HIV associated cell death: Peptide-induced apoptosis restricts viral transmission.

The human immunodeficiency virus (HIV) is still a global pandemic and despite the successful use of anti-retroviral therapy, a well-established cure remains to be identified. Viral modulation of cell death has a significant role in HIV pathogenesis. Here we sought to understand the major mechanisms of HIV- induced death of lymphocytes and the effects on viral transmission. Flow cytometry analysis of lymphocytes from five latent HIV-infected patients, and HIV IIIB-infected MT2 cells demonstrated both necrosis and apoptosis to be the major mechanisms of cell death in CD4+ and CD4-/CD8- lymphocytes. Significantly, pro-apoptotic tumor necrosis factor (TNF) peptide (P13) was found to inhibit HIV-related cell death and reduced viral transmission. Whereas pro-necrotic TNF peptide (P16) had little effect on HIV-related cell death and viral transmission. Understanding mechanisms by which cell death can be manipulated may provide additional drug targets to reduce the loss of CD4+ cells and the formation of a viral reservoir in HIV infection

A REPRESENTATION OF GM-VARIATION IN WAVES BY THE VOLTERRA SYSTEM

ABSTRACT As known, the variation of the metacentric height of a ship in irregular waves is not a pure linear process, particular when a ship has large beam to draught ratio and large flare near the waterline at bow and stern. This kind of unconventional hull form is usually adopted for modern RoRo-ship, cruise-ship etc. which allows large cargo space and high service speed. In this paper, the GM-variation is derived into a function series with respect to the variation order and represented by the Volterra system. The transfer functions for the different orders are integrated numerically or analytically through expressing the sectional beam, area and moment in Taylor's series as function of the momentary water line. Thereby the explicit relationship between the hull form and GM-variation can be obtained. The numerical result has shown the significant effect of the second order term in the Volterra system on the GM-variation in waves. Hence, the non-linear characteristics of the GM-variation in an irregular wave can be easily analyzed by means of available nonlinear probability theories or Monte-Carlo simulation technique

Metal-Free Carbocyclization of Homoallylic Silyl Ethers Leading to Cyclopropanes and Cyclobutanes

© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimWe have developed a Hosomi-Sakurai type carbocyclization of homoallylic silyl ethers in reaction with silyl nucleophiles, catalyzed by Lewis acidic silylium salt. It offers cyclopropane and cyclobutane products with high efficiency and selectivity. A range of silyl nucleophiles could be engaged in this transformation to give small-sized carbocycles incorporating allyl, allenyl, carbonyl, indole or thioether groups. Diastereoselectivity in the cyclobutane formation was observed to be dependent on the steric bulkiness of incoming nucleophiles.11sciescopu

Gut bacteria responding to dietary change encode sialidases that exhibit preference for red meat-associated carbohydrates.

Dietary habits have been associated with alterations of the human gut resident microorganisms contributing to obesity, diabetes and cancer1. In Western diets, red meat is a frequently eaten food2, but long-term consumption has been associated with increased risk of disease3,4. Red meat is enriched in N-glycolylneuraminic acid (Neu5Gc) that cannot be synthesized by humans5. However, consumption can cause Neu5Gc incorporation into cell surface glycans6, especially in carcinomas4,7. As a consequence, an inflammatory response is triggered when Neu5Gc-containing glycans encounter circulating anti-Neu5Gc antibodies8,9. Although bacteria can use free sialic acids as a nutrient source10-12, it is currently unknown if gut microorganisms contribute to releasing Neu5Gc from food. We found that a Neu5Gc-rich diet induces changes in the gut microbiota, with Bacteroidales and Clostridiales responding the most. Genome assembling of mouse and human shotgun metagenomic sequencing identified bacterial sialidases with previously unobserved substrate preference for Neu5Gc-containing glycans. X-ray crystallography revealed key amino acids potentially contributing to substrate preference. Additionally, we verified that mouse and human sialidases were able to release Neu5Gc from red meat. The release of Neu5Gc from red meat using bacterial sialidases could reduce the risk of inflammatory diseases associated with red meat consumption, including colorectal cancer4 and atherosclerosis13

Proteomics and Mass Spectrometry for Cancer Biomarker Discovery

Proteomics is a rapidly advancing field not only in the field of biology but also in translational cancer research. In recent years, mass spectrometry and associated technologies have been explored to identify proteins or a set of proteins specific to a given disease, for the purpose of disease detection and diagnosis. Such biomarkers are being investigated in samples including cells, tissues, serum/plasma, and other types of body fluids. When sufficiently refined, proteomic technologies may pave the way for early detection of cancer or individualized therapy for cancer. Mass spectrometry approaches coupled with bioinformatic tools are being developed for biomarker discovery and validation. Understanding basic concepts and application of such technology by investigators in the field may accelerate the clinical application of protein biomarkers in disease management

