sugarcane yellow leaf virus infection in asymptomatic leaves with hyperspectral remote sensing and associated leaf pigment changes

Journal of Virological Methods ◽

10.1016/j.jviromet.2010.03.024 ◽

2010 ◽

Vol 167 (2) ◽

pp. 140-145 ◽

Cited By ~ 32

Author(s):

Michael P. Grisham ◽

Richard M. Johnson ◽

Paul V. Zimba

Keyword(s):

Remote Sensing ◽

Virus Infection ◽

Hyperspectral Remote Sensing ◽

Yellow Leaf ◽

Sugarcane Yellow Leaf Virus ◽

Leaf Pigment ◽

Leaf Virus

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Sugarcane yellow leaf virus. [Distribution map].

Distribution Maps of Plant Diseases ◽

10.1079/dmpd/20183138502 ◽

2018 ◽

Author(s):

Keyword(s):

Tamil Nadu ◽

Andhra Pradesh ◽

Uttar Pradesh ◽

Saccharum Officinarum ◽

Yellow Leaf ◽

Distribution Map ◽

Sugarcane Yellow Leaf Virus ◽

Leaf Virus ◽

Virus Distribution ◽

New Distribution

Abstract A new distribution map is provided for Sugarcane yellow leaf virus. Luteoviridae: Polerovirus. Hosts: sugarcane (Saccharum officinarum), barley (Hordeum vulgare), sorghum (Sorghum bicolor). Information is given on the geographical distribution in Asia (China, Fujian, Guangdong, Guangxi, Guizhou, Hainan, Jiangxi, Yunnan, India, Andhra Pradesh, Bihar, Haryana, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Punjab, Tamil Nadu, Uttar Pradesh, Uttarakhand, Indonesia, Malaysia, Philippines, Sri Lanka, Taiwan, Thailand), Africa (Egypt, Kenya, Mauritius, Reunion, South Africa, Tunisia), North America (USA, Florida, Hawaii, Louisiana, Texas), Central America & Caribbean (Barbados, Costa Rica, Cuba, Guadeloupe, Guatemala, Martinique, Nicaragua, Puerto Rico), South America (Argentina, Brazil, Colombia, Ecuador, Peru, Venezuela).

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Genome-wide approaches for the identification of markers and genes associated with sugarcane yellow leaf virus resistance

Scientific Reports ◽

10.1038/s41598-021-95116-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ricardo José Gonzaga Pimenta ◽

Alexandre Hild Aono ◽

Roberto Carlos Villavicencio Burbano ◽

Alisson Esdras Coutinho ◽

Carla Cristina da Silva ◽

...

Keyword(s):

Virus Resistance ◽

Machine Learning Algorithms ◽

Energy Crop ◽

Yellow Leaf ◽

Sugarcane Yellow Leaf Virus ◽

Association Analyses ◽

Sugarcane Breeding ◽

Genetic Base ◽

Genome Wide ◽

Leaf Virus

AbstractSugarcane yellow leaf (SCYL), caused by the sugarcane yellow leaf virus (SCYLV) is a major disease affecting sugarcane, a leading sugar and energy crop. Despite damages caused by SCYLV, the genetic base of resistance to this virus remains largely unknown. Several methodologies have arisen to identify molecular markers associated with SCYLV resistance, which are crucial for marker-assisted selection and understanding response mechanisms to this virus. We investigated the genetic base of SCYLV resistance using dominant and codominant markers and genotypes of interest for sugarcane breeding. A sugarcane panel inoculated with SCYLV was analyzed for SCYL symptoms, and viral titer was estimated by RT-qPCR. This panel was genotyped with 662 dominant markers and 70,888 SNPs and indels with allele proportion information. We used polyploid-adapted genome-wide association analyses and machine-learning algorithms coupled with feature selection methods to establish marker-trait associations. While each approach identified unique marker sets associated with phenotypes, convergences were observed between them and demonstrated their complementarity. Lastly, we annotated these markers, identifying genes encoding emblematic participants in virus resistance mechanisms and previously unreported candidates involved in viral responses. Our approach could accelerate sugarcane breeding targeting SCYLV resistance and facilitate studies on biological processes leading to this trait.

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