Additional file 1 of Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development

Additional file 1: Figure S1. a Datarepresentation. Combination 1: concatenating 100-dimensional compoundembeddings with the sum of three 100-dimensional 3-g protein sequence embeddings. Combination 2: concatenating300-dimensional compound embeddings with the sum of three 300-dimensional 3-g proteinsequence embeddings. Combination 3: concatenating 300-dimensional compound embeddings with the concatenation ofthree 100-dimensional 3-g protein sequence embeddings. b Performancecomparison of models fedwith different data representations. FigureS2. a Partial architecture ofPCM-GAN. The input of generator issampled from low-dimensional Gaussian distribution (orange).The output of generator is 600-dimensional feature (purple) + one-dimensionalcorresponding label/activity (green). b Partial architecture of PCM-AAE. The input of encoder and output of decoder are 600-dimensionalfeature (purple) + one-dimensional corresponding label/activity (green). FigureS3. a Cumulative variance explained by thenumber of components in PCA. b Runningtime behavior of t-SNE and PCA+t-SNE methods for dimensionality reduction. Figure S4. Performance of fourdifferent machine learning models at four levels. (CV2: new target prediction; CV3:new drug prediction; CV4: pair prediction of new target and new drug). Figure S5. Lossplot of PCM-GAN which a didnot use batch-normalization (BN); bused BN in the generator. FigureS6. Performancecomparison among NB model (non-balanced model) and 24 reconstructed models fedwith data augmented by 24 generators respectively. Figure S7. Performance comparison between PCM-AAEand EPA. Statistical significance of the difference between theperformance of EPA and PCM-AAE was determined by paired t-test. ns: p > 0.05;*: p  0.05; *: p < 0.05; **: p < 0.01; ***: p < 0.001;****: p < 0.0001. FigureS9. Correlationcoefficient between every two datasets. FigureS10. Scatterplots of ENB predicted selectivity score and experimentalselectivity score of inhibitors in various sets. Figure S11. Phylogenetic tree to display the performance of ENB onkinases from the Metz’s set. Circles represent the kinases included in trainingset. Squares represent the kinases excluded from training set. Table S1. Non-balanced model performancein training set and test set. Algorithm S1. TrainingPCM-AAE

International Criminal Tribunal for Rwanda

ROC curves of phenotype similarity matrices constructed with or without title portions. ROC analysis with the two benchmark datasets (A: Phenotypic Series, B: Linked OMIM Record Pairs) suggested that the similarity matrix constructed with both the text and title portions of OMIM records outperformed the matrix constructed with the text portion only. The range of false positive rates was restricted to (0, 0.1) in order to highlight the differences between each curve. (PDF 270 kb

Presentation_1_HLA-DPB1 genotype variants predict DP molecule cell surface expression and DP donor specific antibody binding capacity.pptx

The contribution of alloresponses to mismatched HLA-DP in solid organ transplantation and hematopoietic stem cell transplantation (HCT) has been well documented. Exploring the regulatory mechanisms of DPB1 alleles has become an important question to be answered. In this study, our initial investigation focused on examining the correlation between the rs9277534G/A SNP and DPB1 mRNA expression. The result showed that there was a significant increase in DPB1 mRNA expression in B lymphoblastoid cell lines (BLCLs) with the rs9277534GG genotype compared to rs9277534AA genotype. In addition, B cells with the rs9277534GG exhibited significantly higher DP protein expression than those carrying the rs9277534AA genotype in primary B cells. Furthermore, we observed a significant upregulation of DP expression in B cells following treatment with Interleukin 13 (IL-13) compared to untreated B cells carrying rs9277534GG-linked DPB1 alleles. Fluorescence in situ hybridization (FISH) analysis of DPB1 in BLCL demonstrated significant differences in both the cytoplasmic (p=0.0003) and nuclear (p=0.0001) localization of DP mRNA expression comparing DPB1*04:01 (rs9277534AA) and DPB1*05:01 (rs9277534GG) homozygous cells. The study of the correlation between differential DPB1 expression and long non-coding RNAs (lncRNAs) showed that lnc-HLA-DPB1-13:1 is strongly associated with DP expression (r=0.85), suggesting the potential involvement of lncRNA in regulating DP expression. The correlation of DP donor specific antibody (DSA) with B cell flow crossmatch (B-FCXM) results showed a better linear correlation of DP DSA against GG and AG donor cells (R2 = 0.4243, p=0.0025 and R2 = 0.6172, p=0.0003, respectively), compared to DSA against AA donor cells (R2 = 0.0649, p=0.4244). This explained why strong DP DSA with a low expression DP leads to negative B-FCXM. In conclusion, this study provides evidence supporting the involvement of lncRNA in modulating HLA-DP expression, shedding lights on the intricate regulatory mechanisms of DP, particularly under inflammatory conditions in transplantation.</p

