Recombinant Solanum demissum Putative late blight resistance protein homolog R1C-3 (R1C-3), partial

What is the structural organization of R1C-3 and how does it relate to other resistance proteins in Solanum species?

R1C-3 belongs to the R1 resistance gene cluster in Solanum demissum and is classified as a CC-NBS-LRR (Coiled-Coil Nucleotide-Binding Site Leucine-Rich Repeat) resistance protein. This protein family is characterized by three distinct functional domains: a coiled-coil domain at the N-terminus, a central nucleotide-binding site, and leucine-rich repeats at the C-terminus . The R1 homologues form three distinct evolutionary clades with evidence of frequent sequence exchanges occurring within but not between these clades .

Structural comparison with related resistance proteins reveals:

The partial nature of R1C-3 could affect its functional capacity relative to complete resistance proteins, potentially impacting either effector recognition or downstream signaling capabilities.

How does R1C-3 contribute to the molecular mechanisms of late blight resistance?

R1C-3 likely functions within the gene-for-gene resistance model typical of NBS-LRR proteins. In this framework, the LRR domain recognizes specific effector proteins (Avr proteins) secreted by Phytophthora infestans during infection. This recognition triggers a signal transduction cascade that leads to the hypersensitive response (HR), effectively restricting pathogen spread.

The molecular action of R1C-3 and related R proteins involves:

• Recognition of pathogen effectors, potentially including Avr3a or similar effectors as observed with related proteins

• Activation of MAPK signaling cascades following recognition

• Initiation of defense responses including reactive oxygen species production, transcriptional reprogramming, and programmed cell death

Studies of related R proteins suggest that the LRR domain of R1C-3 contains the specificity determinants for effector recognition, while the NBS domain functions as a molecular switch regulating activation .

What evolutionary processes have shaped the R1C-3 gene and the broader R1 gene cluster?

The R1 resistance gene cluster shows substantial structural variation among haplotypes in S. demissum, with evidence suggesting three independently evolving groups of type I resistance genes . These genes are characterized by:

The evolution of these genes appears driven by pathogen pressure, with sequence exchanges serving as a mechanism for generating novel recognition specificities. Unlike type II resistance genes that evolve more slowly, the R1 homologues (including R1C-3) represent fast-evolving type I genes that likely contribute to the ongoing evolutionary arms race with P. infestans .

The R1 genes originated in wild Solanum species and have been introgressed into cultivated potatoes through breeding programs, representing an important source of resistance in modern potato cultivars.

What isolation and characterization techniques are most effective for studying the partial R1C-3 protein?

A systematic approach for isolating and characterizing R1C-3 should include:

• Genomic DNA isolation: Use modified CTAB extraction methods optimized for Solanum species, which effectively handle high polysaccharide and secondary metabolite content.

• PCR amplification strategy: Design primers based on conserved regions of known R1 homologues, targeting:

  • Conserved motifs in the NBS domain (P-loop, kinase-2, GLPL)

  • Previously sequenced regions of R1 homologues

  • Regions specific to the clade containing R1C-3

• Cloning and sequence verification:

  • TOPO-TA or Gateway cloning systems for efficient capture

  • Sanger sequencing to confirm identity

  • NGS approaches for capturing the complete sequence context

• Expression system selection:

  • Bacterial expression (E. coli) for protein fragment analysis

  • Plant expression systems (N. benthamiana) for functional studies

  • Yeast systems for interaction studies

For partial proteins, special consideration should be given to:

• Domain integrity assessment

• Protein stability and solubility testing

• Strategic design of fusion tags to enhance stability and purification

How can RNA-sequencing approaches be optimized to study R1C-3 expression patterns during pathogen infection?

RNA-sequencing represents a powerful approach for understanding the temporal dynamics of R1C-3 expression during infection. Based on successful studies of late blight resistance genes, the following methodology is recommended :

• Experimental design considerations:

  • Include multiple time points (0, 12, 24, 48, 72 hours post-infection) to capture the full response trajectory

  • Compare resistant (R1C-3-containing) and susceptible genotypes

  • Include biological replicates (minimum n=3) for statistical power

  • Consider both compatible and incompatible P. infestans isolates

• RNA extraction optimization:

  • Use specialized RNA extraction methods for potato tissue

  • Implement stringent quality control (RIN > 7)

  • Include DNase treatment to eliminate genomic contamination

• Library preparation strategy:

