Comparison of three genomic DNA extraction methods to obtain high DNA quality from maize

Transmission patterns of a Mycobacterium avium subsp. paratuberculosis clone within a single heard investigated by Whole Genome Sequencing

Mycobacterium avium subsp. paratuberculosis (MAP) is characterized by a low genomic rate of mutation. Current subtyping tools, such as Mini-Micro-satellite analyses, do to have not sufficient discriminatory power to disclose MAP's evolution on small spatial and temporal scales. The aim of the study was to investigate the population structure of MAP inside a single dairy herd using whole genome sequencing (WGS) approaches. For this purpose, the genomes of 43 field isolates, recovered from the faeces of 36 cows of the same dairy herd from 2012 to 2016, were sequenced by WGS. The isolates' genomes showed a low number (43) of polymorphic sites (SNPs), confirming the clonal origin of the herd infection. However, despite the limited genomic diversity found in WGS, the phylogenetic analysis was discriminatory enough to detect the presence of different genomic clades and sub-clades inside the herd population. In addition, the phylodynamic reconstruction showed the existence of an ancestor clade from which the other clades and sub-clades originated. Moreover, by reconstructing the putative within-herd transmission networks using WGS data, we demonstrated that: (i) in a herd where MAP is endemic, multiple isolates recovered from a single animal and differing from each other by few (three/four) SNPs can originate from different transmission or passive shedding events and not from intra-host evolution; and (ii) variability of minisatellites coupled with a few microsatellites does not represent reliable tracers of within-herd infection chains. Our findings show that WGS, coupled with relevant epidemiological information, represents a valuable tool to work out fine epidemiological and micro-evolutionary relationships such as those at herd-level scale.

Differential impact of Bt-transgenic rice plantings on bacterial community in three niches over consecutive years

Transgenic-Bacillus thuringiensis (Bt) crops express insecticidal proteins, which can accumulate in plants and soil where they may influence microbial populations. The impact of Bt crops on bacterial communities has only been assessed under short-term, and results have been contradictory. Here, we analyzed the bacterial communities in three niches, rhizosphere soil (RS), root endosphere (RE) and leaf endosphere (LE), of three Bt rice and their non-Bt parental lines for three consecutive years by high-throughput sequencing. In principal coordinate analysis (PCoA) and PERMANOVA (Adonis) analysis, operational taxonomic units (OTUs) were clustered primarily by niche type and differed significantly in the RE and LE but not in the RS between each of three Bt lines compared with the non-Bt rice line, and not in each respective niche among the three Bt rice lines. The bacterial communities in the RS of different rice lines over the 3 years were clustered mainly by year rather than by lines. The differential bacterial taxa among the lines did not overlap between years, presumably because Cry proteins are rapidly degraded in the soil. A network analysis of RS bacterial communities showed that the network complexity and density for the three Bt rice lines did not decrease compared with those for the non-Bt line. In conclusion, our results demonstrated that bacterial communities differed significantly in RE and LE between Bt and non-Bt rice lines, but the differences were mild and transient, and had no adverse impact on RS over the 3 years. This study provides favorable evidence in support of the commercialization of Bt rice.

Technical note: Polyvinylpyrrolidone (PVP) and proteinase-K improve the efficiency of DNA extraction from Japanese larch wood and PCR success rate

Illegal distribution of timber disrupts the timber market and depletes forest resources. DNA markers are used to verify the legal distribution of wood. However, it is difficult to obtain the quantity and quality of DNA suitable for genetic analysis because of the physicochemical properties of wood; therefore, an efficient wood DNA extraction method is required. In this study, to prepare an efficient DNA extraction method from Japanese larch (Larix kaempferi) wood, we investigated the ability of polyvinylpyrrolidone (PVP) and proteinase-K to improve DNA extraction efficiency and PCR success rate. It was found that the addition of PVP resulted in a significant increase in the DNA concentration of the treatment group compared to that of the control group, while the purity (A260/A280) showed no difference. Moreover, the addition of proteinase-K significantly increased both the DNA concentration and purity of the treatment group compared to those of the control group. Further analysis showed that the PCR success rate of psbC (approximately 350 bp) was higher than 90% in the control, PVP treatment, and proteinase-K treatment groups. However, in the PCR success rate of rbcL (approximately 1.3 kb) was higher in the proteinase-K and PVP treatment groups than in the control group. The addition of PVP and proteinase-K increased the success rate of PCR amplification for long regions by preventing DNA damage caused by phenolic compounds and proteins in the wood. The results of this study can thus develop DNA extraction methods to identify the species and origin of woods.

TaPCNA plays a role in programmed cell death after UV-B exposure in wheat (Triticum aestivum)

Ultraviolet (UV)-B is a component of sunlight and shows a significant effect on DNA damage, which can be regulated by proliferating cell nuclear antigen (PCNA). The role of TaPCNA in wheat (Triticum aestivum L.) programmed cell death (PCD) under UV-B has not been investigated previously. Here, we explored the function of TaPCNA in wheat exposed to UV-B utilising Barley Stripe Mosaic Virus-virus-induced gene silencing (VIGS). The results showed that the expression of TaPCNA was downregulated, and curly wheat leaves with several spots were determined by VIGS. The growth rate and mesophyll cell length were significantly inhibited after TaPCNA was silenced. The activity of superoxide dismutase and the contents of soluble sugar and soluble protein decreased, whereas the activities of peroxidase and catalase and malondialdehyde content increased in TaPCNA-silenced and UV-B treatment groups. DNA laddering and propidium iodide staining results showed that DNA fragments and micronucleus accumulated after TaPCNA silencing with or without UV-B. Thus, TaPCNA participates in plant growth and DNA damage and PCD under UV-B. This study suggests an idea for the exploration of the function of certain genes in such complex wheat genomes and offers a theoretical basis to improve wheat agronomic traits.

Antibacterial Activity of Chitosan and PAβN on MexAB Expression in Clinical Isolates of Ciprofloxacin Resistant Pseudomonas aeruginosa

Background: Pseudomonas aeruginosa is a nosocomial pathogen, acquiring resistance to a wide range of antibiotics. The MexAB-OprM pump can lead to resistance in this organism. Thus, the study was conducted to determine the effect of chitosan and phenylalanine arginyl ß-naphthylamide (PaβN) on the expression of MexAB in isolated ciprofloxacin resistant P. aeruginosa. Objectives: This study investigated the effect of an antibiotic combination on the MexABP. aeruginosa expression. Methods: A total of 30 ciprofloxacin-resistant isolates of P. aeruginosa were collected in this project. Then, chitosan nanoparticles were prepared using the ionic gelation method. Minimum inhibitory concentration (MIC) values were determined for ciprofloxacin, ciprofloxacin + PAßN, chitosan + ciprofloxacin, and chitosan + ciprofloxacin + PAßN using the micro-dilution method. Moreover, the expression level of MexAB genes was measured using real-time polymerase chain reaction. Results: In total, 76.7% of the isolates were identified as multidrug resistant. A significant decrease in the MIC value was observed in groups treated with PAβN compared to those without PAβN. Moreover, the MIC value was significantly lower in the ciprofloxacin chitosan group than in groups without ciprofloxacin. Decreased MexA and MexB mRNA levels were observed in all antibiotic-treated strains compared to the ciprofloxacin-treated group. Conclusions: There is a significant relationship between the increased MexAB expression and resistance to ciprofloxacin (P-value < 0.05). One of the therapeutic concerns is multidrug resistant bacteria, which needs to be addressed by finding new and more effective antibiotics.

MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

Genetic diversity and evolutionary patterns of Taraxacum kok-saghyz Rodin

Taraxacum kok-saghyz Rodin (TKS) is an important potential alternative source of natural inulin and rubber production, which has great significance for the production of industrial products. In this study, we sequenced 58 wild TKS individuals collected from four different geography regions worldwide to elucidate the population structure, genetic diversity, and the patterns of evolution. Also, the first flowering time, crown diameter, morphological characteristics of leaf, and scape of all TKS individuals were measured and evaluated statistically. Phylogenetic analysis based on SNPs and cluster analysis based on agronomic traits showed that all 58 TKS individuals could be roughly divided into three distinct groups: (a) Zhaosu County in Xinjiang (population AB, including a few individuals from population C and D); (b) Tekes County in Xinjiang (population C); and (c) Tuzkol lake in Kazakhstan (population D). Population D exhibited a closer genetic relationship with population C compared with population AB. Genetic diversity analysis further revealed that population expansion from C and D to AB occurred, as well as gene flow between them. Additionally, some natural selection regions were identified in AB population. Function annotation of candidate genes identified in these regions revealed that they mainly participated in biological regulation processes, such as transporter activity, structural molecule activity, and molecular function regulator. We speculated that the genes identified in selective sweep regions may contribute to TKS adaptation to the Yili River Valley of Xinjiang. In general, this study provides new insights in clarifying population structure and genetic diversity analysis of TKS using SNP molecular markers and agronomic traits.

Applications of deep-learning approaches in horticultural research: a review

Deep learning is known as a promising multifunctional tool for processing images and other big data. By assimilating large amounts of heterogeneous data, deep-learning technology provides reliable prediction results for complex and uncertain phenomena. Recently, it has been increasingly used by horticultural researchers to make sense of the large datasets produced during planting and postharvest processes. In this paper, we provided a brief introduction to deep-learning approaches and reviewed 71 recent research works in which deep-learning technologies were applied in the horticultural domain for variety recognition, yield estimation, quality detection, stress phenotyping detection, growth monitoring, and other tasks. We described in detail the application scenarios reported in the relevant literature, along with the applied models and frameworks, the used data, and the overall performance results. Finally, we discussed the current challenges and future trends of deep learning in horticultural research. The aim of this review is to assist researchers and provide guidance for them to fully understand the strengths and possible weaknesses when applying deep learning in horticultural sectors. We also hope that this review will encourage researchers to explore some significant examples of deep learning in horticultural science and will promote the advancement of intelligent horticulture.

Optimum DNA Extraction Methods for Edible Bird's Nest Identification Using Simple Additive Weighting Technique

A simple additive weighting (SAW) technique was used to determine and compare the overall performance of five DNA extraction methods from conventional (SDS method) to commercial kits (Qiagen, Wizard, and NucleoSpin) for identifying origins of edible bird’s nest (EBN) using end-point polymerase chain reaction (PCR). A hybrid method (SDS/Qiagen) which has been developed by combining the conventional SDS method with commercialised Qiagen was determined as the most suitable in terms of speed and cost-effectiveness. The determination of optimum extraction method was by the performances on efficiency and feasibility, extracted DNA concentration, purity, PCR amplifiability, handling time and safety of reagents used. The hybrid SDS/Qiagen method is less costly compared to the commercial kits and offered a more rapid alternative to the conventional SDS method with significant improvement in the yield, purity and PCR amplifiability. The developed hybrid SDS/Qiagen method provides a more practical alternative over the lengthy process using conventional method and expensive process using commercial kits. Using the simple additive weighting (SAW) technique and analysis, the Qiagen method is considered the most efficient and feasible method without consideration of cost as it yielded the purest extracted DNA and achieved the highest PCR amplifiability with the shortest turnaround time.

Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry

Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained. Yet, fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry (Fragaria x ananassa) images. The pipeline segments, classifies, and labels the images and extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominance effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable. Although we study strawberry images, the algorithm can be applied to other fruits, as shown in the GitHub repository.

Sticky problems: extraction of nucleic acids from molluscs

Traditional molecular methods and omics-techniques across molluscan taxonomy increasingly inform biology of Mollusca. Recovery of DNA and RNA for such studies is challenged by common biological properties of the highly diverse molluscs. Molluscan biomineralization, adhesive structures and mucus involve polyphenolic proteins and mucopolysaccharides that hinder DNA extraction or copurify to inhibit enzyme-catalysed molecular procedures. DNA extraction methods that employ the detergent hexadecyltrimethylammoniumbromide (CTAB) to remove these contaminants importantly facilitate molecular-level study of molluscs. Molluscan pigments may stain DNA samples and interfere with spectrophotometry, necessitating gel electrophoresis or fluorometry for accurate quantification. RNA can reliably be extracted but the 'hidden break' in 28S rRNA of molluscs (like most protostomes) causes 18S and 28S rRNA fragments to co-migrate electrophoretically. This challenges the standard quality control based on the ratio of 18S and 28S rRNA, developed for deuterostome animals. High-AT content in molluscan rRNA prevents the effective purification of polyadenylated mRNA. Awareness of these matters aids the continuous expansion of molecular malacology, enabling work also with museum specimens and next-generation sequencing, with the latter imposing unprecedented demands on DNA quality. Alternative methods to extract nucleic acids from molluscs are available from literature and, importantly, from communications with others who study the molecular biology of molluscs. This article is part of the Theo Murphy meeting issue 'Molluscan genomics: broad insights and future directions for a neglected phylum'.

The Influence of Endothelial Nitric Oxide Synthase (eNOS) Genetic Polymorphisms on Cholesterol Blood Levels Among Type 2 Diabetic Patients on Atorvastatin Therapy

Background:

Endothelial nitric oxide synthase (eNOS) plays a major role in the response of anti-hypercholesterol statin drugs. Genetic polymorphisms in the eNOS gene affect the activity of eNOS thereby modulating the statin response.

Objectives:

This study investigated the influence of major functional eNOS gene polymorphisms (rs2070744, rs1799983, and rs61722009) on the lipid profile of type 2 diabetes mellitus (T2DM) Jordanian patients treated with atorvastatin.

Methods:

The sample comprised 103 T2DM patients who attended the diabetes clinic of Jordan University Hospital. The T2DM patients had regularly been taking 20 mg atorvastatin. The atorvastatin response was calculated by measuring the lipid profile before and after three months of atorvastatin treatment. The eNOS genotypes of the subjects were analyzed using polymerase chain reaction (PCR) followed by restriction fragment length polymorphism (RFLP) assay.

Results:

No significant association was found between eNOS genetic polymorphisms and the response to atorvastatin (ANOVA, p > 0.05). In addition, no significant difference in the frequency of eNOS genotypes was found between T2DM patients and healthy subjects. However, patients with eNOS rs1799983, 4a/4a, and rs61722009 G/G genotypes showed significantly lower levels of baseline total cholesterol (TC) and low density lipoprotein (LDL) than did patients carrying the rs1799983 4b/4b or rs61722009 T/T genotype (p < 0.05). The eNOS rs1799983 and rs61722009 polymorphisms were in complete linkage disequilibrium (D' = 1).

Conclusion:

Although no association was found between eNOS genetic polymorphisms and atorvastatin response, there was a significant association between the rs1799983 and rs61722009 genotypes and baselines levels of TC and LDL in Jordanian T2DM patients. These genetic variants affect cholesterol levels and may play a role in the susceptibility to cardiovascular diseases in T2DM patients. Further studies are needed to validate these findings.

The Effects of Herbarium Specimen Characteristics on Short-Read NGS Sequencing Success in Nearly 8000 Specimens: Old, Degraded Samples Have Lower DNA Yields but Consistent Sequencing Success

Phylogenetic datasets are now commonly generated using short-read sequencing technologies unhampered by degraded DNA, such as that often extracted from herbarium specimens. The compatibility of these methods with herbarium specimens has precipitated an increase in broad sampling of herbarium specimens for inclusion in phylogenetic studies. Understanding which sample characteristics are predictive of sequencing success can guide researchers in the selection of tissues and specimens most likely to yield good results. Multiple recent studies have considered the relationship between sample characteristics and DNA yield and sequence capture success. Here we report an analysis of the relationship between sample characteristics and sequencing success for nearly 8,000 herbarium specimens. This study, the largest of its kind, is also the first to include a measure of specimen quality ("greenness") as a predictor of DNA sequencing success. We found that taxonomic group and source herbarium are strong predictors of both DNA yield and sequencing success and that the most important specimen characteristics for predicting success differ for DNA yield and sequencing: greenness was the strongest predictor of DNA yield, and age was the strongest predictor of proportion-on-target reads recovered. Surprisingly, the relationship between age and proportion-on-target reads is the inverse of expectations; older specimens performed slightly better in our capture-based protocols. We also found that DNA yield itself is not a strong predictor of sequencing success. Most literature on DNA sequencing from herbarium specimens considers specimen selection for optimal DNA extraction success, which we find to be an inappropriate metric for predicting success using next-generation sequencing technologies.

