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Background: A key step in clinical flow cytometry data analysis is gating, which involves the identification of cell populations. The process of gating produces a set of reportable results, which are typically described by gating definitions. The non-standardized, non-interpreted nature of gating definitions represents a hurdle for data interpretation and data sharing across and within organizations. Interpreting and standardizing gating definitions for subsequent analysis of gating results requires a curation effort from experts. Machine learning approaches have the potential to help in this process by predicting expert annotations associated with gating definitions., Methods: We created a gold-standard dataset by manually annotating thousands of gating definitions with cell type and functional marker annotations. We used this dataset to train and test a machine learning pipeline able to predict standard cell types and functional marker genes associated with gating definitions., Results: The machine learning pipeline predicted annotations with high accuracy for both cell types and functional marker genes. Accuracy was lower for gating definitions from assays belonging to laboratories from which limited or no prior data was available in the training. Manual error review ensured that resulting predicted annotations could be reused subsequently as additional gold-standard training data., Conclusions: Machine learning methods are able to consistently predict annotations associated with gating definitions from flow cytometry assays. However, a hybrid automatic and manual annotation workflow would be recommended to achieve optimal results., (© 2022 International Clinical Cytometry Society.)

Purpose: Idasanutlin is a selective small-molecule MDM2 antagonist. It activates the tumor suppressor TP53 and is in phase 3 clinical trial for acute myeloid leukemia. Nonclinical studies have shown that glucuronidation is the major metabolizing mechanism for idasanutlin and UGT1A3 is the major metabolizing enzyme. There are reported examples of UGT polymorphisms associated with drug metabolism or response. Thus, the aim of this analysis is to investigate if UGT polymorphism is associated with idasanutlin pharmacokinetics., Method: Idasanutlin clearance was derived and normalized from two phase I studies. Its clearance level was compared between patients with different genotypes at 44 non-monomorphic UGT SNPs. Several single-locus and multi-locus association analysis, including haplotype association analysis and pairwise SNP interaction (epistasis) analyses were performed to investigate if there is any association between UGT genotypes and idasanutlin clearance., Results and Conclusion: A total of 69 patients who have both idasanutlin pharmacokinetic data and UGT genotyping data were analyzed for association. The major clearance enzyme for idasanutlin, UGT1A3, has no association with idasanutlin clearance. Further single-locus and multi-locus association analyses also suggest that no significant UGT polymorphism association with idasanutlin clearance can be detected with the current datasets. However, the possibility of association with rare allele(s) of UGT family genes cannot be excluded due to the limited sample size of the current phase I studies.

A central problem in structure-based drug design is understanding protein-ligand interactions quantitatively and qualitatively. Several recent studies have highlighted from a qualitative perspective the nature of these interactions and their utility in drug discovery. However, a common limitation is a lack of adequate tools to mine these interactions comprehensively, since exhaustive searches of the protein data bank are time-consuming and difficult to perform. Consequently, fundamental questions remain unanswered: How unique or how common are the protein-ligand interactions observed in a given drug design project when compared to all complexed structures in the protein data bank? Which interaction patterns might explain the affinity of a tool compound toward unwanted targets? To answer these questions and to enable the systematic and comprehensive study of protein-ligand interactions, we introduce PROLIX (Protein Ligand Interaction Explorer), a tool that uses sophisticated fingerprint representations of protein-ligand interaction patterns for rapid data mining in large crystal structure databases. Our implementation strategy pursues a branch-and-bound technique that enables mining against thousands of complexes within a few seconds. Key elements of PROLIX include (i) an intuitive interface that enables users to formulate complex queries easily, (ii) exceptional speed for results retrieval, and (iii) a sophisticated results summarization. Herein we describe the algorithms developed to enable complex queries and fast retrieval of search results, as well as the intuitive aspects of the user interface and summarization viewer.

Motivation: Cell-based phenotypic screens using small molecule inhibitors is an important technology for early drug discovery if the relationship between the disease-related cellular phenotype and inhibitors' biological targets can be determined. However, chemical inhibitors are rightfully believed to be less specific than perturbation by biological agents, such as antibody and small inference RNA. Therefore, it is often a challenge in small molecule phenotypic screening to infer the causality between a particular cellular phenotype and the inactivation of the responsible protein due to the off-target effect of the inhibitors., Results: In this article, we present a Roche in-house effort of screening 746 structurally diverse compounds for their cytotoxicity in HeLa cells measured by high content imaging technology. These compounds were also systematically profiled for the targeted and off-target binding affinity to a panel of 25 pre-selected protein kinases in a cell-free system. In an effort to search for the kinases whose activities are crucial for cell survival, we found that the simple association method such as the chi-square test yields a large number of false positives because the observed cytotoxic phenotype is likely to be the result of promiscuous action of less specific inhibitors instead of true consequence of inactivation of single relevant target. We demonstrated that a stratified categorical data analysis technique such as the Cochran-Mantel-Haenszel test is an effective approach to extract the meaningful biological connection from the spurious correlation resulted from confounding covariates. This study indicates that, empowered by appropriate statistical adjustment, small molecule inhibitor perturbation remains a powerful tool to pin down the relevant biomarker for drug safety and efficacy research., Contact: xin.wei@roche.com, Supplementary Information: Supplementary data are available at Bioinformatics online.

