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Traditional linkage analysis and genome-wide association studies have identified HLA and a number of non-HLA genes as genetic factors for islet autoimmunity (IA) and type 1 diabetes (T1D). However, the relative risk associated with previously identified non-HLA genes is usually very small as measured in cases/controls from mixed populations. Genetic associations for IA and T1D may be more accurately assessed in prospective cohorts. In this study, 5806 subjects from the TEDDY (The Environmental Determinants of Diabetes in the Young) study, an international prospective cohort study, were genotyped for 176,586 SNPs on the ImmunoChip. Cox proportional hazards analyses were performed to discover the SNPs associated with the risk for IA, T1D, or both. Three regions were associated with the risk of developing any persistent confirmed islet autoantibody: one known region near SH2B3 (HR = 1.35, p = 3.58 × 10 −7 ) with Bonferroni-corrected significance and another known region near PTPN22 ( HR = 1.46, p = 2.17 × 10 −6 ) and one novel region near PPIL2 (HR = 2.47, p = 9.64 × 10 −7 ) with suggestive evidence (p < 10 −5 ). Two known regions ( PTPN22 : p = 2.25 × 10 −6 , INS ; p = 1.32 × 10 −7 ) and one novel region ( PXK/PDHB: p = 8.99 × 10 −6 ) were associated with the risk for multiple islet autoantibodies. First appearing islet autoantibodies differ with respect to association. Two regions ( INS : p = 5.67 × 10 −6 and TTC34/PRDM16 : 6.45 × 10 −6 ) were associated if the fist appearing autoantibody was IAA and one region ( RBFOX1 : p = 8.02 × 10 −6 ) was associated if the first appearing autoantibody was GADA. The analysis of T1D identified one region already known to be associated with T1D ( INS : p = 3.13 × 10 −7 ) and three novel regions ( RNASET2, PLEKHA1, and PPIL2 ; 5.42 × 10 −6  > p > 2.31 × 10 −6 ). These results suggest that a number of low frequency variants influence the risk of developing IA and/or T1D and these variants can be identified by large prospective cohort studies using a survival analysis approach. [ABSTRACT FROM AUTHOR]

Functional annotation of the gigantic metagenomic data is one of the major time-consuming and computationally demanding tasks, which is currently a bottleneck for the efficient analysis. The commonly used homology-based methods to functionally annotate and classify proteins are extremely slow. Therefore, to achieve faster and accurate functional annotation, we have developed an orthology-based functional classifier ‘Woods’ by using a combination of machine learning and similarity-based approaches. Woods displayed a precision of 98.79% on independent genomic dataset, 96.66% on simulated metagenomic dataset and > 97% on two real metagenomic datasets. In addition, it performed > 87 times faster than BLAST on the two real metagenomic datasets. Woods can be used as a highly efficient and accurate classifier with high-throughput capability which facilitates its usability on large metagenomic datasets. [ABSTRACT FROM AUTHOR]

Highlights: [•] An overview of the various softwares and databases available for DNA barcoding of plants is provided. [•] The need for innovative bioinformatics approach in this field has been emphasized. [•] A brief description of currently available algorithms and their shortcomings has been given. [ABSTRACT FROM AUTHOR]

