Core Subset Research Articles

Pan-genome and multi-parental framework for high-resolution trait dissection in melon (Cucumis melo).

Linking genotype with phenotype is a fundamental goal in biology and requires robust data for both. Recent advances in plant-genome sequencing have expedited comparisons among multiple-related individuals. The abundance of structural genomic within-species variation that has been discovered indicates that a single reference genome cannot represent the complete sequence diversity of a species, leading to the expansion of the pan-genome concept. For high-resolution forward genetics, this unprecedented access to genomic variation should be paralleled and integrated with phenotypic characterization of genetic diversity. We developed a multi-parental framework for trait dissection in melon (Cucumis melo), leveraging a novel pan-genome constructed for this highly variable cucurbit crop. A core subset of 25 diverse founders (MelonCore25), consisting of 24 accessions from the two widely cultivated subspecies of C. melo, encompassing 12 horticultural groups, and 1 feral accession was sequenced using a combination of short- and long-read technologies, and their genomes were assembled de novo. The construction of this melon pan-genome exposed substantial variation in genome size and structure, including detection of ~300 000 structural variants and ~9 million SNPs. A half-diallel derived set of 300 F2 populations, representing all possible MelonCore25 parental combinations, was constructed as a framework for trait dissection through integration with the pan-genome. We demonstrate the potential of this unified framework for genetic analysis of various melon traits, including rind color intensity and pattern, fruit sugar content, and resistance to fungal diseases. We anticipate that utilization of this integrated resource will enhance genetic dissection of important traits and accelerate melon breeding.

Optimisation of the core subset for the APY approximation of genomic relationships

BackgroundBy entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have been proposed to address this challenge, such as the Algorithm for Proven and Young (APY). In APY, genotyped animals are partitioned into core and non-core subsets, which induces a sparser inverse of the genomic relationship matrix. This partitioning is often done at random. While APY is a good approximation of the full model, random partitioning can make results unstable, possibly affecting accuracy or even reranking animals. Here we present a stable optimisation of the core subset by choosing animals with the most informative genotype data.MethodsWe derived a novel algorithm for optimising the core subset based on a conditional genomic relationship matrix or a conditional single nucleotide polymorphism (SNP) genotype matrix. We compared the accuracy of genomic predictions with different core subsets for simulated and real pig data sets. The core subsets were constructed (1) at random, (2) based on the diagonal of the genomic relationship matrix, (3) at random with weights from (2), or (4) based on the novel conditional algorithm. To understand the different core subset constructions, we visualise the population structure of the genotyped animals with linear Principal Component Analysis and non-linear Uniform Manifold Approximation and Projection.ResultsAll core subset constructions performed equally well when the number of core animals captured most of the variation in the genomic relationships, both in simulated and real data sets. When the number of core animals was not sufficiently large, there was substantial variability in the results with the random construction but no variability with the conditional construction. Visualisation of the population structure and chosen core animals showed that the conditional construction spreads core animals across the whole domain of genotyped animals in a repeatable manner.ConclusionsOur results confirm that the size of the core subset in APY is critical. Furthermore, the results show that the core subset can be optimised with the conditional algorithm that achieves an optimal and repeatable spread of core animals across the domain of genotyped animals.

Social media use in the context of the Personal Social Media Ecosystem Framework

Abstract The rapid proliferation and maturation of social media platforms have led to numerous challenges in understanding the correlates of social media use among users. To advance this research, the present article proposes a new way to think about social media with the Personal Social Media Ecosystem Framework (PSMEF). This perspective defines social media as a user-centric digital environment made up of a central set of individual, yet interrelated digital spaces (e.g., in-app pages) that are themselves embedded within a broader ecology (e.g., operating system, the Internet, offline contexts). By leveraging the PSMEF and data from focus groups involving adolescent participants (N = 59), we identify a core subset of salient environmental contexts within participants’ PSMEs that can generalize across platforms, which are differentially associated with popular social media platforms. The theoretical and practical implications of this work are discussed.