LC–MS-Based Metabolomics and Lipidomics Study of High-Density-Lipoprotein-Modulated Glucose Metabolism with an apoA‑I Knockout Mouse Model

Type 2 diabetes mellitus (T2DM) has become a tremendous problem in public health nowadays. High-density lipoprotein (HDL) refers to a group of heterogeneous particles that circulate in blood, and a recent research finds that HDL acts a pivotal part of glucose metabolism. To understand systemic metabolic changes correlated with HDL in glucose metabolism, we applied LC–MS-based metabolomics and lipidomics to detect metabolomic and lipidomic profiles of plasma from apoA-I knockout mice fed a high-fat diet. Multivariate analysis was applied to differentiate apoA-I knockout mice and controls, and potential biomarkers were found. Pathway analysis demonstrated that several metabolic pathways such as aminoacyl-tRNA biosynthesis, arginine and proline metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis were dysregulated in apoA-I knockout mice. This study may provide a new insight into the underlying pathogenesis in T2DM and prove that LC–MS-based metabolomics and lipidomics are powerful approaches in finding potential biomarkers and disturbed pathways

Development and Evaluation of a Parallel Reaction Monitoring Strategy for Large-Scale Targeted Metabolomics Quantification

Recent advances in mass spectrometers which have yielded higher resolution and faster scanning speeds have expanded their application in metabolomics of diverse diseases. Using a quadrupole-Orbitrap LC–MS system, we developed an efficient large-scale quantitative method targeting 237 metabolites involved in various metabolic pathways using scheduled, parallel reaction monitoring (PRM). We assessed the dynamic range, linearity, reproducibility, and system suitability of the PRM assay by measuring concentration curves, biological samples, and clinical serum samples. The quantification performances of PRM and MS1-based assays in Q-Exactive were compared, and the MRM assay in QTRAP 6500 was also compared. The PRM assay monitoring 237 polar metabolites showed greater reproducibility and quantitative accuracy than MS1-based quantification and also showed greater flexibility in postacquisition assay refinement than the MRM assay in QTRAP 6500. We present a workflow for convenient PRM data processing using Skyline software which is free of charge. In this study we have established a reliable PRM methodology on a quadrupole-Orbitrap platform for evaluation of large-scale targeted metabolomics, which provides a new choice for basic and clinical metabolomics study

Additional file 1: Figure S1. of SoftPanel: a website for grouping diseases and related disorders for generation of customized panels

ROC curves of phenotype similarity matrices constructed with or without title portions. ROC analysis with the two benchmark datasets (A: Phenotypic Series, B: Linked OMIM Record Pairs) suggested that the similarity matrix constructed with both the text and title portions of OMIM records outperformed the matrix constructed with the text portion only. The range of false positive rates was restricted to (0, 0.1) in order to highlight the differences between each curve. (PDF 270 kb

PTEN regulates PLK1 and controls chromosomal stability during cell division

<p>PTEN functions as a guardian of the genome through multiple mechanisms. We have previously established that PTEN maintains the structural integrity of chromosomes. In this report, we demonstrate a fundamental role of PTEN in controlling chromosome inheritance to prevent gross genomic alterations. Disruption of <i>PTEN</i> or depletion of PTEN protein phosphatase activity causes abnormal chromosome content, manifested by enlarged or polyploid nuclei. We further identify polo-like kinase 1 (PLK1) as a substrate of PTEN phosphatase. PTEN can physically associate with PLK1 and reduce PLK1 phosphorylation in a phosphatase-dependent manner. We show that PTEN deficiency leads to PLK1 phosphorylation and that a phospho-mimicking PLK1 mutant causes polyploidy, imitating functional deficiency of PTEN phosphatase. Inhibition of PLK1 activity or overexpression of a non-phosphorylatable PLK1 mutant reduces the polyploid cell population. These data reveal a new mechanism by which PTEN controls genomic stability during cell division.</p

