Determining the hierarchical order by which intestinal tract, administered diet, and individual relay can shape the gut microbiome of fattening quails.

A bacterial metabarcoding approach was used to compare the microbiome composition of caecal and faecal samples from fattening Japanese quails (Coturnix coturnix japonica) fed three different diet regimes. The tested feedstuffs included (1) a commercial diet for fattening quails, (2) a commercial diet containing 12% full-fat silkworm (Bombyx mori) pupae meal, and (3) a commercial diet containing 12% defatted silkworm pupae meal. The aim of the experiment was to verify the relative effect of three variables (diet type, gut tract comparing caecum to rectum, and individual animal) in determining the level of bacterial community dissimilarity to rank the relevance of each of the three factors in affecting and shaping community composition. To infer such ranking, the communities resulting from the high-throughput sequencing from each sample were used to calculate the Bray-Curtis distances in all the pairwise combinations, whereby identical communities would score 0 and totally different ones would yield the maximum distance, equal to 1. The results indicated that the main driver of divergence was the gut tract, as distances between caecal and faecal samples were higher on average, irrespective of diet composition, which scored second in rank, and of whether they had been sampled from the same individual, which was the least effective factor. Simpson's species diversity indexes was not significantly different when comparing tracts or diets, while community evenness was reduced in full-fat silkworm diet-fed animals. The identities of the differentially displayed taxa that were statistically significant as a function of gut tract and diet regimen are discussed in light of their known physiological and functional traits.

Genetic approaches to exploit landraces for improvement of Triticum turgidum ssp. durum in the age of climate change.

Addressing the challenges of climate change and durum wheat production is becoming an important driver for food and nutrition security in the Mediterranean area, where are located the major producing countries (Italy, Spain, France, Greece, Morocco, Algeria, Tunisia, Turkey, and Syria). One of the emergent strategies, to cope with durum wheat adaptation, is the exploration and exploitation of the existing genetic variability in landrace populations. In this context, this review aims to highlight the important role of durum wheat landraces as a useful genetic resource to improve the sustainability of Mediterranean agroecosystems, with a focus on adaptation to environmental stresses. We described the most recent molecular techniques and statistical approaches suitable for the identification of beneficial genes/alleles related to the most important traits in landraces and the development of molecular markers for marker-assisted selection. Finally, we outline the state of the art about landraces genetic diversity and signature of selection, already identified from these accessions, for adaptability to the environment.

Bacterial endophytes as indicators of susceptibility to Cercospora Leaf Spot (CLS) disease in Beta vulgaris L.

The fungus Cercospora beticola causes Cercospora Leaf Spot (CLS) of sugar beet (Beta vulgaris L.). Despite the global importance of this disease, durable resistance to CLS has still not been obtained. Therefore, the breeding of tolerant hybrids is a major goal for the sugar beet sector. Although recent studies have suggested that the leaf microbiome composition can offer useful predictors to assist plant breeders, this is an untapped resource in sugar beet breeding efforts. Using Ion GeneStudio S5 technology to sequence amplicons from seven 16S rRNA hypervariable regions, the most recurring endophytes discriminating CLS-symptomatic and symptomless sea beets (Beta vulgaris L.ssp. maritima) were identified. This allowed the design of taxon-specific primer pairs to quantify the abundance of the most representative endophytic species in large naturally occurring populations of sea beet and subsequently in sugar beet breeding genotypes under either CLS symptomless or infection stages using qPCR. Among the screened bacterial genera, Methylobacterium and Mucilaginibacter were found to be significantly (p < 0.05) more abundant in symptomatic sea beets with respect to symptomless. In cultivated sugar beet material under CLS infection, the comparison between resistant and susceptible genotypes confirmed that the susceptible genotypes hosted higher contents of the above-mentioned bacterial genera. These results suggest that the abundance of these species can be correlated with increased sensitivity to CLS disease. This evidence can further prompt novel protocols to assist plant breeding of sugar beet in the pursuit of improved pathogen resistance.

SNP Alleles Associated With Low Bolting Tendency in Sugar Beet.