Epidemiology, aetiology, interventions and genomics in children with arthrogryposis multiplex congenita: protocol for a multisite registry

IntroductionArthrogryposis multiplex congenita (AMC) is an umbrella term including hundreds of conditions with the common clinical manifestation of multiple congenital contractures. AMC affects 1 in 3000 live births and is...

Measurement of the Kinetics and Thermodynamics of the Thermal Degradation for a Flame Retardant Polyurethane-Based Aerogel

The current work aims to study the thermal degradation of the flame retardant polyurethane aerogel (FR_PU_aerogel) through multiple milligram-scale experimental methods. A systemic methodology for measuring the reaction kinetics and thermodynamics of the thermal degradation of FR_PU_aerogel is detailed. Specifically, the thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were performed simultaneously in inert atmosphere to measure the mass loss and heat flow data, and a numerical framework called ThermaKin2Ds was used to inversely model these experimental data. First, a reaction mechanism with six first-order consecutive reactions was developed based on the inverse analysis of the TGA data. The corresponding reaction kinetics were optimized using the hill climbing optimization algorithm. Subsequently, the heat capacities of each condensed phase component and the heat of the reactions were obtained through inversely modeling the heat flow data. Furthermore, the heat of the complete combustion of each gaseous component were derived based on the heat release rates measured in the milligram-scale combustion calorimeter (MCC) experiments. It is noted that the developed reaction mechanism was further validated against the mass loss data obtained at different heating rates. The parameters determined in this work serve as a core subset of inputs for the pyrolysis model development, which is essential for the quantitative understanding of the ignition and the combustion behavior of solid materials.

Emotions as Affective Position-Takings and as Nonconceptual Meta-Representations: A Comparison

The theory of emotions as affective position-takings (PT) is investigated from the perspective of a computational model of the belief-desire theory of emotions (CBDTE) proposed by the author. Both theories assume that a core subset of typical emotion episodes are the products of an evaluation process in which cognized states of affairs are evaluated for their congruence with the person's desires; and that emotions are, on the conscious level, feelings of pleasure and displeasure. However, according to PT the evaluation is a concern-based construal and emotional feelings are intrinsically object-directed, whereas according to CBDTE, the evaluation is performed by a hardwired mechanism that produces nonintentional feelings. Furthermore, CBDTE suggests that hedonic feelings are simultaneously reactions to the detection of value, and representations of the detected value.

SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction

BackgroundProtein-protein interactions (PPIs) dominate intracellular molecules to perform a series of tasks such as transcriptional regulation, information transduction, and drug signalling. The traditional wet experiment method to obtain PPIs information is costly and time-consuming.ResultIn this paper, SDNN-PPI, a PPI prediction method based on self-attention and deep learning is proposed. The method adopts amino acid composition (AAC), conjoint triad (CT), and auto covariance (AC) to extract global and local features of protein sequences, and leverages self-attention to enhance DNN feature extraction to more effectively accomplish the prediction of PPIs. In order to verify the generalization ability of SDNN-PPI, a 5-fold cross-validation on the intraspecific interactions dataset of Saccharomyces cerevisiae (core subset) and human is used to measure our model in which the accuracy reaches 95.48% and 98.94% respectively. The accuracy of 93.15% and 88.33% are obtained in the interspecific interactions dataset of human-Bacillus Anthracis and Human-Yersinia pestis, respectively. In the independent data set Caenorhabditis elegans, Escherichia coli, Homo sapiens, and Mus musculus, all prediction accuracy is 100%, which is higher than the previous PPIs prediction methods. To further evaluate the advantages and disadvantages of the model, the one-core and crossover network are conducted to predict PPIs, and the data show that the model correctly predicts the interaction pairs in the network.ConclusionIn this paper, AAC, CT and AC methods are used to encode the sequence, and SDNN-PPI method is proposed to predict PPIs based on self-attention deep learning neural network. Satisfactory results are obtained on interspecific and intraspecific data sets, and good performance is also achieved in cross-species prediction. It can also correctly predict the protein interaction of cell and tumor information contained in one-core network and crossover network.The SDNN-PPI proposed in this paper not only explores the mechanism of protein-protein interaction, but also provides new ideas for drug design and disease prevention.

