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Climate change has exacerbated the effects of abiotic stresses on plant growth and productivity. Drought is one of the most important abiotic stress factors that interfere with plant growth and development. Plant selection and breeding as well as genetic engineering methods used to improve crop drought tolerance are expensive and time consuming. Plants use a myriad of adaptative mechanisms to cope with the adverse effects of drought stress including the association with beneficial microorganisms such as plant growth promoting rhizobacteria (PGPR). Inoculation of plant roots with different PGPR species has been shown to promote drought tolerance through a variety of interconnected physiological, biochemical, molecular, nutritional, metabolic, and cellular processes, which include enhanced plant growth, root elongation, phytohormone production or inhibition, and production of volatile organic compounds. Therefore, plant colonization by PGPR is an eco-friendly agricultural method to improve plant growth and productivity. Notably, the processes regulated and enhanced by PGPR can promote plant growth as well as enhance drought tolerance. This review addresses the current knowledge on how drought stress affects plant growth and development and describes how PGPR can trigger plant drought stress responses at the physiological, morphological, and molecular levels. Highlight: This review aims to highlight the recent advances in understanding the effects of plant growth-promoting rhizobacteria in enhancing plant growth and drought stress tolerance. [ABSTRACT FROM AUTHOR]

Background: Population structure and cryptic relatedness between individuals (samples) are two major factors affecting false positives in genome-wide association studies (GWAS). In addition, population stratification and genetic relatedness in genomic selection in animal and plant breeding can affect prediction accuracy. The methods commonly used for solving these problems are principal component analysis (to adjust for population stratification) and marker-based kinship estimates (to correct for the confounding effects of genetic relatedness). Currently, many tools and software are available that analyze genetic variation among individuals to determine population structure and genetic relationships. However, none of these tools or pipelines perform such analyses in a single workflow and visualize all the various results in a single interactive web application. Results: We developed PSReliP, a standalone, freely available pipeline for the analysis and visualization of population structure and relatedness between individuals in a user-specified genetic variant dataset. The analysis stage of PSReliP is responsible for executing all steps of data filtering and analysis and contains an ordered sequence of commands from PLINK, a whole-genome association analysis toolset, along with in-house shell scripts and Perl programs that support data pipelining. The visualization stage is provided by Shiny apps, an R-based interactive web application. In this study, we describe the characteristics and features of PSReliP and demonstrate how it can be applied to real genome-wide genetic variant data. Conclusions: The PSReliP pipeline allows users to quickly analyze genetic variants such as single nucleotide polymorphisms and small insertions or deletions at the genome level to estimate population structure and cryptic relatedness using PLINK software and to visualize the analysis results in interactive tables, plots, and charts using Shiny technology. The analysis and assessment of population stratification and genetic relatedness can aid in choosing an appropriate approach for the statistical analysis of GWAS data and predictions in genomic selection. The various outputs from PLINK can be used for further downstream analysis. The code and manual for PSReliP are available at https://github.com/solelena/PSReliP. [ABSTRACT FROM AUTHOR]

Background: Despite continuing technological advances, the cost for large-scale genotyping of a high number of samples can be prohibitive. The purpose of this study is to design a cost-saving strategy for SNP genotyping. We suggest making use of pooling, a group testing technique, to drop the amount of SNP arrays needed. We believe that this will be of the greatest importance for non-model organisms with more limited resources in terms of cost-efficient large-scale chips and high-quality reference genomes, such as application in wildlife monitoring, plant and animal breeding, but it is in essence species-agnostic. The proposed approach consists in grouping and mixing individual DNA samples into pools before testing these pools on bead-chips, such that the number of pools is less than the number of individual samples. We present a statistical estimation algorithm, based on the pooling outcomes, for inferring marker-wise the most likely genotype of every sample in each pool. Finally, we input these estimated genotypes into existing imputation algorithms. We compare the imputation performance from pooled data with the Beagle algorithm, and a local likelihood-aware phasing algorithm closely modeled on MaCH that we implemented. Results: We conduct simulations based on human data from the 1000 Genomes Project, to aid comparison with other imputation studies. Based on the simulated data, we find that pooling impacts the genotype frequencies of the directly identifiable markers, without imputation. We also demonstrate how a combinatorial estimation of the genotype probabilities from the pooling design can improve the prediction performance of imputation models. Our algorithm achieves 93% concordance in predicting unassayed markers from pooled data, thus it outperforms the Beagle imputation model which reaches 80% concordance. We observe that the pooling design gives higher concordance for the rare variants than traditional low-density to high-density imputation commonly used for cost-effective genotyping of large cohorts. Conclusions: We present promising results for combining a pooling scheme for SNP genotyping with computational genotype imputation on human data. These results could find potential applications in any context where the genotyping costs form a limiting factor on the study size, such as in marker-assisted selection in plant breeding. [ABSTRACT FROM AUTHOR]