Differential lead-fluoride and nickel-fluoride uptake in co-polluted soil variably affects the overall physiome in an aromatic rice cultivar

The present study aimed to show that nickel and fluoride exhibited synchronized co-inhibited uptake in the aromatic rice cultivar, Gobindobhog, since bioaccumulation of the two elements was lower than that during individual stress, so that overall growth under combined stress was similar to control seedlings. On the contrary, lead and fluoride stimulated their co-uptake which triggered oxidative damages, NADPH oxidase activity, methylglyoxal accumulation, photosynthetic inhibition, membrane-protein damages, necrosis and genomic template degradation. Accumulation of proline, anthocyanins, non-protein thiols and phytochelatins was stimulated for systemic protection against reactive oxygen species (ROS) and xenobiotic-mediated injuries during lead-fluoride toxicity. ROS accumulation during nickel-fluoride stress was insignificant due to which enhanced accumulation of most antioxidants was not required. Glutathione depletion during combined lead-fluoride toxicity was due to its utilization in the glyoxalase cycle and also inhibition of glutathione reductase. However, the nickel-fluoride-treated sets maintained glutathione reserves and glyoxalase activity similar to those in control. Presence of fluoride 'safeguarded' the glutathione-utilizing enzymes like glutathione reductase, glutathione peroxidase and glutathione-S-transferase during dual lead-fluoride stress. This was because these enzymes showed higher activity compared to that under lead toxicity alone. Enzymatic antioxidants like superoxide dismutase, ascorbate peroxidase and guaiacol peroxidase were activated during lead-fluoride toxicity due to altered iron and copper homeostasis. Catalase activity was strongly inhibited, resulting in the inability to scavenge H₂O₂ and suppression of the fluoride-adaptable phenotype. However, none of the enzymatic antioxidants were inhibited during nickel-fluoride stress, which cumulatively allowed the seedlings to maintain normal physiology. Overall our findings holistically reveal the physiological plasticity of Gobindobhog in response to two different heavy metals under the influence of fluoride.

A low-cost pipeline for soil microbiome profiling

Common bottlenecks in environmental and crop microbiome studies are the consumable and personnel costs necessary for genomic DNA extraction and sequencing library construction. This is harder for challenging environmental samples such as soil, which is rich in Polymerase Chain Reaction (PCR) inhibitors. To address this, we have established a low‐cost genomic DNA extraction method for soil samples. We also present an Illumina‐compatible 16S and ITS rRNA gene amplicon library preparation workflow that uses common laboratory equipment. We evaluated the performance of our genomic DNA extraction method against two leading commercial soil genomic DNA kits (MoBio PowerSoil® and MP Biomedicals™ FastDNA™ SPIN) and a recently published non‐commercial extraction method by Zou et al. (PLoS Biology, 15, e2003916, 2017). Our benchmarking experiment used four different soil types (coniferous, broad‐leafed, and mixed forest plus a standardized cereal crop compost mix) assessing the quality and quantity of the extracted genomic DNA by analyzing sequence variants of 16S V4 and ITS rRNA amplicons. We found that our genomic DNA extraction method compares well to both commercially available genomic DNA extraction kits in DNA quality and quantity. The MoBio PowerSoil® kit, which relies on silica column‐based DNA extraction with extensive washing, delivered the cleanest genomic DNA, for example, best A260:A280 and A260:A230 absorbance ratios. The MP Biomedicals™ FastDNA™ SPIN kit, which uses a large amount of binding material, yielded the most genomic DNA. Our method fits between the two commercial kits, producing both good yields and clean genomic DNA with fragment sizes of approximately 10 kb. Comparative analysis of detected amplicon sequence variants shows that our method correlates well with the two commercial kits. Here, we present a low‐cost genomic DNA extraction method for soil samples that can be coupled to an Illumina‐compatible simple two‐step amplicon library construction workflow for 16S V4 and ITS marker genes. Our method delivers high‐quality genomic DNA at a fraction of the cost of commercial kits and enables cost‐effective, large‐scale amplicon sequencing projects. Notably, our extracted gDNA molecules are long enough to be suitable for downstream techniques such as full gene sequencing or even metagenomics shotgun approaches using long reads (PacBio or Nanopore), 10x Genomics linked reads, and Dovetail genomics.

Optimization of high molecular weight DNA extraction methods in shrimp for a long-read sequencing platform

Marine organisms are important to global food security as they are the largest source of animal proteins feeding mankind. Genomics-assisted aquaculture can increase yield while preserving the environment to ensure sufficient and sustainable production for global food security. However, only few high-quality genome sequences of marine organisms, especially shellfish, are available to the public partly because of the difficulty in the sequence assembly due to the complex nature of their genomes. A key step for a successful genome sequencing is the preparation of high-quality high molecular weight (HMW) genomic DNA. This study evaluated the effectiveness of five DNA extraction protocols (CTAB, Genomic-tip, Mollusc DNA, TIANamp Marine Animals DNA, and Sbeadex livestock kits) in obtaining shrimp HMW DNA for a long-read sequencing platform. DNA samples were assessed for quality and quantity using a Qubit fluorometer, NanoDrop spectrophotometer and pulsed-field gel electrophoresis. Among the five extraction methods examined without further optimization, the Genomic-tip kit yielded genomic DNA with the highest quality. However, further modifications of these established protocols might yield even better DNA quality and quantity. To further investigate whether the obtained genomic DNA could be used in a long-read sequencing application, DNA samples from the top three extraction methods (CTAB method, Genomic-tip and Mollusc DNA kits) were used for Pacific Biosciences (PacBio) library construction and sequencing. Genomic DNA obtained from Genomic-tip and Mollusc DNA kits allowed successful library construction, while the DNA obtained from the CTAB method did not. Genomic DNA isolated using the Genomic-tip kit yielded a higher number of long reads (N50 of 14.57 Kb) than those obtained from Mollusc DNA kits (N50 of 9.74 Kb). Thus, this study identified an effective extraction method for high-quality HMW genomic DNA of shrimp that can be applied to other marine organisms for a long-read sequencing platform.

The influence of five metallic nanoparticles on the expression of major drug-metabolizing enzyme genes with correlation of inflammation in mouse livers

Metallic nanoparticles (NPs) are widely used in medical preparations. The present study aims to find out the influence of widely used five metallic NPs on the expression of major hepatic drug-metabolizing enzyme (DME) genes. Six groups of BALB/C mice, 7 mice each, were exposed to: Gold NPs, silver NPs, copper oxide NPs, silicon dioxide NPs and zinc oxide NPs, for 21 days. Liver biopsies from all mice were subjected to mouse cyp3a11, cyp2c29, ugt2b1 and interleukin-6 (il6) gene expression quantification using real-time polymerase chain reaction, in addition to inflammatory cell infiltration examination. All tested NPs caused a sharp and significant (ANOVA, p value <0.05) downregulation in the expression of DME genes, with the highest influence was observed in mice exposed to copper oxide NPs. Additionally, all NPs induced hepatic inflammation and upregulated the expression of il6 gene, which were inversely correlated with the expression of DMEs. It is concluded that all tested NPs downregulated the expression of DME genes, with the highest influence exhibited by copper oxide NPs, in correlation with inflammation and il6 gene induction in the liver. Further studies are needed to find out the effect of anti-inflammatory compounds against the alterations induced by metallic NPs exposure on hepatic DMEs.

The Protective Effect of Metformin against Oxandrolone-Induced Infertility in Male Rats

Background:

Oxandrolone is a synthetic testosterone analogue that is widely used among bodybuilders and athletes. However, oxandrolone causes male infertility. Recently, it was found that metformin reduces the risk of infertility associated with diabetes mellitus.

Aim:

This study aimed to investigate the protective effects of metformin against oxandrolone-induced infertility in male rats.

