Cell morphology changes with high-MGO honey and high-hydrogen peroxide honey treatment^a.

a<p>Actual mean cell lengths and statistics are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055898#pone.0055898.s003" target="_blank">Table S1</a>.</p><p><b>↓</b>Statistically significant decrease compared to no-honey treated cells (<i>p&lt;0.05</i>).</p><p><b>↑</b>Statistically significant increase compared to no-honey treated cells (<i>p&lt;0.05</i>).</p><p>–No change.</p

Effect of sugar, MGO and catalase on growth of bacteria.

<p>Overnight cultures of <i>B. subtilis, E. coli, S. aureus</i> and <i>P. aeruginosa</i> were treated with various components, including catalase, MGO, sugar, and a combination of MGO and sugar at various concentrations equivalent to honeys at the corresponding concentrations shown on the x-axis. The MGO/sugar experiments were performed in the absence (left-hand graphs) and presence (right-hand graphs) of catalase as indicated. The MGO levels correspond to honeys M1 (651.4 mg/kg MGO), M2 (1004.3 mg/kg MGO) and M3 (1541.3 mg/kg MGO) at 1%–32% (w/v). Optical density was recorded at 595 nm every hour for 24 hours. For each component concentration, the time it takes for the culture to reach log phase (assessed as at least 10% of the final culture absorbance of the untreated culture) is plotted on the x-axis. The derivation of this value is described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055898#s2" target="_blank">Materials and Methods</a>. A value of 24 hours on the y-axis denotes ‘no growth’. An untreated control was performed alongside each particular treatment, and the starting OD₅₉₅ (zero time-point on x-axis) is plotted for that particular honey experiment.</p

Summary of growth and morphological effects of honeys and control treatments on all organisms.

a<p>this data includes data from both the log and lag phases of growth.</p>b<p>The number of crosses increases the more growth is inhibited.</p>c<p>Means no effect.</p

Effect of New Zealand manuka, kanuka and manuka-kanuka blended honeys on bacterial growth.

<p>Overnight cultures of <i>B. subtilis, E. coli, S. aureus</i> and <i>P. aeruginosa</i> were treated with ten different honeys, plus or minus catalase: three manuka honeys, M1, M2 and M3; two kanuka honeys, K1 and K2; four manuka-kanuka blended honeys, MK1, MK2, MK3 and MK4; and one clover honey, C, at various concentrations (from 1%–32% (w/v), increasing in 2-fold series). Optical density was recorded at 595 nm every hour for 24 hours. For each honey concentration, the time it takes for the culture to reach log phase (assessed as at least 10% of the final culture absorbance of the untreated culture) is plotted on the x-axis. The derivation of this value is described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055898#s2" target="_blank">Materials and Methods</a>. A value of 24 hours on the y-axis denotes ‘no growth’. Where symbols for a particular honey overlap, we have surrounded the point on the graph by all the symbols relevant to that point. This occurs in several cases for 16% and 32% honey treatments. An untreated control was also performed alongside each particular honey treatment, and the starting OD₅₉₅ (zero time-point on x-axis) is plotted for that particular honey experiment.</p

Floral source, MGO and H₂O₂ Levels of Honeys.

a<p>As reported in Stephens <i>et al.</i> (2010).</p>b<p>MGO (methylglyoxal) levels, reported in Stephens <i>et al.</i> (2010).</p>c<p>H₂O₂ (hydrogen peroxide) levels are expressed as mean H₂O₂ production rate in 1 mL of 10% w/v honey.</p>d<p>Samples collected from hive sites.</p>e<p>Aged samples from drums supplied by apiarists and purchased as designated type.</p>f<p>Obtained commercially.</p

Cellular morphology of bacterial cells treated with a high-MGO honey and a high-hydrogen peroxide honey.

<p>The effects of 4% (w/v) of a high-MGO honey (M3) and a high-hydrogen peroxide honey (MK1) on bacterial cellular morphology were examined. Overnight cultures of <i>B. subtilis, E. coli, S. aureus</i> and <i>P. aeruginosa</i> were treated with these honeys, cells collected at both lag and log phases of growth as indicated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055898#pone.0055898.s002" target="_blank">Figure S2</a>, fixed with glutaraldehyde, stained with DAPI and imaged using fluorescence microscopy. All images are overlays of the phase-contrast image and the DAPI-stained (red) fluorescence image. The two left-hand panels show the no-honey treated control cells, the two middle panels M3 honey-treated cells, and the two right-hand panels show the MK1 honey-treated cells. In all images, condensed DNA is shown by green arrows; and dispersed DNA in <i>B. subtilis</i> cells is shown by blue arrows. An asterisk indicates lysed cells for <i>B. subitlis</i> (MK1, lag-phase cells). The scale bar represents 2 µm, except for <i>S. aureus</i> images, where it represents 1 µm.</p

Overlap of significantly associated rSNPs identified by ASE and GTE.