Abstract: Recently, we reported development of the C57BL/6.NOD-Aec1Aec2 mouse carrying two genetic intervals derived from the NOD mouse. These two genetic regions confer Sjögren''s syndrome (SjS)-like disease in SjS-non-susceptible C57BL/6 mice. In an attempt to define the molecular bases underlying onset of dacryoadenitis and subsequently keratoconjunctivitis sicca (or xerophthalmia) in the C57BL/6.NOD-Aec1Aec2 mouse model, we have carried out a study utilizing microarray technology. Using oligonucleotide microarrays, gene expression profiles of lacrimal glands at 4, 8, 12, 16 and 20weeks of age were generated for C57BL/6.NOD-Aec1Aec2 male mice. Analyses using Linear Models for Microarray Analysis package and B-statistics, 552 genes were identified as being differentially expressed (adjusted p-value <0.01 and B <1.5) during the development of SjS-like disease. These 552 genes could be arranged into four clusters, with each cluster defining a unique pattern of temporal expression, while the individual genes within each cluster could be grouped according to related function. Using a pair-wise analysis, temporal changes in gene expressions provided profiles indicating that individual genes were differentially expressed at specific time points during development of SjS. In addition, multiple genes that have been reported to show, either in humans or mouse models, an association with autoimmunity and/or SjS, e.g., ApoE, Baff, Clu, Ctla4, Fas/Fasl, Irf5, Lyzs, Nfkb, Socs3, Stat4, Tap2, Tgfβ1, Tnfa, and Vcam1 were also found to exhibit differential expressions, both quantitatively and temporally. Selecting a few families of genes, e.g., cystatins, cathepsins, metalloproteinases, lipocalins, complement, kallikreins, carbonic anhydrases and tumor necrosis factors, it was noted that only a limited number of family members showed differential expressions, suggesting a restricted glandular expression. Utilizing these genes, pathways of inter-reactive genes have been constructed for apoptosis and fatty acid homeostasis, leading to modeling of possible underlying events inducing disease. Thus, these different approaches to analyze microarray data permit identification of multiple sets of genes of interest whose expressions and expression profiles may correlate with molecular mechanisms, signaling pathways and/or immunological processes involved in the development and onset of SjS in this mouse model, thereby providing new insight into the underlying cause or regulation of this disease. [Copyright &y& Elsevier]

Identification of biofilm inhibitory small molecules appears promising for therapeutic intervention against biofilm-forming bacteria. However, the experimental identification of such molecules is a time-consuming task, and thus, the computational approaches emerge as promising alternatives. We developed the 'Molib' tool to predict the biofilm inhibitory activity of small molecules. We curated a training dataset of biofilm inhibitory molecules, and the structural and chemical features were used for feature selection, followed by algorithms optimization and building of machine learning-based classification models. On five-fold cross validation, Random Forest-based descriptor, fingerprint and hybrid classification models showed accuracies of 0.93, 0.88 and 0.90, respectively. The performances of all models were evaluated on two different validation datasets including biofilm inhibitory and non-inhibitory molecules, attesting to its accuracy (≥ 0.90). The Molib web server would serve as a highly useful and reliable tool for the prediction of biofilm inhibitory activity of small molecules. • Molib has 3 models based on molecular descriptors, fingerprints and hybrid model for prediction of anti-biofilm molecules. • On 5-CV, RF-based descriptor, fingerprint and hybrid models showed accuracies of 0.93, 0.88 and 0.90, respectively. • Molib gives prediction score and label for each molecule based on which anti-biofilm potential of molecules can be estimated. • Molib helps also in identifying molecules with lower anti-biofilm potential which can be modified to increase their potency. • Inclusion of multiple machine learning models in Molib helps in reliable prediction rather than using a single model. [ABSTRACT FROM AUTHOR]

Abstract Vascular inflammation plays a critical role in the pathogenesis of diabetic retinopathy. Recently, Interleukin-6 (IL-6) trans-signaling via soluble IL-6 receptor (sIL-6R) has emerged as a prominent regulator of inflammation in endothelial cells. This study was designed to test the hypothesis that selective inhibition of the IL-6 trans-signaling pathway will attenuate inflammation and subsequent barrier disruption in retinal endothelial cells. Human retinal endothelial cells (HRECs) were exposed to IL-6 and sIL-6R to induce IL-6 trans-signaling and the commercially available compound sgp130Fc (soluble gp-130 fused chimera) was used to selectively inhibit IL-6 trans-signaling. IL-6 trans-signaling activation caused a significant increase in STAT3 phosphorylation, expression of adhesion molecules, ROS production and apoptosis in HRECs whereas a significant decrease in mitochondrial membrane potential and NO production was observed in IL-6 trans-signaling activated cells. These changes were not observed in cells pre-treated with sgp130Fc. IL-6 trans-signaling activation was sufficient to cause barrier disruption in endothelial monolayers and pre-treatment of HRECs with sgp130Fc, maintained endothelial barrier function similar to that of untreated cells. Thus, in conclusion, these results indicate that IL-6 trans-signaling is an important mediator of inflammation, apoptosis and barrier disruptive effects in the retinal endothelial cells and inhibition of the IL-6 trans-signaling pathway using sgp130-Fc attenuates vascular inflammation and endothelial barrier disruption. Highlights • IL-6 trans-signaling is an important mediator of inflammation and barrier disruption in the retinal endothelial cells. • An anti-inflammatory molecule, sgp130Fc selectively inhibits the IL-6 trans-signaling pathway. • sgp130Fc treatment attenuates IL-6 trans-signaling mediated inflammation and apoptosis in the retinal endothelial cells. [ABSTRACT FROM AUTHOR]