The identification of efficient molecular markers related to low bolting tendency is a priority in sugar beet (Beta vulgaris L.) breeding. This study aimed to identify SNP markers associated with low bolting tendency by establishing a genome-wide association study. An elaborate 3-year field trial comprising 13 sugar beet lines identified L14 as the one exhibiting the lowest bolting tendency along with an increased survival rate after autumnal sowing. For SNP discovery following phenotyping, contrasting phenotypes of 24 non-bolting and 15 bolting plants of the L14 line were sequenced by restriction site-associated DNA sequencing (RAD-seq). An association model was established with a set of 10,924 RAD-based single nucleotide polymorphism (SNP) markers. The allelic status of the most significantly associated SNPs ranked based on their differential allelic status between contrasting phenotypes (p < 0.01) was confirmed on three different validation datasets comprising diverse sugar beet lines and varieties adopting a range of SNP detection technologies. This study has led to the identification of SNP_36780842 and SNP_48607347 linked to low bolting tendency and can be used for marker-assisted breeding and selection in sugar beet.

Endophytic Microbiome Responses to Sulfur Availability in Beta vulgaris (L.).

Sulfur is an essential plant macronutrient, and its adequate supply allows an efficient root storage and sugar extractability in sugar beets (Beta vulgaris L.). In this study, we investigated the effect of changes in sulfur availability on the endophytic community structure of sugar beets. Plants were hydroponically grown in a complete nutrient solution (S-supplied), a nutrient solution without MgSO4 (S-deprived), and a nutrient solution without MgSO4 for six days and resupplied with 100 µM MgSO4 for 48 h (S-resupplied). The sulfur status was monitored by inductively coupled plasma ICP-OES, and combustion analysis together with the evaluation of microRNA395 as a biomarker for sulfate status. Metabarcoding of the bacterial 16S rRNA gene was carried out in order to determine leaf endophytic community structure. The Shannon diversity index significantly differed (p < 0.05) between sulfate-supplied and sulfate-deprived seedlings. Validation by Real-Time PCR showed a significant increase (p < 0.05) of Burkholderia spp. in sulfate-deprived plants as compared to sulfate-supplied ones. The study sheds new light on the effects of nutrient deficiency on the microbiome of sugar beet plants.

Novel Effects of Leonardite-Based Applications on Sugar Beet.

The present study aimed to explore the effects of foliar application of a leonardite-based product on sugar beet (Beta vulgaris L.) plants grown in the field. The approach concerned the evaluation of the community compositional structure of plant endophytic bacteria through a metabarcoding approach, the expression level of a gene panel related to hormonal metabolism and signaling, and the main sugar beet productivity traits. Results indicated that plants treated with leonardite (dosage of 2,000 ml ha-1, dilution 1:125, 4 mg C l-1) compared with untreated ones had a significant increase (p < 0.05) in (i) the abundance of Oxalicibacterium spp., recognized to be an endophyte bacterial genus with plant growth-promoting activity; (ii) the expression level of LAX2 gene, coding for auxin transport proteins; and (iii) sugar yield. This study represents a step forward to advance our understanding of the changes induced by leonardite-based biostimulant in sugar beet.

Quantification of rhizomania virus by automated RNA isolation and PCR based methods in sugar beet.

Rhizomania is a grave disease affecting sugar beet (Beta vulgaris L.). It is caused by the Beet Necrotic Yellow Vein Virus (BNYVV), an RNA virus transmitted by the plasmodiophorid vector Polymyxa betae. Genetic resistance to the virus has been accomplished mostly using phenotype-genotype association studies. As yet, the most convenient method to ascertain plant resistance has been the quantification of viral titer in roots through the ELISA test. This method is particularly time-consuming and clashes with the necessities of modern plant breeding. Here, we propose an alternative and successful phenotyping method based on the automatic extraction of the viral RNA from sugar beet roots and its relative and absolute quantification by quantitative real-time PCR (qRT-PCR) and digital PCR (dPCR), respectively. Such a method enables an improved standardization of the study, as well as an accurate quantification of the virus also in those samples presenting low virus titer, with respect to the ELISA test. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13337-021-00674-7.

Transcriptional and Physiological Analyses to Assess the Effects of a Novel Biostimulant in Tomato.

