Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways

Bioactive compounds can be valuable research tools and drug leads, but it is often difficult to identify their mechanism of action or cellular target. Here we investigate the potential for integration of chemical-genetic and genetic interaction data to reveal information about the pathways and targets of inhibitory compounds. Taking advantage of the existing complete set of yeast haploid deletion mutants, we generated drug-hypersensitivity (chemical-genetic) profiles for 12 compounds. In addition to a set of compound-specific interactions, the chemical-genetic profiles identified a large group of genes required for multidrug resistance. In particular, yeast mutants lacking a functional vacuolar H(+)-ATPase show multidrug sensitivity, a phenomenon that may be conserved in mammalian cells. By filtering chemical-genetic profiles for the multidrug-resistant genes and then clustering the compound-specific profiles with a compendium of large-scale genetic interaction profiles, we were able to identify target pathways or proteins. This method thus provides a powerful means for inferring mechanism of action.     

Chapter 17: Bioimage Informatics for Systems Pharmacology

Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.

What to Learn in This Chapter

• What automated approaches are necessary for analysis of phenotypic changes, especially for drug and target discovery?
• What quantitative features and machine learning approaches are commonly used for quantifying phenotypic changes?
• What resources are available for bioimage informatics studies?

This article is part of the “Translational Bioinformatics" collection for PLOS Computational Biology.

     

Membrane fatty acids as phenotypic markers in the polyphasic taxonomy of methylotrophs within the Proteobacteria.

A polyphasic approach to bacterial taxonomy attempts to integrate phylogenetic relationships with phenotypic marker analysis. This study describes the application of membrane fatty acids as a phenotypic marker for methylotrophs. Detailed phospholipid, ester-linked fatty acid (PLFA) profiles are reported for 17 methylotrophic eubacterial strains. These profiles included verification of double bond positions and geometries, both critical features for this analysis. Multivariate cluster analysis was used to indicate groupings of these strains along with literature values of both methylotrophs and non-methylotrophs based on the PLFA phenotype. Like many phenotypic characteristics, PLFA profiles were influenced by environmental conditions. The instabilities displayed, however, were predictable from physiological studies including increased trans/cis and cyclopropyl/cis ratios. Cluster analysis of PLFA profiles generated by separate investigators with different culture conditions indicated reproducibility by strain and species. The PLFA phenotype relationships compare favourably with phylogenetic associations based on 16S rRNA data for methylotrophs and will continue to be a valuable phenotypic marker for Proteobacteria taxonomy.     

影像基因组学分析方法研究进展

在生物医学大数据背景下,精准医学的研究重点之一是基因型数据和表型数据的融合及关联分析,通过数据融合及关联分析,认识疾病表型特征与基因多态性及基因活动之间的关系。影像基因组学作为一个新兴研究领域,它将疾病影像数据和基因组数据整合,并挖掘两者之间的联系,从而发现能够反映基因多态或表达的影像特征,在此基础上建立基于影像特征的非侵入式疾病诊断方法,是目前生物医学最有前景的研究领域之一。综述了影像基因组学领域的研究方法,包括基因组数据分析、影像数据分析以及基因组数据-影像数据融合分析方法。在此基础上,介绍了影像基因组学目前在临床上的典型应用,包括疾病的辅助诊断、预后预测和疗效评估。最后,对影像基因组学的未来发展进行了展望。     

Identification and validation of bioactive small molecule target through phenotypic screening

For effective bioactive small molecule discovery and development into new therapeutic drug, a systematic screening and target protein identification is required. Different from the conventional screening system, herein phenotypic screening in combination with multi-omics-based target identification and validation (MOTIV) is introduced. First, phenotypic screening provides visual effect of bioactive small molecules in the cell or organism level. It is important to know the effect on the cell or organism level since small molecules affect not only a single target but the entire cellular mechanism within a cell or organism. Secondly, MOTIV provides systemic approach to discover the target protein of bioactive small molecule. With the chemical genomics and proteomics approach of target identification methods, various target protein candidates are identified. Then network analysis and validations of these candidates result in identifying the biologically relevant target protein and cellular mechanism. Overall, the combination of phenotypic screening and MOTIV will provide an effective approach to discover new bioactive small molecules and their target protein and mechanism identification.     