Background: Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3'end mRNA sequencing of different lengths as predictors. Results: The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone. Conclusions: The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3'end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs. [ABSTRACT FROM AUTHOR]

Background: Multiplex CRISPR-Cas9-based genome editing is an efficient method for targeted disruption of gene function in plants. Use of CRISPR-Cas9 has increased rapidly in recent years and is becoming a routine method for generating single and higher order Arabidopsis thaliana mutants. Low entry, reliable assembly of CRISPR/Cas9 vectors and efficient mutagenesis is necessary to enable a maximum of researchers to break through the genetic redundancy within plant multi-gene families and allow for a plethora of gene function studies that have been previously unachievable. It will also allow routine de novo generation of mutations in ever more complex genetic backgrounds that make introgression of pre-existing alleles highly cumbersome. Results: To facilitate rapid and efficient use of CRISPR/Cas9 for Arabidopsis research, we developed a CRISPR/Cas9-based toolbox for generating mutations at multiple genomic loci, using two-color fluorescent seed selection. In our system, up-to eight gRNAs can be routinely introduced into a binary vector carrying either a FastRed, FastGreen or FastCyan fluorescent seed selection cassette. FastRed and FastGreen binary vectors can be co-transformed as a cocktail via floral dip to introduce sixteen gRNAs at the same time. The seeds can be screened either for red or green fluorescence, or for the presence of both colors. Importantly, in the second generation after transformation, Cas9 free plants are identified simply by screening the non-fluorescent seeds. Our collection of binary vectors allows to choose between two widely-used promoters to drive Cas enzymes, either the egg cell-specific (pEC1.2) from A. thaliana or the constitutive promoter from Petroselinum crispum (PcUBi4-2). Available enzymes are "classical" Cas9 codon-optimized for A. thaliana and a recently reported, intron-containing version of Cas9 codon-optimized for Zea mays, zCas9i. We observed the highest efficiency in producing knockout phenotypes by using intron-containing zCas9i driven under egg-cell specific pEC1.2 promoter. Finally, we introduced convenient restriction sites flanking promoter, Cas9 and fluorescent selection cassette in some of the T-DNA vectors, thus allowing straightforward swapping of all three elements for further adaptation and improvement of the system. Conclusion: A rapid, simple and flexible CISPR/Cas9 cloning system was established that allows assembly of multi-guide RNA constructs in a robust and reproducible fashion, by avoiding generation of very big constructs. The system enables a flexible, fast and efficient screening of single or higher order A. thaliana mutants. [ABSTRACT FROM AUTHOR]

Our civilization has to enhance food production to feed world's expected population of 9.7 billion by 2050. These food demands can be met by implementation of innovative technologies in agriculture. This transformative agricultural concept, also known as digital farming, aims to maximize the crop yield without an increase in the field footprint while simultaneously minimizing environmental impact of farming. There is a growing body of evidence that Raman spectroscopy, a non-invasive, non-destructive, and laser-based analytical approach, can be used to: (i) detect plant diseases, (ii) abiotic stresses, and (iii) enable label-free phenotyping and digital selection of plants in breeding programs. In this review, we critically discuss the most recent reports on the use of Raman spectroscopy for confirmatory identification of plant species and their varieties, as well as Raman-based analysis of the nutrition value of seeds. We show that high selectivity and specificity of Raman makes this technique ideal for optical surveillance of fields, which can be used to improve agriculture around the world. We also discuss potential advances in synergetic use of RS and already established imaging and molecular techniques. This combinatorial approach can be used to reduce associated time and cost, as well as enhance the accuracy of diagnostics of biotic and abiotic stresses. [ABSTRACT FROM AUTHOR]