This work aimed to study the effects in tomato (Solanum lycopersicum L.) of foliar applications of a novel calcium-based biostimulant (SOB01) using an omics approach involving transcriptomics and physiological profiling. A calcium-chloride fertilizer (SOB02) was used as a product reference standard. Plants were grown under well-watered (WW) and water stress (WS) conditions in a growth chamber. We firstly compared the transcriptome profile of treated and untreated tomato plants using the software RStudio. Totally, 968 and 1,657 differentially expressed genes (DEGs) (adj-p-value < 0.1 and |log2(fold change)| ≥ 1) were identified after SOB01 and SOB02 leaf treatments, respectively. Expression patterns of 9 DEGs involved in nutrient metabolism and osmotic stress tolerance were validated by real-time quantitative reverse transcription PCR (RT-qPCR) analysis. Principal component analysis (PCA) on RT-qPCR results highlighted that the gene expression profiles after SOB01 treatment in different water regimes were clustering together, suggesting that the expression pattern of the analyzed genes in well water and water stress plants was similar in the presence of SOB01 treatment. Physiological analyses demonstrated that the biostimulant application increased the photosynthetic rate and the chlorophyll content under water deficiency compared to the standard fertilizer and led to a higher yield in terms of fruit dry matter and a reduction in the number of cracked fruits. In conclusion, transcriptome and physiological profiling provided comprehensive information on the biostimulant effects highlighting that SOB01 applications improved the ability of the tomato plants to mitigate the negative effects of water stress.

QuantStudio™ 12K Flex OpenArray® System as a Tool for High-Throughput Genotyping and Gene Expression Analysis.

Real time technology provides great advancements over PCR-based methods for a broad range of applications. With the increased availability of sequencing information, there is a need for the development and application of high-throughput real time PCR genotyping and gene expression methods that significantly broaden the current screening capabilities. Thermo Fisher Scientific (USA) has released a platform (QuantStudio™ 12K Flex system coupled with OpenArray® technology) with key elements required for high-throughput SNP genotyping and gene expression analysis. This allows for a rapid screening of large numbers of TaqMan® assays (up to 256) in many samples (up to 480) per run. This advanced real-time method involves the use of an array composed of 3,000 through-holes running on the QuantStudio™ 12K with OpenArray® block. The aim of this chapter is to outline the OpenArray® approach while providing a comprehensive in-depth review of the scientific literature on this topic. In agreement with a large number of independent studies, we conclude that the use of OpenArray® technology is a rapid and accurate method for high-throughput and large-scale systems biology studies with high specificity and sensitivity.

Expression Profiling of Candidate Genes in Sugar Beet Leaves Treated with Leonardite-Based Biostimulant.

Leonardite-based biostimulants are a large class of compounds, including humic acid substances. Foliar application of biostimulants at field level improves plant growth, yield and quality through metabolic changes and stimulation of plant proton pumps. The present study aimed at identifying optimum dosage of BLACKJAK, a humic acid-based substance, which is able to modify genes involved in sugar beet growth. Thirty-three genes belonging to various biochemical pathway categories were tested in leaves of treated sugar beet (Beta vulgaris L.) samples to assess gene expression profiling in response to BLACKJAK. Seedlings of a diploid and multigerm variety were grown in plastic pots and sprayed with two dilutions of BLACKJAK (dilution 1:500-1.0 mg C L-1 and dilution 1:1000-0.5 mg C L-1). Leaf samples were collected after 24, 48, and 72 h treatment with BLACKJAK for each dilution. RNA was extracted and the quantification of gene expression was performed while using an OpenArray platform. Results of analysis of variance demonstrated that, 15 genes out of a total of 33 genes tested with OpenArray qPCR were significantly affected by treatment and exposure time. Analysis for annotation of gene products and pathways revealed that genes belonging to the mitochondrial respiratory pathways, nitrogen and hormone metabolisms, and nutrient uptake were up-regulated in the BLACKJAK treated samples. Among the up-regulated genes, Bv_PHT2;1 and Bv_GLN1 expression exerted a 2-fold change in 1:1000 and 1:500 BLACKJAK concentrations. Overall, the gene expression data in the BLACKJAK treated leaves demonstrated the induction of plant growth-related genes that were contributed almost to amino acid and nitrogen metabolism, plant defense system, and plant growth.