Phenotypic biomonitoring using multivariate flow cytometric analysis of multi-stained microorganisms

A new method for monitoring phenotypic profiles of pure cultures and complex microbial communities was evaluated. The approach was to stain microorganisms with a battery of fluorescent dyes prior to flow cytometry analysis (FCM) and to analyse the data using multivariate methods, including principal component analysis and partial least squares. The FCM method was quantitatively evaluated using different mixtures of pure cultures as well as microbial communities. The results showed that the method could quantitatively and reproducibly resolve both populations and communities of microorganisms with 5% abundance in a diverse microbial background. The feasibility of monitoring complex microbial communities over time during the biodegradation of naphthalene using the FCM method was demonstrated. The biodegradation of naphthalene occurred to differing extents in microcosms representing three different types of aromatic-contaminated groundwater and a sample of bio-basin water. The FCM method distinguished each of these four microbial communities. The phenotypic profiles were compared with genotypic profiles generated by random-amplified polymorphic DNA analysis. The genotypic profiles of the microbial communities described only the microbial composition, and not their functional change, whereas the phenotypic profiles seemed to contain information on both the composition and the functional change of the microorganisms. Furthermore, event analysis of the FCM data showed that microbial communities with initially differing compositions could converge towards a similar composition if they had a capacity for high levels of degradation, whereas microbial communities with similar initial compositions could diverge if they differed in biodegrading ability.     

Virtual Prototyping and Parametric Design of 3D-Printed Tablets Based on the Solution of Inverse Problem

The problem of designing tablet geometry and its internal structure that results into a specified release profile of the drug during dissolution was considered. A solution method based on parametric programming, inspired by CAD (computer-aided design) approaches currently used in other fields of engineering, was proposed and demonstrated. The solution of the forward problem using a parametric series of structural motifs was first carried out in order to generate a library of drug release profiles associated with each structural motif. The inverse problem was then solved in three steps: first, the combination of basic structural motifs whose superposition provides the closest approximation of the required drug release profile was found by a linear combination of pre-calculated release profiles. In the next step, the final tablet design was constructed and its dissolution curve found computationally. Finally, the proposed design was 3D printed and its dissolution profile was confirmed experimentally. The computational method was based on the numerical solution of drug diffusion in a boundary layer surrounding the tablet, coupled with erosion of the tablet structure encoded by the phase volume function. The tablets were 3D printed by fused deposition modelling (FDM) from filaments produced by hot-melt extrusion. It was found that the drug release profile could be effectively controlled by modifying the tablet porosity. Custom release profiles were obtained by combining multiple porosity regions in the same tablet. The computational method yielded accurate predictions of the drug release rate for both single- and multi-porosity tablets.     

Alignment error envelopes for single particle analysis

To determine the structure of a biological particle to high resolution by electron microscopy, image averaging is required to combine information from different views and to increase the signal-to-noise ratio. Starting from the number of noiseless views necessary to resolve features of a given size, four general factors are considered that increase the number of images actually needed: (1) the physics of electron scattering introduces shot noise, (2) thermal motion and particle inhomogeneity cause the scattered electrons to describe a mixture of structures, (3) the microscope system fails to usefully record all the information carried by the scattered electrons, and (4) image misalignment leads to information loss through incoherent averaging. The compound effect of factors 2-4 is approximated by the product of envelope functions. The problem of incoherent image averaging is developed in detail through derivation of five envelope functions that account for small errors in 11 "alignment" parameters describing particle location, orientation, defocus, magnification, and beam tilt. The analysis provides target error tolerances for single particle analysis to near-atomic (3.5 A) resolution, and this prospect is shown to depend critically on image quality, defocus determination, and microscope alignment.     

