Serum Stabilities of Short Tryptophan- and Arginine-Rich Antimicrobial Peptide Analogs

Several short antimicrobial peptides that are rich in tryptophan and arginine residues were designed with a series of simple modifications such as end capping and cyclization. The two sets of hexapeptides are based on the Trp- and Arg-rich primary sequences from the "antimicrobial centre" of bovine lactoferricin as well as an antimicrobial sequence obtained through the screening of a hexapeptide combinatorial library.HPLC, mass spectrometry and antimicrobial assays were carried out to explore the consequences of the modifications on the serum stability and microbicidal activity of the peptides. The results show that C-terminal amidation increases the antimicrobial activity but that it makes little difference to its proteolytic degradation in human serum. On the other hand, N-terminal acetylation decreases the peptide activities but significantly increases their protease resistance. Peptide cyclization of the hexameric peptides was found to be highly effective for both serum stability and antimicrobial activity. However the two cyclization strategies employed have different effects, with disulfide cyclization resulting in more active peptides while backbone cyclization results in more proteolytically stable peptides. However, the benefit of backbone cyclization did not extend to longer 11-mer peptides derived from the same region of lactoferricin. Mass spectrometry data support the serum stability assay results and allowed us to determine preferred proteolysis sites in the peptides. Furthermore, isothermal titration calorimetry experiments showed that the peptides all had weak interactions with albumin, the most abundant protein in human serum.Taken together, the results provide insight into the behavior of the peptides in human serum and will therefore aid in advancing antimicrobial peptide design towards systemic applications

Selective Release of MicroRNA Species from Normal and Malignant Mammary Epithelial Cells

MicroRNAs (miRNAs) in body fluids are candidate diagnostics for a variety of conditions and diseases, including breast cancer. One premise for using extracellular miRNAs to diagnose disease is the notion that the abundance of the miRNAs in body fluids reflects their abundance in the abnormal cells causing the disease. As a result, the search for such diagnostics in body fluids has focused on miRNAs that are abundant in the cells of origin. Here we report that released miRNAs do not necessarily reflect the abundance of miRNA in the cell of origin. We find that release of miRNAs from cells into blood, milk and ductal fluids is selective and that the selection of released miRNAs may correlate with malignancy. In particular, the bulk of miR-451 and miR-1246 produced by malignant mammary epithelial cells was released, but the majority of these miRNAs produced by non-malignant mammary epithelial cells was retained. Our findings suggest the existence of a cellular selection mechanism for miRNA release and indicate that the extracellular and cellular miRNA profiles differ. This selective release of miRNAs is an important consideration for the identification of circulating miRNAs as biomarkers of disease

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb - 1 . A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