Methods:

Rats continuously received one of four treatments (n=7) over 14 days: control DMSO administration, oxandrolone administration, metformin administration, or co-administration of oxandrolone and metformin. Doses were equivalent to those used for human treatment. Subsequently, testicular and blood samples were collected for morphological, biochemical, and histological examination. In addition, gene expression of the testosterone synthesizing enzyme CYP11A1 was analyzed in the testes using RT-PCR.

Results:

Oxandrolone administration induced male infertility by significantly reducing relative weights of testes by 48%, sperm count by 82%, and serum testosterone levels by 96% (ANOVA, P value < 0.05). In addition, histological examination determined that oxandrolone caused spermatogenic arrest which was associated with 2-fold downregulation of testicular CYP11A1 gene expression. However, co-administration of metformin with oxandrolone significantly ameliorated toxicological alterations induced by oxandrolone exposure (ANOVA, P value < 0.05).

Conclusion:

Metformin administration protected against oxandrolone-induced infertility in male rats. Further clinical studies are needed to confirm the protective effect of metformin against oxandrolone-induced infertility among athletes.

Procedures for DNA Extraction from Opium Poppy (Papaver somniferum L.) and Poppy Seed-Containing Products

Several commonly used extraction procedures and commercial kits were compared for extraction of DNA from opium poppy (Papaver somniferum L.) seeds, ground seeds, pollen grains, poppy seed filling from a bakery product, and poppy oil. The newly developed extraction protocol was much simpler, reduced the cost and time required for DNA extraction from the native and ground seeds, and pollen grains. The quality of extracted DNA by newly developed protocol was better or comparable to the most efficient ones. After being extended by a simple purification step on a silica membrane column, the newly developed protocol was also very effective in extracting of poppy DNA from poppy seed filling. DNA extracted from this poppy matrix was amplifiable by PCR analysis. DNA extracted from cold-pressed poppy oil and suitable for amplifications was obtained only by methods developed previously for olive oil. Extracted poppy DNA from all tested matrices was analysed by PCR using primers flanking a microsatellite locus (156 bp) and two different fragments of the reference tubulin gene (553 bp and 96 bp). The long fragment of the reference gene was amplified in DNA extracted from native seeds, ground seeds, and pollen grains. Poppy DNA extracted from the filling of bakery product was confirmed only by amplification of short fragments (96 bp and 156 bp). DNA extracted from cold-pressed poppy oil was determined also only by amplification of these two short fragments.

Coffee Flower Identification Using Binarization Algorithm Based on Convolutional Neural Network for Digital Images

Crop-type identification is one of the most significant applications of agricultural remote sensing, and it is important for yield estimation prediction and field management. At present, crop identification using datasets from unmanned aerial vehicle (UAV) and satellite platforms have achieved state-of-the-art performances. However, accurate monitoring of small plants, such as the coffee flower, cannot be achieved using datasets from these platforms. With the development of time-lapse image acquisition technology based on ground-based remote sensing, a large number of small-scale plantation datasets with high spatial-temporal resolution are being generated, which can provide great opportunities for small target monitoring of a specific region. The main contribution of this paper is to combine the binarization algorithm based on OTSU and the convolutional neural network (CNN) model to improve coffee flower identification accuracy using the time-lapse images (i.e., digital images). A certain number of positive and negative samples are selected from the original digital images for the network model training. Then, the pretrained network model is initialized using the VGGNet and trained using the constructed training datasets. Based on the well-trained CNN model, the coffee flower is initially extracted, and its boundary information can be further optimized by using the extracted coffee flower result of the binarization algorithm. Based on the digital images with different depression angles and illumination conditions, the performance of the proposed method is investigated by comparison of the performances of support vector machine (SVM) and CNN model. Hence, the experimental results show that the proposed method has the ability to improve coffee flower classification accuracy. The results of the image with a 52.5° angle of depression under soft lighting conditions are the highest, and the corresponding Dice (F1) and intersection over union (IoU) have reached 0.80 and 0.67, respectively.

The Association of 3-Hydroxy-3-Methylglutaryl-CoA Reductase, Apolipoprotein E, and Solute Carrier Organic Anion Genetic Variants with Atorvastatin Response among Jordanian Patients with Type 2 Diabetes

Atorvastatin is commonly used among type 2 diabetic (DM2) patients at the University of Jordan Hospital to prevent cardiovascular complication. However, we noticed that there is a wide inter-individual variation in the efficacy and toxicity of atorvastatin. This study aimed to find out the effects of major genetic variants in 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR), Apolipoprotein E (APOE), and Solute Carrier Organic Anion (SLCO1B1) genes on atorvastatin response among DM2 patients. A sample of 139 DM2 patients on 20 mg of atorvastatin was included in this study. The lipid and glycemic profile and the levels of hepatic enzymes alanine aminotransferase (ALT) and aspartate transaminase were recorded before and after 3 months of atorvastatin treatment. Additionally, the genetic variants HMGCR rs17244841,APOE rs7412 and rs429357, and SLCO1B1 rs2306283 and rs11045818 were genotyped using an Applied Biosystems DNA sequencing method (ABI3730×1). We found that atorvastatin reduced total cholesterol and low-density lipoprotein (LDL) more significantly (p-value < 0.05) in patients with wild genotype than variant alleles APOE rs7412C > T and SLCO1B1 rs2306283A > G. Furthermore, the ALT level was elevated significantly (p-value < 0.05) by 27% in patients with heterozygous SLCO1B1 rs11045818 G/A genotype, while it was not elevated among wild genotype carriers. Additionally, atorvastatin reduced total cholesterol more significantly (p-value < 0.05) in patients with SLCO1B1 rs2306283A and rs11045818G haplotypes and increased ALT levels by 27% (p-value < 0.05) in patients with SLCO1B1 rs2306283G and rs11045818A haplotypes. In conclusion, it was found in this study that APOE rs7412, SLCO1B1 rs2306283, and rs11045818 genotypes can be considered as potential genetic biomarkers of atorvastatin response among DM2 patients of Jordanian Arabic origin. Further clinical studies with larger sample numbers are needed to confirm these findings.

SILEX: a fast and inexpensive high-quality DNA extraction method suitable for multiple sequencing platforms and recalcitrant plant species

BACKGROUND

The use of sequencing and genotyping platforms has undergone dramatic improvements, enabling the generation of a wealth of genomic information. Despite this progress, the availability of high-quality genomic DNA (gDNA) in sufficient concentrations is often a main limitation, especially for third-generation sequencing platforms. A variety of DNA extraction methods and commercial kits are available. However, many of these are costly and frequently give either low yield or low-quality DNA, inappropriate for next generation sequencing (NGS) platforms. Here, we describe a fast and inexpensive DNA extraction method (SILEX) applicable to a wide range of plant species and tissues.

RESULTS

SILEX is a high-throughput DNA extraction protocol, based on the standard CTAB method with a DNA silica matrix recovery, which allows obtaining NGS-quality high molecular weight genomic plant DNA free of inhibitory compounds. SILEX was compared with a standard CTAB extraction protocol and a common commercial extraction kit in a variety of species, including recalcitrant ones, from different families. In comparison with the other methods, SILEX yielded DNA in higher concentrations and of higher quality. Manual extraction of 48 samples can be done in 96 min by one person at a cost of 0.12 €/sample of reagents and consumables. Hundreds of tomato gDNA samples obtained with either SILEX or the commercial kit were successfully genotyped with Single Primer Enrichment Technology (SPET) with the Illumina HiSeq 2500 platform. Furthermore, DNA extracted from Solanum elaeagnifolium using this protocol was assessed by Pulsed-field gel electrophoresis (PFGE), obtaining a suitable size ranges for most sequencing platforms that required high-molecular-weight DNA such as Nanopore or PacBio.

CONCLUSIONS

A high-throughput, fast and inexpensive DNA extraction protocol was developed and validated for a wide variety of plants and tissues. SILEX offers an easy, scalable, efficient and inexpensive way to extract DNA for various next-generation sequencing applications including SPET and Nanopore among others.

