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Many bird species are vulnerable to environmental change, so knowledge of their diet and its variation can help understand population status and flexibility to respond to change. Insectivorous species are predicted to have a flexible diet within and between individuals, which can respond to naturally fluctuating prey abundance, thus allowing opportunistic exploitation of available resources. We analysed the diet of a nocturnal, aerial insectivore, the European Nightjar Caprimulgus europaeus, using high-throughput metabarcoding. We quantified diet diversity and composition of 130 faecal samples from nests and roosts on a northern breeding site in the UK from 2015 to 2018, and compared differences among individuals and years. Although dominated by moths, diet varied significantly between individual faecal samples and between years and months. Prey species composition varied between years, and was more variable between samples in 2017 than in other years. Faecal samples were significantly more likely to contain moth species with a wingspan of > 60 mm and less likely to contain moth species of

Additional file 1: Table S1. Primer sequences used for MHC sequencing. Degenerate bases are shown according to IUPAC codes: Y = C/T, N = any base. Table S2. Repeatability of MHC-I (n = 26) and MHC-II (n = 24) genotyping for different dominant frequency thresholds. Minimum amplicon frequency was kept constant at 0.3%. Threshold with the greatest repeatability is in bold. Table S3. Repeatability of MHC-I (n = 26) and MHC-II (n = 24) genotyping for different minimum amplicon frequencies. Minimum dominant frequency threshold was kept constant at 25%. Minimum amplicon frequency with the greatest repeatability for each MHC class is in bold. Table S5. Core families present in 281 faecal samples, collected from 224 Seychelles warblers. Core microbiome is defined as bacterial families that appeared in at least 50% of samples, with a minimum relative abundance of 0.1%. Total number of reads, and % of all reads are included. Table S5. The effect of host-associated variables on gut microbiome diversity in the Seychelles warbler (n = 195). GLMMs for three metrics of alpha diversity: Shannon diversity, Chao 1(log transformed) and Faiths phylogenetic diversity (log transformed): A) including the presence/absence of MHC alleles or, B) MHC diversity. A Linear model was used to generate conditional model-averaged estimates (��), their standard error (SE), z value, P value, and relative importance (��) are shown for all predictors featuring in the top model set (��AICc ��� 7). All continuous factors were standardised. Estimates are in reference to MHC allele = absent, TLR3 genotype = TLR3AA, sex = female, age class = fledgling, field period = Major 2017. Significant terms are in bold and underlined. *** P < 0.001, ** P < 0.01, * P < 0.05. Figure S1. (A) Sample completeness and (B) rarefaction curves in Seychelles warbler faecal samples. Each line represents a single faecal sample (281 faecal samples, collected from 224 Seychelles warblers). Curves were generated using the R package iNEXT 2.0.20, with 50 bootstrap replicates per sample. The dashed line represents the number of reads used as a cut-off for retaining samples in downstream analysis (all samples with fewer than 10,000 reads were removed). Figure S2. Prevalence and total abundance of all ASV���s separated by phylum. Each phylum is shown in a separate plot, and a different colour. Dashed lines represent the values used as cut-offs for filtering rare taxa before alpha and beta diversity analyses (minimum abundance = 50), and additional filtering for beta diversity (prevalence threshold = 2.5%). Figure S3. Individual repeatability of alpha and beta diversity measures in the Seychelles warbler. This was tested by sequecnoing multiple samples taken from the same individuals; these samples were collected during the same field season (n = 115 faecal samples from 51 individuals. Pairwise Euclidean distances were calculated between samples taken from different individuals, versus those from within the same individual, in the same season for A). Shannon dissimilarity B) unweighted UniFrac dissimilarity and C) weighted UniFrac dissimilarity. Boxes span the interquartile (25% - 75%) range. Whiskers extend to 1.5 times the interquartile range. The median is marked by a horizontal line and the mean is marked by a diamond. Dark blue points in A) indicate pairwise comparisons involving two outliers. Significant differences are shown, and P-values are derived from Kruskal���Wallis tests: *** P < 0.001, * P < 0.05. Figure S4. The repeatability of sequencing methods. This was tested by sequencing 37 faecal samples taken from individual Seychelles warblers twice. A) Relative abundance (%) of the 10 most abundant taxa at the phylum level for the 37 duplicated samples. Each column represents one sample, black lines separate duplicated samples. All other taxa within each sample are collapsed into the low abundance category. B) The pairwise Euclidean dissimilarity between different samples, versus within pairs of duplicated samples (same DNA sequenced twice) for i. Shannon dissimilarity ii. unweighted UniFrac dissimilarity and iii. weighted UniFrac dissimilarity. Boxes span the interquartile (25% - 75%) range. Whiskers extend to 1.5 times the interquartile range. The median is marked by a horizontal line and the mean is marked by a diamond. Significant differences are shown, and P-values were derived from Kruskal���Wallis tests: *** P < 0.001. Figure S5. Beta diversity of Seychelles warbler gut microbiome composition in different age classes. The principal coordinate plots are based on A) unweighted UniFrac distances, and B) weighted UniFrac distances. Points represent a single faecal sample from a different individual (n = 195). Sample sizes are specified in brackets in the legend, and colours indicate the age class which was either fledgling (yellow), old-fledgling (green), sub-adult (indigo) and adult (purple). Ellipses represent a 95% confidence interval around the cluster centroids. Figure S6. Differentially abundant ASV���s in the gut microbiome of Seychelles warblers between different age categories (FL = fledgling, OFL = old fledgling, SA = sub-adult, A = adult). ASVs are grouped at the level of bacterial order and coloured according to bacterial phylum. Differential ASV abundance was assessed using negative binomial Wald tests and P values were adjusted using the Benjamini and Hochberg false-discovery rate correction with a significance cut-off of P < 0.01. ASVs shown with a log2 fold change greater than zero are significantly more abundant in the age classes on the left and ASVs with a log2 fold change smaller than zero are significantly more abundant in age classes on the right. Figure S7. Differentially abundant ASV���s in the gut microbiome of Seychelles warblers, between seasons. Comparisons are A) Major 2017 vs Minor 2018, B) Major 2018 vs Minor 2017, or C) Major 2017 vs Major 2018. ASVs are grouped at the level of bacterial order and coloured according to bacterial phylum. Differential ASV abundance was assessed using negative binomial Wald tests and P values were adjusted using the Benjamini and Hochberg false-discovery rate correction with a significance cut-off of P < 0.01. ASVs shown with a log2 fold change greater than zero are significantly more abundant in seasons on the left and ASVs with a log2 fold change smaller than zero are significantly more abundant in seasons on the right.

