Hen egg white lysozyme fibrillation: a deep‐UV resonance Raman spectroscopic study

Amyloid fibrils are associated with numerous degenerative diseases. The molecular mechanism of the structural transformation of native protein to the highly ordered cross‐β structure, the key feature of amyloid fibrils, is under active investigation. Conventional biophysical methods have limited application in addressing the problem because of the heterogeneous nature of the system. In this study, we demonstrated that deep‐UV resonance Raman (DUVRR) spectroscopy in combination with circular dichroism (CD) and intrinsic tryptophan fluorescence allowed for quantitative characterization of protein structural evolution at all stages of hen egg white lysozyme fibrillation in vitro. DUVRR spectroscopy was found to be complimentary to the far‐UV CD because it is (i) more sensitive to β ‐sheet than to α ‐helix, and (ii) capable of characterizing quantitatively inhomogeneous and highly light‐scattering samples. In addition, phenylalanine, a natural DUVRR spectroscopic biomarker of protein structural rearrangements, exhibited substantial changes in the Raman cross section of the 1000‐cm^–1 band at various stages of fibrillation. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

NMR characterization of copper and lipid interactions of the C2B domain of synaptotagmin I—relevance to the non-classical secretion of the human acidic fibroblast growth factor (hFGF-1)

Human fibroblast growth factor (hFGF-1) is a ∼ 17 kDa heparin binding cytokine. It lacks the conventional hydrophobic N-terminal signal sequence and is secreted through non-classical secretion routes. Under stress, hFGF-1 is released as a multiprotein complex consisting of hFGF-1, S100A13 (a calcium binding protein), and p40 synaptotagmin (Syt1). Copper (Cu²⁺) is shown to be required for the formation of the multiprotein hFGF-1 release complex (Landriscina et al. ,2001; Di Serio et al., 2008). Syt1, containing the lipid binding C2B domain, is believed to play an important role in the eventual export of the hFGF-1 across the lipid bilayer. In this study, we characterize Cu²⁺ and lipid interactions of the C2B domain of Syt1 using multidimensional NMR spectroscopy. The results highlight how Cu²⁺ appears to stabilize the protein bound to pS vesicles. Cu²⁺ and lipid binding interface mapped using 2D ¹H-¹⁵N heteronuclear single quantum coherence experiments reveal that residues in β-strand I contributes to the unique Cu²⁺ binding site in the C2B domain. In the absence of metal ions, residues located in Loop II and β-strand IV contribute to binding to unilamelar pS vesicles. In the presence of Cu²⁺, additional residues located in Loops I and III appear to stabilize the protein-lipid interactions. The results of this study provide valuable information towards understanding the molecular mechanism of the Cu²⁺-induced non-classical secretion of hFGF-1.     

Length-weight relationship of fish species captured around an artificial offshore reef (northern Rio de Janeiro,Brazil)

The present work provides length-weight relationships (LWRs) for fish species captured around artificial offshore reef deployed 5 km from Guaxindiba Port, northern Rio de Janeiro, Brazil. The fish were captured during two sampling periods each year between 1996 and 2017 using bottom gill nets (25 m_length × 3 m_depth; 30 mm mesh). The fish were kept on ice and the biometric data, including total length (cm) and total wet weight (g) were determined in the laboratory. A total of 16 species belonging to 10 families were analyzed with Sciaenidae being the species richest family (5 spp) in the samples. The new value of LWRs for Sphyraena guachancho and new maximum sizes recorded for seven species highlight the scarcity of information on biological aspects of South America coastal fishes. These LWRs should assist fisheries scientist and managers to complement their further studies of population parameters to improve management decisions.     

Cytotoxic Effects of the Selective Ligands of Membrane Progesterone Receptors in Human Pancreatic Adenocarcinoma Cells BxPC3

Biochemistry (Moscow) - Progesterone and its synthetic analogues act on cells through different types of receptors, affecting proliferation and apoptosis. These compounds exert their effect through...     

Mechanisms of Candida Resistance to Antimycotics and Promising Ways to Overcome It: The Role of Probiotics

Probiotics and Antimicrobial Proteins - Pathogenic Candida and infections caused by those species are now considered as a serious threat to public health. The treatment of candidiasis is...     

An evolution-based high-fidelity method of epistasis measurement: Theory and application to influenza

Linkage effects in a multi-locus population strongly influence its evolution. The models based on the traveling wave approach enable us to predict the average speed of evolution and the statistics of phylogeny. However, predicting statistically the evolution of specific sites and pairs of sites in the multi-locus context remains a mathematical challenge. In particular, the effects of epistasis, the interaction of gene regions contributing to phenotype, is difficult to predict theoretically and detect experimentally in sequence data. A large number of false-positive interactions arises from stochastic linkage effects and indirect interactions, which mask true epistatic interactions. Here we develop a proof-of-principle method to filter out false-positive interactions. We start by demonstrating that the averaging of haplotype frequencies over multiple independent populations is necessary but not sufficient for epistatic detection, because it still leaves high numbers of false-positive interactions. To compensate for the residual stochastic noise, we develop a three-way haplotype method isolating true interactions. The fidelity of the method is confirmed analytically and on simulated genetic sequences evolved with a known epistatic network. The method is then applied to a large sequence database of neurominidase protein of influenza A H1N1 obtained from various geographic locations to infer the epistatic network responsible for the difference between the pre-pandemic virus and the pandemic strain of 2009. These results present a simple and reliable technique to measure epistatic interactions of any sign from sequence data.