<p>The percentage of overlapping rSNPs detected by allele-specific expression (ASE) and genotype expression (GTE) analysis is plotted for varying numbers of samples. The top 9536 SNPs from the GTE analysis are compared with the top 38203 SNPs from the ASE analysis, which corresponds to a Bonferroni threshold of p = 0.05 for a GTE sample size of 395 and an ASE sample size of 188. The p-value cut-offs were adapted so that the same SNP top-list sizes were obtained at all sample sizes for both GTE (p-value of 1.17E-7, 1.06E-4, 1.93E-3, 6.12E-3 for n = 395, n = 188, n = 95, and n = 50 respectively) and ASE (p-value of 8.06E-8, 9.35E-5, 4.90E-3 for n = 188, n = 95, and n = 50 respectively). The vertical axes show the percentage of SNPs in the top-lists detected by both GTE and ASE analysis and the horizontal axes show the number of samples analyzed using GTE and ASE, respectively. The percentage overlap is calculated by dividing the number of overlaps with the number of top SNPs in the GTE analysis. In (A), each line shows the effect on the number of overlapping SNPs detected by ASE analysis of a specific sample size when the sample size in GTE analysis was increased. In (B), each line shows the effect on the number of overlapping rSNPs detected by GTE analysis of a specific sample size when the samples size in ASE analysis is increased.</p

The ability of ASE and GTE analysis to detect significantly associated rSNPs at different MAF.

<p>Fractions of rSNPs are shown for different minor allele frequencies (MAF) with significant association signals according to a Bonferroni-corrected p-value of 0.05. Each data point underlying the curves represents the fraction of significant associations within a 1% MAF bin. Sliding 5% MAF window averages are plotted for different sample sizes analyzed by ASE and GTE. Both methods detect a lower fraction of low frequency rSNPs, compared to the fraction of all the SNPs at the same frequency (black line). The ASE method detects a higher fraction of the SNPs (solid lines) with a MAF <15% than GTE (dashed lines) regardless of sample size except for the largest GTE sample set.</p

Large-Scale Gene-Centric Analysis Identifies Novel Variants for Coronary Artery Disease

Coronary artery disease (CAD) has a significant genetic contribution that is incompletely characterized. To complement genome-wide association (GWA) studies, we conducted a large and systematic candidate gene study of CAD susceptibility, including analysis of many uncommon and functional variants. We examined 49,094 genetic variants in ~2,100 genes of cardiovascular relevance, using a customised gene array in 15,596 CAD cases and 34,992 controls (11,202 cases and 30,733 controls of European descent; 4,394 cases and 4,259 controls of South Asian origin). We attempted to replicate putative novel associations in an additional 17,121 CAD cases and 40,473 controls. Potential mechanisms through which the novel variants could affect CAD risk were explored through association tests with vascular risk factors and gene expression. We confirmed associations of several previously known CAD susceptibility loci (eg, 9p21.3:p<10−33; LPA:p<10−19; 1p13.3:p<10−17) as well as three recently discovered loci (COL4A1/COL4A2, ZC3HC1, CYP17A1:p<5×10−7). However, we found essentially null results for most previously suggested CAD candidate genes. In our replication study of 24 promising common variants, we identified novel associations of variants in or near LIPA, IL5, TRIB1, and ABCG5/ABCG8, with per-allele odds ratios for CAD risk with each of the novel variants ranging from 1.06–1.09. Associations with variants at LIPA, TRIB1, and ABCG5/ABCG8 were supported by gene expression data or effects on lipid levels. Apart from the previously reported variants in LPA, none of the other ~4,500 low frequency and functional variants showed a strong effect. Associations in South Asians did not differ appreciably from those in Europeans, except for 9p21.3 (per-allele odds ratio: 1.14 versus 1.27 respectively; P for heterogeneity = 0.003). This large-scale gene-centric analysis has identified several novel genes for CAD that relate to diverse biochemical and cellular functions and clarified the literature with regard to many previously suggested genes