Secondary metabolite as therapeutic agent from endophytic fungi Alternaria longipes strain VITN14G of mangrove plant Avicennia officinalis

Endophytic fungi, especially from mangrove plants, are rich source of secondary metabolites, which plays a major role in various pharmacological actions preferably in cancer and bacterial infections. To perceive its role in antidiabetic activity we isolated and tested the metabolites derived from a novel strain Alternaria longipes strain VITN14G obtained from mangrove plant Avicennia officinalis. The crude extract was analyzed for antidiabetic activity and subjected to column chromatography. The isolated fractions were screened in vitro for α-glucosidase and α-amylase inhibitory activities. The cytotoxicity of the isolated fractions was studied on L929 cell lines. Following which, the screened fraction 2 was allowed for structure elucidation using gas chromatography-mass spectrometry, one-dimensional, two-dimensional nuclear magnetic resonance spectroscopy, ultraviolet, and Fourier-transform infrared analysis. The binding energies of the isolated fraction 2 with glycolytic enzymes were calculated by molecular docking studies using AutoDock Vina. The isolated fraction 2 identified as 2,4,6-triphenylaniline, showed no significant difference in α-amylase inhibition rates and a significant difference of 10% in α-glucosidase inhibition rates than that of the standard drug acarbose. Further, the cytotoxicity assay of the isolated fraction 2 resulted in a cell viability of 73.96%. Supportingly, in silico studies showed 2,4,6-triphenylaniline to produce a stronger binding affinity toward the glycolytic enzyme targets. The compound 2,4,6-triphenylaniline isolated from A. longipes strain VITN14G exhibited satisfactory antidiabetic activity for type 2 diabetes in vitro, which will further be confirmed by in vivo studies. Successful outcome of the study will result in a natural substitute for existing synthetic antidiabetic drugs.     

Morpho-colorimetric characterization by image analysis to identify diaspores of wild plant species

Digital images of ex situ germplasm stored in the Sardinian Germplasm Bank (BG-SAR) were used for the application of image analysis techniques at the Stazione Sperimentale di Granicoltura per la Sicilia. The analysed accessions refer to 148 taxonomic units belonging to 102 genera and 47 families, typical of the Sardinian flora, and of the Mediterranean basin in general.The images of diaspores were acquired by a flatbed scanner and elaborated with a macro specially developed for the morphometric and colorimetric measurements. This method allowed carrying out a database for the characterization of autochthonous germplasm in entry to the bank and the realization of statistic classifiers for the discrimination of genera and species within the following families: Apiaceae, Boraginaceae, Caryophyllaceae, Cistaceae, Fabaceae and Scrophulariaceae. Such classifiers, based on the linear discriminant analysis (LDA) technique and checked by cross-validation, showed a performance included between 74.3% and 96.4%.In addition, for the genus Astragalus, it was possible to elaborate a classifier able to identify very similar taxa of a species complex, obtaining a performance between 83.7% and 100%. Such analysis proved the validity of the methodology also from the taxonomic point of view.Suggestions for subsequent methodological progress, which could offer applications in other research issues, such as ecological analysis, soil seed bank and archaeological botany are proposed.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Site-Specific Structural Variations Accompanying Tubular Assembly of the HIV-1 Capsid Protein

The 231-residue capsid (CA) protein of human immunodeficiency virus type 1 (HIV-1) spontaneously self-assembles into tubes with a hexagonal lattice that is believed to mimic the surface lattice of conical capsid cores within intact virions. We report the results of solid-state nuclear magnetic resonance (NMR) measurements on HIV-1 CA tubes that provide new information regarding changes in molecular structure that accompany CA self-assembly, local dynamics within CA tubes, and possible mechanisms for the generation of lattice curvature. This information is contained in site-specific assignments of signals in two- and three-dimensional solid-state NMR spectra, conformation-dependent ¹⁵N and ¹³C NMR chemical shifts, detection of highly dynamic residues under solution NMR conditions, measurements of local variations in transverse spin relaxation rates of amide ¹H nuclei, and quantitative measurements of site-specific ¹⁵N–¹⁵N dipole–dipole couplings. Our data show that most of the CA sequence is conformationally ordered and relatively rigid in tubular assemblies and that structures of the N-terminal domain (NTD) and the C-terminal domain (CTD) observed in solution are largely retained. However, specific segments, including the N-terminal β-hairpin, the cyclophilin A binding loop, the inter-domain linker, segments involved in intermolecular NTD–CTD interactions, and the C-terminal tail, have substantial static or dynamical disorder in tubular assemblies. Other segments, including the 3₁₀-helical segment in CTD, undergo clear conformational changes. Structural variations associated with curvature of the CA lattice appear to be localized in the inter-domain linker and intermolecular NTD–CTD interface, while structural variations within NTD hexamers, around local 3-fold symmetry axes, and in CTD–CTD dimerization interfaces are less significant.     

Pore-forming Activity of the Escherichia coli Type III Secretion System Protein EspD

Enterohemorrhagic Escherichia coli is a causative agent of gastrointestinal and diarrheal diseases. Pathogenesis associated with enterohemorrhagic E. coli involves direct delivery of virulence factors from the bacteria into epithelial cell cytosol via a syringe-like organelle known as the type III secretion system. The type III secretion system protein EspD is a critical factor required for formation of a translocation pore on the host cell membrane. Here, we show that recombinant EspD spontaneously integrates into large unilamellar vesicle (LUV) lipid bilayers; however, pore formation required incorporation of anionic phospholipids such as phosphatidylserine and an acidic pH. Leakage assays performed with fluorescent dextrans confirmed that EspD formed a structure with an inner diameter of ∼2.5 nm. Protease mapping indicated that the two transmembrane helical hairpin of EspD penetrated the lipid layer positioning the N- and C-terminal domains on the extralumenal surface of LUVs. Finally, a combination of glutaraldehyde cross-linking and rate zonal centrifugation suggested that EspD in LUV membranes forms an ∼280–320-kDa oligomeric structure consisting of ∼6–7 subunits.     

免疫细胞浸润在非小细胞肺癌诊断与预后中的应用

免疫细胞浸润对癌症的诊断与预后有着重要意义。文中收集TCGA数据库已收录的非小细胞肺癌肿瘤与正常组织基因表达数据,利用CIBERSORT工具得到22种免疫细胞占比来评估免疫细胞浸润情况。以22种免疫细胞占比为特征,用机器学习方法构建了非小细胞肺癌肿瘤与正常组织的分类模型,其中随机森林方法构建的模型分类效果AUC=0.987、敏感性0.98及特异性0.84。并且用随机森林方法构建的肺腺癌和肺鳞癌肿瘤组织分类模型效果AUC=0.827、敏感性0.75及特异性0.77。用LASSO回归筛选22种免疫细胞特征,保留8种强相关特征组成的免疫细胞评分结合临床特征构建了非小细胞肺癌预后模型。经评估及验证,预后模型C-index=0.71并且3年和5年的校准曲线拟合良好,可以对预后风险度进行准确预测。本研究基于免疫细胞浸润所构建的分类模型与预后模型,旨在对非小细胞肺癌的诊断与预后研究提供新的策略。