Mutation screening of the breast cancer genes BRCA1 and BRCA2 identifies a large fraction of variants of uncertain clinical significance (VUS) whose functional and clinical interpretations pose a challenge for genomic medicine. Likewise, an increasing amount of evidence indicates that genetic variants can have deleterious effects on pre-mRNA splicing. Our goal was to investigate the impact on splicing of a set of reported variants of BRCA2 exons 17 and 18 to assess their role in hereditary breast cancer and to identify critical regulatory elements that may constitute hotspots for spliceogenic variants. A splicing reporter minigene with BRCA2 exons 14 to-20 (MGBR2_ex14-20) was constructed in the pSAD vector. Fifty-two candidate variants were selected with splicing prediction programs, introduced in MGBR2_ex14-20 by site-directed mutagenesis and assayed in triplicate in MCF-7 cells. Wild type MGBR2_ex14-20 produced a stable transcript of the expected size (1,806 nucleotides) and structure (V1-[BRCA2_exons_14–20]–V2). Functional mapping by microdeletions revealed essential sequences for exon recognition on the 3’ end of exon 17 (c.7944-7973) and the 5’ end of exon 18 (c.7979-7988, c.7999-8013). Thirty out of the 52 selected variants induced anomalous splicing in minigene assays with >16 different aberrant transcripts, where exon skipping was the most common event. A wide range of splicing motifs were affected including the canonical splice sites (15 variants), novel alternative sites (3 variants), the polypyrimidine tract (3 variants) and enhancers/silencers (9 variants). According to the guidelines of the American College of Medical Genetics and Genomics (ACMG), 20 variants could be classified as pathogenic (c.7806-2A>G, c.7806-1G>A, c.7806-1G>T, c.7806-1_7806-2dup, c.7976+1G>A, c.7977-3_7978del, c.7977-2A>T, c.7977-1G>T, c.7977-1G>C, c.8009C>A, c.8331+1G>T and c.8331+2T>C) or likely pathogenic (c.7806-9T>G, c.7976G>C, c.7976G>A, c.7977-7C>G, c.7985C>G, c.8023A>G, c.8035G>T and c.8331G>A), accounting for 30.8% of all pathogenic/likely pathogenic variants of exons 17–18 at the BRCA Share database. The remaining 8 variants (c.7975A>G, c.7977-6T>G, c.7988A>T, c.7992T>A, c.8007A>G, c.8009C>T, c.8009C>G, and c.8072C>T) induced partial splicing anomalies with important ratios of the full-length transcript (≥70%), so that they remained classified as VUS. Aberrant splicing is therefore especially prevalent in BRCA2 exons 17 and 18 due to the presence of active ESEs involved in exon recognition. Splicing functional assays with minigenes are a valuable strategy for the initial characterization of the splicing outcomes and the subsequent clinical interpretation of variants of any disease-gene, although these results should be checked, whenever possible, against patient RNA.

Grafting is typically utilized to merge adapted seedling rootstocks with highly productive clonal scions. This process implies the interaction of multiple genomes to produce a unique tree phenotype. However, the interconnection of both genotypes obscures individual contributions to phenotypic variation (rootstock-mediated heritability), hampering tree breeding. Therefore, our goal was to quantify the inheritance of seedling rootstock effects on scion traits using avocado (Persea americana Mill.) cv. Hass as a model fruit tree. We characterized 240 diverse rootstocks from 8 avocado cv. Hass orchards with similar management in three regions of the province of Antioquia, northwest Andes of Colombia, using 13 microsatellite markers simple sequence repeats (SSRs). Parallel to this, we recorded 20 phenotypic traits (including morphological, biomass/reproductive, and fruit yield and quality traits) in the scions for 3 years (2015–2017). Relatedness among rootstocks was inferred through the genetic markers and inputted in a “genetic prediction” model to calculate narrow-sense heritabilities (h2) on scion traits. We used three different randomization tests to highlight traits with consistently significant heritability estimates. This strategy allowed us to capture five traits with significant heritability values that ranged from 0.33 to 0.45 and model fits (r) that oscillated between 0.58 and 0.73 across orchards. The results showed significance in the rootstock effects for four complex harvest and quality traits (i.e., total number of fruits, number of fruits with exportation quality, and number of fruits discarded because of low weight or thrips damage), whereas the only morphological trait that had a significant heritability value was overall trunk height (an emergent property of the rootstock–scion interaction). These findings suggest the inheritance of rootstock effects, beyond root phenotype, on a surprisingly wide spectrum of scion traits in “Hass” avocado. They also reinforce the utility of polymorphic SSRs for relatedness reconstruction and genetic prediction of complex traits. This research is, up to date, the most cohesive evidence of narrow-sense inheritance of rootstock effects in a tropical fruit tree crop. Ultimately, our work highlights the importance of considering the rootstock–scion interaction to broaden the genetic basis of fruit tree breeding programs while enhancing our understanding of the consequences of grafting.

