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The timing and extent to which D. suzukii utilize non-crop resources is not well understood and likely varies on a regional basis. This is an especially pertinent question for the northern range of D. suzukii, as cold winters may kill off a subset of overwintering adults [16,17] and scant information exists on what happens after D. suzukii emerge from overwintering sites before adequate fruit hosts ripen, but see [18,19]. D. suzukii can potentially exploit locally-available springtime fruiting non-crop hosts in northern temperate regions to increase adult population levels that later infest summer-fruiting cultivated hosts. If these flies begin their annual infestation cycles in non-crop plants first, those plant species could act as sentinels for early season monitoring and predict infestation risk for growers. At the end of the growing season in northern latitudes, most commercial fruit production ends in October. However, D. suzukii may continue to infest available non-host crops before going into reproductive diapause, which is brought on by a number of factors, including cooler temperatures and shortened day length [20,21,22].
At each site, D. suzukii presence was monitored using two clear deli cup traps with 12.5 mm diameter entry holes placed approximately 25 mm from the top of each cup. A yeast, sugar, apple cider vinegar, whole-wheat flour, water mixture was placed in a small insert (118 mL specimen cup, Coviden, Mansfield, MA, USA) within the container, covered with mesh, and surrounded by an apple cider vinegar, ethanol and surfactant drowning solution . The two traps were hung ~1 m off the ground in the canopy of plants not known to be associated with D. suzukii, approximately 1 m from a known non-crop host, and at least 10 m from each other. In 2013, traps were placed at all sites, while in 2014, trapping was done at six of the eight sites. Trap catch was collected and bait redeployed each week. Contents of the traps were drained from the drowning solution and examined for D. suzukii and other Drosophila spp. using a dissecting scope. A subsampling procedure was implemented for any trap catch weighing more than 2 g after draining, whereby 10% by weight of the trap catch was randomly removed, counted in its entirety, and used to estimate total D. suzukii numbers. For catches greater than 15 g, a 5% subsample was counted. This methodology was verified by comparing subsample estimates to the full count of the entire sample for five trap collections. The subsample procedure has ~5% margin of error.
Seed dormancy is a genetically controlled block preventing the germination of imbibed seeds in favorable conditions. It requires a period of dry storage (after-ripening) or certain environmental conditions to be overcome. Dormancy is an important seed trait, which is under selective pressure, to control the seasonal timing of seed germination. Dormant and non-dormant (after-ripened) seeds are characterized by large sets of differentially expressed genes. However, little information is available concerning the temporal and spatial transcriptional changes during early stages of rehydration in dormant and non-dormant seeds. We employed genome-wide transcriptome analysis on seeds of the model plant Arabidopsis thaliana to investigate transcriptional changes in dry seeds upon rehydration. We analyzed gene expression of dormant and after-ripened seeds of the Cvi accession over four time points and two seed compartments (the embryo and surrounding single cell layer endosperm), during the first 24 h after sowing. This work provides a global view of gene expression changes in dormant and non-dormant seeds with temporal and spatial detail, and these may be visualized via a web accessible tool ( ). A large proportion of transcripts change similarly in both dormant and non-dormant seeds upon rehydration, however, the first differences in transcript abundances become visible shortly after the initiation of imbibition, indicating that changes induced by after-ripening are detected and responded to rapidly upon rehydration. We identified several gene expression profiles which contribute to differential gene expression between dormant and non-dormant samples. Genes with enhanced expression in the endosperm of dormant seeds were overrepresented for stress-related Gene Ontology categories, suggesting a protective role for the endosperm against biotic and abiotic stress to support persistence of the dormant seed in its environment.
FIGURE 1. Dormancy release of Cvi seeds and the experimental set-up for the transcriptome analysis in D and AR seeds. (A) Loss of dormancy by dry seed storage (after-ripening) of the Cvi batch. Line graph shows germination percentage (average ± SD of four replicates). (B) Graph shows the rate of testa and endosperm rupture (average ± SD of four replicates) of seeds after 16 months of dry seed storage. (C) Photographs show the seed compartments used for gene expression analysis, micropylar and chalazal endosperm (MCE) and radicle+hypocotyl (RAD) indicated by the red dashed outer lines. (D) Picture summarizes the experimental set-up of the seed sampling. Freshly harvested seeds were fully dormant. These D seeds were imbibed for 3, 7, 12, and 24 h. The imbibed seeds were dissected to obtain two compartments, the radicle+hypocotyl (RAD) and the MCE at each time point. Together with non-dissected D dry seeds these total nine dormant samples which are indicated in blue. Next we followed AR of the seed batch by performing germination assays during the AR period. When seeds were fully capable to germinate the seeds are fully AR and thus non-dormant. Similar sampling was performed to obtain another nine AR samples, indicated in red.
FIGURE 2. Spatial and temporal gene expression in the Cvi seed dataset. (A) Gene expression plot from the web tool showing the expression of XTR8 as an example in various seed samples. (B) Number of expressed genes in various (sub) samples. (C) Number of genes expressed at different time points after sowing. (D) Numbers of specifically expressed genes in seed compartments, dormancy state or combined. The seed compartment specific sets were further refined using the gene expression data set of dissected germinating Col-0 seeds, with. the 42 MCE specific genes not present (expression value < 5 log2) in Col-0 RAD, cotyledons and peripheral endosperm as well and similarly, the 48 genes specific to the RAD are not present in the Col-0 cotyledons and endosperm samples. D, dormant; AR, after-ripened; MCE, micropylar and chalazal endosperm; RAD, radicle and hypocotyl; HAS, hours after sowing.
We compared our results obtained using full haematopoietic expression profiles with those obtained using minimal gene sets we defined to represent each lineage (Supplementary Data 3 and Methods). Of the 132 lineages whose full expression profiles were significantly associated with patient survival in the univariate analysis, 63 had significantly prognostic gene sets (Supplementary Data 4). These lineages were primarily developmental B cells, developmental T cells, stem cells, CD8+ effector T cells and NK cells. This result suggested that certain lineages survival contribution could be recapitulated using a small number of genes; however, the low overall number of consistent gene sets underscored the importance of using the entire gene expression profile when identifying survival-associated haematopoietic programmes.
The above described miRNAs matched 50 gene-targets from the 80-probe signature. In our study, hsa-miR-200c* and -29c have been associated with HJURP expression levels in G1, hsa-miR-19b-1* with CXCR6 in G2, and hsa-miR-17 with CTSK in G3, which are among the most important genes in the signature. None of these associations, however, have been reported in the literature. On the other hand, studies have demonstrated hits on the gene regulation between hsa-miR-142-5p and CD24 , hsa-miR-29 and DNMT3B [87, 88], hsa-miR-142-3p and EGR2 , hsa-miR-150 and EGR2 , hsa-miR-34a and IKZF3 , hsa-miR-150 and MIAT , hsa-miR-342-3p and PSMG3[93, 94], hsa-miR-17 and TIMP3 . Our results further suggested an important correlation between miRNAS and gene expression values in both Basal I and Basal II, identified by this in silico approach. These and other correlations are, however, highly complex and not fully understood. Additional analysis using in vitro and in vivo models are required to validate our achievements.
When an antigen matching the antigen-binding site binds to a naïve or memory B cell, it activates the B cell to produce and secrete more antigen-specific antibodies. Once a B cell fully matures, it is known as a plasma cell and will continue to produce and secrete antigen-specific antibodies for the remainder of its life cycle.
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