Sequential MyD88-Independent and -Dependent Activation of Innate Immune Responses to Intracellular Bacterial Infection

AbstractMicrobial infections induce chemokine and cytokine cascades that coordinate innate immune defenses. Infection with the intracellular bacterial pathogen Listeria monocytogenes induces CCR2-dependent monocyte recruitment and activation, an essential response for host survival. Herein we show that invasive L. monocytogenes, but not killed or noninvasive bacteria, induce secretion of MCP-1, the requisite chemokine for monocyte recruitment. Induction of MCP-1, but not TNF or IL-12, following L. monocytogenes infection is MyD88 independent. Consistent with these results, MyD88 deficiency does not impair monocyte recruitment to L. monocytogenes infected spleens, but prevents monocyte activation. Our results indicate that distinct microbial signals activate innate immune responses in an ordered, step-wise fashion, providing a mechanism to specify and modulate antimicrobial effector functions

Nonenzymatic β‑Carotene Degradation in Provitamin A‑Biofortified Crop Plants

Provitamin A biofortification, the provision of provitamin A carotenoids through agriculture, is regarded as an effective and sustainable intervention to defeat vitamin A deficiency, representing a global health problem. This food-based intervention has been questioned in conjunction with negative outcomes for smokers and asbestos-exposed populations of the CARET and ATBC trials in which very high doses of β-carotene were supplemented. The current notion that β-carotene cleavage products (apocarotenoids) represented the harmful agents is the basis of the here-presented research. We quantitatively analyzed numerous plant food items and concluded that neither the amounts of apocarotenoids nor β-carotene provided by plant tissues, be they conventional or provitamin A-biofortified, pose an increased risk. We also investigated β-carotene degradation pathways over time. This reveals a substantial nonenzymatic proportion of carotene decay and corroborates the quantitative relevance of highly oxidized β-carotene polymers that form in all plant tissues investigated

binned_subsets_2_14

contains the evenly distributed genotype subsets used in Spindel et al., 201

Additional file 8: Table S4. of Development and validation of allele-specific SNP/indel markers for eight yield-enhancing genes using whole-genome sequencing strategy to increase yield potential of rice, Oryza sativa L.

Markers for Fluidigm SNP genotyping platform. (DOC 36 kb

Additional file 1: Figure S1. of Development and validation of allele-specific SNP/indel markers for eight yield-enhancing genes using whole-genome sequencing strategy to increase yield potential of rice, Oryza sativa L.

Screening of DNA polymorphisms in the Gn1a gene using WGS data. Sequencing reads were aligned to the reference genome sequence (IRGSP-1.0). Note that the Gn1a gene lay on the opposite strand of the reference sequence. Screen-captured images of IGV software showed nucleotide variations that were used for marker development. (A) Three SNPs located in the Gn1a promoter region were shown with their genomic location on chromosome 1. Chr1: 5276405, Chr1: 5276521, and Chr1: 5276591 SNPs were used for Gn1a-17SNP/Gn1a-17SNP-FD, Gn1a-18SNP-FD, and Gn1a-19SNP-FD markers, respectively. (B) About a 70-bp deletion (red circles) near the 3’ UTR of Gn1a was found in varieties NSIC Rc158 and NSIC Rc238. This indel was used for designing the Gn1a-indel3 marker. (DOC 178 kb

Additional file 3: Figure S3. of Development and validation of allele-specific SNP/indel markers for eight yield-enhancing genes using whole-genome sequencing strategy to increase yield potential of rice, Oryza sativa L.

Screening of ST12-specific DNA variation in the OsSPL14 promoter region through WGS data analysis. Note that the OsSPL14 gene lay on the opposite strand of the reference sequence. The reference genome sequence was shown at the bottom of the image. Screen-captured image of IGV software showed an ST12-specific SNP located at the Chr 8: 25282790 nucleotide position (IRGSP-1.0). (DOC 77 kb

MET_crfilt_.90_outliers_removed_for_RRBlup

Post-imputation GBS dataset used specifically for GS cross-validation in Spindel et al., 2015. Dataset contains all markers with call rates >= .9 and lines that were included in the GS analysis (i.e., sub-population outliers are removed from this dataset). The data are formatted for use with the R rrBLUP package

Additional file 7: Table S3. of Development and validation of allele-specific SNP/indel markers for eight yield-enhancing genes using whole-genome sequencing strategy to increase yield potential of rice, Oryza sativa L.

Primers for preparation of PCR products and PCR product sequencing. (DOC 35 kb

Mean accuracies of cross-validation for prediction of grain yield (Kg/ha) (top row), flowering time (days to 50% flowering) (middle row), and plant height (cm) (bottom row) in the 2012 dry season, using 10 selections of SNP subsets either distributed evenly throughout the genome (right column) or chosen at random (left column) and five different statistical methods, error bars constructed using 1 standard error from the mean.

<p>The training population consisted of data from years 2009–2011, both seasons per year.</p

CV experiments.

<p>*AS = all seasons, DS = dry season only, WS = wet season only</p><p>CV experiments.</p