VCU Theses and Dissertations

Removal of protein activity by genetic mutation or pharmacological inhibition has been used extensively to understand the normal function of a protein. However, null mutations eliminate gene function in all cells and pharmacological agents can diffuse through tissues to have similar global effects that can obscure the physiological function of a protein. This is a particular problem when studying proteins that function in many cell types or that have different cell-specific activities. The most direct strategy to study the function of a protein is to reduce or eliminate its activity only in specific cell types, rather than in all cells of an organism. The idea of targeting gene knockdown to specific cell types or to individual cells is not new and many strategies aim to do just this. However, these strategies result in variable knockdown efficiencies and can have silencing effects in neighboring cells and therefore knockdown is never cell-specific. We developed a novel method to knock down the expression of any gene and to restrict this knockdown to specific cell types in C. elegans. In this method we replaced endogenous genes with single copy integrated transgenes containing an engineered sequence tag that introduces premature stop codons (PTCs) into transgene mRNA. This tag causes the natural stop codon to be recognized as a PTC by the host's nonsense-mediated decay (NMD) machinery and does not disrupt gene function. In NMD-competent animals, a PTC-containing transgene is degraded and in NMD-defective animals, a PTC-containing transgene is expressed. Therefore, the expression of PTC-containing transgenes can be controlled by cell-specific activation of NMD. Using this technique, we replaced two endogenous genes with PTC-containing transgenes and directed degradation of their mRNA to specific cell types by restoring NMD activity in these cells. The single copy transgenes were expressed at levels comparable to the endogenous genes and were knocked down to ∼10% of endogenous by NMD, resulting in both global and cell-specific null-like phenotypes. This knockdown strategy can be used to cell-specifically knock down essentially any gene in the C. elegans genome and should provide new insights into understanding protein function.

VCU Theses and Dissertations