The Analysis of Potential Diagnostic and Therapeutic Targets for the Occurrence and Development of Gastric Cancer Based on Bioinformatics.

Objective Gastric cancer is among the most common malignant tumors of the digestive system. This study explored the molecular mechanisms and potential therapeutic targets for gastric cancer occurrence and progression using bioinformatics. Methods The gastric cancer microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database. The R package was used for data mining and screening differentially expressed genes (DEGs). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Based on the protein-protein interaction (PPI) network analysis, core targets and core subsets were screened. Then, the relationship between the expression level of the core genes and the prognosis of gastric cancer patients was analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) database. Results Using the GSE19826 and GSE54129 datasets, a total of 550 DEGs were identified, including 248 upregulated and 302 downregulated genes. GO and KEGG analyses showed that the upregulated DEGs were mainly enriched in the extracellular matrix (ECM) organization of the biological process (BP), the collagen-containing ECM of cellular component (CC), and the ECM structural constituent of molecular function (MF). DEGs were also enriched in human papillomavirus infections, the focal adhesion pathway, PI3K-Akt signaling pathway, and among others. The downregulated DEGs were mainly enriched in digestion, basal part of the cell, and aldo-keto reductase (NADP) activity. And the above pathways were enriched primarily in the metabolism of xenobiotics by cytochrome P450, drug metabolism-cytochrome P450, and retinol metabolism. Five core genes, including COL1A2, COL3A1, BGN, FN1, and VCAN, were significantly highly expressed in gastric cancer patients and were associated with poor prognosis. Conclusion This study identified new potential molecular targets closely related to gastric cancer occurrence and development via mining public data using bioinformatics analysis methods.

Borylated 2,3,4,5-Tetrachlorophthalimide and Their 2,3,4,5-Tetrachlorobenzamide Analogues: Synthesis, Their Glycosidase Inhibition and Anticancer Properties in View to Boron Neutron Capture Therapy.

Tetrachlorinated phthalimide analogues bearing a boron-pinacolate ester group were synthesised via two synthetic routes and evaluated in their glycosidase modulating and anticancer properties, with a view to use them in boron neutron capture therapy (BNCT), a promising radiation type for cancer, as this therapy does little damage to biological tissue. An unexpected decarbonylation/decarboxylation to five 2,3,4,5-tetrachlorobenzamides was observed and confirmed by X-ray crystallography studies, thus, giving access to a family of borylated 2,3,4,5-tetrachlorobenzamides. Biological evaluation showed the benzamide drugs to possess good to weak potencies (74.7–870 μM) in the inhibition of glycosidases, and to have good to moderate selectivity in the inhibition of a panel of 18 glycosidases. Furthermore, in the inhibition of selected glycosidases, there is a core subset of three animal glycosidases, which is always inhibited (rat intestinal maltase α-glucosidase, bovine liver β-glucosidase and β-galactosidase). This could indicate the involvement of the boron atom in the binding. These glycosidases are targeted for the management of diabetes, viral infections (via a broad-spectrum approach) and lysosomal storage disorders. Assays against cancer cell lines revealed potency in growth inhibition for three molecules, and selectivity for one of these molecules, with the growth of the normal cell line MCF10A not being affected by this compound. One of these molecules showed both potency and selectivity; thus, it is a candidate for further study in this area. This paper provides numerous novel aspects, including expedited access to borylated 2,3,4,5-tetrachlorophthalimides and to 2,3,4,5-tetrachlorobenzamides. The latter constitutes a novel family of glycosidase modulating drugs. Furthermore, a greener synthetic access to such structures is described.