Drug resistance poses a threatening challenge for mankind, as the development of resistance to already well-established drugs causes serious therapeutic problems. Resistance to paclitaxel (Ptxl), a complex diterpenoid working as microtubule stabilizer, is one such issue in cancer treatment. Microtubule stabilizer drugs, stabilises microtubules upon binding to β-tubulin subunit of tubulin heterodimer thus causing mitotic arrest leading to death of cancer cell. Leucine point mutations viz. L215H, L217R, and L225M were reported for Ptxl resistance in various cancers. In the current study, molecular mechanism of these resistance causing mutations was explored using molecular docking, molecular dynamics (MD) simulation, binding energy estimation (MMPBSA), free energy decomposition, principle component analysis (PCA) and free energy landscape (FEL) methods. A total of five systems including unbound βI-tubulin (Apo), docked wild+Ptxl, L215H+Ptxl, L217R+Ptxl and L225M+Ptxl were prepared, and 50 ns MD simulation was performed for each system. Binding energy estimation indicated that leucine mutation reduces the binding affinity of Ptxl in mutant types (MTs) as compared to wild type (WT). Further, in contrast to WT Ptxl interactions with the M-loop (PHE270-VAL286), S6-S7 loop and H9–H10 were significantly altered in MTs. Results showed that in MTs, Ptxl had weak interaction with M-loop residues, while having strong affinity with S6–S7 loop and H6–H7 loop. Moreover, PCA and FEL analysis revealed that M-loop flexible region (THR274-LEU284) was strongly bound with Ptxl in WT preventing its flexible movement and the causing factor for microtubule stabilization. In MTs due to poor interaction with Ptxl, M-loop flexible region retains its flexibility, therefore unable to stabilize microtubule. This study will give an insight into the importance of M-loop flexible region interaction with Ptxl for microtubule stabilization. In addition, it clearly provides the molecular basis of Ptxl resistance mechanism in leucine MTs. This work will help in developing novel microtubule stabilizers molecules active against MTs. [ABSTRACT FROM AUTHOR]

An easy synthesis of two 1,3,4-oxadiazole derivatives, namely, 2-phenyl-5-(pyridin-3-yl)-1,3,4-oxadiazole (POX) and 2-(4-methoxyphenyl)-5-(pyridin-3-yl)-1,3,4-oxadiazole (4-PMOX), and their corrosion-inhibition efficacy against mild steel corrosion in 1 N HCl, is evaluated using weight loss from 303 to 323 K, Electrochemical Impedance Spectroscopy (EIS), Potentiodynamic Polarization (PDP), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray (EDX), UV–Vis spectroscopy, along with theoretical evaluation. Both POX and 4-PMOX exhibit excellent inhibition efficiency, with values reaching 97.83% and 98% at 500 ppm, respectively. The PDP analysis reveals that both derivatives act as mixed-type inhibitors. The Langmuir adsorption isotherm provides insights into the adsorption phenomena, demonstrating that 4-PMOX exhibits superior adsorption behavior on the mild steel surface compared to POX. This finding is further supported by SEM, DFT, RDF, and MSD analyses. Quantum mechanical parameters, including E HOMO , E LUMO , dipole moment (μ), energy gap (ΔE), etc., are in good agreement with the effectiveness of inhibition performance revealing ΔE values of 3.10 and 2.75 for POX and 4-PMOX, respectively. The results obtained from this study hold significant implications for researchers aiming to design more efficient organic inhibitors to combat metal corrosion. • A convenient synthesis of two 1,3,4-oxadiazole-pyridine hybrids is reported. • Excellent corrosion inhibition efficacy (∼98% at 500 ppm) is expressed by these molecules. • Corrosion inhibition performance iscomprehensively evaluated using EIS, PDP, SEM, EDX, and UV–Vis spectroscopy. • The adsorption phenomenon and the mechanism of corrosion inhibition are theoretically correlated using DFT and MD simulation. [ABSTRACT FROM AUTHOR]