Rhizobial infection triggers systemic transport of endogenous RNAs between shoots and roots in soybean

Legumes have evolved a symbiotic relationship with rhizobial bacteria and their roots form unique nitrogen-fixing organs called nodules. Studies have shown that abiotic and biotic stresses alter the profile of gene expression and transcript mobility in plants. However, little is known about the systemic transport of RNA between roots and shoots in response to rhizobial infection on a genome-wide scale during the formation of legume-rhizobia symbiosis. In our study, we found that two soybean (Glycine max) cultivars, Peking and Williams, show a high frequency of single nucleotide polymorphisms; this allowed us to characterize the origin and mobility of transcripts in hetero-grafts of these two cultivars. We identified 4,552 genes that produce mobile RNAs in soybean, and found that rhizobial infection triggers mass transport of mRNAs between shoots and roots at the early stage of nodulation. The majority of these mRNAs are of relatively low abundance and their transport occurs in a selective manner in soybean plants. Notably, the mRNAs that moved from shoots to roots at the early stage of nodulation were enriched in many nodule-related responsive processes. Moreover, the transcripts of many known symbiosis-related genes that are induced by rhizobial infection can move between shoots and roots. Our findings provide a deeper understanding of endogenous RNA transport in legume-rhizobia symbiotic processes.

Growth monitoring of greenhouse lettuce based on a convolutional neural network

Growth-related traits, such as aboveground biomass and leaf area, are critical indicators to characterize the growth of greenhouse lettuce. Currently, nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. In this study, a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network (CNN). Taking lettuce images as the input, a CNN model was trained to learn the relationship between images and the corresponding growth-related traits, i.e., leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA). To compare the results of the CNN model, widely adopted methods were also used. The results showed that the values estimated by CNN had good agreement with the actual measurements, with R2 values of 0.8938, 0.8910, and 0.9156 and normalized root mean square error (NRMSE) values of 26.00, 22.07, and 19.94%, outperforming the compared methods for all three growth-related traits. The obtained results showed that the CNN demonstrated superior estimation performance for the flat-type cultivars of Flandria and Tiberius compared with the curled-type cultivar of Locarno. Generalization tests were conducted by using images of Tiberius from another growing season. The results showed that the CNN was still capable of achieving accurate estimation of the growth-related traits, with R2 values of 0.9277, 0.9126, and 0.9251 and NRMSE values of 22.96, 37.29, and 27.60%. The results indicated that a CNN with digital images is a robust tool for the monitoring of the growth of greenhouse lettuce.

Genome-wide assessment of population structure and genetic diversity of Chinese Lou onion using specific length amplified fragment (SLAF) sequencing

Lou onion (Allium fistulosum L. var. viviparum) is an abundant source of flavonols which provides additional health benefits to diseases. Genome-wide specific length amplified fragment (SLAF) sequencing method is a rapidly developed deep sequencing technologies used for selection and identification of genetic loci or markers. This study aimed to elucidate the genetic diversity of 122 onion accessions in China using the SLAF-seq method. A set of 122 onion accessions including 107 A.fistulosum L. var. viviparum Makino, 3 A.fistulosum L. var. gigantum Makino, 3 A.mongolicum Regel and 9 A.cepa L. accessions (3 whites, 3 reds and 3 yellows) from different regions in China were enrolled. Genomic DNA was isolated from young leaves and prepared for the SLAF-seq, which generated a total of 1,387.55 M reads and 162,321 high quality SNPs (integrity >0.5 and MAF >0.05). These SNPs were used for the construction of neighbor-joining phylogenetic tree, in which 10 A.fistulosum L. var. viviparum Makino accessions from Yinchuan (Ningxia province) and Datong (Qinghai province) had close genetic relationship. The 3 A.cepa L. clusters (red, white and yellow) had close genetic relationship especially with the 97 A.fistulosum L. var. viviparum Makino accessions. Population structure analysis suggested entire population could be clustered into 3 groups, while principal component analysis (PCA) showed there were 4 genetic groups. We confirmed the SLAF-seq approach was effective in genetic diversity analysis in red onion accessions. The key findings would provide a reference to the Lou onion germplasm in China.

Epigenomic Regulatory Mechanism in Vegetative Phase Transition of Malus hupehensis

In woody plants, phase transitions substantially affect growth and development. Although there has been considerable interest in the regulatory mechanisms underlying phase changes, the associated epigenetic modifications remain relatively uncharacterized. We examined the DNA methylation changes and the transcriptional responses in adult and juvenile Malus hupehensis leaves. The DNA methylations were 66.61% and 68.3% in the CG context, 49.12% and 52.44% in the CHG context, and 7.02% and 8.22% in the CHH context for the adult and juvenile leaves, respectively. The number of differentially methylated regions in all contexts distributed in the genic regions varied. Additionally, inhibited DNA methylation in adult leaves activated the transcription of indole-3-acetic acid related genes in the signaling, response, and transport pathways. Moreover, the opposite methylation and expression patterns were observed for the SPL and AP2 family genes between the adult and juvenile leaves. Both gene families contribute to the M. hupehensis vegetative phase transition. Furthermore, the hyper-/hypomethylation of the gene body or promoter of transcription factor genes may lead to up-/downregulated gene expression. The methylation levels of the WRKY (22), NAC (21), ERF (8), WOX (2), KNAT (6), EIN3 (2), SCL (7), ZAT (7), and HSF (4) genes were higher in the adult leaves than in the juvenile leaves, whereas the opposite pattern was observed for the TCP (2), MADS-box (11), and DOF (3) genes. An analysis of the correlation between methylation and transcription indicated the methylation of the gene body in all contexts and the methylation of the promoter in the CG and CHG contexts are negatively correlated with gene expression. However, the methylation of the promoter in the CHH context is positively correlated with gene expression. These findings reflect the diversity in the epigenetic regulation of gene expression and may be useful for elucidating the epigenetic regulatory mechanism underlying the M. hupehensis vegetative phase transition.

A Modified SDS – Based Method Applied for Extraction of High-Quality DNA from Raw Corn and Roasted Soybean

The probability of contamination of non-transgenic varieties with genetically modified (GM) products increase as a result of global expansion of areas sown with transgenic crops. DNA-based methods as accurate, efficient and reliable methods are preferable for detection of GM material in raw or highly processed foods. Isolation of high quality DNA with a suitable and efficient DNA extraction protocol is crucial for getting precise results in DNA amplification. In this study, we performed modifications of previously known Sodium dodecyl sulfate (SDS)-based DNA extraction method regarding the incubation period, DNA pellet washing and addition of organic solvent extraction, to improve DNA quality and to reduce costs. Raw corn kernels and roasted soybean seed were used as samples. DNA was extracted following three protocols, modifications of Edwards protocol. The type of detergent used in raw corn sample did not cause significant effects on extracted DNA yield and purity, while in roasted soybean samples the 2% (w/v) SDS lysis buffer gave the highest DNA yield. The additional incubation step raised the DNA yield from raw corn for 121%, while the purest DNA from soybean sample was obtained using organic solvent extraction. Electrophoretic determination of DNA integrity showed varying degree of DNA smearing from roasted soybean. Contrary, all extraction protocols used on raw corn kernels produced a high molecular weight DNA. Thus, our in-house DNA extraction protocol is as efficient but more cost effective compared to commercial kits and can be used for raw corn, while the protocol for roasted soybean needs further improvement.

Improving sustainable use of genetic resources in biodiversity archives

Tissue sample databases housed in biodiversity archives represent a vast trove of genetic resources, and these tissues are often destructively subsampled and provided to researchers for DNA extractions and subsequent sequencing. While obtaining a sufficient quantity of DNA for downstream applications is vital for these researchers, it is also important to preserve tissue resources for future use given that the original material is destructively and consumptively sampled with each use. It is therefore necessary to develop standardized tissue subsampling and loaning procedures to ensure that tissues are being used efficiently. In this study, we specifically focus on the efficiency of DNA extraction methods by using anuran liver and muscle tissues maintained at a biodiversity archive. We conducted a series of experiments to test whether current practices involving coarse visual assessments of tissue size are effective, how tissue mass correlates with DNA yield and concentration, and whether the amount of DNA recovered is correlated with sample age. We found that tissue samples between 2 and 8 mg resulted in the most efficient extractions, with tissues at the lower end of this range providing more DNA per unit mass and tissues at the higher end of this range providing more total DNA. Additionally, we found no correlation between tissue age and DNA yield. Because we find that even very small tissue subsamples tend to yield far more DNA than is required by researchers for modern sequencing applications (including whole genome shotgun sequencing), we recommend that biodiversity archives consider dramatically improving sustainable use of their archived material by providing researchers with set quantities of extracted DNA rather than with the subsampled tissues themselves.