  • Strand-specific libraries to detect potential antisense transcription

  • Poly(A) selection for coding transcripts

  • Consider small RNA sequencing as a complementary approach

• Bioinformatic analysis pipeline:

  • Quality filtering and adapter trimming

  • Alignment to reference genome (consider using both S. tuberosum and S. demissum references)

  • Differential expression analysis with DESeq2 or edgeR

  • GO enrichment and pathway analysis

  • Co-expression network construction

Research has shown that NBS-LRR genes like R1C-3 often show differential expression at specific time points after infection, with some being induced only at later stages (e.g., 72 hpi) , highlighting the importance of comprehensive temporal sampling.

What functional assays can validate the role of R1C-3 in recognition of P. infestans effectors?

Multiple complementary approaches can establish the functional role of R1C-3 in effector recognition:

• Transient expression assays:

  • Agrobacterium-mediated expression in N. benthamiana

  • Co-infiltration of R1C-3 with candidate effectors

  • Quantification of hypersensitive response using ion leakage measurements or DAB staining

• Protein-protein interaction studies:

  • Yeast two-hybrid screening with a library of P. infestans effectors

  • Co-immunoprecipitation to confirm interactions in planta

  • Bimolecular fluorescence complementation (BiFC) for visualizing interactions in living cells

• Domain mapping experiments:

  • Create chimeric constructs swapping domains with related R proteins

  • Perform deletion analysis to identify essential recognition regions

  • Site-directed mutagenesis of predicted interface residues

• Transgenic complementation:

  • Transform susceptible potato varieties with R1C-3

  • Challenge with diverse P. infestans isolates

  • Assess resistance phenotypes

A systematic testing approach might involve:

Such methodologies have been successfully applied to characterize other resistance proteins like R1B-13 and their interactions with effectors such as Avr3a.

How does the structure-function relationship in R1C-3 determine specificity for particular P. infestans effectors?

The specific recognition of pathogen effectors by R1C-3 likely depends on structural features within its LRR domain, which serves as the primary determinant of recognition specificity in NBS-LRR proteins. Research on related resistance proteins provides insights into several key aspects :

• LRR domain architecture:

  • The leucine-rich repeats form a horseshoe-shaped structure

  • The concave surface contains variable residues that interact with effectors

  • Specific solvent-exposed residues create the recognition interface

• Recognition mechanisms:

  • Direct recognition: Physical interaction between R1C-3 and the effector

  • Indirect recognition: R1C-3 monitors modifications of host targets by effectors (the "guard hypothesis")

  • Bait-and-switch: R1C-3 may use decoy domains that mimic effector targets

• Structural dynamics:

  • Conformational changes upon effector binding release auto-inhibition

  • The NBS domain functions as a molecular switch, likely through nucleotide exchange

  • Intramolecular interactions between domains regulate activation threshold

Molecular analyses of R1 homologues reveal that they contain three independently evolving clades with evidence of sequence exchanges within but not between clades . This pattern suggests that distinct recognition specificities are maintained while allowing for diversification within each evolutionary lineage.

What signaling pathways are activated downstream of R1C-3 recognition and how do they differ from other resistance mechanisms?

Following effector recognition, R1C-3 likely initiates a complex signaling cascade leading to defense activation. Based on studies of related CC-NBS-LRR proteins, this signaling network involves :

• Early signaling events:

  • Conformational changes in R1C-3 structure

  • Oligomerization and/or interaction with signaling partners

  • Activation of MAPK cascades through direct or indirect mechanisms

• Secondary messengers:

  • Calcium influx and calmodulin signaling

  • Production of reactive oxygen species through NADPH oxidases

  • Nitric oxide production and signaling

• Transcriptional reprogramming:

  • Activation of specific transcription factors (WRKYs, TGAs)

  • Induction of pathogenesis-related (PR) genes

  • Metabolic pathway reconfiguration

• Defense outputs:

  • Hypersensitive response (localized cell death)

  • Cell wall reinforcement

  • Antimicrobial compound production

  • Systemic signal generation

The temporal dynamics of these responses can be visualized through time-course experiments, which have revealed that NBS-LRR genes like R1C-3 may be differentially expressed at specific timepoints during infection . Some resistance proteins show expression as early as the first few hours after infection, while others (including several NBS-LRR coding genes) are only differentially expressed at later timepoints such as 72 hours post-infection .