Background: Rice leaf blight, which is a devastating disease worldwide, is caused by the bacterium Xanthomonas oryzae pv. oryzae (Xoo). The upregulated by transcription activator-like 1 (UPT) effector box in the promoter region of the rice Xa13 gene plays a key role in Xoo pathogenicity. Mutation of a key bacterial protein-binding site in the UPT box of Xa13 to abolish PXO99-induced Xa13 expression is a way to improve rice resistance to bacteria. Highly efficient generation and selection of transgene-free edited plants are helpful to shorten and simplify the gene editing-based breeding process. Selective elimination of transgenic pollen of T0 plants can enrich the proportion of T1 transgene-free offspring, and expression of a color marker gene in seeds makes the selection of T2 plants very convenient and efficient. In this study, a genome editing and multiplexed selection system was used to generate bacterial leaf blight-resistant and transgene-free rice plants. Results: We introduced site-specific mutations into the UPT box using CRISPR/Cas12a technology to hamper with transcription-activator-like effector (TAL) protein binding and gene activation and generated genome-edited rice with improved bacterial blight resistance. Transgenic pollen of T0 plants was eliminated by pollen-specific expression of the α-amylase gene Zmaa1, and the proportion of transgene-free plants increased from 25 to 50% among single T-DNA insertion events in the T1 generation. Transgenic seeds were visually identified and discarded by specific aleuronic expression of DsRed, which reduced the cost by 50% and led to up to 98.64% accuracy for the selection of transgene-free edited plants. Conclusion: We demonstrated that core nucleotide deletion in the UPT box of the Xa13 promoter conferred resistance to rice blight, and selection of transgene-free plants was boosted by introducing multiplexed selection. The combination of genome editing and transgene-free selection is an efficient strategy to accelerate functional genomic research and plant breeding. [ABSTRACT FROM AUTHOR]

Background: The main research topic in this paper is how to compare multiple biological experiments using transcriptome data, where each experiment is measured and designed to compare control and treated samples. Comparison of multiple biological experiments is usually performed in terms of the number of DEGs in an arbitrary combination of biological experiments. This process is usually facilitated with Venn diagram but there are several issues when Venn diagram is used to compare and analyze multiple experiments in terms of DEGs. First, current Venn diagram tools do not provide systematic analysis to prioritize genes. Because that current tools generally do not fully focus to prioritize genes, genes that are located in the segments in the Venn diagram (especially, intersection) is usually difficult to rank. Second, elucidating the phenotypic difference only with the lists of DEGs and expression values is challenging when the experimental designs have the combination of treatments. Experiment designs that aim to find the synergistic effect of the combination of treatments are very difficult to find without an informative system. Results: We introduce Venn-diaNet, a Venn diagram based analysis framework that uses network propagation upon protein-protein interaction network to prioritizes genes from experiments that have multiple DEG lists. We suggest that the two issues can be effectively handled by ranking or prioritizing genes with segments of a Venn diagram. The user can easily compare multiple DEG lists with gene rankings, which is easy to understand and also can be coupled with additional analysis for their purposes. Our system provides a web-based interface to select seed genes in any of areas in a Venn diagram and then perform network propagation analysis to measure the influence of the selected seed genes in terms of ranked list of DEGs. Conclusions: We suggest that our system can logically guide to select seed genes without additional prior knowledge that makes us free from the seed selection of network propagation issues. We showed that Venn-diaNet can reproduce the research findings reported in the original papers that have experiments that compare two, three and eight experiments. Venn-diaNet is freely available at: http://biohealth.snu.ac.kr/software/venndianet [ABSTRACT FROM AUTHOR]