A comparison of topologically associating domain callers over mammals at high resolution

BackgroundTopologically associating domains (TADs) are locally highly-interacting genome regions, which also play a critical role in regulating gene expression in the cell. TADs have been first identified while investigating the 3D genome structure over High-throughput Chromosome Conformation Capture (Hi-C) interaction dataset. Substantial degree of efforts have been devoted to develop techniques for inferring TADs from Hi-C interaction dataset. Many TAD-calling methods have been developed which differ in their criteria and assumptions in TAD inference. Correspondingly, TADs inferred via these callers vary in terms of both similarities and biological features they are enriched in.ResultWe have carried out a systematic comparison of 27 TAD-calling methods over mammals. We use Micro-C, a recent high-resolution variant of Hi-C, to compare TADs at a very high resolution, and classify the methods into 3 categories: feature-based methods, Clustering methods, Graph-partitioning methods. We have evaluated TAD boundaries, gaps between adjacent TADs, and quality of TADs across various criteria. We also found particularly CTCF and Cohesin proteins to be effective in formation of TADs with corner dots. We have also assessed the callers performance on simulated datasets since a gold standard for TADs is missing. TAD sizes and numbers change remarkably between TAD callers and dataset resolutions, indicating that TADs are hierarchically-organized domains, instead of disjoint regions. A core subset of feature-based TAD callers regularly perform the best while inferring reproducible domains, which are also enriched for TAD related biological properties.ConclusionWe have analyzed the fundamental principles of TAD-calling methods, and identified the existing situation in TAD inference across high resolution Micro-C interaction datasets over mammals. We come up with a systematic, comprehensive, and concise framework to evaluate the TAD-calling methods performance across Micro-C datasets. Our research will be useful in selecting appropriate methods for TAD inference and evaluation based on available data, experimental design, and biological question of interest. We also introduce our analysis as a benchmarking tool with publicly available source code.

Core implementation strategies for improving cirrhosis care in the Veterans Health Administration.

The Veterans Health Administration (VHA) provides care for more than 80,000 veterans with cirrhosis. This longitudinal, multimethod evaluation of a cirrhosis care quality improvement program aimed to (1) identify implementation strategies associated with evidence-based, guideline-concordant cirrhosis care over time, and (2) use qualitative interviews to operationalize strategies for a manualized intervention. VHA providers were surveyed annually about the use of 73 implementation strategies to improve cirrhosis care in fiscal years 2018 (FY18) and 2019 (FY19). Implementation strategies linked to guideline-concordant cirrhosis care were identified using bivariate statistics and comparative configurational methods. Semistructured interviews were conducted with 12 facilities in the highest quartile of cirrhosis care to specify the successful implementation strategies and their mechanisms of change. A total of 106 VHA facilities (82%) responded at least once over the 2-year period (FY18, n = 63; FY19, n = 100). Facilities reported using a median of 12 (interquartile range [IQR] 20) implementation strategies in FY18 and 10 (IQR 19) in FY19. Of the 73 strategies, 35 (48%) were positively correlated with provision of evidence-based cirrhosis care. Configurational analysis identified multiple strategy pathways directly linked to more guideline-concordant cirrhosis care. Across both methods, a subset of eight strategies was determined to be core to cirrhosis care improvement and specified using qualitative interviews. In a national cirrhosis care improvement initiative, a multimethod approach identified a core subset of successful implementation strategy combinations. This process of empirically identifying and specifying implementation strategies may be applicable to other implementation challenges in hepatology.

