Mini Review: Potential Applications of Non-host Resistance for Crop Improvement

Plant breeding for disease resistance is crucial to sustain global crop production. For decades, plant breeders and researchers have extensively used host plant resistance genes (R-genes) to develop disease resistant cultivars. However, the general instability of R-genes in crop cultivars when challenged with diverse pathogen populations emphasizes the need for more stable means of resistance. Alternatively, nonhost resistance is recognized as the most durable, broad-spectrum form of resistance against the majority of potential pathogens in plants and has gained great attention as an alternative target for managing resistance. While transgenic approaches have been utilized to transfer nonhost resistance to host species, conventional breeding applications have been more elusive. Nevertheless, avenues for discovery and deployment of genetic loci for nonhost resistance via hybridization are increasingly abundant, particularly when transferring genes among closely related species. In this mini review, we discuss current and developing applications of nonhost resistance for crop improvement with a focus on the overlap between host and nonhost mechanisms and the potential impacts of new technology

Exploring deep learning for complex trait genomic prediction in polyploid outcrossing species

Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks. A potential advantage of DL for GP over standard linear model methods is that DL can potentially take into account all genetic interactions, including dominance and epistasis, which are expected to be of special relevance in most polyploids. In this study, we evaluated the predictive accuracy of linear and DL techniques in two important small fruits or berries: strawberry and blueberry. The two datasets contained a total of 1,358 allopolyploid strawberry (2n=8x=112) and 1,802 autopolyploid blueberry (2n=4x=48) individuals, genotyped for 9,908 and 73,045 single nucleotide polymorphism (SNP) markers, respectively, and phenotyped for five agronomic traits each. DL depends on numerous parameters that influence performance and optimizing hyperparameter values can be a critical step. Here we show that interactions between hyperparameter combinations should be expected and that the number of convolutional filters and regularization in the first layers can have an important effect on model performance. In terms of genomic prediction, we did not find an advantage of DL over linear model methods, except when the epistasis component was important. Linear Bayesian models were better than convolutional neural networks for the full additive architecture, whereas the opposite was observed under strong epistasis. However, by using a parameterization capable of taking into account these non-linear effects, Bayesian linear models can match or exceed the predictive accuracy of DL. A semiautomatic implementation of the DL pipeline is available at https://github.com/lauzingaretti/deepGP/

Exploring deep learning for complex trait genomic prediction in polyploid outcrossing species

Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks. A potential advantage of DL for GP over standard linear model methods is that DL can potentially take into account all genetic interactions, including dominance and epistasis, which are expected to be of special relevance in most polyploids. In this study, we evaluated the predictive accuracy of linear and DL techniques in two important small fruits or berries: strawberry and blueberry. The two datasets contained a total of 1,358 allopolyploid strawberry (2n=8x=112) and 1,802 autopolyploid blueberry (2n=4x=48) individuals, genotyped for 9,908 and 73,045 single nucleotide polymorphism (SNP) markers, respectively, and phenotyped for five agronomic traits each. DL depends on numerous parameters that influence performance and optimizing hyperparameter values can be a critical step. Here we show that interactions between hyperparameter combinations should be expected and that the number of convolutional filters and regularization in the first layers can have an important effect on model performance. In terms of genomic prediction, we did not find an advantage of DL over linear model methods, except when the epistasis component was important. Linear Bayesian models were better than convolutional neural networks for the full additive architecture, whereas the opposite was observed under strong epistasis. However, by using a parameterization capable of taking into account these non-linear effects, Bayesian linear models can match or exceed the predictive accuracy of DL. A semiautomatic implementation of the DL pipeline is available at https://github.com/lauzingaretti/deepGP/.info:eu-repo/semantics/publishedVersio

A roadmap for research in octoploid strawberry

The cultivated strawberry (Fragaria × ananassa) is an allo-octoploid species, originating nearly 300 years ago from wild progenitors from the Americas. Since that time the strawberry has become the most widely cultivated fruit crop in the world, universally appealing due to its sensory qualities and health benefits. The recent publication of the first high-quality chromosome-scale octoploid strawberry genome (cv. Camarosa) is enabling rapid advances in genetics, stimulating scientific debate and provoking new research questions. In this forward-looking review we propose avenues of research toward new biological insights and applications to agriculture. Among these are the origins of the genome, characterization of genetic variants, and big data approaches to breeding. Key areas of research in molecular biology will include the control of flowering, fruit development, fruit quality, and plant–pathogen interactions. In order to realize this potential as a global community, investments in genome resources must be continually augmented.Peer reviewe