Background: In recent decades, detecting protein complexes (PCs) from protein-protein interaction networks (PPINs) has been an active area of research. There are a large number of excellent graph clustering methods that work very well for identifying PCs. However, most of existing methods usually overlook the inherent core-attachment organization of PCs. Therefore, these methods have three major limitations we should concern. Firstly, many methods have ignored the importance of selecting seed, especially without considering the impact of overlapping nodes as seed nodes. Thus, there may be false predictions. Secondly, PCs are generally supposed to be dense subgraphs. However, the subgraphs with high local modularity structure usually correspond to PCs. Thirdly, a number of available methods lack handling noise mechanism, and miss some peripheral proteins. In summary, all these challenging issues are very important for predicting more biological overlapping PCs. Results: In this paper, to overcome these weaknesses, we propose a clustering method by core-attachment and local modularity structure, named CALM, to detect overlapping PCs from weighted PPINs with noises. Firstly, we identify overlapping nodes and seed nodes. Secondly, for a node, we calculate the support function between a node and a cluster. In CALM, a cluster which initially consists of only a seed node, is extended by adding its direct neighboring nodes recursively according to the support function, until this cluster forms a locally optimal modularity subgraph. Thirdly, we repeat this process for the remaining seed nodes. Finally, merging and removing procedures are carried out to obtain final predicted clusters. The experimental results show that CALM outperforms other classical methods, and achieves ideal overall performance. Furthermore, CALM can match more complexes with a higher accuracy and provide a better one-to-one mapping with reference complexes in all test datasets. Additionally, CALM is robust against the high rate of noise PPIN. Conclusions: By considering core-attachment and local modularity structure, CALM could detect PCs much more effectively than some representative methods. In short, CALM could potentially identify previous undiscovered overlapping PCs with various density and high modularity. [ABSTRACT FROM AUTHOR]

Background: Adaptation of pathogens to their hosts depends critically on factors affecting pathogen reproductive rate. While pathogen reproduction is the end result of an intricate interaction between host and pathogen, the relative contributions of host and pathogen genotype to variation in pathogen life history within the host are not well understood. Untangling these contributions allows us to identify traits with sufficient genetic variation for selection to act and to identify mechanisms of coevolution between pathogens and their hosts. We investigated the effects of pathogen and host genotype on three life-history components of pathogen fitness; infection efficiency, latent period, and sporulation capacity, in the oat crown rust fungus, Puccinia coronata f.sp. avenae, as it infects oats (Avena sativa). Results: We show that both pathogen and host genotype significantly affect total spore production but do so through their effects on different life-history stages. Pathogen genotype has the strongest effect on the early stage of infection efficiency, while host genotype most strongly affects the later life-history stages of latent period and sporulation capacity. In addition, host genotype affected the relationship between pathogen density and the later life-history traits of latent period and sporulation capacity. We did not find evidence of pathogen-by-host genotypic (GxG) interactions. Conclusion: Our results illustrate mechanisms by which variation in host populations will affect the evolution of pathogen life history. Results show that different pathogen life-history stages have the potential to respond differently to selection by host or pathogen genotype and suggest mechanisms of antagonistic coevolution.Pathogen populations may adapt to host genotypes through increased infection efficiency while their plant hosts may adapt by limiting the later stages of pathogen growth and spore production within the host. [ABSTRACT FROM AUTHOR]

