Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Biocatalytic Synthesis of Polymers of Precisely Defined Structures

The fabrication of functional nanoscale devices requires the construction of complex architectures at length scales characteristic of atoms and molecules. Currently microlithography and micro-machining of macroscopic objects are the preferred methods for construction of small devices, but these methods are limited to the micron scale. An intriguing approach to nanoscale fabrication involves the association of individual molecular components into the desired architectures by supramolecular assembly. This process requires the precise specification of intermolecular interactions, which in turn requires precise control of molecular structure

MicroRNA profiling of cisplatinresistant oral squamous cell carcinoma cell lines enriched withcancer-stem-cell-like and epithelial-mesenchymal transition-type features

Oral cancer is of major public health problem in India. Current investigation was aimed to identify the specific deregulated miRNAs which are responsible for development of resistance phenotype through regulating their resistance related target gene expression in oral squamous cell carcinoma (OSCC). Cisplatin-resistant OSCC cell lines were developed from their parental human OSCC cell lines and subsequently characterised. The resistant cells exhibited enhanced proliferative, clonogenic capacity with significant up-regulation of P-glycoprotein (ABCB1), c-Myc, survivin, β-catenin and a putative cancer-stem-like signature with increased expression of CD44, whereas the loss of E-cadherin signifies induced EMT phenotype. A comparative analysis of miRNA expression profiling in parental and cisplatin-resistant OSCC cell lines for a selected sets (deregulated miRNAs in head and neck cancer) revealed resistance specific signature. Moreover, we observed similar expression pattern for these resistance specific signature miRNAs in neoadjuvant chemotherapy treated and recurrent tumours compared to those with newly diagnosed primary tumours in patients with OSCC. All these results revealed that these miRNAs play an important role in the development of cisplatin-resistance mainly through modulating cancer stem-cell-like and EMT-type properties in OSCC

Interaction Between Convection and Pulsation

This article reviews our current understanding of modelling convection dynamics in stars. Several semi-analytical time-dependent convection models have been proposed for pulsating one-dimensional stellar structures with different formulations for how the convective turbulent velocity field couples with the global stellar oscillations. In this review we put emphasis on two, widely used, time-dependent convection formulations for estimating pulsation properties in one-dimensional stellar models. Applications to pulsating stars are presented with results for oscillation properties, such as the effects of convection dynamics on the oscillation frequencies, or the stability of pulsation modes, in classical pulsators and in stars supporting solar-type oscillations.Comment: Invited review article for Living Reviews in Solar Physics. 88 pages, 14 figure

Polyenylpyrrole Derivatives Inhibit NLRP3 Inflammasome Activation and Inflammatory Mediator Expression by Reducing Reactive Oxygen Species Production and Mitogen-Activated Protein Kinase Activation

10.1371/journal.pone.0076754PLoS ONE810-POLN

Current strategies for treatment of intervertebral disc degeneration: substitution and regeneration possibilities

Background: Intervertebral disc degeneration has an annual worldwide socioeconomic impact masked as low back pain of over 70 billion euros. This disease has a high prevalence over the working age class, which raises the socioeconomic impact over the years. Acute physical trauma or prolonged intervertebral disc mistreatment triggers a biochemical negative tendency of catabolic-anabolic balance that progress to a chronic degeneration disease. Current biomedical treatments are not only ineffective in the long-run, but can also cause degeneration to spread to adjacent intervertebral discs. Regenerative strategies are desperately needed in the clinics, such as: minimal invasive nucleus pulposus or annulus fibrosus treatments, total disc replacement, and cartilaginous endplates decalcification. Main Body: Herein, it is reviewed the state-of-the-art of intervertebral disc regeneration strategies from the perspective of cells, scaffolds, or constructs, including both popular and unique tissue engineering approaches. The premises for cell type and origin selection or even absence of cells is being explored. Choice of several raw materials and scaffold fabrication methods are evaluated. Extensive studies have been developed for fully regeneration of the annulus fibrosus and nucleus pulposus, together or separately, with a long set of different rationales already reported. Recent works show promising biomaterials and processing methods applied to intervertebral disc substitutive or regenerative strategies. Facing the abundance of studies presented in the literature aiming intervertebral disc regeneration it is interesting to observe how cartilaginous endplates have been extensively neglected, being this a major source of nutrients and water supply for the whole disc. Conclusion: Severalinnovative avenues for tackling intervertebral disc degeneration are being reported â from acellular to cellular approaches, but the cartilaginous endplates regeneration strategies remain unaddressed. Interestingly, patient-specific approaches show great promise in respecting patient anatomy and thus allow quicker translation to the clinics in the near future.The authors would like to acknowledge the support provided by the Portuguese Foundation for Science and Technology (FCT) through the project EPIDisc (UTAP-EXPL/BBBECT/0050/2014), funded in the Framework of the “International Collaboratory for Emerging Technologies, CoLab”, UT Austin|Portugal Program. The FCT distinctions attributed to J. Miguel Oliveira (IF/00423/2012 and IF/01285/ 2015) and J. Silva-Correia (IF/00115/2015) under the Investigator FCT program are also greatly acknowledged.info:eu-repo/semantics/publishedVersio

A functional variant in the Stearoyl-CoA desaturase gene promoter enhances fatty acid desaturation in pork