Hydrophobic Proteome Analysis of Triple Negative and Hormone-Receptor-Positive-Her2-Negative Breast Cancer by Mass Spectrometer

It is widely believed that discovery of specific, sensitive, and reliable tumor biomarkers can improve the treatment of cancer. Currently, there are no obvious targets that can be used in treating triple-negative breast cancer (TNBC). To better understand TNBC and find potential biomarkers for targeted treatment, we combined a novel hydrophobic fractionation protocol with mass spectrometry LTQ-orbitrap to explore and compare the hydrophobic sub-proteome of TNBC with another subtype of breast cancer, hormone-receptor-positive-Her2-negative breast cancer (non-TNBC). Hydrophobic sub-proteome of breast cancer is rich in membrane proteins. Hundreds of proteins with various defined key cellular functions were identified from TNBC and non-TNBC tumors. In this study, protein profiles of TNBC and non-TNBC were systematically examined, compared, and validated. We have found that nine keratins are down-regulated and several heat shock proteins are up-regulated in TNBC tissues. Our study may provide insights of molecules that are responsible for the aggressiveness of TNBC. The initial results obtained using a combination of hydrophobic fractionation and nano-LC mass spectrometry analysis of these proteins appear promising in the discovery of potential cancer biomarkers and bio-signatures. When sufficiently refined, this approach may prove useful in improving breast cancer treatment

A New Descriptor for Amino Acids and Its Applications in Peptide QSAR

To establish a new amino acid structure descriptor that can be applied in peptide quantitative structure activity relationship (QSAR) Keywords: amino acids, peptides, quantitative structure-activity relationship (QSAR), SVMW descriptor Peptides are essential substance to sustain life In this paper, SVMW, which derived by principal components analysis of the matrix of 160 MoRSE descriptors and 99 WHIM descriptors of amino acids, were examined through principal component analysis (PCA). Applying SVMW to 58 angiotensin-converting enzyme inhibitors (dipeptide), 55 angiotensin-converting enzyme inhibitors (tri-peptides) and 48 bitter tasting thresholds, satisfying results were obtained from the constructed QSAR models. Experimental part Principle and Methodology Principal component analysis (PCA) Based on quantum chemistry calculation level of density function theory (DFT) Partial least square Partial least square (PLS) Stepwise multiple regression (SMR) was carried out for variable selection because it was less time-consuming and easy to implement. PLS was implemented by software of Simca-P 10.0. Matlab 7.0 was used for PCA, and SPSS 10.0 was used for stepwise multiple variable selection. Results and discussions QSAR model for angiotensin-converting enzyme inhibitors (dipeptide) Angiotensin converting enzyme inhibitor (dipeptide) is an inhibitor of angiotensin-converting enzyme (ACE)

Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response

Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC) tumors. Twenty proteins were found to correctly classify all HER2 positive and 7 of the 11 TNBC tumors. Among them, galectin-3-binding protein and ALDH1A1 were found preferentially elevated in TNBC, whereas CK19, transferrin, transketolase, and thymosin β4 and β10 were elevated in HER2-positive cancers. In addition, several proteins such as enolase, vimentin, peroxiredoxin 5, Hsp 70, periostin precursor, RhoA, cathepsin D preproprotein, and annexin 1 were found to be associated with the tumor responses to treatment within each subtype. The MS-based proteomic findings appear promising in guiding tumor classification and predicting response. When sufficiently validated, some of these candidate protein markers could have great potential in improving breast cancer treatment

Constraints on modified Chaplygin gas from recent observations and a comparison of its status with other models

In this Letter, a modified Chaplygin gas (MCG) model of unifying dark energy and dark matter with the exotic equation of state $p_{MCG}=B\rho_{MCG} -\frac A{\rho_{MCG}^\alpha}$ is constrained from recently observed data: the 182 Gold SNe Ia, the 3-year WMAP and the SDSS baryon acoustic peak. It is shown that the best fit value of the three parameters ($B$,$B_{s}$,$\alpha$) in MCG model are (-0.085,0.822,1.724). Furthermore, we find the best fit $w(z)$ crosses -1 in the past and the present best fit value $w(0)=-1.114<-1$, and the $1\sigma$ confidence level of $w(0)$ is $-0.946\leq w(0)\leq-1.282$. Finally, we find that the MCG model has the smallest $\chi^{2}_{min}$ value in all eight given models. According to the Alaike Information Criterion (AIC) of model selection, we conclude that recent observational data support the MCG model as well as other popular models.Comment: 8 pages, 1 figur