Several immobilization methods were explored for the preparation of high-performance affinity monolithic columns containing human serum albumin (HSA). These monoliths were based on a copolymer of glycidyl methacrylate and ethylene dimethacrylate. In one method, the epoxy groups of this copolymer were used directly for the immobilization of HSA through its amine residues (i.e., the epoxy method); in other approaches, these epoxy groups were converted to diols for later use in the carbonyldiimidazole, disuccinimidyl carbonate, and Schiff base methods. Each HSA monolith was evaluated in terms of its total protein content and its retention of several model compounds, including (R/S)-warfarin and D/L-tryptophan. The greatest amount of immobilized HSA was obtained by the Schiff base method, whereas the epoxy method gave the lowest protein content. The Schiff base method also gave the best resolution in chiral separations of (R/S)-warfarin and D/L-tryptophan. All of the immobilization methods gave similar relative activities for HSA in its binding to (R)- and (S)-warfarin, but some differences were noted in the activity of the immobilized HSA for D- and L-tryptophan. The efficiency of these monoliths was found to be greater than that of silica-based HSA columns for (R/S)-warfarin (i.e., analytes with high retention), but little or no difference was seen for D- and L-tryptophan (analytes with weak retention).
Affinity monoliths based on a copolymer of glycidyl methacrylate and ethylene dimethacrylate were developed for ultrafast immunoextractions. Rabbit immunoglobulin G (IgG) and anti-FITC antibodies were used as model ligands for this work. The antibody content of the monoliths was optimized by varying both the polymerization and immobilization conditions for preparing such supports. The temperature and porogen composition used during polymerization showed significant effects on monolith morphology and on the amount of antibodies that could be coupled to these materials. The effects of various immobilization procedures and coupling conditions were also evaluated, including the coupling temperature, pH, protein concentration, and use of high buffer concentrations. The maximum ligand density obtained for rabbit IgG was approximately 60 mg/g. When a 4.5 mm i.d. x 0.95 mm monolith disk containing anti-FITC antibodies was used, 95% extraction of fluorescein was achieved in 100 ms. These properties make such monoliths attractive for work in the rapid isolation of analytes from biological samples. Similar columns can be developed for other targets by varying the types of antibodies or binding agents placed within the monoliths.
Background & Aims Genome-wide association studies (GWASs) have identified 140 Crohn’s disease (CD) susceptibility loci. For most loci, the variants that cause disease are not known and the genes affected by these variants have not been identified. We aimed to identify variants that cause CD through detailed sequencing, genetic association, expression, and functional studies. Methods We sequenced whole exomes of 42 unrelated subjects with Crohn’s disease (CD) and 5 healthy individuals (controls), and then filtered single-nucleotide variants by incorporating association results from meta-analyses of CD GWASs and in silico mutation effect prediction algorithms. We then genotyped 9348 patients with CD, 2868 with ulcerative colitis, and 14,567 controls, and associated variants analyzed in functional studies using materials from patients and controls and in vitro model systems. Results We identified rare missense mutations in PR domain-containing1 (PRDM1) and associated these with CD. These increased proliferation of T cells and secretion of cytokines upon activation, and increased expression of the adhesion molecule L-selectin. A common CD risk allele, identified in GWASs, correlated with reduced expression of PRDM1 in ileal biopsies and peripheral blood mononuclear cells (combined P=1.6×0−8). We identified an association between CD and a common missense variant, Val248Ala, in nuclear domain 10 protein 52 (NDP52) (P=4.83×10−9). We found that this variant impairs the regulatory functions of NDP52 to inhibit NFκB activation of genes that regulate inflammation and affect stability of proteins in toll-like receptor pathways. Conclusions We have extended GWAS results and provide evidence that variants in PRDM1 and NDP52 determine susceptibility to CD. PRDM1 maps adjacent to a CD interval identified in GWASs and encodes a transcription factor expressed by T and B cells. NDP52 is an adaptor protein that functions in selective autophagy of intracellular bacteria and signaling molecules, supporting the role for autophagy in pathogenesis of CD.
Accurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks. Inspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear endto-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning.
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