Creation and validation of core subset of potato (Solanum tuberosum) germplasm

Potato (Solanum tuberosum L.) is the only non-cereal staple food crop globally. The progress of this temperate crop in the sub-tropical country relies strongly on improvement activities utilizing the genetic resources acquired over a time period. However, proper maintenance, evaluation and utilization of germplasm resources is a cumbersome task. Creation of core set of conserved genetic resources is an efficient and cost effective method of management of conserved germplasm and its use in breeding program. Indian collection of potato (Solanum tuberosum ssp tuberosum) constitutes 1399 accessions emanating from the five continents maintained under ex situ conservation. A core subset of 140 potato germplasm was developed using Power Core software based on 19 morphological traits. Majority of entries in the core subset were from South America (42.14%). Comparison of mean data using Newman-Keul's test and variances using Levene's test for different traits revealed representation of actual diversity of base population by the core subset. Correlations among the traits governed by co-adapted gene complexes were conserved in the core subset. Shannon-Weaver diversity indices indicate that the potato core subset maximized the phenotypic diversity of the potato germplasm. Validation of core subset using principal component analysis reveals that the first 4 PCs represents 55.27% of total variations in core subset compared to 52.50% that in entire collection. The identified core subset can be used in Indian potato improvement programs as well as in genomics studies.

MLL::AF9 degradation induces rapid changes in transcriptional elongation and subsequent loss of an active chromatin landscape

MLL rearrangements produce fusion oncoproteins that drive leukemia development, but the direct effects of MLL-fusion inactivation remain poorly defined. We designed models with degradable MLL::AF9 where treatment with small molecules induces rapid degradation. We leveraged the kinetics of this system to identify a core subset of MLL::AF9 target genes where MLL::AF9 degradation induces changes in transcriptional elongation within 15minutes. MLL::AF9 degradation subsequently causes loss of a transcriptionally active chromatin landscape. We used this insight to assess the effectiveness of small molecules that target members of the MLL::AF9 multiprotein complex, specifically DOT1L and MENIN. Combined DOT1L/MENIN inhibition resembles MLL::AF9 degradation, whereas single-agent treatment has more modest effects on MLL::AF9 occupancy and gene expression. Our data show that MLL::AF9 degradation leads to decreases in transcriptional elongation prior to changes in chromatin landscape at select loci and that combined inhibition of chromatin complexes releases the MLL::AF9 oncoprotein from chromatin globally.

QTL mapping and genomic analyses of earliness and fruit ripening traits in a melon Recombinant Inbred Lines population supported by de novo assembly of their parental genomes.

Earliness and ripening behavior are important attributes of fruits on and off the vine, and affect quality and preference of both growers and consumers. Fruit ripening is a complex physiological process that involves metabolic shifts affecting fruit color, firmness, and aroma production. Melon is a promising model crop for the study of fruit ripening, as the full spectrum of climacteric behavior is represented across the natural variation. Using Recombinant Inbred Lines (RILs) population derived from the parental lines “Dulce” (reticulatus, climacteric) and “Tam Dew” (inodorus, non-climacteric) that vary in earliness and ripening traits, we mapped QTLs for ethylene emission, fruit firmness and days to flowering and maturity. To further annotate the main QTL intervals and identify candidate genes, we used Oxford Nanopore long-read sequencing in combination with Illumina short-read resequencing, to assemble the parental genomes de-novo. In addition to 2.5 million genome-wide SNPs and short InDels detected between the parents, we also highlight here the structural variation between these lines and the reference melon genome. Through systematic multi-layered prioritization process, we identified 18 potential polymorphisms in candidate genes within multi-trait QTLs. The associations of selected SNPs with earliness and ripening traits were further validated across a panel of 177 diverse melon accessions and across a diallel population of 190 F1 hybrids derived from a core subset of 20 diverse parents. The combination of advanced genomic tools with diverse germplasm and targeted mapping populations is demonstrated as a way to leverage forward genetics strategies to dissect complex horticulturally important traits.