Allelic Variation of MYB10 Is the Major Force Controlling Natural Variation in Skin and Flesh Color in Strawberry (Fragaria spp.) Fruit

Independent mutations in the transcription factor MYB10 cause most of the anthocyanin variation observed in diploid woodland strawberry (Fragaria vesca) and octoploid cultivated strawberry (Fragaria x ananassa). The fruits of diploid and octoploid strawberry (Fragaria spp) show substantial natural variation in color due to distinct anthocyanin accumulation and distribution patterns. Anthocyanin biosynthesis is controlled by a clade of R2R3 MYB transcription factors, among which MYB10 is the main activator in strawberry fruit. Here, we show that mutations in MYB10 cause most of the variation in anthocyanin accumulation and distribution observed in diploid woodland strawberry (F. vesca) and octoploid cultivated strawberry (F. xananassa). Using a mapping-by-sequencing approach, we identified a gypsy-transposon in MYB10 that truncates the protein and knocks out anthocyanin biosynthesis in a white-fruited F. vesca ecotype. Two additional loss-of-function mutations in MYB10 were identified among geographically diverse white-fruited F. vesca ecotypes. Genetic and transcriptomic analyses of octoploid Fragaria spp revealed that FaMYB10-2, one of three MYB10 homoeologs identified, regulates anthocyanin biosynthesis in developing fruit. Furthermore, independent mutations in MYB10-2 are the underlying cause of natural variation in fruit skin and flesh color in octoploid strawberry. We identified a CACTA-like transposon (FaEnSpm-2) insertion in the MYB10-2 promoter of red-fleshed accessions that was associated with enhanced expression. Our findings suggest that cis-regulatory elements in FaEnSpm-2 are responsible for enhanced MYB10-2 expression and anthocyanin biosynthesis in strawberry fruit flesh.Peer reviewe

Allelic Variation of MYB10 is the Major Force Controlling Natural Variation of Skin and Flesh Color in Strawberry (Fragaria spp.) fruit

Anthocyanins are the principal color-producing compounds synthesized in developing fruits of strawberry (Fragaria spp.). Substantial natural variation in color have been observed in fruits of diploid and octoploid accessions, resulting from distinct accumulation and distribution of anthocyanins in fruits. Anthocyanin biosynthesis is controlled by a clade of R2R3 MYB transcription factors, among which MYB10 has been shown as the main activator in strawberry fruit. Here, we show that MYB10 mutations cause most of the anthocyanin variation observed in diploid woodland strawberry (F. vesca) and octoploid cultivated strawberry (F. ×ananassa). Using a mapping-by-sequencing approach, we identified a gypsytransposon insertion in MYB10 that truncates the protein and knocks out anthocyanin biosynthesis in a white-fruited F. vesca ecotype. Two additional lossof-function MYB10 mutations were identified among geographically diverse whitefruited F. vesca ecotypes. Genetic and transcriptomic analyses in octoploid Fragaria spp. revealed that FaMYB10-2, one of three MYB10 homoeologs identified, residing in the F. iinumae-derived subgenome, regulates the biosynthesis of anthocyanins in developing fruit. Furthermore, independent mutations in MYB10-2 are the underlying cause of natural variation in fruit skin and flesh color in octoploid strawberry. We identified a CACTA-like transposon (FaEnSpm-2) insertion in the MYB10-2 promoter of red-fleshed accessions that was associated with enhanced expression and anthocyanin accumulation. Our findings suggest that putative cis regulatory elements provided by FaEnSpm-2 are required for high and ectopic MYB10-2 expression and induction of anthocyanin biosynthesis in fruit flesh. We developed MYB10-2 (sub-genome) specific DNA markers for marker-assisted selection that accurately predicted anthocyanin phenotypes in octoploid segregating populations

