Genome-Wide Divergence and Linkage Disequilibrium Analyses for Capsicum baccatum Revealed by Genome-Anchored Single Nucleotide Polymorphisms

Principal component analysis (PCA) with 36,621 polymorphic genome-anchored single nucleotide polymorphisms (SNPs) identified collectively for Capsicum annuum and Capsicum baccatum was used to show the distribution of these 2 important incompatible cultivated pepper species. Estimated mean nucleotide diversity (π) and Tajima’s D across various chromosomes revealed biased distribution toward negative values on all chromosomes (except for chromosome 4) in cultivated C. baccatum, indicating a population bottleneck during domestication of C. baccatum. In contrast, C. annuum chromosomes showed positive π and Tajima’s D on all chromosomes except chromosome 8, which may be because of domestication at multiple sites contributing to wider genetic diversity. For C. baccatum, 13,129 SNPs were available, with minor allele frequency (MAF) ≥0.05; PCA of the SNPs revealed 283 C. baccatum accessions grouped into 3 distinct clusters, for strong population structure. The fixation index (FST) between domesticated C. annuum and C. baccatum was 0.78, which indicates genome-wide divergence. We conducted extensive linkage disequilibrium (LD) analysis of C. baccatum var. pendulum cultivars on all adjacent SNP pairs within a chromosome to identify regions of high and low LD interspersed with a genome-wide average LD block size of 99.1 kb. We characterized 1742 haplotypes containing 4420 SNPs (range 9–2 SNPs per haplotype). Genome-wide association study of peduncle length, a trait that differentiates wild and domesticated C. baccatum types, revealed 36 genome-wide SNPs significantly associated. Population structure, identity by state (IBS) and LD patterns across the genome will be of potential use for future genome-wide association study of economically important traits in C. baccatum peppers

Critical weather limits for paddy rice under diverse ecosystems of India

Rice yields are largely influenced by variability in weather. Here, we demonstrate the effect of weather variables viz., maximum and minimum temperatures, rainfall, morning and evening relative humidity, bright sunshine hours on the yield of rice cv. Swarna, grown across five rice ecologies of India through field experiments during kharif (wet) season (Jun-Sept.). Critical thresholds of weather elements were identified for achieving above average, average and below average yield for each ecology. The investigation could determine how different weather elements individually and collectively affect rice yield in different rice ecosystems of India. While a sudden increase in minimum temperature by 8-10 °C (> 30 °C) during reproductive period resulted in 40-50 per cent yield reduction at Mohanpur, a sudden decrease (< 20 °C) caused yield decline at Dapoli. The higher yields may be attributed to a significant difference in bright sunshine hours between reproductive phases of above-average and below-average yield years (ranging from 2.8 to 7.8 hours during P5 stages and 1.7 to 5.1 during P4 stages). Rice cultivar Swarna performed differently at various sowing dates in a location as well as across locations (6650 kg ha-1 at Dapoli to 1101 kg ha-1 at Samastipur). It was also found that across all locations, the above average yield could be associated with higher range of maximum temperature compared to that of below average yield. Principal component analysis explained 77 per cent of cumulative variance among the variables at first growth stage, whereas 70 per cent at second growth stage followed by 74 per cent and 66 per cent at subsequent growth stages. We found that coastal locations, in contrast to inland ones, could maximize the yield potential of the cultivar Swarna, due to the longer duration of days between panicle initiation to physiological maturity. We anticipate that the location-specific thresholds of weather factors will encourage rice production techniques that are climate resilient

Metagenomic and Metatranscriptomic Analyses of Diverse Watermelon Cultivars Reveal the Role of Fruit Associated Microbiome in Carbohydrate Metabolism and Ripening of Mature Fruits

The plant microbiome is a key determinant of plant health and productivity, and changes in the plant microbiome can alter the tolerance to biotic and abiotic stresses and the quality of end produce. Little is known about the microbial diversity and its effect on carbohydrate metabolism in ripe fruits. In this study, we aimed to understand the diversity and function of microorganisms in relation to carbohydrate metabolism of ripe watermelon fruits. We used 16S metagenomics and RNAseq metatranscriptomics for analysis of red (PI459074, Congo, and SDRose) and yellow fruit-flesh cultivars (PI227202, PI435990, and JBush) of geographically and metabolically diverse watermelon cultivars. Metagenomics data showed that Proteobacteria were abundant in SDRose and PI227202, whereas Cyanobacteria were most abundant in Congo and PI4559074. In the case of metatranscriptome data, Proteobacteria was the most abundant in all cultivars. High expression of genes linked to infectious diseases and the expression of peptidoglycan hydrolases associated to pathogenicity of eukaryotic hosts was observed in SDRose, which could have resulted in low microbial diversity in this cultivar. The presence of GH28, associated with polygalacturonase activity in JBush and SDRose could be related to cell wall modifications including de-esterification and depolymerization, and consequent loss of galacturonic acid and neutral sugars. Moreover, based on the KEGG annotation of the expressed genes, nine α-galactosidase genes involved in key processes of galactosyl oligosaccharide metabolism, such as raffinose family were identified and galactose metabolism pathway was reconstructed. Results of this study underline the links between the host and fruit-associated microbiome in carbohydrate metabolism of the ripe fruits. The cultivar difference in watermelon reflects the quantum and diversity of the microbiome, which would benefit watermelon and other plant breeders aiming at the holobiont concept to incorporate associated microbiomes in breeding programs