Label-Free Detecting of the Compaction and Decompaction of ctDNA Molecules Induced by Surfactants with SERS Based on a nanoPAA-ZnCl2-AuLs Solid Substrate

DNA molecular compaction/decompaction is of great significance for the exploration of basic life processes, the research of biomedical and genetic engineering, and so forth. However, the detailed mechanism of DNA compaction/decompaction caused by surfactants remains an open and challenging problem that has not been fully solved so far. In this paper, a sort of novel solid substrate, nanoPAA-ZnCl₂-AuLs, with good stability and high sensitivity, was prepared by a self-assembly method. Based on this substrate, the surface-enhanced Raman scattering (SERS) technology was employed to investigate characteristics of interactions between DNA molecules and surfactants at a single molecular level. SERS spectra of calf thymus DNA (ctDNA), cetyl trimethyl ammonium bromide (CTAB), and sodium dodecyl sulfate (SDS) with a concentration as low as 10^-9 M, and SERS spectra of ctDNA-CTAB and ctDNA-CTAB-SDS composites were collected, respectively. The interactions between ctDNA and surfactants were analyzed by changes in SERS spectra, for example, disappearances and appearances of SERS bands and relative changes of peak intensity, in which CTAB resulted in the compaction of the DNA molecule while SDS induced the decompaction of the ctDNA-CTAB complex. Moreover, UV-visible spectrophotometry was employed to demonstrate the compaction/decompaction of ctDNA molecules caused by surfactants. The local binding modes of ctDNA molecules and surfactant molecules were expounded. This work will be helpful for understanding biological processes such as DNA compaction and recombination within nucleus or/and cells and for the development of gene therapy technologies.

Auxin Response Factor 2A Is Part of the Regulatory Network Mediating Fruit Ripening Through Auxin-Ethylene Crosstalk in Durian

Fruit ripening is a highly coordinated developmental process driven by a complex hormonal network. Ethylene is the main regulator of climacteric fruit ripening. However, a putative role of other key phytohormones in this process cannot be excluded. We previously observed an increasing level of auxin during the post-harvest ripening of the durian fruit, which occurred concomitantly with the rise in the climacteric ethylene biosynthesis. Herein, we connect the key auxin signaling component, auxin response factors (ARFs), with the regulatory network that controls fruit ripening in durian through the identification and functional characterization of a candidate ripening-associated ARF. Our transcriptome-wide analysis identified 15 ARF members in durian (DzARFs), out of which 12 were expressed in the fruit pulp. Most of these DzARFs showed a differential expression, but DzARF2A had a marked ripening-associated expression pattern during post-harvest ripening in Monthong, a commercial durian cultivar from Thailand. Phylogenetic analysis of DzARF2A based on its tomato orthologue predicted a role in ripening through the regulation of ethylene biosynthesis. Transient expression of DzARF2A in Nicotiana benthamiana leaves significantly upregulated the expression levels of ethylene biosynthetic genes, pointing to a ripening-associated role of DzARF2A through the transcriptional regulation of ethylene biosynthesis. Dual-luciferase reporter assay determined that DzARF2A trans-activates durian ethylene biosynthetic genes. We previously reported significantly higher auxin level during post-harvest ripening in a fast-ripening cultivar (Chanee) compared to a slow-ripening one (Monthong). DzARF2A expression was significantly higher during post-harvest ripening in the fast-ripening cultivars (Chanee and Phuangmanee) compared to that of the slow-ripening ones (Monthong and Kanyao). Thus, higher auxin level could upregulate the expression of DzARF2A during ripening of a fast-ripening cultivar. The auxin-induced expression of DzARF2A confirmed its responsiveness to exogenous auxin treatment in a dose-dependent manner, suggesting an auxin-mediated role of DzARF2A in fruit ripening. We suggest that high DzARF2A expression would activate ARF2A-mediated transcription of ethylene biosynthetic genes, leading to increased climacteric ethylene biosynthesis (auxin-ethylene crosstalk) and faster ripening. Hence, we demonstrated DzARF2A as a new component of the regulatory network possibly mediating durian fruit ripening through transcriptional regulation of ethylene biosynthetic genes.

Polyphenol-Rich Extracts from Brown Macroalgae Lessonia trabeculate Attenuate Hyperglycemia and Modulate Gut Microbiota in High-Fat Diet and Streptozotocin-Induced Diabetic Rats

Brown macroalgae are an important source of polyphenols with multiple health functions. In this work, polyphenol extracts from Lessonia trabeculate were purified and investigated for the antidiabetic activity in vitro and in vivo. The purified polyphenol extracts exhibited good antioxidant activities, α-glucosidase and lipase inhibition activities (IC50 < 0.25 mg/mL). The HPLC-DAD-ESI-MS/MS analysis indicated that the compounds in polyphenol extracts were mainly phlorotannin derivatives, phenolic acid derivatives, and gallocatechin derivatives. In vivo, C57BL/6J rats treated with polyphenol extracts for 4 weeks had lower fasting blood glucose levels, insulin levels, as well as better serum lipid profiles and antioxidant stress parameters, compared with the diabetic control (DC) group. Histopathology revealed that polyphenol extracts preserved the architecture and function of the liver. Short-chain fatty acid contents in rats' fecal samples with polyphenols administration were significantly recovered as compared with the DC group. Furthermore, the gut microflora of rats was investigated with high-throughput 16S rRNA gene sequencing and results indicated that polyphenol extracts had a positive effect on regulating the dysbiosis of the microbial ecology in diabetic rats. All of the results from the study provided a scientific reference of the potentially beneficial effects of L. trabeculate polyphenols on diabetes management.

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

BACKGROUND

Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm.

RESULTS

Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58-0.81) was higher than the other lines (r = 0.21-0.59) included in this study with different genetic backgrounds.

CONCLUSIONS

With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing.

The biochemistry underpinning industrial seed technology and mechanical processing of sugar beet

Seed-processing technologies such as polishing and washing enhance crop seed quality by limited removal of the outer layers and by leaching. Combined, this removes chemical compounds that inhibit germination. Industrial processing to deliver high-quality commercial seed includes removing chemical inhibitors of germination, and is essential to produce fresh sprouts, achieve vigorous crop establishment, and high yield potential in the field. Sugar beet (Beta vulgaris subsp. vulgaris var. altissima Doell.), the main sugar source of the temperate agricultural zone, routinely undergoes several processing steps during seed production to improve germination performance and seedling growth. Germination assays and seedling phenotyping was carried out on unprocessed, and processed (polished and washed) sugar beet fruits. Pericarp-derived solutes, known to inhibit germination, were tested in germination assays and their osmolality and conductivity assessed (ions). Abscisic acid (ABA) and ABA metabolites were quantified in both the true seed and pericarp tissue using UPLC-ESI(+)-MS/MS. Physical changes in the pericarp structures were assessed using scanning electron microscopy (SEM). We found that polishing and washing of the sugar beet fruits both had a positive effect on germination performance and seedling phenotype, and when combined, this positive effect was stronger. The mechanical action of polishing removed the outer pericarp (fruit coat) tissue (parenchyma), leaving the inner tissue (sclerenchyma) unaltered, as revealed by SEM. Polishing as well as washing removed germination inhibitors from the pericarp, specifically, ABA, ABA metabolites, and ions. Understanding the biochemistry underpinning the effectiveness of these processing treatments is key to driving further innovations in commercial seed quality.