How can functional stacking of R1C-3 with other resistance genes be optimized for durable field resistance?

Functional stacking of multiple resistance genes represents a promising strategy for developing durable resistance to late blight. Evidence indicates that combining multiple R genes provides more robust protection than individual genes . For optimizing R1C-3 stacking:

• Strategic gene combinations:

  • Include genes with complementary recognition spectra

  • Combine R1C-3 with broad-spectrum resistance genes like RB/Rpi-blb1, Rpi-blb2, and Rpi-stol1

  • Integrate both qualitative R genes (like R1C-3) and quantitative resistance genes (like Rpi-Smira2)

• Molecular strategies for stacking:

  • Multigene transformation constructs

  • CRISPR-based gene editing to modify native loci

  • Conventional breeding pyramiding with marker-assisted selection

• Expression optimization:

  • Selection of appropriate promoters to avoid silencing

  • Consideration of expression levels to minimize fitness costs

  • Potential use of pathogen-inducible promoters

Successful examples of resistance gene stacking include the combination of Rpi-sto1, Rpi-vnt1.1, and Rpi-blb3 in the susceptible potato cultivar Desiree, and the pyramiding of R3a, R3b, R4, Rpi-Smira1, and Rpi-Smira2 . These approaches have demonstrated cumulative effects on late blight resistance.

How has the R1 gene cluster evolved in Solanum species and what does this reveal about R1C-3's evolutionary history?

The R1 resistance gene cluster shows remarkable structural variation and evolutionary dynamics across Solanum species. Studies of S. demissum have revealed several key insights that inform our understanding of R1C-3's evolution :

The presence of R1C-3 as a partial homolog may reflect either:

• Ongoing evolution through recombination events

• Gene conversion resulting in partial gene structures

• Pseudogenization of once-functional variants

These evolutionary patterns provide critical context for understanding the functional capabilities and potential durability of R1C-3-mediated resistance.

What methods are most effective for analyzing the evolutionary relationships between R1C-3 and other resistance gene homologs?

Robust phylogenetic and evolutionary analysis of R1C-3 requires specialized approaches to address the complex evolutionary history of resistance genes. Recommended methodologies include:

• Sequence acquisition and alignment:

  • Long-read sequencing to capture complete gene structures and surrounding regions

  • Structural variant detection to identify copy number variations

  • MAFFT or MUSCLE with parameters optimized for divergent sequences

  • Manual curation of alignments, particularly in variable regions

• Phylogenetic analysis:

  • Maximum likelihood methods (RAxML, IQ-TREE) with appropriate substitution models

  • Bayesian inference (MrBayes, BEAST) for complex evolutionary models

  • Gene tree/species tree reconciliation to account for incomplete lineage sorting

• Recombination and gene conversion detection:

  • RDP4 suite for comprehensive recombination detection

  • GENECONV for detecting gene conversion events

  • Sliding window analysis of sequence similarity

• Selection analysis:

  • Site-specific selection models (PAML, HyPhy)

  • McDonald-Kreitman tests comparing within-species polymorphism and between-species divergence

  • Domain-specific analysis of selection patterns

• Structural evolution analysis:

  • Protein structure prediction and comparison

  • Identification of critical functional residues

  • Mapping selection patterns onto predicted structures

Such methods have successfully revealed that R1 homologues form three distinct clades with evidence of frequent sequence exchanges occurring within but not between these clades , providing a framework for positioning R1C-3 within this evolutionary context.

How do environmental factors influence the expression and effectiveness of R1C-3 and related resistance genes?

Environmental conditions significantly impact both the expression of resistance genes like R1C-3 and the effectiveness of the resistance they confer. Key environmental factors and their effects include:

• Temperature effects:

  • Many R gene-mediated resistances are temperature-sensitive

  • Higher temperatures may suppress resistance responses

  • Temperature fluctuations can affect stability of resistance

• Humidity and moisture:

  • High humidity favors P. infestans infection and disease progression

  • Leaf wetness duration affects infection success

  • Water stress can modulate plant defense capacity

• Light conditions:

  • Light quality and intensity influence defense gene expression

  • Photoperiod affects circadian regulation of immunity

  • Shading can compromise resistance mechanisms

• Soil conditions and nutrition:

  • Nutrient status affects basal immunity and R gene function

  • Particularly important are nitrogen, phosphorus, and potassium levels

  • Micronutrient availability influences defense compound production

Experimental approaches to study these interactions should include:

Understanding these environmental influences is crucial for developing deployment strategies that maximize the effectiveness and durability of R1C-3-mediated resistance across diverse growing conditions.