Intraspecific genetic variation among

Context The genus Sehima is an important component of grasslands worldwide and is the dominant grass of Sehima–Dichanthium grasslands of India. It is an excellent fodder and a good source of lignocellulosic material and several industrially important biomolecules. Aims We aimed to characterise and conserve locally adapted diverse germplasm of S. nervosum, collected from different agro-climatic zones, for effective utilisation. Methods Morphological and nutritive traits were measured and statistically analysed for diversity in a germplasm collection of S. nervosum genotypes from diverse agro-climatic conditions of India, and a representative core subset was created. Key results Morphological traits and nutritive parameters such as crude protein content and digestibility showed wide variability among accessions. Principal component analysis established that plant height, number of tillers per tussock and number of nodes per tiller accounted for half of the variation present. Tiller internode length, stem diameter, and leaf blade length and width also contributed >5 unit points each to the cumulative proportion of variance accounted for. Conclusions The set of germplasm, possessing substantial variability, is a valuable genetic resource for developing new cultivars. Genotypes identified with high protein content could be used as forage, and those with high cellulose and hemicelluloses as a biofuel resource. Implications Genotypes can be separately identified for forage and biofuel, and the core subset can effectively be used for evaluation and selection of genotypes in target environments. Additionally, the core subset can be used to establish and rejuvenate sustainable pastures by allowing natural selection.

Morphological and nutritional diversity among accessions of marvel grass (Dichanthium annulatum(Forssk.) Stapf) and development of a core collection

AbstractDichanthium annulatumis one of the dominant grasses of India, North Africa, Southeast Asia, China, Australia, Fiji, New Guinea, Cuba, Haiti and Puerto Rico. This drought-tolerant grass is an excellent fodder in mixed pastures. Developing varieties with improved quality and tolerance to various abiotic stresses is hampered due to its apomictic nature. Germplasm collection, characterization, genetic diversity analysis and core subset development followed by selection for desirable traits seems to be the most plausible breeding tool for developing new cultivars. In the present study, 498 genotypes collected from different agro-ecological zones in India were included. Genotypes were characterized for various metric and non-numeric traits; and the nutritional parameters. Agglomerative clustering analysis, using the Euclidean distance method, showed 14 distinct clusters. High variability was recorded for green forage yield, quantitative traits and nutritive quality parameters. A core subset of 50 accessions was identified, which captured most of the morphological and nutritional variability present in the total germplasm. Clustering of genotypes was observed to be related to the climatic conditions of the place of collection. High genetic variability observed for various morphological traits as well as forage yield indicated that these genotypes or subset of genotypes can be evaluated in different abiotic stress conditions such as salt, light and moisture stress for the identification of suitable varieties for the respective areas. Variability was attributed to inter-generic, inter-specific crossing together with the occasional presence of sexual plants in nature.

Genetic diversity and population structure of <i>Heteropogon contortus</i> L. germplasm collected from diverse agro-climatic regions in India and development of a core germplasm set

Heteropogon contortus, an important constituent of major grasslands of India, Australia and many countries in Africa, Asia and the Americas, is important for pasture and grassland productivity. Hence genetic improvement of the grass needs attention. A genetic variability study, including development of a core subset, was carried out by evaluating 235 accessions collected from different agro-ecological zones of India. The study, based on 16 metric and 14 non-metric traits along with 8 nutritional parameters, indicated that considerable genetic variability existed among the germplasm and selection could result in identification of suitable types for target environments. Clustering and subclustering was performed to select 35 accessions to form a core subset. The statistical analysis indicated that the core subset captured almost all the variability present in the entire germplasm. The study will help researchers to focus future studies on this core subset in developing genetic improvement programs.

