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Pine wilt disease (PWD) is a worldwide forest disease caused by pine wood nematode (PWN). In this article, we investigated the composition, organization, correlation, and function of the endophytic microbial community in Pinus massoniana field with and without PWN. Samples were taken from branches, upper, middle, and lower trunks, as well as soil, from both healthy and infected trees. The results showed that the fungal diversity of healthy pines is around 1.1 times that of infected pines, while the bacterial diversity is about 0.75 times that of infected pines at the OTUs level. An increase of the abundance of pathogenic fungus such as Saitozyma, Graphilbum, Diplodia, Candida, Pseudoxanthomonas, Dyella and Pantoea was witnessed in infected pines according to the result of LEfSe. Furthermore, Ophiostoma and saprophytic fungus such as Entomocorticium, ganoderma, tomentella, entomocorticium were exclusively prominent in infected pines, which were substantially and highly connected with other species (p < 0.05), indicating the trees' vulnerability and making the wood blue. In healthy pines, the top three functional guilds are parasites, plant pathogens, and saprotrophs. Parasites (36.52%) are primarily found in the branches, plant pathogens (29.12%) are primarily found in the lower trunk, and saprotrophs (67.88%) are primarily found in the upper trunk of disease trees. Pines' immunity is being eroded due to an increase in the quantity and types of diseases. PICRUSt2 research revealed that NADH or NADPH, as well as carbon-nitrogen bonds, were more abundant in healthy pines, but acid anhydrides and transferring phosphorus-containing groups were more abundant in infected pines. The shift in resin secretion lowers the tree's potential and encourages pine wilt and mortality. In total, PWN may have disrupted the microbiological ecology and worked with the community to hasten the demise of pines. [ABSTRACT FROM AUTHOR]

Background: Prairie grass (Bromus catharticus) is a typical cool-season forage crop with high biomass production and fast growth rate during winter and spring. However, its genetic research and breeding has remained stagnant due to limited available genomic resources. The aim of this study was to generate large-scale genomic data using high-throughput transcriptome sequencing, and perform a preliminary validation of EST-SSR markers of B. catharticus. Results: Eleven tissue samples including seeds, leaves, and stems were collected from a new high-yield strain of prairie grass BCS1103. A total of 257,773 unigenes were obtained, of which 193,082 (74.90%) were annotated. Comparison analysis between tissues identified 1803, 3030, and 1570 genes specifically and highly expressed in seed, leaf, and stem, respectively. A total of 37,288 EST-SSRs were identified from unigene sequences, and more than 80,000 primer pairs were designed. We synthesized 420 primer pairs and selected 52 ones with high polymorphisms to estimate genetic diversity and population structure in 24 B. catharticus accessions worldwide. Despite low diversity indicated by an average genetic distance of 0.364, the accessions from South America and Asia and wild accessions showed higher genetic diversity. Moreover, South American accessions showed a pure ancestry, while Asian accessions demonstrated mixed internal relationships, which indicated a different probability of gene flow. Phylogenetic analysis clustered the studied accessions into four clades, being consistent with phenotypic clustering results. Finally, Mantel analysis suggested the total phenotypic variation was mostly contributed by genetic component. Stem diameter, plant height, leaf width, and biomass yield were significantly correlated with genetic data (r > 0.6, P < 0.001), and might be used in the future selection and breeding. Conclusion: A genomic resource was generated that could benefit genetic and taxonomic studies, as well as molecular breeding for B. catharticus and its relatives in the future. [ABSTRACT FROM AUTHOR]

Background: Monochamus alternatus Hope is one of the insect vectors of pinewood nematode (Bursaphelenchus xylophilus), which causes the destructive pine wilt disease. The microorganisms within the ecosystem, comprising plants, their environment, and insect vectors, form complex networks. This study presents a systematic analysis of the bacterial microbiota in the M. alternatus midgut and its habitat niche. Methods: Total DNA was extracted from 20 types of samples (with three replicates each) from M. alternatus and various tissues of healthy and infected P. massoniana (pines). 16S rDNA amplicon sequencing was conducted to determine the composition and diversity of the bacterial microbiota in each sample. Moreover, the relative abundances of bacteria in the midgut of M. alternatus larvae were verified by counting the colony-forming units. Results: Pinewood nematode infection increased the microbial diversity in pines. Bradyrhizobium, Burkholderia, Dyella, Mycobacterium, and Mucilaginibacter were the dominant bacterial genera in the soil and infected pines. These results indicate that the bacterial community in infected pines may be associated with the soil microbiota. Interestingly, the abundance of the genus Gryllotalpicola was highest in the bark of infected pines. The genus Cellulomonas was not found in the midgut of M. alternatus, but it peaked in the phloem of infected pines, followed by the phloem of heathy pines. Moreover, the genus Serratia was not only present in the habitat niche, but it was also enriched in the M. alternatus midgut. The colony-forming unit assays showed that the relative abundance of Serratia sp. peaked in the midgut of instar II larvae (81%). Conclusions: Overall, the results indicate that the bacterial microbiota in the soil and in infected pines are correlated. The Gryllotalpicola sp. and Cellulomonas sp. are potential microbial markers of pine wilt disease. Additionally, Serratia sp. could be an ideal agent for expressing insecticidal protein in the insect midgut by genetic engineering, which represents a new use of microbes to control M. alternatus. [ABSTRACT FROM AUTHOR]