Extraction of DNA with magnetic ionic liquids using in situ dispersive liquid-liquid microextraction

A new class of magnetic ionic liquids (MILs) with metal-containing cations was applied in in situ dispersive liquid-liquid microextraction (DLLME) for the extraction of long and short double-stranded DNA. For developing the method, MILs comprised of N-substituted imidazole ligands (with butyl-, benzyl-, or octyl-groups as substituents) coordinated to different metal centers (Ni²⁺, Mn²⁺, or Co²⁺) as cations, and chloride anions were investigated. These water-soluble MILs were reacted with the bis[(trifluoromethyl)sulfonyl]imide anion during the extraction to generate a water-immiscible MIL capable of preconcentrating DNA. The feasibility of combining the extraction methodology with anion-exchange high-performance liquid chromatography with diode array detection (HPLC-DAD) or fluorescence spectroscopy was studied. The method with the Ni²⁺- and Co²⁺-based MILs was easily combined with fluorescence spectroscopy and provided a faster and more sensitive method than HPLC-DAD for the determination of DNA. In addition, the method was compared to conventional DLLME using analogous water-immiscible MILs. The developed in situ MIL-DLLME method required only 3 min for DNA extraction and yielded 1.1-1.5 times higher extraction efficiency (EFs) than the conventional MIL-DLLME method. The in situ MIL-DLLME method was also compared to the trihexyl(tetradecyl)phosphonium tris(hexafluorocetylaceto)nickelate(II) MIL, which has been used in previous DNA extraction studies. EFs of 42-99% were obtained using the new generation of MILs, whereas EFs of only 20-38% were achieved with the phosphonium MIL. This new class of MILs is simple and inexpensive to prepare. In addition, the MILs present operational advantages such as easier manipulation in comparison to hydrophobic MILs, which can have high viscosities. These MILs are a promising new class of DNA extraction solvents that can be manipulated using an external magnetic field. Graphical abstract Magnetic ionic liquids with metal-containing cations are applied in in situ dispersive liquid-liquid microextraction for the extraction of long and short double-stranded DNA.

A Low-Cost High-Throughput Method for Plant Genomic DNA Isolation

Many of the functional genomics methods require isolation of genomic DNA from large population of plants. The selection of DNA isolation protocols depends on several factors such as choice of starting material, ease of handling, time and labor required for isolation, the final quantity as well as the quality of genomic DNA. We outline here a high-throughput method of DNA extraction from different plant species including cereal crops. The protocol can be used for extraction of DNA in single tubes as well as for large formats in 96-well plates. The protocol includes steps for eliminating interfering secondary products such as phenolics. This protocol can be applied for high-throughput isolation of DNA for varied applications such as TILLING, mapping, fingerprinting, etc. as a cost-effective protocol compared to commercial kits.

Comparative RNA-Sequencing and DNA Methylation Analyses of Apple (Malus domestica Borkh.) Buds with Diverse Flowering Capabilities Reveal Novel Insights into the Regulatory Mechanisms of Flower Bud Formation

In plants, DNA methylation (i.e. chromatin modification) is important for various biological processes, including growth, development and flowering. Because 'Fuji' apple trees are alternate bearing and have a long ripening period and poor-quality flower buds, we used bud types with diverse flowering capabilities to investigate the epigenetic regulatory mechanisms influencing flower bud formation. We examined the DNA methylation changes and the transcriptional responses in the selected apple bud types. We observed that in the apple genome, approximately 79.5%, 67.4% and 23.7% of the CG, CHG and CHH sequences are methylated, respectively. For each sequence context, differentially methylated regions exhibited distinct methylation patterns among the analyzed apple bud types. Global methylation and transcriptional analyses revealed that nonexpressed genes or genes expressed at low levels were highly methylated in the gene-body regions, suggesting that gene-body methylation is negatively correlated with gene expression. Moreover, genes with methylated promoters were more highly expressed than genes with unmethylated promoters, implying promoter methylation and gene expression are positively correlated. Additionally, flowering-related genes (e.g. SOC1, AP1 and SPLs) and some transcription factor genes (e.g. GATA, bHLH, bZIP and WOX) were highly expressed in spur buds (highest flowering rate), but were associated with low methylation levels in the gene-body regions. Our findings indicate a potential correlation between DNA methylation and gene expression in apple buds with diverse flowering capabilities, suggesting an epigenetic regulatory mechanism influences apple flower bud formation.

Refinement of a clearing protocol to study crassinucellate ovules of the sugar beet (Beta vulgaris L., Amaranthaceae)

BACKGROUND

Clearing methods allow relatively quick processing of plant material and examination of cellular structures by rendering tissues and organs translucent. They have been adapted for plant embryology, primarily to study ovule development, megasporogenesis, megagametogenesis and embryogenesis. Such clearing methods overcome several disadvantages of the conventional embedding-sectioning techniques that are arduous and time-consuming. Although numerous protocols with different clearing solutions have been described, there have been no reports to date proposing a reliable method to clear the crassinucellate ovules of the sugar beet (Beta vulgaris L.), an economically important crop. Therefore, this study aims to find a suitable approach to improve the tissue transparency of sugar beet ovules at different developmental stages.

RESULTS

We established a methyl salicylate-based protocol that significantly improved the transparency of the B. vulgaris ovule structures, which allowed us to observe the megagameto- and embryogenesis of that species. This was achieved by (1) chemical softening of the tissues; (2) vacuum pump-assisted infiltration step; (3) shaking-assisted incubation with clearing mixtures; and (4) manual removal of the chemically softened seed coat.

CONCLUSIONS

The effectiveness of our method is due to the strategy combining various approaches at different stages of the procedure aiming at increasing the accessibility of the internal ovule structures to the clearing solution. The results of this study may be applied in sugar beet breeding programs, and it will provide a basis for further investigation of numerous aspects of the species' embryology. Moreover, that unique approach may be easily adapted to other species developing crassinucellate ovules.

Optimized Nuclear Pellet Method for Extracting Next-Generation Sequencing Quality Genomic DNA from Fresh Leaf Tissue

Next-generation sequencing (NGS) is a revolutionary advancement allowing large-scale discovery of functional molecular markers that has many applications, including plant breeding. High-quality genomic DNA (gDNA) is a prerequisite for successful NGS library preparation and sequencing; however, few reliable protocols to obtain such plant gDNA exist. A previously reported nuclear pellet (NP) method enables extraction of high-yielding gDNA from fresh leaf tissue of maize (Zea mays L.), but the quality does not meet the stringent requirements of NGS. In this study, we optimized the NP method for whole-genome sequencing of rice (Oryza sativa L.) through the integration of simple purification steps. The optimized NP method relied on initial nucleus enrichment, cell lysis, extraction, and subsequent gDNA purification buffers. The purification steps used proteinase K, RNase A, phenol/chloroform/isoamyl alcohol (25:24:1), and chloroform/isoamyl alcohol (24:1) treatments for protein digestion and RNA, protein, and phenol removal, respectively. Our data suggest that this optimized NP method allowed extraction of consistently high-yielding and high-quality undegraded gDNA without contamination by protein and RNA. Moreover, the extracted gDNA fulfilled the quality metrics of NGS library preparation for the Illumina HiSeq X Ten platform by the TruSeq DNA PCR-Free Library Prep Kit (Illumina). We provide a reliable step-by-step guide to the extraction of high-quality gDNA from fresh leaf tissues of rice for molecular biologists with limited resources.

Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production

Aerial imagery is regularly used by crop researchers, growers and farmers to monitor crops during the growing season. To extract meaningful information from large-scale aerial images collected from the field, high-throughput phenotypic analysis solutions are required, which not only produce high-quality measures of key crop traits, but also support professionals to make prompt and reliable crop management decisions. Here, we report AirSurf, an automated and open-source analytic platform that combines modern computer vision, up-to-date machine learning, and modular software engineering in order to measure yield-related phenotypes from ultra-large aerial imagery. To quantify millions of in-field lettuces acquired by fixed-wing light aircrafts equipped with normalised difference vegetation index (NDVI) sensors, we customised AirSurf by combining computer vision algorithms and a deep-learning classifier trained with over 100,000 labelled lettuce signals. The tailored platform, AirSurf-Lettuce, is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution across the field, based on which associated global positioning system (GPS) tagged harvest regions have been identified to enable growers and farmers to conduct precision agricultural practises in order to improve the actual yield as well as crop marketability before the harvest.