Straw return and low N addition modify the partitioning of dissimilatory nitrate reduction by increasing conversion to ammonium in paddy fields

Dissimilatory nitrate reduction to ammonium (DNRA) contributes to nitrogen (N) conservation, whereas denitrification and anaerobic ammonium oxidation (anammox) lead to N loss in agroecosystems. However, their relative roles in nitrate (NO3−) partitioning for total dissimilatory NO3− reduction and the effects of long-term straw retention on such mechanisms remain largely unclear. In this study, after implementing straw return and N addition for seven consecutive years in a paddy field, we used 15N tracing combined with molecular biological techniques to investigate the rates of denitrification, anammox, DNRA, and their associated abundance of nosZ, hszB, and nrfA genes, respectively, as well as the characteristics of the DNRA community. The results showed that the rates of denitrification, anammox, and DNRA varied from 20.7 to 33.3, 0.12–0.34, and 0.88–3.20 nmol N g−1 h−1, respectively. Straw return significantly increased denitrification, anammox, DNRA activities, and the abundance of associated genes, except for the anammox hszB gene. With increasing N input rate, the denitrification rate increased (P < 0.05), the DNRA rate decreased (P < 0.05), whereas the abundance of the three functional genes did not change significantly. Compared with chronic high N addition under straw retention, low N amendment increased the DNRA-based NO3− partitioning by 61–111%, although denitrification dominated the dissimilatory NO3− reduction process. The significant increase in soil DOC:NO3− ratio (R2 = 0.89), and the reduction of soil pH (R2 = 0.87) and standing water NO3− concentration (R2 = 0.70), promoted N conservation through DNRA. The core subset of DNRA communities (R2 = 0.99), belonging to Bacteroidetes, Proteobacteria, Euryarchaeota, and Firmicutes phyla, were responsible for DNRA activities. This is the first study to examine all three dissimilatory NO3− reduction processes in response to long-term straw return and N addition. We propose that straw return with low N addition can largely favor DNRA partitioning among the three dissimilatory NO3− reduction processes through biotic and abiotic regulators.

Genetic Dissection of Seedling Root System Architectural Traits in a Diverse Panel of Hexaploid Wheat through Multi-Locus Genome-Wide Association Mapping for Improving Drought Tolerance.

Cultivars with efficient root systems play a major role in enhancing resource use efficiency, particularly water absorption, and thus in drought tolerance. In this study, a diverse wheat association panel of 136 wheat accessions including mini core subset was genotyped using Axiom 35k Breeders’ Array to identify genomic regions associated with seedling stage root architecture and shoot traits using multi-locus genome-wide association studies (ML-GWAS). The association panel revealed a wide variation of 1.5- to 50-fold and were grouped into six clusters based on 15 traits. Six different ML-GWAS models revealed 456 significant quantitative trait nucleotides (QTNs) for various traits with phenotypic variance in the range of 0.12–38.60%. Of these, 87 QTNs were repeatedly detected by two or more models and were considered reliable genomic regions for the respective traits. Among these QTNs, eleven were associated with average diameter and nine each for second order lateral root number (SOLRN), root volume (RV) and root length density (RLD). A total of eleven genomic regions were pleiotropic and each controlled two or three traits. Some important candidate genes such as Formin homology 1, Ubiquitin-like domain superfamily and ATP-dependent 6-phosphofructokinase were identified from the associated genomic regions. The genomic regions/genes identified in this study could potentially be targeted for improving root traits and drought tolerance in wheat.