Identification and validation of SNP markers linked to seed toxicity in Jatropha curcas L.

Edible/non-toxic varieties of Jatropha curcas L. are gaining increasing attention, providing both oil as biofuel feedstock or even as edible oil and the seed kernel meal as animal feed ingredient. They are a viable alternative to the limitation posed by the presence of phorbol esters in toxic varieties. Accurate genotyping of toxic/non-toxic accessions is critical to breeding management. The aim of this study was to identify SNP markers linked to seed toxicity in J. curcas. For SNP discovery, NGS technology was used to sequence the whole genomes of a toxic and non-toxic parent along with a bulk of 51 toxic and 30 non-toxic F2 plants. To ascertain the association between SNP markers and seed toxicity trait, candidate SNPs were genotyped on 672 individuals segregating for seed toxicity and two collections of J. curcas composed of 96 individuals each. In silico SNP discovery approaches led to the identification of 64 candidate SNPs discriminating non-toxic and toxic samples. These SNPs were mapped on Chromosome 8 within the Linkage Group 8 previously identified as a genomic region important for phorbol ester biosynthesis. The association study identified two new SNPs, SNP_J22 and SNP_J24 significantly linked to low toxicity with R2 values of 0.75 and 0.54, respectively. Our study released two valuable SNP markers for high-throughput, marker-assisted breeding of seed toxicity in J. curcas.

Molecular and Morphological Changes Induced by Leonardite-based Biostimulant in Beta vulgaris L.

Humic substances extracted from leonardite are widely considered to be bioactive compounds, influencing the whole-plant physiology and the crop yield. The aim of this work was to evaluate the effect of a new formulate based on leonardite in the early stage of growth of sugar beet (Beta vulgaris L.). A commercial preparation of leonardite (BLACKJAK) was characterized by ionomic analysis, solid-state 13C MAS NMR spectroscopy. Seedlings of sugar beet were grown in Hoagland's solution under controlled conditions. After five days of growth, an aliquot of the concentrated BLACKJAK was added to the solution to obtain a final dilution of 1:1000 (0.5 mg C L-1). The sugar beet response in the early stage of growth was determined by evaluating root morphological traits as well as the changes in the expression of 53 genes related to key morphophysiological processes. Root morphological traits, such as total root length, fine root length (average diameter < 0.5 mm), and number of root tips, were significantly (p < 0.001) increased in plants treated with BLACKJAK, compared to the untreated plants at all sampling times. At the molecular level, BLACKJAK treatment upregulated many of the evaluated genes. Moreover, both Real Time PCR and digital PCR showed that genes involved in hormonal response, such as PIN, ARF3, LOGL 10, GID1, and BRI1, were significantly (p < 0.05) upregulated by treatment with BLACKJAK. Our study provides essential information to understand the effect of a leonardite-based formulate on plant growth hormone metabolism, although the molecular and physiological basis for these complicated regulatory mechanisms deserve further investigations.

Comparison of three PCR-based assays for SNP genotyping in plants.

BACKGROUND: PCR allelic discrimination technologies have broad applications in the detection of single nucleotide polymorphisms (SNPs) in genetics and genomics. The use of fluorescence-tagged probes is the leading method for targeted SNP detection, but assay costs and error rates could be improved to increase genotyping efficiency. A new assay, rhAmp, based on RNase H2-dependent PCR (rhPCR) combined with a universal reporter system attempts to reduce error rates from primer/primer and primer/probe dimers while lowering costs compared to existing technologies. Before rhAmp can be widely adopted, more experimentation is required to validate its effectiveness versus established methods. RESULTS: The aim of this study was to compare the accuracy, sensitivity and costs of TaqMan, KASP, and rhAmp SNP genotyping methods in sugar beet (Beta vulgaris L.). For each approach, assays were designed to genotype 33 SNPs in a set of 96 sugar beet individuals obtained from 12 parental lines. The assay sensitivity was tested using a series of dilutions from 100 to 0.1 ng per PCR reaction. PCR was carried out on the QuantStudio 12K Flex Real-Time PCR System (Thermo Fisher Scientific, USA). The call-rate, defined as the percentage of genotype calls relative to the possible number of calls, was 97.0, 97.6, and 98.1% for TaqMan, KASP, and rhAmp, respectively. For rhAmp SNP, 24 of the 33 SNPs demonstrated 100% concordance with other two technologies. The genotype concordance with either technologies for the other 9 targets was above 99% (99.34-99.89%). CONCLUSION: The sensitivity test demonstrated that TaqMan and rhAmp were able to successfully determine SNP genotypes using as little as 0.2 ng DNA per reaction, while the KASP was unable to ascertain SNP states below 0.9 ng of DNA per reaction. Comparative cost per reaction was also analyzed with rhAmp SNP offering the lowest cost per reaction. In conclusion, rhAmp produced more calls than either TaqMan or KASP, higher signal to NTC data while offering the lowest cost per reaction.