Chemotography for multi-target SAR analysis in the context of biological pathways

The increasing amount of chemogenomics data, that is, activity measurements of many compounds across a variety of biological targets, allows for better understanding of pharmacology in a broad biological context. Rather than assessing activity at individual biological targets, today understanding of compound interaction with complex biological systems and molecular pathways is often sought in phenotypic screens. This perspective poses novel challenges to structure-activity relationship (SAR) assessment. Today, the bottleneck of drug discovery lies in the understanding of SAR of rich datasets that go beyond single targets in the context of biological pathways, potential off-targets, and complex selectivity profiles. To aid in the understanding and interpretation of such complex SAR, we introduce Chemotography (chemotype chromatography), which encodes chemical space using a color spectrum by combining clustering and multidimensional scaling. Rich biological data in our approach were visualized using spatial dimensions traditionally reserved for chemical space. This allowed us to analyze SAR in the context of target hierarchies and phylogenetic trees, two-target activity scatter plots, and biological pathways. Chemotography, in combination with the Kyoto Encyclopedia of Genes and Genomes (KEGG), also allowed us to extract pathway-relevant SAR from the ChEMBL database. We identified chemotypes showing polypharmacology and selectivity-conferring scaffolds, even in cases where individual compounds have not been tested against all relevant targets. In addition, we analyzed SAR in ChEMBL across the entire Kinome, going beyond individual compounds. Our method combines the strengths of chemical space visualization for SAR analysis and graphical representation of complex biological data. Chemotography is a new paradigm for chemogenomic data visualization and its versatile applications presented here may allow for improved assessment of SAR in biological context, such as phenotypic assay hit lists.     

Drug metabolism by cultured human hepatocytes: how far are we from the in vivo reality?

The investigation of metabolism is an important milestone in the course of drug development. Drug metabolism is a determinant of drug pharmacokinetics variability in human beings. Fundamental to this are phenotypic differences, as well as genotypic differences, in the expression of the enzymes involved in drug metabolism. Genotypic variability is easy to identify by means of polymerase chain reaction-based or DNA chip-based methods, whereas phenotypic variability requires direct measurement of enzyme activities in liver, or, indirectly, measurement of the rate of metabolism of a given compound in vivo. There is a great deal of phenotypic variability in human beings, only a minor part being attributable to gene polymorphisms. Thus, enzyme activity measurements in a series of human livers, as well as in vivo studies with human volunteers, show that phenotypic variability is, by far, much greater than genotypic variability. In vitro models are currently used to investigate the hepatic metabolism of new compounds. Cultured human hepatocytes are considered to be the closest model to the human liver. However, the fact that hepatocytes are placed in a microenvironment that differs from that of the cells in the liver raises the question of to what extent drug metabolism variability observed in vitro actually reflects that in the liver in vivo. This issue has been examined by investigating the metabolism of the model compound, aceclofenac (an approved analgesic/anti-inflammatory drug), both in vitro and in vivo. Hepatocytes isolated from programmed liver biopsies were incubated with aceclofenac, and the metabolites formed were investigated by HPLC. The patients were given the drug during the course of clinical recovery, and the metabolites, largely present in urine, were analysed. In vitro and in vivo data from the same individual were compared. There was a good correlation between the in vitro and in vivo relative abundance of oxidised metabolites (4'-OH-aceclofenac + 4'-OH-diclofenac; Spearman's rho = 0.855), and the hydrolysis of aceclofenac (diclofenac + 4'-OH-aceclofenac + 4'-OH-diclofenac; rho = 0.691), while the conjugation of the drug in vitro was somewhat lower than in vivo. Globally, the metabolism of aceclofenac in vitro correlated with the amount of metabolites excreted in urine after 16 hours (rho = 0.95). Overall, although differing among assays, the in vitro/in vivo metabolism data for each patient were surprisingly similar. Thus, the variability observed in vitro appears to reflect genuine phenotypic variability among the donors.     

Dimension reduction methods for microarrays with application to censored survival data

MOTIVATION: Recent research has shown that gene expression profiles can potentially be used for predicting various clinical phenotypes, such as tumor class, drug response and survival time. While there has been extensive studies on tumor classification, there has been less emphasis on other phenotypic features, in particular, patient survival time or time to cancer recurrence, which are subject to right censoring. We consider in this paper an analysis of censored survival time based on microarray gene expression profiles. RESULTS: We propose a dimension reduction strategy, which combines principal components analysis and sliced inverse regression, to identify linear combinations of genes, that both account for the variability in the gene expression levels and preserve the phenotypic information. The extracted gene combinations are then employed as covariates in a predictive survival model formulation. We apply the proposed method to a large diffuse large-B-cell lymphoma dataset, which consists of 240 patients and 7399 genes, and build a Cox proportional hazards model based on the derived gene expression components. The proposed method is shown to provide a good predictive performance for patient survival, as demonstrated by both the significant survival difference between the predicted risk groups and the receiver operator characteristics analysis. AVAILABILITY: R programs are available upon request from the authors. SUPPLEMENTARY INFORMATION: http://dna.ucdavis.edu/~hli/bioinfo-surv-supp.pdf.     