A Markush, or generic structure, is a widely used convention in chemical and pharmaceutical patents. The flexibility and complexity of this format, however, preclude an easy understanding and analysis of chemical space. In this paper, an application package called MarVis (Markush Visualization) is introduced to help chemists visualize Markush structures in chemical patents. MarVis can output a report with the Markush structure showing the query substructure and also an R-group table of all the possible R-groups described in the patent. MarVis also has a unique interactive interface that allows chemists to explore and zoom in the chemical space to find a subset of interest. SMILES, with minimal extensions, was used to facilitate a variety of patent Markush structure studies.

Relationships between drug targets and associated diseases have traditionally been investigated by means of sequence similarity, comparative protein modeling, and pathway analysis. Recently, a complementary paradigm has emerged to link targets and drugs via biological responses within activity data and visualize findings in networks. It has been indicated that one of the obstacles towards the identification of novel interactions is the sparsity of available data. In this article, we provide a survey of estimation methods that address the challenge of data sparsity. Each method is described in terms of its advantages and limitations, and an exemplary application on compound-target activity data is demonstrated. With such imputation methods in-hand, the opportunity to combine efforts in molecular informatics can be realized, yielding novel insights into ligand-target space., (Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Glucose flux through the hexosamine biosynthetic pathway leads to the post-translational modification of cytoplasmic and nuclear proteins by O-linked beta-N-acetylglucosamine (O-GlcNAc). This tandem system serves as a nutrient sensor to couple systemic metabolic status to cellular regulation of signal transduction, transcription, and protein degradation. Here we show that O-GlcNAc transferase (OGT) harbours a previously unrecognized type of phosphoinositide-binding domain. After induction with insulin, phosphatidylinositol 3,4,5-trisphosphate recruits OGT from the nucleus to the plasma membrane, where the enzyme catalyses dynamic modification of the insulin signalling pathway by O-GlcNAc. This results in the alteration in phosphorylation of key signalling molecules and the attenuation of insulin signal transduction. Hepatic overexpression of OGT impairs the expression of insulin-responsive genes and causes insulin resistance and dyslipidaemia. These findings identify a molecular mechanism by which nutritional cues regulate insulin signalling through O-GlcNAc, and underscore the contribution of this modification to the aetiology of insulin resistance and type 2 diabetes.

Alström syndrome is a monogenic recessive disorder featuring an array of clinical manifestations, with systemic fibrosis and multiple organ involvement, including retinal degeneration, hearing loss, childhood obesity, diabetes mellitus, dilated cardiomyopathy (DCM), urological dysfunction, and pulmonary, hepatic, and renal failure. We evaluated a large cohort of patients with Alström syndrome for mutations in the ALMS1 gene. In total, 79 disease-causing variants were identified, of which 55 are novel mutations. The variants are primarily clustered in exons 8, 10, and 16, although we also identified novel mutations in exons 12 and 18. Most alleles were identified only once (45/79), but several were found recurrently. Founder effects are likely in families of English and Turkish descent. We also identified 66 SNPs and assessed the functional significance of these variants based on the conserved identity of the protein and the severity of the resulting amino acid substitution. A genotype-phenotype association study examining 18 phenotypic parameters in a subset of 58 patients found suggestive associations between disease-causing variants in exon 16 and the onset of retinal degeneration before the age of 1 year (P = 0.02), the occurrence of urological dysfunction (P = 0.02), of DCM (P = 0.03), and of diabetes (P = 0.03). A significant association was found between alterations in exon 8 and absent, mild, or delayed renal disease (P = 0.0007). This data may have implications for the understanding of the molecular mechanisms of ALMS1 and provides the basis for further investigation of how alternative splicing of ALMS1 contributes to the severity of the disease., (2007 Wiley-Liss, Inc.)

To study the insulin effects on gene expression in skeletal muscle, muscle biopsies were obtained from 20 insulin sensitive individuals before and after euglycemic hyperinsulinemic clamps. Using microarray analysis, we identified 779 insulin-responsive genes. Particularly noteworthy were effects on 70 transcription factors, and an extensive influence on genes involved in both protein synthesis and degradation. The genetic program in skeletal muscle also included effects on signal transduction, vesicular traffic and cytoskeletal function, and fuel metabolic pathways. Unexpected observations were the pervasive effects of insulin on genes involved in interacting pathways for polyamine and S-adenoslymethionine metabolism and genes involved in muscle development. We further confirmed that four insulin-responsive genes, RRAD, IGFBP5, INSIG1, and NGFI-B (NR4A1), were significantly up-regulated by insulin in cultured L6 skeletal muscle cells. Interestingly, insulin caused an accumulation of NGFI-B (NR4A1) protein in the nucleus where it functions as a transcription factor, without translocation to the cytoplasm to promote apoptosis. The role of NGFI-B (NR4A1) as a new potential mediator of insulin action highlights the need for greater understanding of nuclear transcription factors in insulin action.

Alström syndrome is a homogeneous autosomal recessive disorder that is characterized by childhood obesity associated with hyperinsulinemia, chronic hyperglycemia and neurosensory deficits. The gene involved in Alström syndrome probably interacts with genetic modifiers, as subsets of affected individuals present with additional features such as dilated cardiomyopathy, hepatic dysfunction, hypothyroidism, male hypogonadism, short stature and mild to moderate developmental delay, and with secondary complications normally associated with type 2 diabetes, such as hyperlipidemia and atherosclerosis. Our detection of an uncharacterized transcript, KIAA0328, led us to identify the gene ALMS1, which contains sequence variations, including four frameshift mutations and two nonsense mutations, that segregate with Alström syndrome in six unrelated families. ALMS1 is ubiquitously expressed at low levels and does not share significant sequence homology with other genes reported so far. The identification of ALMS1 provides an entry point into a new pathway leading toward the understanding of both Alström syndrome and the common diseases that characterize it.