Innovative Approaches to Evaluate Sugar Beet Responses to Changes in Sulfate Availability.

In this study, a system based on omics profiling was set-up for sugar beet (Beta vulgaris L. subsp. vulgaris) evaluation after changes in sulfate availability. Seedlings were grown on sulfate-deprived Hoagland solution. Six days after germination, 100 µM MgSO4 was added to the solution. Root samples were collected 36 h after treatments. WinRHIZO root-scanning approach was used for the automated image analysis of plant root morphology. Inductively Coupled Plasma Spectrometry (ICP-OES) and quadrupole-time-of-flight mass spectrometry (Q-TOF) were used for ionomic and metabolic analysis, respectively. Nanofluidic real-time PCR (OpenArray system) was used for molecular profiling. OpenArray chips were designed with TaqMan probes for 53 sugar beet genes putatively involved in sulfate nutrition. At morphological level treated seedlings showed significantly higher values (P < 0.01) than untreated plants for root traits related to soil exploration and nutrient uptake, such as total root length, fine roots length and root tips number. ICP-OES, Q-TOF and transcriptomic data revealed changes due to sulfate availability in sugar beet samples. Two key results are highlighted in sulfate-supplied roots and leaves. Firstly, high expression levels of auxin efflux carrier component 1 (PIN) and 5-phosphoribosyl-anthranilate, precursor of tryptophan and auxin synthesis, were observed in roots. Secondly, high levels of 2-Cys peroxiredoxin BAS1, chloroplastic, thioredoxin reductase (NADPH) and cysteine synthase, chloroplastic/chromoplastic, O-acetylserine sulfhydrylase, involved in protection against oxidative stress and cysteine synthase activity, respectively, were observed in leaves. Based on our findings, the combination of evaluated omics approaches could become a key system for the evaluation of the nutritional status of sugar beet under different nutrient availability conditions.

Targeted Next-Generation Sequencing Identification of Mutations in Disease Resistance Gene Analogs (RGAs) in Wild and Cultivated Beets.

Resistance gene analogs (RGAs) were searched bioinformatically in the sugar beet (Beta vulgaris L.) genome as potential candidates for improving resistance against different diseases. In the present study, Ion Torrent sequencing technology was used to identify mutations in 21 RGAs. The DNA samples of ninety-six individuals from six sea beets (Beta vulgaris L. subsp. maritima) and six sugar beet pollinators (eight individuals each) were used for the discovery of single-nucleotide polymorphisms (SNPs). Target amplicons of about 200 bp in length were designed with the Ion AmpliSeq Designer system in order to cover the DNA sequences of the RGAs. The number of SNPs ranged from 0 in four individuals to 278 in the pollinator R740 (which is resistant to rhizomania infection). Among different groups of beets, cytoplasmic male sterile lines had the highest number of SNPs (132) whereas the lowest number of SNPs belonged to O-types (95). The principal coordinates analysis (PCoA) showed that the polymorphisms inside the gene Bv8_184910_pkon (including the CCCTCC sequence) can effectively differentiate wild from cultivated beets, pointing at a possible mutation associated to rhizomania resistance that originated directly from cultivated beets. This is unlike other resistance sources that are introgressed from wild beets. This gene belongs to the receptor-like kinase (RLK) class of RGAs, and is associated to a hypothetical protein. In conclusion, this first report of using Ion Torrent sequencing technology in beet germplasm suggests that the identified sequence CCCTCC can be used in marker-assisted programs to differentiate wild from domestic beets and to identify other unknown disease resistance genes in beet.