Synthesis and structure–activity analysis of diphenylpyrazolodiazene inhibitors of the HIV-1 Nef virulence factor

HIV-1 Nef is a critical AIDS progression factor yet underexplored target for antiretroviral drug discovery. A recent high-throughput screen for pharmacological inhibitors of Nef-dependent Src-family kinase activation identified a diphenylpyrazolodiazene hit compound with submicromolar potency in HIV-1 replication assays against a broad range of primary Nef variants. This compound, known as ‘B9’, binds directly to Nef and inhibits its dimerization in cells as a possible mechanism of action. Here were synthesized a diverse set of B9 analogs and identified structural features essential to antiretroviral activity. Chemical modifications to each of the three rings present in the parent compound were identified that did not compromise antiviral action. These analogs will guide the development of next-generation compounds with appropriate pharmacological profiles for assessment of antiretroviral activity in vivo.     

Statistical multivariate metabolite profiling for aiding biomarker pattern detection and mechanistic interpretations in GC/MS based metabolomics

A strategy for robust and reliable mechanistic statistical modelling of metabolic responses in relation to drug induced toxicity is presented. The suggested approach addresses two cases commonly occurring within metabonomic toxicology studies, namely; 1) A pre-defined hypothesis about the biological mechanism exists and 2) No such hypothesis exists. GC/MS data from a liver toxicity study consisting of rat urine from control rats and rats exposed to a proprietary AstraZeneca compound were resolved by means of hierarchical multivariate curve resolution (H-MCR) generating 287 resolved chromatographic profiles with corresponding mass spectra. Filtering according to significance in relation to drug exposure rendered in 210 compound profiles, which were subjected to further statistical analysis following correction to account for the control variation over time. These dose related metabolite traces were then used as new observations in the subsequent analyses. For case 1, a multivariate approach, named Target Batch Analysis, based on OPLS regression was applied to correlate all metabolite traces to one or more key metabolites involved in the pre-defined hypothesis. For case 2, principal component analysis (PCA) was combined with hierarchical cluster analysis (HCA) to create a robust and interpretable framework for unbiased mechanistic screening. Both the Target Batch Analysis and the unbiased approach were cross-verified using the other method to ensure that the results did match in terms of detected metabolite traces. This was also the case, implying that this is a working concept for clustering of metabolites in relation to their toxicity induced dynamic profiles regardless if there is a pre-existing hypothesis or not. For each of the methods the detected metabolites were subjected to identification by means of data base comparison as well as verification in the raw data. The proposed strategy should be seen as a general approach for facilitating mechanistic modelling and interpretations in metabolomic studies.     

A new Multi-PCR-SSCP assay for simultaneous detection of isoniazid and rifampin resistance in Mycobacterium tuberculosis

Prompt detection of drug resistance in Mycobacterium tuberculosis is essential for effective control of tuberculosis (TB). We developed a Multi-PCR-SSCP method that detects more than 80% commonly observed isoniazid (INH) and rifampin (RIF) resistance M. tuberculosis in a single assay. The usefulness of the newly developed method was evaluated with 116 clinical isolates of M. tuberculosis. Distinct SSCP patterns were observed for different mutations and the correlation between Multi-PCR-SSCP results and DNA sequencing data was strong. Using the culture-based phenotypic drug susceptibility testing as a reference, the sensitivity of the newly developed Multi-PCR-SSCP assay was determined to be 80% and 81.8% for INH and RIF, respectively. The specificity of the assay was 100% and 92%, for INH and RIF, respectively. Multi-PCR-SSCP provides a rapid and potentially more cost-effective method of detecting multidrug-resistant TB.