Barley (Hordeum vulgare L.) Mla (Mildew resistance locus a) and its nucleotide-binding, leucine-rich-repeat receptor (NLR) orthologs protect many cereal crops from diseases caused by fungal pathogens. However, large segments of the Mla pathway and its mechanisms remain unknown. To further characterize the molecular interactions required for NLR-based immunity, we used fast-neutron mutagenesis to screen for plants compromised in MLA-mediated response to the powdery mildew fungus, Blumeria graminis f. sp. hordei. One variant, m11526, contained a novel mutation, designated rar3 (required for Mla6 resistance3), that abolishes race-specific resistance conditioned by the Mla6, Mla7, and Mla12 alleles, but does not compromise immunity mediated by Mla1, Mla9, Mla10, and Mla13. This is analogous to, but unique from, the differential requirement of Mla alleles for the co-chaperone Rar1 (required for Mla12 resistance1). We used bulked-segregant-exome capture and fine mapping to delineate the causal mutation to an in-frame Lys-Leu deletion within the SGS domain of SGT1 (Suppressor of G-two allele of Skp1, Sgt1ΔKL308–309), the structural region that interacts with MLA proteins. In nature, mutations to Sgt1 usually cause lethal phenotypes, but here we pinpoint a unique modification that delineates its requirement for some disease resistances, while unaffecting others as well as normal cell processes. Moreover, the data indicate that the requirement of SGT1 for resistance signaling by NLRs can be delimited to single sites on the protein. Further study could distinguish the regions by which pathogen effectors and host proteins interact with SGT1, facilitating precise editing of effector incompatible variants.

Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.

Mapping protein–protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate complementary DNA (cDNA) libraries represent an alternative approach that optimizes scale, cost and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are evaluated as to whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and it recognized all validated interactions. NGPINT can process interaction data from any biosystem with an available genome or transcriptome reference, thus facilitating the discovery of protein–protein interactions in model and non-model organisms.

Grafting is typically utilized to merge adapted seedling rootstocks with highly productive clonal scions. This process implies the interaction of multiple genomes to produce a unique tree phenotype. Yet, the interconnection of both genotypes obscures individual contributions to phenotypic variation (i.e. rootstock-mediated heritability), hampering tree breeding. Therefore, our goal was to quantify the inheritance of seedling rootstock effects on scion traits using avocado (Persea americana Mill.) cv. Hass as model fruit tree. We characterized 240 rootstocks from 8 avocado cv. Hass orchards in three regions of the province of Antioquia, in the northwest Andes of Colombia, using 13 microsatellite markers (simple sequence repeats – SSRs). Parallel to this, we recorded 20 phenotypic traits (including morphological, eco-physiological, and fruit yield and quality traits) in the scions for three years (2015–2017). Relatedness among rootstocks was inferred through the genetic markers and inputted in a ‘genetic prediction’ model in order to calculate narrow-sense heritabilities (h2) on scion traits. We used three different randomization tests to highlight traits with consistently significant heritability estimates. This strategy allowed us to capture five traits with significant heritability values that ranged from 0.33 to 0.45 and model fits (R2) that oscillated between 0.58 and 0.74 across orchards. The results showed significance in the rootstock effects for four complex harvest and quality traits (i.e. total number of fruits, number of fruits with exportation quality, and number of fruits discarded because of low weight or thrips damage), while the only morphological trait that had a significant heritability value was overall trunk height (an emergent property of the rootstock-scion interaction). These findings suggest the inheritance of rootstock effects, beyond root phenotype, on a surprisingly wide spectrum of scion traits in ‘Hass’ avocado. They also reinforce the utility of SSR markers for relatedness reconstruction and genetic prediction of complex traits. This research is, up to date, the most cohesive evidence of narrow-sense inheritance of rootstock effects in a tropical fruit tree crop. Ultimately, our work reinforces the importance of considering the rootstock-scion interaction to broaden the genetic basis of fruit tree breeding programs, while enhancing our understanding of the consequences of grafting.

Interactomes embody one of the most effective representations of cellular behavior by revealing function through protein associations. In order to build these models at the organism scale, highthroughput techniques are required to identify interacting pairs of proteins. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate protein-protein interaction networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we describe a statistical framework, designated Y2HSCORES, for analyzing high-throughput Y2H screens that considers key aspects of experimental design, normalization, and controls. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS identified conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeastbased interaction screenings, accelerating the biological interpretation of experimental results. Proof-of-concept was demonstrated by discovery and validation of a novel interaction between the barley powdery mildew effector, AVRA13, with the vesicle-mediated thylakoid membrane biogenesis protein, HvTHF1.

The barley MLA nucleotide-binding leucine-rich-repeat (NLR) receptor and its orthologs confer recognition specificity to many fungal diseases, including powdery mildew, stem- and stripe rust. We used interolog inference to construct a barley protein interactome (HvInt) comprising 66133 edges and 7181 nodes, as a foundation to explore signaling networks associated with MLA. HvInt was compared to the experimentally validated Arabidopsis interactome of 11253 proteins and 73960 interactions, verifying that the two networks share scale-free properties, including a power-law distribution and small-world network. Then, by successive layering of defense-specific ‘omics’ datasets, HvInt was customized to model cellular response to powdery mildew infection. Integration of HvInt with expression quantitative trait loci (eQTL) enabled us to infer disease modules and responses associated with fungal penetration and haustorial development. Next, using HvInt and infection-time-course RNA sequencing of immune signaling mutants, we assembled resistant (R) and susceptible (S) subnetworks. The resulting differentially co-expressed (R-S) interactome is essential to barley immunity, facilitates the flow of signaling pathways and is linked to Mla through trans eQTL associations. Lastly, we anchored HvInt with new and previously identified interactors of the MLA coiled coli (CC) + nucleotide binding (NB) domains and extended these to additional MLA alleles, orthologs and NLR outgroups to predict receptor localization and conservation of signaling response. These results link genomic, transcriptomic, and physical interactions during MLA-specified immunity.