Gene space completeness in complex plant genomes

Genome annotations offer ample opportunities to study gene functions, biochemical and regulatory pathways, or quantitative trait loci in plants. Determining the quality and completeness of a genome annotation, and maintaining the balance between them, are major challenges, even for genomes of well-studied model organisms. In this review, we present a historical overview of the complexity in different plant genomes and discuss the hurdles and possible solutions in obtaining a complete and high-quality genome annotation. We illustrate there is no clear-cut answer to solve these challenges for different gene types, but provide tips on guiding the iterative process of generating a superior genome annotation, which is a moving target as our knowledge about plant genomics increases and additional data sources become available.

Development of an innovative and sustainable one-step method for rapid plant DNA isolation for targeted PCR using magnetic ionic liquids

BACKGROUND

Nowadays, there is an increasing demand for fast and reliable plant biomolecular analyses. Conventional methods for the isolation of nucleic acids are time-consuming and require multiple and often non-automatable steps to remove cellular interferences, with consequence that sample preparation is the major bottleneck in the bioanalytical workflow. New opportunities have been created by the use of magnetic ionic liquids (MILs) thanks to their affinity for nucleic acids.

RESULTS

In the present study, a MIL-based magnet-assisted dispersive liquid-liquid microextraction (maDLLME) method was optimized for the extraction of genomic DNA from Arabidopsis thaliana (L.) Heynh leaves. MILs containing different metal centers were tested and the extraction method was optimized in terms of MIL volume and extraction time for purified DNA and crude lysates. The proposed approach yielded good extraction efficiency and is compatible with both quantitative analysis through fluorimetric-based detection and qualitative analysis as PCR amplification of multi and single locus genes. The protocol was successfully applied to a set of plant species and tissues.

CONCLUSIONS

The developed MIL-based maDLLME approach exhibits good enrichment of nucleic acids for extraction of template suitable for targeted PCR; it is very fast, sustainable and potentially automatable thereby representing a powerful tool for screening plants rapidly using DNA-based methods.

A modified protocol for rapid DNA isolation from cotton (Gossypium spp.)

Extraction of high-quality DNA from Gossypium (Cotton) species is notoriously difficult due to high contents of polysaccharides, quinones and polyphenols other secondary metabolites. Here, we describe a simple, rapid and modified procedure for high-quality DNA extraction from cotton, which is amenable for downstream analyses. In contrast to other CTAB methods, the described procedure is rapid, omits the use of liquid nitrogen, phenol, CsCl gradient ultracentrifugation, uses inexpensive and less hazardous reagents, and requires only ordinary laboratory equipment. The procedure employed the high concentration of NaCl and use of PVP-10 to rid the problems associated with polysaccharides and polyphenols, respectively. The average yield was approximately 10–15 μg of good quality DNA from 100 mg of tissue weight, which is adequate for projects, like genetic mapping or marker-assisted plant breeding. This protocol can be performed in as little as 3 h and may be adapted to high-throughput DNA isolation.

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Buffers A and B were redesigned from Paterson et al. (1993) and Porebski et al. (1997), respectively.

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Ribonuclease A was added before chloroform extraction.

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A simple, rapid and inexpensive DNA extraction method is described.

A low-cost and open-source platform for automated imaging

BACKGROUND

Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments. The field setting introduces a wide range of uncontrolled environmental variables that make validation and interpretation of spectral responses challenging, and as such lab- and greenhouse-deployed systems for plant studies and phenotyping are of increasing interest. In this study, we have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner. The reliability and accuracy of HyperScanner were validated using drought and salt stress experiments with Arabidopsis thaliana.

RESULTS

A robust, scalable, and reliable system was created. The system was built using open-sourced parts, and all custom parts, operational methods, and data have been made publicly available in order to maintain the open-source aim of HyperScanner. The gathered reflectance images showed changes in narrowband red and infrared reflectance spectra for each of the stress tests that was evident prior to other visual physiological responses and exhibited congruence with measurements using full-range contact spectrometers.

CONCLUSIONS

HyperScanner offers the potential for reliable and inexpensive laboratory hyperspectral imaging systems. HyperScanner was able to quickly collect accurate reflectance curves on a variety of plant stress experiments. The resulting images showed spectral differences in plants shortly after application of a treatment but before visual manifestation. HyperScanner increases the capacity for spectroscopic and imaging-based analytical tools by providing more access to hyperspectral analyses in the laboratory setting.

An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat

Wheat is one of the major crops in the world, with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply. The continual pressure to sustain wheat yield due to the world's growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments. We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour. We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties. Based on these image series, we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds. As a first step towards robust measurement of key yield traits in the field, we present a promising approach that employ Fully Convolutional Network (FCN) to perform semantic segmentation of images to segment wheat spike regions. We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets. We found that the FCN architecture had achieved a Mean classification Accuracy (MA) >82% on validation data and >76% on test data and Mean Intersection over Union value (MIoU) >73% on validation data and and >64% on test datasets. Through this phenomics research, we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike, which can be used to assist yield-focused wheat breeding objectives in near future.

Comparing Two Common DNA Extraction Kits for the Characterization of Symbiotic Microbial Communities from Ascidian Tissue

Various DNA extraction methods are often used interchangeably for the characterization of microbial communities despite indications that different techniques produce disparate results. The microbiomes of two ascidian species were herein characterized using two common DNA extraction kits, the DNeasy Blood and Tissue Kit (Qiagen) and the PowerSoil DNA Isolation Kit (Mo Bio Laboratories), followed by next-generation (Illumina) sequencing of partial 16S rRNA genes. Significant differences were detected in microbial community diversity and structure between ascidian species, but not between kits, suggesting similar recovery of biological variation and low technical variation between the two extraction methods for ascidian microbiome characterization.

Detection and analysis of wheat spikes using Convolutional Neural Networks

BACKGROUND

Field phenotyping by remote sensing has received increased interest in recent years with the possibility of achieving high-throughput analysis of crop fields. Along with the various technological developments, the application of machine learning methods for image analysis has enhanced the potential for quantitative assessment of a multitude of crop traits. For wheat breeding purposes, assessing the production of wheat spikes, as the grain-bearing organ, is a useful proxy measure of grain production. Thus, being able to detect and characterize spikes from images of wheat fields is an essential component in a wheat breeding pipeline for the selection of high yielding varieties.

RESULTS

We have applied a deep learning approach to accurately detect, count and analyze wheat spikes for yield estimation. We have tested the approach on a set of images of wheat field trial comprising 10 varieties subjected to three fertilizer treatments. The images have been captured over one season, using high definition RGB cameras mounted on a land-based imaging platform, and viewing the wheat plots from an oblique angle. A subset of in-field images has been accurately labeled by manually annotating all the spike regions. This annotated dataset, called SPIKE, is then used to train four region-based Convolutional Neural Networks (R-CNN) which take, as input, images of wheat plots, and accurately detect and count spike regions in each plot. The CNNs also output the spike density and a classification probability for each plot. Using the same R-CNN architecture, four different models were generated based on four different datasets of training and testing images captured at various growth stages. Despite the challenging field imaging conditions, e.g., variable illumination conditions, high spike occlusion, and complex background, the four R-CNN models achieve an average detection accuracy ranging from 88 to 94 % across different sets of test images. The most robust R-CNN model, which achieved the highest accuracy, is then selected to study the variation in spike production over 10 wheat varieties and three treatments. The SPIKE dataset and the trained CNN are the main contributions of this paper.

CONCLUSION

With the availability of good training datasets such us the SPIKE dataset proposed in this article, deep learning techniques can achieve high accuracy in detecting and counting spikes from complex wheat field images. The proposed robust R-CNN model, which has been trained on spike images captured during different growth stages, is optimized for application to a wider variety of field scenarios. It accurately quantifies the differences in yield produced by the 10 varieties we have studied, and their respective responses to fertilizer treatment. We have also observed that the other R-CNN models exhibit more specialized performances. The data set and the R-CNN model, which we make publicly available, have the potential to greatly benefit plant breeders by facilitating the high throughput selection of high yielding varieties.