What emerging technologies could advance our understanding of R1C-3 function and evolution?

Several cutting-edge technologies offer promising approaches for deeper insights into R1C-3:

• Advanced genomic technologies:

  • Long-read sequencing (Oxford Nanopore, PacBio) for resolving complex structural variations in the R1 cluster

  • Hi-C and optical mapping for chromosomal-scale organization

  • Single-molecule real-time sequencing for haplotype resolution

• CRISPR technologies:

  • Base editing for precise modification of recognition specificity

  • Prime editing for more complex sequence alterations

  • CRISPR activation/interference for modulating expression

• Structural biology advances:

  • Cryo-EM for elucidating complete R1C-3 structure

  • AlphaFold and similar AI approaches for structure prediction

  • In situ structural biology to visualize conformational changes during activation

• Single-cell approaches:

  • Single-cell RNA-seq to capture cellular heterogeneity in defense responses

  • Spatial transcriptomics to map defense activation patterns

  • Multimodal single-cell profiling (RNA + protein)

• Systems biology integration:

  • Multi-omics integration (transcriptomics, proteomics, metabolomics)

  • Network analysis of defense signaling pathways

  • Predictive modeling of resistance durability

These technologies could address key questions including the complete structure of R1C-3, its precise recognition specificities, and its evolutionary trajectory within the dynamic R1 gene cluster .

How can knowledge of R1C-3 inform breeding strategies for durable late blight resistance?

Translating molecular understanding of R1C-3 into practical breeding applications requires strategic approaches:

• Marker-assisted selection strategies:

  • Development of R1C-3-specific molecular markers

  • Haplotype analysis to identify optimal genetic backgrounds

  • Selection for complementary resistance loci

• Resistance gene deployment strategies:

  • Spatial and temporal deployment planning

  • Consideration of resistance gene rotation or mixtures

  • Integration with quantitative resistance loci

• Durability-focused breeding:

  • Selection for R gene combinations with complementary recognition spectra

  • Integration of multiple resistance mechanisms (R genes, PRRs, antimicrobial proteins)

  • Incorporation of both qualitative and quantitative resistance

• Germplasm resources:

  • Screening of wild Solanum species for novel R1 homologues

  • Creation of pre-breeding lines with optimized resistance gene stacks

  • Development of genetic stocks for research and breeding

• Pathogen monitoring integration:

  • Surveillance of P. infestans populations for virulence evolution

  • Feedback loops between pathogen monitoring and breeding decisions

  • Testing against diverse pathogen isolates to ensure broad effectiveness

Historical examples demonstrate the value of R gene stacking, as seen in the successful combination of multiple resistance genes in potato cultivars, including qualitative R genes (R3a, R3b, R4, Rpi-Smira1) and quantitative resistance genes (Rpi-Smira2) .

What interdisciplinary approaches could enhance our ability to study complex resistance mechanisms like those mediated by R1C-3?

Addressing the complexity of R1C-3-mediated resistance requires integration across multiple scientific disciplines:

• Computational biology and bioinformatics:

  • Machine learning for predicting effector targets

  • Network modeling of defense signaling pathways

  • Evolutionary simulations of R gene/effector coevolution

• Chemical biology:

  • Small molecule modulators of defense responses

  • Chemically-induced proximity approaches for controlling protein interactions

  • Metabolomic profiling of resistance responses

• Synthetic biology:

  • Designer resistance proteins with optimized recognition specificities

  • Engineered signaling circuits for customized defense outputs

  • Modular resistance components for plug-and-play immunity

• Ecology and evolutionary biology:

  • Field studies of resistance durability

  • Ecological modeling of disease dynamics

  • Evolutionary forecasting of resistance breaking

• Quantitative genetics and systems biology:

  • GWAS for identifying genetic modifiers of R1C-3 function

  • Systems-level analysis of resistance networks

  • Predictive modeling of genotype-to-phenotype relationships

The integration of these disciplines could potentially address fundamental challenges in understanding and deploying R1C-3, including:

Such interdisciplinary approaches have proven valuable in understanding complex resistance mechanisms in the Solanum-Phytophthora pathosystem, as evidenced by recent advances in characterizing the R1 gene cluster .