Abstract Objective To investigate the utility of a combined panel of protein biomarkers and clinical factors to predict recurrence in serous ovarian cancer patients. Methods Women at Augusta University diagnosed with ovarian cancer were enrolled between 2005 and 2015 (n = 71). Blood was drawn at enrollment and follow-up visits. Patient serum collected at remission was analyzed using the SOMAscan array (n = 35) to measure levels of 1129 proteins. The best 26 proteins were confirmed using Luminex assays in the same 35 patients and in an additional 36 patients (n total = 71) as orthogonal validation. The data from these 26 proteins was combined with clinical factors using an elastic net multivariate model to find an optimized combination predictive of progression-free survival (PFS). Results Of the 26 proteins, Brain Derived Neurotrophic Factor and Platelet Derived Growth Factor molecules were significant for predicting PFS on both univariate and multivariate analyses. All 26 proteins were combined with clinical factors using the elastic net algorithm. Ten components were determined to predict PFS (HR of 6.55, p-value 1.12 × 10−6, CI 2.57–16.71). This model was named the serous high grade ovarian cancer (SHOC) score. Conclusion The SHOC score can predict patient prognosis in remission. This tool will hopefully lead to early intervention and consolidation therapy strategies in remission patients destined to recur. Highlights • Serum proteins are capable of predicting residual cancer in remission ovarian cancer patients. • BDNF provides excellent prediction of PFS in among both early and advanced stage patients. • A combination of clinical factors and serum proteins provides superior prediction of recurrence among all patients. [ABSTRACT FROM AUTHOR]

• IL-6 signaling using a soluble IL-6 receptor is termed as "IL-6 trans-signaling". • IL-6 trans-signaling is known to cause dysfunction in retinal endothelial cells. • Identified gene expression changes in HRECs after IL-6 trans-signaling activation. • Selective inhibition of this pathway using sgp130Fc, abrogated these changes. Increasing evidence suggests that interleukin-6 (IL-6) trans-signaling plays a critical role in the pathogenesis of diabetic retinopathy (DR). We have previously shown that activation of IL-6 trans-signaling induces barrier dysfunction in human retinal endothelial cells (HRECs). However, the molecular mechanisms underlying these effects are not clear. The purpose of this study was to discover global gene expression changes in HRECs following activation of IL-6 trans-signaling. HRECs were treated with IL-6 and soluble IL-6R to activate IL-6 trans-signaling, and sgp130Fc treatment was used for IL-6 trans-signaling inhibition. RNA-Seq analyses were performed for global gene expression profiling. Differential expression was determined using DESeq2, and bioinformatic analyses were performed to associate the differentially expressed genes with biological functions and pathways. Our analyses revealed 445 differentially expressed genes (318 upregulated and 127 downregulated) in HRECs after IL-6 trans-signaling activation. We identified several novel genes not previously associated with IL-6 signaling or endothelial dysfunction. Leukocyte adhesion, diapedesis and chemokine signaling pathways are highly enriched in differentially expressed genes. Inhibition of IL-6 trans-signaling with sgp130Fc abrogated these changes, thus highlighting the therapeutic potential of this drug. This study identified significant gene expression changes caused by IL-6 trans-signaling activation in HRECs. Identification of such changes has the potential to uncover the precise molecular mechanisms of IL-6 trans-signaling mediated effects in the pathology of DR. [ABSTRACT FROM AUTHOR]