Hereditary sensory neuropathy type I

Hereditary sensory neuropathy type I (HSN I) is a slowly progressive neurological disorder characterised by prominent predominantly distal sensory loss, autonomic disturbances, autosomal dominant inheritance, and juvenile or adulthood disease onset. The exact prevalence is unknown, but is estimated as very low. Disease onset varies between the 2nd and 5th decade of life. The main clinical feature of HSN I is the reduction of sensation sense mainly distributed to the distal parts of the upper and lower limbs. Variable distal muscle weakness and wasting, and chronic skin ulcers are characteristic. Autonomic features (usually sweating disturbances) are invariably observed. Serious and common complications are spontaneous fractures, osteomyelitis and necrosis, as well as neuropathic arthropathy which may even necessitate amputations. Some patients suffer from severe pain attacks. Hypacusis or deafness, or cough and gastrooesophageal reflux have been observed in rare cases. HSN I is a genetically heterogenous condition with three loci and mutations in two genes (SPTLC1 and RAB7) identified so far. Diagnosis is based on the clinical observation and is supported by a family history. Nerve conduction studies confirm a sensory and motor neuropathy predominantly affecting the lower limbs. Radiological studies, including magnetic resonance imaging, are useful when bone infections or necrosis are suspected. Definitive diagnosis is based on the detection of mutations by direct sequencing of the SPTLC1 and RAB7 genes. Correct clinical assessment and genetic confirmation of the diagnosis are important for appropriate genetic counselling and prognosis. Differential diagnosis includes the other hereditary sensory and autonomic neuropathies (HSAN), especially HSAN II, as well as diabetic foot syndrome, alcoholic neuropathy, neuropathies caused by other neurotoxins/drugs, immune mediated neuropathy, amyloidosis, spinal cord diseases, tabes dorsalis, lepra neuropathy, or decaying skin tumours like amelanotic melanoma. Management of HSN I follows the guidelines given for diabetic foot care (removal of pressure to the ulcer and eradication of infection, followed by the use of specific protective footwear) and starts with early and accurate counselling of patients about risk factors for developing foot ulcerations. The disorder is slowly progressive and does not influence life expectancy but is often severely disabling after a long duration of the disease

Two Genes on A/J Chromosome 18 Are Associated with Susceptibility to Staphylococcus aureus Infection by Combined Microarray and QTL Analyses

Although it has recently been shown that A/J mice are highly susceptible to Staphylococcus aureus sepsis as compared to C57BL/6J, the specific genes responsible for this differential phenotype are unknown. Using chromosome substitution strains (CSS), we found that loci on chromosomes 8, 11, and 18 influence susceptibility to S. aureus sepsis in A/J mice. We then used two candidate gene selection strategies to identify genes on these three chromosomes associated with S. aureus susceptibility, and targeted genes identified by both gene selection strategies. First, we used whole genome transcription profiling to identify 191 (56 on chr. 8, 100 on chr. 11, and 35 on chr. 18) genes on our three chromosomes of interest that are differentially expressed between S. aureus-infected A/J and C57BL/6J. Second, we identified two significant quantitative trait loci (QTL) for survival post-infection on chr. 18 using N2 backcross mice (F1 [C18A]×C57BL/6J). Ten genes on chr. 18 (March3, Cep120, Chmp1b, Dcp2, Dtwd2, Isoc1, Lman1, Spire1, Tnfaip8, and Seh1l) mapped to the two significant QTL regions and were also identified by the expression array selection strategy. Using real-time PCR, 6 of these 10 genes (Chmp1b, Dtwd2, Isoc1, Lman1, Tnfaip8, and Seh1l) showed significantly different expression levels between S. aureus-infected A/J and C57BL/6J. For two (Tnfaip8 and Seh1l) of these 6 genes, siRNA-mediated knockdown of gene expression in S. aureus–challenged RAW264.7 macrophages induced significant changes in the cytokine response (IL-1 β and GM-CSF) compared to negative controls. These cytokine response changes were consistent with those seen in S. aureus-challenged peritoneal macrophages from CSS 18 mice (which contain A/J chromosome 18 but are otherwise C57BL/6J), but not C57BL/6J mice. These findings suggest that two genes, Tnfaip8 and Seh1l, may contribute to susceptibility to S. aureus in A/J mice, and represent promising candidates for human genetic susceptibility studies

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.