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GWAS cannot identify functional SNPs (fSNP) from disease-associated SNPs in linkage disequilibrium (LD). Here, we report developing three sequential methodologies including Reel-seq (Regulatory element-sequencing) to identify fSNPs in a high-throughput fashion, SDCP-MS (SNP-specific DNA competition pulldown-mass spectrometry) to identify fSNP-bound proteins and AIDP-Wb (allele-imbalanced DNA pulldown-Western blot) to detect allele-specific protein:fSNP binding. We first apply Reel-seq to screen a library containing 4316 breast cancer-associated SNPs and identify 521 candidate fSNPs. As proof of principle, we verify candidate fSNPs on three well-characterized loci: FGFR2, MAP3K1 and BABAM1. Next, using SDCP-MS and AIDP-Wb, we rapidly identify multiple regulatory factors that specifically bind in an allele-imbalanced manner to the fSNPs on the FGFR2 locus. We finally demonstrate that the factors identified by SDCP-MS can regulate risk gene expression. These data suggest that the sequential application of Reel-seq, SDCP-MS, and AIDP-Wb can greatly help to translate large sets of GWAS data into biologically relevant information.

The MerR-family proteins represent a unique family of bacteria transcription factors (TFs), which activate transcription in a manner distinct from canonical ones. Here, we report a cryo-EM structure of a B. subtilis transcription activation complex comprising B. subtilis six-subunit (2αββ‘ωε) RNA Polymerase (RNAP) core enzyme, σA, a promoter DNA, and the ligand-bound B. subtilis BmrR, a prototype of MerR-family TFs. The structure reveals that RNAP and BmrR recognize the upstream promoter DNA from opposite faces and induce four significant kinks from the −35 element to the −10 element of the promoter DNA in a cooperative manner, which restores otherwise inactive promoter activity by shortening the length of promoter non-optimal −35/−10 spacer. Our structure supports a DNA-distortion and RNAP-non-contact paradigm of transcriptional activation by MerR TFs.

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Background Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid. Methods A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI, was generated. The proposed approach simultaneously fused the topology of complex networks and diverse information from heterogeneous data sources, and coped with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validated the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo. Results The experimental results showed that the DLDTI model achieved promising performance under fivefold cross-validations with AUC values of 0.9172, which was higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models. Conclusions DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.

Stable organic luminescent radicals are a special class of compounds integrating optical, electrical, and magnetic properties. Luminescent radicals not only have potential applications in the field of the organic light-emitting diodes (OLEDs) but can also be applied in the fields of fluorescence sensing, bioimaging, and so forth. Nevertheless, due to the adverse effects of solvent polarity on the luminescent performance of radicals, no feasible approaches have been found in the literature toward fluorescent sensing. In this work, we report two luminescent radicals, 2αPyID-TTM and 2δPyID-TTM, whose emissions show high efficiency and less dependence on solvent polarity. Both radicals show remarkable protonationdeprotonation properties. Besides, 2αPyID-TTM exhibits significant fluorescence quenching and colorimetric response toward Fe 3+ in aqueous solution. This suggests the possibility of a fluorometric/ colorimetric dual-channel probe for Fe 3+ . Moreover, an optimized OLED using 2δPyID-TTM as an emissive dopant shows pure red emission and a maximum external quantum efficiency (EQE) of 10.6%. These results show promise for luminescent radicals as fluorescent probes and electroluminescent emitters.

As the most prominent clinical drug targets for the inhibition of platelet aggregation, P2Y and P2Y have been found to be highly expressed in both platelets and macrophages. However, the roles and function of P2Y in the regulation of macrophage-mediated innate immune responses remain unclear. Here, we demonstrate that adenosine 5'-diphosphate (ADP), the endogenous ligand of P2Y, P2Y and P2Y, was released both in E. coli-infected mice and from macrophages treated with either lipopolysaccharide (LPS) or Pam3CSK4. Furthermore, the expression of P2Y was clearly increased in both LPS-treated macrophages and tuberculosis patients. ADP protected mice from E. coli 0111-induced peritonitis by recruiting more macrophages to the infected sites. Consistent with this, ADP and ADP-treated cell culture medium attracted more macrophages in the transwell assay by enhancing the expression of MCP-1. Nevertheless, P2Y is dispensable for ADP-mediated protection against bacterial infection. However, either P2Y/P2Y deficiency or blocking the downstream signaling of P2Y/P2Y blocked the ADP-mediated immune response and allowed more bacteria to persist in the infected mice. Furthermore, extracellular signal-regulated kinase (ERK) phosphorylation was clearly increased by ADP, and this type of activation could be blocked by either forskolin or analogs of cyclic AMP (cAMP) (for example, 8-bromo-cAMP). Accordingly, ADP-induced MCP-1 production and protection against bacterial infection could also be reduced by U0126, forskolin and 8-bromo-cAMP. Overall, our study reveals a relationship between danger signals and innate immune responses, which suggests the potential therapeutic significance of ADP-mediated purinergic signaling in infectious diseases.