Background: Narrow genetic base, complex allo-tetraploid genome and presence of repetitive elements have led the discovery of single nucleotide polymorphisms (SNPs) in Brassica juncea (AABB; 2n = 4x = 36) at a slower pace. Double digest RAD (ddRAD) - a genome complexity reduction technique followed by NGS was used to generate a total of 23 million paired-end reads from three genotypes each of Indian (Pusa Tarak, RSPR-01 and Urvashi) and Exotic (Donskaja IV, Zem 1 and EC287711) genepools. Results: Sequence data analysis led to the identification of 10,399 SNPs in six genotypes at a read depth of 10x coverage among the genotypes of two genepools. A total of 44 hyper-variable regions (nucleotide variation hotspots) were also found in the genome, of which 93% were found to be a part of coding genes/regions. The functionality of the identified SNPs was estimated by genotyping a subset of SNPs on MassARRAY® platform among a diverse set of B. juncea genotypes. SNP genotyping-based genetic diversity and population studies placed the genotypes into two distinct clusters based mostly on the place of origin. The genotypes were also characterized for six morphological traits, analysis of which revealed a significant difference in the mean values between Indian and Exotic genepools for six traits. The association analysis for six traits identified a total of 45 significant marker-trait associations on 11 chromosomes of A- and B- group of progenitor genomes. Conclusions: Despite narrow diversity, the ddRAD sequencing was able to identify large number of nucleotide polymorphisms between the two genepools. Association analysis led to the identification of common SNPs/genomic regions associated between flowering and maturity traits, thereby underscoring the possible role of common chromosomal regions-harboring genes controlling flowering and maturity in Brassica juncea. [ABSTRACT FROM AUTHOR]

Background: To increase the accuracy of microbiome data analysis, solving the technical limitations of the existing sequencing machines is required. Quality trimming is suggested to reduce the effect of the progressive decrease in sequencing quality with the increased length of the sequenced library. In this study, we examined the effect of the trimming thresholds (0–20 for QIIME1 and 0–30 for QIIME2) on the number of reads that remained after the quality control and chimera removal (the good reads). We also examined the distance of the analysis results to the gold standard using simulated samples. Results: Quality trimming increased the number of good reads and abundance measurement accuracy in Illumina paired-end reads of the V3-V4 hypervariable region. Conclusions: Our results suggest that the pre-analysis trimming step should be included before the application of QIIME1 or QIIME2. [ABSTRACT FROM AUTHOR]

Background: Common bean is a legume of social and nutritional importance as a food crop, cultivated worldwide especially in developing countries, accounting for an important source of income for small farmers. The availability of the complete sequences of the two common bean genomes has dramatically accelerated and has enabled new experimental strategies to be applied for genetic research. DArTseq has been widely used as a method of SNP genotyping allowing comprehensive genome coverage with genetic applications in common bean breeding programs. Results: Using this technology, 6286 SNPs (1 SNP/86.5 Kbp) were genotyped in genic (43.3%) and non-genic regions (56. 7%). Genetic subdivision associated to the common bean gene pools (K = 2) and related to grain types (K = 3 and K = 5) were reported. A total of 83% and 91% of all SNPs were polymorphic within the Andean and Mesoamerican gene pools, respectively, and 26% were able to differentiate the gene pools. Genetic diversity analysis revealed an average HE of 0.442 for the whole collection, 0.102 for Andean and 0.168 for Mesoamerican gene pools (FST = 0.747 between gene pools), 0. 440 for the group of cultivars and lines, and 0.448 for the group of landrace accessions (FST = 0.002 between cultivar/line and landrace groups). The SNP effects were predicted with predominance of impact on non-coding regions (77.8%). SNPs under selection were identified within gene pools comparing landrace and cultivar/line germplasm groups (Andean: 18; Mesoamerican: 69) and between the gene pools (59 SNPs), predominantly on chromosomes 1 and 9. The LD extension estimate corrected for population structure and relatedness (r2SV) was~88 kbp, while for the Andean gene pool was ~395 kbp, and for the Mesoamerican was ~ 130 kbp. Conclusions: For common bean, DArTseq provides an efficient and cost-effective strategy of generating SNPs for large-scale genome-wide studies. The DArTseq resulted in an operational panel of 560 polymorphic SNPs in linkage equilibrium, providing high genome coverage. This SNP set could be used in genotyping platforms with many applications, such as population genetics, phylogeny relation between common bean varieties and support to molecular breeding approaches. [ABSTRACT FROM AUTHOR]

Background: Cotton germplasm resources contain beneficial alleles that can be exploited to develop germplasm adapted to emerging environmental and climate conditions. Accessions and lines have traditionally been characterized based on phenotypes, but phenotypic profiles are limited by the cost, time, and space required to make visual observations and measurements. With advances in molecular genetic methods, genotypic profiles are increasingly able to identify differences among accessions due to the larger number of genetic markers that can be measured. A combination of both methods would greatly enhance our ability to characterize germplasm resources. Recent efforts have culminated in the identification of sufficient SNP markers to establish high-throughput genotyping systems, such as the CottonSNP63K array, which enables a researcher to efficiently analyze large numbers of SNP markers and obtain highly repeatable results. In the current investigation, we have utilized the SNP array for analyzing genetic diversity primarily among cotton cultivars, making comparisons to SSR-based phylogenetic analyses, and identifying loci associated with seed nutritional traits. Results: The SNP markers distinctly separated G. hirsutum from other Gossypium species and distinguished the wild from cultivated types of G. hirsutum. The markers also efficiently discerned differences among cultivars, which was the primary goal when designing the CottonSNP63K array. Population structure within the genus compared favorably with previous results obtained using SSR markers, and an association study identified loci linked to factors that affect cottonseed protein content. Conclusions: Our results provide a large genome-wide variation data set for primarily cultivated cotton. Thousands of SNPs in representative cotton genotypes provide an opportunity to finely discriminate among cultivated cotton from around the world. The SNPs will be relevant as dense markers of genome variation for association mapping approaches aimed at correlating molecular polymorphisms with variation in phenotypic traits, as well as for molecular breeding approaches in cotton. [ABSTRACT FROM AUTHOR]