Genome-Wide Differentiation of Various Melon Horticultural Groups for Use in GWAS for Fruit Firmness and Construction of a High Resolution Genetic Map

Ajuts: Funding support is provided by Gus R. Douglass Institute (Evans Allen Project to Nimmakayala) and USDA-NIFA (2010-02247 and 2012-02511).Melon (Cucumis melo L.) is a phenotypically diverse eudicot diploid (2n = 2x = 24) has climacteric and non-climacteric morphotypes and show wide variation for fruit firmness, an important trait for transportation and shelf life. We generated 13,789 SNP markers using genotyping-by-sequencing (GBS) and anchored them to chromosomes to understand genome-wide fixation indices (Fst) between various melon morphotypes and genomewide linkage disequilibrium (LD) decay. The FST between accessions of cantalupensis and inodorus was 0.23. The FST between cantalupensis and various agrestis accessions was in a range of 0.19-0.53 and between inodorus and agrestis accessions was in a range of 0.21-0.59 indicating sporadic to wide ranging introgression. The EM (Expectation Maximization) algorithm was used for estimation of 1436 haplotypes. Average genome-wide LD decay for the melon genome was noted to be 9.27 Kb. In the current research, we focused on the genome-wide divergence underlying diverse melon horticultural groups. A high-resolution genetic map with 7153 loci was constructed. Genome-wide segregation distortion and recombination rate across various chromosomes were characterized. Melon has climacteric and non-climacteric morphotypes and wide variation for fruit firmness, a very important trait for transportation and shelf life. Various levels of QTLs were identified with high to moderate stringency and linked to fruit firmness using both genome-wide association study (GWAS) and biparental mapping. Gene annotation revealed some of the SNPs are located in β-D-xylosidase, glyoxysomal malate synthase, chloroplastic anthranilate phosphoribosyltransferase, and histidine kinase, the genes that were previously characterized for fruit ripening and softening in other crops

Identification of Gene-Specific Polymorphisms and Association with Capsaicin Pathway Metabolites in <i>Capsicum annuum L.</i> Collections

<div><p>Pepper (<i>Capsicum annuum L.</i>) is an economically important crop with added nutritional value. Production of capsaicin is an important quantitative trait with high environmental variance, so the development of markers regulating capsaicinoid accumulation is important for pepper breeding programs. In this study, we performed association mapping at the gene level to identify single nucleotide polymorphisms (SNPs) associated with capsaicin pathway metabolites in a diverse <i>Capsicum annuum</i> collection during two seasons. The genes <i>Pun1</i>, <i>CCR, KAS</i> and <i>HCT</i> were sequenced and matched with the whole-genome sequence draft of pepper to identify SNP locations and for further characterization. The identified SNPs for each gene underwent candidate gene association mapping. Association mapping results revealed <i>Pun1</i> as a key regulator of major metabolites in the capsaicin pathway mainly affecting capsaicinoids and precursors for acyl moieties of capsaicinoids. Six different SNPs in the promoter sequence of <i>Pun1</i> were found associated with capsaicin in plants from both seasons. Our results support that <i>CCR</i> is an important control point for the flux of p-coumaric acid to specific biosynthesis pathways. <i>KAS</i> was found to regulate the major precursors for acyl moieties of capsaicinoids and may play a key role in capsaicinoid production. Candidate gene association mapping of <i>Pun1</i> suggested that the accumulation of capsaicinoids depends on the expression of <i>Pun1</i>, as revealed by the most important associated SNPs found in the promoter region of <i>Pun1</i>.</p></div

Principal component analysis of transformed concentrations of capsaicin pathway metabolites.

<p>Accessions are labeled by their <i>pun</i>gency level (EV  =  eigenvalue).</p