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16 results for “Fragaria ananassa”
Data from: Sex-determining chromosomes and sexual dimorphism: insights from genetic mapping of sex expression in a natural hybrid Fragaria × ananassa subsp. cuneifolia
We studied the natural hybrid (Fragaria × ananassa subsp. cuneifolia) between two sexually dimorphic octoploid strawberry species (Fragaria virginiana and Fragaria chiloensis) to gain insight into the dynamics of sex chromosomes and the genesis of sexual dimorphism. Male sterility is dominant in both the parental species and thus will be inherited maternally, but the chromosome that houses the sex-determining region differs. Thus, we asked whether (1) the cytotypic composition of hybrid populations represents one or both maternal species, (2) the sex-determining chromosome of the hybrid reflects the location of male sterility within the maternal donor species and (3) crosses from the hybrid species show less sexual dimorphism than the parental species. We found that F. × ananassa subsp. cuneifolia populations consisted of both parental cytotypes but one predominated within each population. Genetic linkage mapping of two crosses showed dominance of male sterility similar to the parental species, however, the map location of male sterility reflected the maternal donor in one cross, but not the other. Moreover, female function mapped to a single region in the first cross, but to two regions in the second cross. Aside from components of female function (fruit set and seed set), other traits that have been found to be significantly sexually dimorphic in the pure species were either not dimorphic or were dimorphic in the opposite direction to the parental species. These results suggest that hybrids experience some disruption of dimorphism in secondary sexual traits, as well as novel location and number of quantitative trait locus (QTL) affecting sex function.
Fig. 4 in Latilactobacillus fragifolii sp. nov., isolated from leaves of a strawberry plant (Fragaria x ananassa)
Fig. 4. Presence of known haem-dependent and manganese-dependent catalase encoding genes in five reference genomes of the genus Latilactobacillus and AMBP162T. Analysis was done by comparing the four known catalase genes from lactic acid bacteria against all predicted proteins from the six Latilactobacillus genomes using BLASTp.
Fig. 2 in Latilactobacillus fragifolii sp. nov., isolated from leaves of a strawberry plant (Fragaria x ananassa)
Fig. 2. Core genome phylogenetic tree of the genus Latilactobacillus. Genes were predicted with Prodigal version 2.6.3 [33] and core genes were determined, aligned and concatenated with SCARAP version 7cd24ba [16]. 662 strict core genes were found. Columns with one or more gaps were trimmed from the alignment with trimAl version 1.4.rev15 [34]. A maximum-likelihood phylogeny was inferred with IQ-Tree version 1.6.11 [31], using the LG+F+I+G4 amino acid substitution model. Bootstrap support was determined with the SH-aLRT test with 1000 replicates and the UFboot test with 1000 replicates. Branches with an SH-aLRT support of at least 80% and UFboot support of at least 95% were considered reliable and were indicated with a dark blue dot. The tree was visualized with ggtree version 3.0.2 [32].
Fig. 3 in Latilactobacillus fragifolii sp. nov., isolated from leaves of a strawberry plant (Fragaria x ananassa)
Fig. 3. Light microscope image at ×1000 magnification of Gram-stained cells of AMBP162T grown for 24 h at 30 °C in de Man–Rogosa–Sharpe broth, shaking at 200 r.p.m.
Competing Technologies: Determining the Geographical Origin of Strawberries (Fragaria × ananassa) using laboratory based Near-Infrared Spectroscopy compared to a simple portable device
<p><span>The application and development of fast and simple screening methods for the authentication of foods has increased continuously in recent years. A widely used analytical technique is Fourier transform near-infrared spectroscopy (FT-NIR). Despite the simple application of FT-NIR analysis, the analyses are usually carried out on benchtop devices in the laboratory. However small, inexpensive and mobile NIR devices could be used on-site. Despite the simple use of FT-NIR analysis, the examinations are usually carried out on a stationary benchtop device in a laboratory. However, in order to be able to perform the application directly on site, the application of small, cost-effective and mobile NIR devices for food analysis is crucial. In this study, both, a benchtop NIR instrument and a handheld NIR device with a lower resolution and analyzed wavenumber range were applied for the differentiation of strawberries from different geographical origins. Distinguishing German and non-German strawberries using linear discriminant analysis (LDA) yielded an accuracy of 91.9 % and 84.0 % using the benchtop and the handheld devices, respec-tively. Relevant variables could be assigned to lipids, carbohydrates and proteins. Overall, our study demonstrated for the first time that analyzing the geographical origin of strawberries using NIR spectroscopy is also possible by means of a handheld device.</span></p>
Fig. 4 in Characterisation of Thai strawberry (Fragaria × ananassa Duch.) cultivars with RAPD markers and metabolite profiling techniques
Fig. 4. Metabolite pathway display shows significant difference between Praratchatan No.80 and Akihime at 25Daa. Changes of metabolites are displayed as ratio>1 (blue), <1 (red), no change (grey) and not detected (white). Analysis comprised six biological replicates per cultivar. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Characterisation of Thai strawberry (Fragaria × ananassa Duch.) cultivars with RAPD markers and metabolite profiling techniques
Fig. 5. PCA analysis of volatile analysis of Akihime and Praratchatan No.80. Freeze-dried tissue was analysed by SPME-GC-MS. Samples and metabolites are displayed as score plot (A) and loading plot (B). Six biological replicates are displayed individually.
