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Ricke, D.

Publications and source records attributed to Ricke, D..

3 recordsLinked to original sources

Zinc and laboratory emerge date impact honey bee gut microbiota and survival

Honey bees (Apis mellifera) may be exposed to a wide variety of chemicals in the environment, including pesticides, antibiotics, and metals. Zinc, for example, is commonly included in fertilizers, pesticides, and feed additives, and is found in agricultural runoff. Honey bees can be exposed to zinc directly or indirectly by consuming zinc-contaminated nectar and pollen. However, there is a paucity of studies addressing the putative effects of zinc on honey bees health. In this study, we tested the effects of zinc on honey bee survivorship and gut microbiota. To evaluate survivorship, we exposed bees to six concentrations of zinc (0, 50, 100, 250, 500, or 1000 mg/L) and assessed survival daily for 10 days. To evaluate effects of zinc on gut microbiota, we exposed bees to 5 or 100 mg/L zinc. Bees were sampled before (day 0) and after zinc exposure (days 3, 6, and 9). Abdominal contents underwent DNA extraction and 16S rRNA sequencing (V3-V4) on an Illumina MiSeq. Sequences were filtered and processed through QIIME2 and DADA2. Zinc treatment had minimal effects on bacterial DNA concentrations and absolute cell counts while emerge date (the date a bee emerged from the brood comb) had a significant effect with decreased bacterial concentrations and cell counts observed at later emerge dates. Survival was only minimally impacted (>89% survival) at zinc concentrations up to 100 mg/L. Zinc had limited effects on overall gut microbial composition, diversity, and taxonomic abundances, with the greatest differences noted in the bee group exposed to the higher concentration of zinc (100 mg/L). In this group, several beneficial taxa (Lactobacillus, Rhizobiaceae, Gilliamella) were found at reduced abundances, while Paenibacillus, a potentially pathogenic taxa, was found at increased abundances. This suggests that zinc exposure, even at relatively low levels, may negatively impact honey bee health, even if survivorship is not dramatically impacted. Notably, emerge date effects were also observed in microbial composition. These results demonstrate the need to include assessments of honey bee gut microbiota in addition to other metrics of honey bee health and survivorship when evaluating the potential effects of agrochemicals on honey bees

ecology↗

Estimating Individual Contributions to Complex DNA SNP Mixtures

Mixture analysis and deconvolution methods can identify both known and unknown individuals contributing to DNA mixtures. These methods may not identify all DNA contributors with the remaining fraction of the mixture being contributed by one or more unknown individuals. The proportion of DNA contributed by individuals to a forensic sample can be estimated using their quantified mixture alleles. For short tandem repeats (STRs), methods to estimate individual contribution concentrations compare capillary electrophoresis peak heights and or peak areas within a mixture. For single nucleotide polymorphisms (SNPs), the major:minor allele ratios or counts, unique to each contributor, can be compared to estimate contributor proportion within the mixture. This article introduces three approaches (mean, median, and slope methods) for estimating individual DNA contributions to forensic mixtures for high throughput sequencing (HTS)/massively parallel sequencing (MPS) SNP panels.

bioinformatics↗

Fast P(RMNE): Fast Forensic DNA Probability of Random Man Not Excluded Calculation

High throughput sequencing (HTS) of DNA forensic samples is expanding from the sizing of short tandem repeats (STRs) to massively parallel sequencing (MPS). HTS panels are expanding from the FBI 20 core Combined DNA Index System (CODIS) loci to include SNPs. The calculation of random man not excluded, P(RMNE), is used in DNA mixture analysis to estimate the probability that a person is present in a DNA mixture. This calculation encounters calculation artifacts with expansion to larger panel sizes. Increasing the floating-point precision of the calculations allows for increased panel sizes but with a corresponding increase in computation time. The Taylor series higher precision libraries used fail on some input data sets leading to algorithm unreliability. Herein, a new formula is introduced for calculating P(RMNE) that scales to larger SNP panel sizes while being computationally efficient (patent pending)[1].

bioinformatics↗