Erythrocyte-mimicking paclitaxel nanoparticles for improving biodistributions of hydrophobic drugs to enhance antitumor efficacy

Recent decades have witnessed several nanocrystal-based hydrophobic drug formulations because of their excellent performance in improving drug loading and controlling drug release as mediate drug forms in tablets or capsules. However, the intravenous administration of drug nanocrystals was usually hampered by their hydrophobic surface properties, causing short half-life time in circulation and low drug distribution in tumor. Here, we proposed to enclose nanocrystals (NC) of hydrophobic drug, such as paclitaxel (PTX) into erythrocyte membrane (EM). By a series of formulation optimizations, spherical PTX nanoparticles (PN) with the particle size of around 280 nm were successfully cloaked in erythrocyte membrane, resulting in a PTX-NP-EM (PNM) system. The PNM could achieve high drug loading of PTX (>60%) and stabilize the particle size significantly compared to PN alone. Besides, the fluorescence-labeling PNM presented better tumor cell uptake, stronger cytotoxicity, and higher drug accumulation in tumor compared to PN. Finally, the PNM was found to be the most effective against tumor growth among all PTX formulations in tumor-bearing mice models, with much lower system toxicity than control formulation. In general, the PNM system with high drug-loading as well as superior bio-distributions in vivo could be served as a promising formulation.</p

Discovery of novel PRMT5 inhibitors bearing a methylpiperazinyl moiety - Supplementary Data

Supplementary Table S1: List of 68 selected compounds via virtual screening and bioactivity evaluation 2. Supplementary Figures: 2.1 Supplementary Figure S1: Jurkat and K-562 cell growth inhibition rates of compounds S1-S68 2.2 Supplementary Figure S2: Anti-proliferative effect of EPZ015666 on Z138 cells 2.3 Supplementary Figure S3: SDMA and PRMT5 levels in Z-138 cells treated by S43, 43b, 43g and the positive control. 2.4 Supplementary Figure S4-S22: NMR and LC-MS spectra of target compounds</p

PTENα regulates mitophagy and maintains mitochondrial quality control

PTEN plays an important role in tumor suppression, and PTEN family members are involved in multiple biological processes in various subcellular locations. Here we report that PTENα, the first identified PTEN isoform, regulates mitophagy through promotion of PARK2 recruitment to damaged mitochondria. We show that PTENα-deficient mice exhibit accumulation of cardiac mitochondria with structural and functional abnormalities, and PTENα-deficient mouse hearts are more susceptible to injury induced by isoprenaline and ischemia-reperfusion. Mitochondrial clearance by mitophagy is also impaired in PTENα-deficient cardiomyocytes. In addition, we found PTENα physically interacts with the E3 ubiquitin ligase PRKN, which is an important mediator of mitophagy. PTENα binds PRKN through the membrane binding helix in its N-terminus, and promotes PRKN mitochondrial translocation through enhancing PRKN self-association in a phosphatase-independent manner. Loss of PTENα compromises mitochondrial translocation of PRKN and resultant mitophagy following mitochondrial depolarization. We propose that PTENα functions as a mitochondrial quality controller that maintains mitochondrial function and cardiac homeostasis. Abbreviations: BECN1 beclin 1; CCCP carbonyl cyanide m-chlorophenylhydrazone; FBXO7 F-box protein 7; FS fraction shortening; HSPA1L heat shock protein family A (Hsp70) member 1 like; HW: BW heart weight:body weight ratio; I-R ischemia-reperfusion; ISO isoprenaline; MAP1LC3/LC3 microtubule associated protein 1 light chain 3; MBH membrane binding helix; MFN1 mitofusin 1; MFN2 mitofusin 2; Nam nicotinamide; TMRM tetramethylrhodamine ethyl ester; WGA wheat germ agglutinin</p