"Noisy beets": impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris.

BACKGROUND: Noise (errors) in scientific data is endemic and may have a detrimental effect on statistical analyses and experimental results. The effects of noisy data have been assessed in genome-wide association studies for case-control experiments in human medicine. Little is known, however, on the impact of noisy data on genomic predictions, a widely used statistical application in plant and animal breeding. RESULTS: In this study, the sensitivity to noise in the data of five classification methods (K-nearest neighbours-KNN, random forest-RF, ridge logistic regression-LR, and support vector machines with linear or radial basis function kernels) was investigated. A sugar beet population of 123 plants phenotyped for a binary trait and genotyped for 192 SNP (single nucleotide polymorphism) markers was used. Labels (0/1 phenotype) were randomly sampled to generate noise. From the base scenario without errors in the labels, increasing proportions of noisy labels-up to 50 %-were generated and introduced in the data. CONCLUSIONS: Local classification methods-KNN and RF-showed higher tolerance to noisy labels compared to methods that leverage global data properties-LR and the two SVM models. In particular, KNN outperformed all other classifiers with AUC (area under the ROC curve) higher than 0.95 up to 20 % noisy labels. The runner-up method, RF, had an AUC of 0.941 with 20 % noise.

A new polymorphism on chromosome 6 associated with bolting tendency in sugar beet.

BACKGROUND: Premature flowering or bolting is an undesirable characteristic that causes severe sugar yield losses and interferes with harvesting. Vernalization is a prerequisite for the floral induction, achieved by exposure to low temperatures for 10-14 weeks. This process is also controlled by other environmental factors, such as long daylight photoperiods and a combination of genetic factors. The objective of this study was the identification of new genetic polymorphisms linked to bolting tendency in sugar beet. RESULTS: Two pollinators characterized by low and high bolting tendency were subjected to RAD-sequencing in order to detect discriminating SNPs between lines. 6,324 putative SNPs were identified. Of these, 192 were genotyped in a set of 19 pollinators, each comprising bolted and non-bolted individuals, for a total of 987 samples. Among the 192 candidate SNPs, the strongest overall association was found for SNP183 on chromosome 6 (p-value = 1.246 10(-13)). The association between SNP183 and bolting tendency was then confirmed in an independent population of 730 plants from 11 breeding lines (p-value = 0.0061). SNP183 is located in the intron of Bv_22330_orky, a sugar beet homolog of a matrix metalloproteinase (MMP) gene that could be implied in flowering in Arabidopsis thaliana. CONCLUSION: Our data support a significant association between an intronic SNP in the MMP gene located on chromosome 6 and the regulation of bolting tendency in sugar beet. The newly identified locus supports the polygenic nature of flowering control. The associated marker can be used to design SNP panels for the discrimination of bolters and non-bolters, to be used in sugar beet breeding programs for the development of improved germplasm with low bolting tendency.

Genome-enabled predictions for binomial traits in sugar beet populations.

BACKGROUND: Genomic information can be used to predict not only continuous but also categorical (e.g. binomial) traits. Several traits of interest in human medicine and agriculture present a discrete distribution of phenotypes (e.g. disease status). Root vigor in sugar beet (B. vulgaris) is an example of binomial trait of agronomic importance. In this paper, a panel of 192 SNPs (single nucleotide polymorphisms) was used to genotype 124 sugar beet individual plants from 18 lines, and to classify them as showing "high" or "low" root vigor. RESULTS: A threshold model was used to fit the relationship between binomial root vigor and SNP genotypes, through the matrix of genomic relationships between individuals in a genomic BLUP (G-BLUP) approach. From a 5-fold cross-validation scheme, 500 testing subsets were generated. The estimated average cross-validation error rate was 0.000731 (0.073%). Only 9 out of 12326 test observations (500 replicates for an average test set size of 24.65) were misclassified. CONCLUSIONS: The estimated prediction accuracy was quite high. Such accurate predictions may be related to the high estimated heritability for root vigor (0.783) and to the few genes with large effect underlying the trait. Despite the sparse SNP panel, there was sufficient within-scaffold LD where SNPs with large effect on root vigor were located to allow for genome-enabled predictions to work.