37 research outputs found

    Helical cationic antimicrobial peptide length and its impact on membrane disruption

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    AbstractCationic antimicrobial peptides (CAMPs) are important elements of innate immunity in higher organisms, representing an ancient defense mechanism against pathogenic bacteria. These peptides exhibit broad-spectrum antimicrobial activities, utilizing mechanisms that involve targeting bacterial membranes. Recently, a 34-residue CAMP (NA-CATH) was identified in cDNA from the venom gland of the Chinese cobra (Naja atra). A semi-conserved 11-residue pattern observed in the NA-CATH sequence provided the basis for generating an 11-residue truncated peptide, ATRA-1A, and its corresponding D-peptide isomer. While the antimicrobial and biophysical properties of the ATRA-1A stereoisomers have been investigated, their modes of action remain unclear. More broadly, mechanistic differences that can arise when investigating minimal antimicrobial units within larger naturally occurring CAMPs have not been rigorously explored. Therefore, the studies reported here are focused on this question and the interactions of full-length NA-CATH and the truncated ATRA-1A isomers with bacterial membranes. The results of these studies indicate that in engineering the ATRA-1A isomers, the associated change in peptide length and charge dramatically impacts not only their antimicrobial effectiveness, but also the mechanism of action they employ relative to that of the full-length parent peptide NA-CATH. These insights are relevant to future efforts to develop shorter versions of larger naturally occurring CAMPs for potential therapeutic applications

    Multi-messenger observations of a binary neutron star merger

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    On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

    Patient and stakeholder engagement learnings: PREP-IT as a case study

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    Distinct Epigenetic Effects of Tobacco Smoking in Whole Blood and among Leukocyte Subtypes - Fig 5

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    <p>Comparison of smoking-associated CpG methylation (A-C) and gene expression (D-F) differences between cell lineages. Methylation levels were measured on 450K arrays and averages for nonsmokers and smokers are displayed (A) cg05575921 (<i>AHRR</i>), (B) cg19859270 (<i>GPR15</i>) and (C) cg03636183 (<i>F2RL3</i>). Gene expression level (D) <i>AHRR</i>, (E) <i>GPR15</i> and (F) <i>F2RL3</i> for each cell type was measured by RT-PCR and averages are shown based on smoking status. Expression is represented by fold change difference between smokers relative to the average of nonsmokers after normalization to β-actin. Bar = Mean ± Standard Error; *<i>p</i>≤0.05, **<i>p</i>≤0.001, Student’s t-test.</p

    Distinct Epigenetic Effects of Tobacco Smoking in Whole Blood and among Leukocyte Subtypes

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    <div><p>Tobacco smoke exposure dramatically alters DNA methylation in blood cells and may mediate smoking-associated complex diseases through effects on immune cell function. However, knowledge of smoking effects in specific leukocyte subtypes is limited. To better characterize smoking–associated methylation changes in whole blood and leukocyte subtypes, we used Illumina 450K arrays and Reduced Representation Bisulfite Sequencing (RRBS) to assess genome-wide DNA methylation. Differential methylation analysis in whole blood DNA from 172 smokers and 81 nonsmokers revealed 738 CpGs, including 616 previously unreported CpGs, genome-wide significantly associated with current smoking (<i>p</i> <1.2x10<sup>-7</sup>, Bonferroni correction). Several CpGs (<i>MTSS1</i>, <i>NKX6-2</i>, <i>BTG2</i>) were associated with smoking duration among heavy smokers (>22 cigarettes/day, n = 86) which might relate to long-term heavy-smoking pathology. In purified leukocyte subtypes from an independent group of 20 smokers and 14 nonsmokers we further examined methylation and gene expression for selected genes among CD14+ monocytes, CD15+ granulocytes, CD19+ B cells, and CD2+ T cells. In 10 smokers and 10 nonsmokers we used RRBS to fine map differential methylation in CD4+ T cells, CD8+ T cells, CD14+, CD15+, CD19+, and CD56+ natural killer cells. Distinct cell-type differences in smoking-associated methylation and gene expression were identified. <i>AHRR</i> (cg05575921), <i>ALPPL2</i> (cg21566642), <i>GFI1</i> (cg09935388), <i>IER3</i> (cg06126421) and <i>F2RL3</i> (cg03636183) showed a distinct pattern of significant smoking-associated methylation differences across cell types: granulocytes> monocytes>> B cells. In contrast <i>GPR15</i> (cg19859270) was highly significant in T and B cells and <i>ITGAL</i> (cg09099830) significant only in T cells. Numerous other CpGs displayed distinctive cell-type responses to tobacco smoke exposure that were not apparent in whole blood DNA. Assessing the overlap between these CpG sites and differential methylated regions (DMRs) with RRBS in 6 cell types, we confirmed cell-type specificity in the context of DMRs. We identified new CpGs associated with current smoking, pack-years, duration, and revealed unique profiles of smoking-associated DNA methylation and gene expression among immune cell types, providing potential clues to hematopoietic lineage-specific effects in disease etiology.</p></div

    RRBS demonstrates regional impact and lineage-specific smoking DMRs within <i>MYO1G</i> and <i>ITGAL</i>.

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    <p>(A) Increased levels of CpG methylation were observed in <i>MYO1G</i> across a ~500-bp region with greater effects in myeloid cell types. (B) At cg22132788 the pattern of change across cell types closely matches 450K array results (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166486#pone.0166486.s003" target="_blank">S3C Fig</a>). (C) RRBS reveal hypomethylation in B cells that was not detected in 450K results. (D) T cell subsets show loss of methylation consistent with 450K array results.</p

    Effects of current smoking on <i>AHRR</i> cg05575921 methylation among monocytes, granulocytes, B cells and T cells.

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    <p>(A) Log2-transformed cotinine levels (ng/ml) were regressed with methylation beta-value measured for cg05575921 in each blood cell type. (B) Strong concordance of DNA methylation at cg05575921 (<i>AHRR</i>) between CD14+ monocytes and CD15+ granulocytes within individual subjects.</p
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