There is growing public concern about reducing saturated fat intake. Stearoyl-CoA desaturase (SCD) is the lipogenic enzyme responsible for the biosynthesis of oleic acid (18:1) by desaturating stearic acid (18:0). Here we describe a total of 18 mutations in the promoter and 3′ non-coding region of the pig SCD gene and provide evidence that allele T at AY487830:g.2228T>C in the promoter region enhances fat desaturation (the ratio 18:1/18:0 in muscle increases from 3.78 to 4.43 in opposite homozygotes) without affecting fat content (18:0+18:1, intramuscular fat content, and backfat thickness). No mutations that could affect the functionality of the protein were found in the coding region. First, we proved in a purebred Duroc line that the C-T-A haplotype of the 3 single nucleotide polymorphisms (SNPs) (g.2108C>T; g.2228T>C; g.2281A>G) of the promoter region was additively associated to enhanced 18:1/18:0 both in muscle and subcutaneous fat, but not in liver. We show that this association was consistent over a 10-year period of overlapping generations and, in line with these results, that the C-T-A haplotype displayed greater SCD mRNA expression in muscle. The effect of this haplotype was validated both internally, by comparing opposite homozygote siblings, and externally, by using experimental Duroc-based crossbreds. Second, the g.2281A>G and the g.2108C>T SNPs were excluded as causative mutations using new and previously published data, restricting the causality to g.2228T>C SNP, the last source of genetic variation within the haplotype. This mutation is positioned in the core sequence of several putative transcription factor binding sites, so that there are several plausible mechanisms by which allele T enhances 18:1/18:0 and, consequently, the proportion of monounsaturated to saturated fat.This research was supported by grants from the Spanish Ministry of Science and Innovation (AGL2009-09779 and AGL2012-33529). RRF is recipient of a PhD scholarship from the Spanish Ministry of Science and Innovation (BES-2010-034607). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of manuscript

Detailed estimation of bioinformatics prediction reliability through the Fragmented Prediction Performance Plots

<p>Abstract</p> <p>Background</p> <p>An important and yet rather neglected question related to bioinformatics predictions is the estimation of the amount of data that is needed to allow reliable predictions. Bioinformatics predictions are usually validated through a series of figures of merit, like for example sensitivity and precision, and little attention is paid to the fact that their performance may depend on the amount of data used to make the predictions themselves.</p> <p>Results</p> <p>Here I describe a tool, named Fragmented Prediction Performance Plot (FPPP), which monitors the relationship between the prediction reliability and the amount of information underling the prediction themselves. Three examples of FPPPs are presented to illustrate their principal features. In one example, the reliability becomes independent, over a certain threshold, of the amount of data used to predict protein features and the intrinsic reliability of the predictor can be estimated. In the other two cases, on the contrary, the reliability strongly depends on the amount of data used to make the predictions and, thus, the intrinsic reliability of the two predictors cannot be determined. Only in the first example it is thus possible to fully quantify the prediction performance.</p> <p>Conclusion</p> <p>It is thus highly advisable to use FPPPs to determine the performance of any new bioinformatics prediction protocol, in order to fully quantify its prediction power and to allow comparisons between two or more predictors based on different types of data.</p

Gut Microbial Signatures for Glycemic Responses of GLP-1 Receptor Agonists in Type 2 Diabetic Patients: A Pilot Study

BACKGROUNDS: Glucagon-like peptide-1 receptor agonist (GLP-1 RA) is probably one of more effective antidiabetic agents in treatment of type 2 diabetes mellitus (T2D). However, the heterogenicity in responses to GLP-1 RA may be potentially related to gut microbiota, although no human evidence has been published. This pilot study aims to identify microbial signatures associated with glycemic responses to GLP-1 RA. MATERIALS AND METHODS: Microbial compositions of 52 patients with T2D receiving GLP-1 RA were determined by 16S rRNA amplicon sequencing. Bacterial biodiversity was compared between responders versus non-responders. Pearson’s correlation and random forest tree algorithm were used to identify microbial features of glycemic responses in T2D patients and multivariable linear regression models were used to validate clinical relevance. RESULTS: Beta diversity significantly differed between GLP-1 RA responders (n = 34) and non-responders (n = 18) (ADONIS, P = 0.004). The top 17 features associated with glycohemoglobin reduction had a 0.96 diagnostic ability, based on area under the ROC curve: Bacteroides dorei and Roseburia inulinivorans, the two microbes having immunomodulation effects, along with Lachnoclostridium sp. and Butyricicoccus sp., were positively correlated with glycemic reduction; Prevotella copri, the microbe related to insulin resistance, together with Ruminococcaceae sp., Bacteroidales sp., Eubacterium coprostanoligenes sp., Dialister succinatiphilus, Alistipes obesi, Mitsuokella spp., Butyricimonas virosa, Moryella sp., and Lactobacillus mucosae had negative correlation. Furthermore, Bacteroides dorei, Lachnoclostridium sp. and Mitsuokella multacida were significant after adjusting for baseline glycohemoglobin and C-peptide concentrations, two clinical confounders. CONCLUSIONS: Unique gut microbial signatures are associated with glycemic responses to GLP-RA treatment and reflect degrees of dysbiosis in T2D patients