Fig. 3 in Characterisation of Thai strawberry (Fragaria × ananassa Duch.) cultivars with RAPD markers and metabolite profiling techniques
Fig. 3. Heatmap of metabolites detected in strawberry fruits of Praratchatan No.80 and Akihime. Metabolites were analysed by GC-MS and LC-MS from polar extracts and by GC-MS from non-polar extracts. Ripening stages are displayed as 7, 10, 13, 16, 19, 22, 25, 28Daa. Only significant compounds, as determined by twoway ANOVA analysis, are displayed. Biological replicates are displayed individually. A more detailed version of this figure is available as Supplementary Fig. 3.
Fig. 2 in Characterisation of Thai strawberry (Fragaria × ananassa Duch.) cultivars with RAPD markers and metabolite profiling techniques
Fig. 2. Stages of fruit development from flower (FL) to overripe fruit of strawberry cultivar Akihime (A) and Praratchatan No.80 (B). Stages are labelled as days after anthesis (Daa). PCA analysis of metabolite composition of Praratchatan No.80 (circles) and Akihime (squares) at eight different fruit ripening stages (C). Metabolites of polar and non-polar extracts were analysed by GC-MS and LC-MS. Data includes 134 identified metabolites including primary and specialised metabolism. Analysis comprised six biological replicates, which are displayed individually.
Fig. 1 in Characterisation of Thai strawberry (Fragaria × ananassa Duch.) cultivars with RAPD markers and metabolite profiling techniques
Fig. 1. Dendrograms of six strawberry varieties based on their genetic (A) and metabolic (B) similarities. The genetic information is based on the degree of band sharing of 228 polymorphic alleles. The metabolic data comprises 129 metabolites identified in polar and non-polar leaf extracts by GC-MS. (C) Representative leaf of six strawberry cultivars.
Data from: Sex-determining chromosomes and sexual dimorphism: insights from genetic mapping of sex expression in a natural hybrid Fragaria × ananassa subsp. cuneifolia
Open the record for dataset details and reuse information.
High-throughput sequencing and degradome analysis identify miRNAs and their targets involved in fruit senescence of Fragaria ananassa
GEO Series GSE48055. Fragaria x ananassa. 3 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Construction of a subtracted gDNA microarray for rapid discovery of molecular markers associated with day-neutrality in strawberry (Fragaria x ananassa)
GEO Series GSE70145. Fragaria x ananassa. 4 samples. Type: Genome variation profiling by array.
Integrative “omic” analysis reveals distinctive cold responses in leaves and roots of strawberry, Fragaria × ananassa ‘Korona’
GEO Series GSE73488. Arabidopsis thaliana; Fragaria x ananassa. 8 samples. Type: Expression profiling by array.
Fig. 1. 16S in Latilactobacillus fragifolii sp. nov., isolated from leaves of a strawberry plant (Fragaria x ananassa)
Fig. 1. 16S rRNA tree of the genus Latilactobacillus Reference genomes for five species of Latilactobacillus and the outgroup species Paralactobacillus selangorensis were determined from the Genome Taxonomy Database release 89 [18] and downloaded from the NCBI. The in-house-sequenced genome of strain AMBP162T was added. 16S rRNA sequences were extracted from whole genome sequences using Barrnap version 0.9 [29] and aligned with MAFFT version 7.407 [30]. A maximum-likelihood phylogeny was inferred with IQ-Tree version 1.6.12 [31], using a TN+F+I nucleotide substitution model. The tree was visualized with ggtree version 3.0.2 [32].
Host-pathogen interactions between Xanthomonas fragariae and its host Fragaria × ananassa investigated with a dual RNA-seq analysis
GEO Series GSE150636. Fragaria x ananassa. 6 samples. Type: Expression profiling by high throughput sequencing.
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