Tomato production is limited to a large extent by climates with high temperatures. Yield-related traits in tomatoes are generally thought to be quantitative inherited traits that are significantly affected by variation in environmental factors. Breeding for heat tolerance is restricted due to the complexity of the traits. The objective of this study was to identify random amplified polymorphic DNA (RAPD) markers linked to heat-tolerance traits in tomatoes under heat stress. Forty-three F7 recombinant inbred lines (RILs) derived from a wild cross between CL 5915 (heat-tolerant) and L4422 (heat-sensitive) were obtained and scored for flower number, fruit number, fruit set, fruit weight and yield under screen house conditions during the hot (summer) season of 2003. The distributions of average fruit weight, fruit number, fruit set and yield in the F7 population were strongly skewed towards heat susceptibility, characteristic of L4422. Significant positive correlation was observed between fruit weight and yield, and between fruit number, fruit set and yield. However, the increase in yield and fruit set by selecting for large flower number may be fairly minor due to non-significant correlation between these traits. Selecting for flower number may not be a useful tool for improving yield under heat stress. A total of 200 RAPD primers were screened, among which 14 were identified as associated with heat-tolerance using bulk segregant analysis (BSA) based on the F7 population grown in a heat-stressed environment. Some RAPD markers were unique to one specific trait, and others were linked to two traits. All markers for heat tolerance related traits had positive gene effects as a result of the contribution of the CL5915 gene that bolstered these traits. One hundred F2 plants derived from the same parents (CL5915×L4422) were grown in the same location during the summer of 2004 to test for the stability and reliability of the 14 markers identified. Selection for the desired heat-tolerance genotypes corresponded well with targeting heat tolerance traits using the selected heat tolerance RAPD markers identified. Marker-assisted selection (MAS) for heat tolerance may be efficiently conducted by selecting individuals that contain high fruit number, high fruit weight, and high yield markers (P06, X01, D06 and D11), which would thus facilitate conventional breeding using CL5915 as a donor parent. [ABSTRACT FROM AUTHOR]

An abstract of the conference paper "Effect of BLUP prediction on genomic selection: Practical considerations to achieve greater accuracy in genomic selection," by Patricio Munoz and colleagues is presented.

Whilst parthenogenesis has evolved multiple times from sexual invertebrate and vertebrate lineages, the drivers and consequences of the sex-asex transition remain mostly uncertain. A model by Stouthamer et al. recently published in BMC Evolutionary Biology shows a pathway by which obligate asexuality could be selected for following endosymbiont infection. [ABSTRACT FROM AUTHOR]

Background: The detection of protein complexes is of great significance for researching mechanisms underlying complex diseases and developing new drugs. Thus, various computational algorithms have been proposed for protein complex detection. However, most of these methods are based on only topological information and are sensitive to the reliability of interactions. As a result, their performance is affected by false-positive interactions in PPINs. Moreover, these methods consider only density and modularity and ignore protein complexes with various densities and modularities. Results: To address these challenges, we propose an algorithm to exploit protein complexes in PPINs by a Seed-Extended algorithm based on Density and Modularity with Topological structure and GO annotations, named SE-DMTG to improve the accuracy of protein complex detection. First, we use common neighbors and GO annotations to construct a weighted PPIN. Second, we define a new seed selection strategy to select seed nodes. Third, we design a new fitness function to detect protein complexes with various densities and modularities. We compare the performance of SE-DMTG with that of thirteen state-of-the-art algorithms on several real datasets. Conclusion: The experimental results show that SE-DMTG not only outperforms some classical algorithms in yeast PPINs in terms of the F-measure and Jaccard but also achieves an ideal performance in terms of functional enrichment. Furthermore, we apply SE-DMTG to PPINs of several other species and demonstrate the outstanding accuracy and matching ratio in detecting protein complexes compared with other algorithms. [ABSTRACT FROM AUTHOR]

Background: Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. We introduce Magic-BLAST, a new aligner based on ideas from the Magic pipeline. Results: Magic-BLAST uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome. Conclusions: We show that Magic-BLAST is the best at intron discovery over a wide range of conditions and the best at mapping reads longer than 250 bases, from any platform. It is versatile and robust to high levels of mismatches or extreme base composition, and reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI. [ABSTRACT FROM AUTHOR]

An abstract of the conference paper "SNP discovery in apple cultivars using next generation sequencing," by Sérgio Alencar and colleagues is presented.

An abstract of the conference paper "Use of kanamycin for selection of Eucalyptus saligna genetically transformed plants," by Yohana Oliveira and colleagues is presented.

An abstract of the conference paper "Genomic selection in tree breeding: testing accuracy of prediction models including dominance effect," by Marie Denis and Jean-Marc Bouvet is presented.