Differential Role of Plasma and Platelet-Derived Factor V In Vivo

Coagulation factor V (FV) has a profound effect on coagulation and deficiency states lead to bleeding. In blood, there are two pools of FV with 80% found in plasma and 20% contained in the α-granules of platelets. The biological basis for this separation is not clear; however, structural distinctions between the pools have been identified suggesting they may have unique effects or play different roles following injury. Broadly however, in vivo evidence for this is lacking. Transgenic mice with synthesis limited to liver or megakaryocytes have been generated to study the role of plasma and platelet FV (Sun et al, Blood 102:2856, 2003). Either pool is sufficient for basal hemostasis and tail-bleeding times were only slightly prolonged compared to wild-type (wt) mice. The impact of the two pools following different hemostatic challenges in different vessels is not known. To expand our in vivo understanding of the FV pools on clot formation, we further studied mice with only platelet FV (platelet-FV+) or plasma FV (plasma-FV+). As a control, we also generated mice with essentially little to no total FV (FV-low). To generate platelet-FV+ mice, wt mice were treated with a single dose of a liver targeted antisense oligonucleotide (ASO) to knockdown plasma FV (ASO-FV; 40 mg/kg). After 3 weeks of ASO-FV treatment, the plasma FV levels assessed with a murine FV-specific ELISA were at the lower limits of detection while platelet FV levels were normal. This confirms prior work showing platelet FV originates from megakaryocyte synthesis in mice, which is different from humans. To generate plasma-FV+ mice, we used previously established transgenic mice only expressing FV (~30% of wt) from the liver with no platelet FV. To create FV-low mice, plasma-FV+ mice were treated with ASO-FV; this resulted in mice with markedly reduced (<0.5%) plasma FV and no platelet FV. Using whole blood from these mice, ROTEM assessment showed that compared to wt mice (n = 5; 5.9 ± 0.3 min), the clot time (CT) for plasma-FV+ mice (n = 3; 5.9 ± 0.4 min) was normal while the CT was prolonged 2-fold for platelet-FV+ mice (n = 10; 13 ± 1.2 min). As expected, the almost complete lack of both pools (FV-low) substantially increased the CT (n = 3; 24 ± 2.2 min). Evaluation of these mice in several injury models probing different vessels was next pursued to examine the hemostatic potential of either pool. Using either a partial (mild) or total (severe) tail injury model, total blood loss for both wt (n = 5) and platelet-FV+ mice (n=10) were similar. In contrast, plasma-FV+ mice (n=5, p<0.001) exhibited a 2- (mild injury) or 5-fold (severe injury) increase in blood loss compared to wt mice. As expected, FV-low mice (n=5; p<0.001) had the highest levels of blood loss. Further, we used a microcirculatory model of hemostasis to image the distribution of plasma vs. platelet FV following laser injury and assess fibrin and platelet deposition. In plasma-FV+ mice, a rapid and robust fluorescence signal for murine FV/Va was detected on the vessel wall surrounding the microscopic injury and on a subset of platelets adherent at the injury site. For platelet-FV+ mice, murine FV/Va was detected only on core subset of platelets, which we speculate are fully activated. Further, analysis of the median integrated florescence as a function of time in multiple mice (n=3-5/group; 15-25 injuries) revealed that the absence of either platelet or plasma FV did not considerably alter the kinetics of platelet accumulation (~2-fold less than wt). However, in contrast to plasma-FV+ mice, platelet-FV+ mice had significantly reduced and delayed fibrin deposition following laser injury suggesting the FV released from platelets is not sufficient to produce normal amounts of fibrin in this model. Our data indicate that the overall impact and contribution of the platelet and plasma FV pools is different depending on the type of injury and/or vessel type. In the microcirculation, plasma FV appears to play an important role in binding to damaged endothelium and contributing to robust thrombin formation, while platelet FV may play a reduced role. However, in more severe injury models, platelet FV plays a prominent role. Collectively these data highlight potentially different roles for the FV pools and/or indicate that the injury models employed impact FV activation or release from platelet α-granules to different extents. DisclosuresIvanciu:Bayer: Research Funding; Novo Nordisk: Research Funding. Crosby:Ionis Pharmaceuticals: Employment, Research Funding. Revenko:Ionis Pharmaceuticals: Employment, Research Funding. MacLeod:Ionis Pharmaceuticals: Employment. Monia:Ionis Pharmaceuticals: Employment. Davidson:Spark Therapeutics: Consultancy. Camire:Bayer: Consultancy, Research Funding; Pfizer: Consultancy, Patents & Royalties, Research Funding; Spark Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novo Nordisk: Research Funding.