Drug target validation and identification of secondary drug target effects using DNA microarrays

k- Strong Inference Algorithm: A Hybrid Information Theory Based Gene Network Inference Algorithm

Gene networks allow researchers to understand the underlying mechanisms between diseases and genes while reducing the need for wet lab experiments. Numerous gene network inference (GNI) algorithms have been presented in the literature to infer accurate gene networks. We proposed a hybrid GNI algorithm, k-Strong Inference Algorithm (ksia), to infer more reliable and robust gene networks from omics datasets. To increase reliability, ksia integrates Pearson correlation coefficient (PCC) and Spearman rank correlation coefficient (SCC) scores to determine mutual information scores between molecules to increase diversity of relation predictions. To infer a more robust gene network, ksia applies three different elimination steps to remove redundant and spurious relations between genes. The performance of ksia was evaluated on microbe microarrays database in the overlap analysis with other GNI algorithms, namely ARACNE, C3NET, CLR, and MRNET. Ksia inferred less number of relations due to its strict elimination steps. However, ksia generally performed better on Escherichia coli (E.coli) and Saccharomyces cerevisiae (yeast) gene expression datasets due to F- measure and precision values. The integration of association estimator scores and three elimination stages slightly increases the performance of ksia based gene networks. Users can access ksia R package and user manual of package via https://github.com/ozgurcingiz/ksia .

Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas

Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.

Microarray Data Preprocessing: From Experimental Design to Differential Analysis

DNA microarray data preprocessing is of utmost importance in the analytical path starting from the experimental design and leading to a reliable biological interpretation. In fact, when all relevant aspects regarding the experimental plan have been considered, the following steps from data quality check to differential analysis will lead to robust, trustworthy results. In this chapter, all the relevant aspects and considerations about microarray preprocessing will be discussed. Preprocessing steps are organized in an orderly manner, from experimental design to quality check and batch effect removal, including the most common visualization methods. Furthermore, we will discuss data representation and differential testing methods with a focus on the most common microarray technologies, such as gene expression and DNA methylation.

Algorithms to Preprocess Microarray Image Data

Microarray is a powerful technology that enables the monitoring of expression levels for thousands of genes simultaneously, providing scientists with a full overview about DNA and RNA investigation. The process is made of three main phases: interaction with biological samples, data extraction, and data analysis. In particular, the data extraction phase strongly relies on image processing algorithms, since the expression levels are revealed by the interaction of light with fluorescent markers. More in detail, in order to extract quantitative information from probes image, three steps are required: (1) gridding, (2) segmentation, and (3) intensity quantification. Errors in one of these steps can deeply affect the process outcome. In this chapter each of the above mentioned steps will be analyzed and discussed. Software platforms dedicated to this purpose will be reported as well.

Drug repurposing strategies of relevance for Parkinson's disease

Parkinson's disease is a highly disabling, progressive neurodegenerative disease that manifests as a mix of motor and non-motor signs. Although we are equipped with some symptomatic treatments, especially for the motor signs of the disease, there are still no established disease-modifying drugs so the disease progresses unchecked. Standard drug discovery programs for disease-modifying therapies have provided key insights into the pathogenesis of Parkinson's disease but, of the many positive candidates identified in pre-clinical studies, none has yet translated into a successful clinically efficacious drug. Given the huge cost of drug discovery programs, it is not surprising that much attention has turned toward repurposing strategies. The trialing of an established therapeutic has the advantage of bypassing the need for preclinical safety testing and formulation optimization, thereby cutting both time and costs involved in getting a treatment to the clinic. Additional reduced failure rates for repurposed drugs are also a potential bonus. Many different strategies for drug repurposing are open to researchers in the Parkinson's disease field. Some of these have already proven effective in identifying suitable drugs for clinical trials, lending support to such approaches. In this review, we present a summary of the different strategies for drug repurposing, from large-scale epidemiological correlation analysis through to single-gene transcriptional approaches. We provide examples of past or ongoing studies adopting each strategy, where these exist. For strategies that have yet to be applied to Parkinson's disease, their utility is illustrated using examples taken from other disorders.

Calcineurin-dependent regulation of endocytosis by a plasma membrane ubiquitin ligase adaptor, Rcr1

Rsp5, the Nedd4 family member in yeast, is an E3 ubiquitin ligase involved in numerous cellular processes, many of which require Rsp5 to interact with PY-motif containing adaptor proteins. Here, we show that two paralogous transmembrane Rsp5 adaptors, Rcr1 and Rcr2, are sorted to distinct cellular locations: Rcr1 is a plasma membrane (PM) protein, whereas Rcr2 is sorted to the vacuole. Rcr2 is delivered to the vacuole using ubiquitin as a sorting signal. Rcr1 is delivered to the PM by the exomer complex using a newly uncovered PM sorting motif. Further, we show that Rcr1, but not Rcr2, is up-regulated via the calcineurin/Crz1 signaling pathway. Upon exogenous calcium treatment, Rcr1 ubiquitinates and down-regulates the chitin synthase Chs3. We propose that the PM-anchored Rsp5/Rcr1 ubiquitin ligase-adaptor complex can provide an acute response to degrade unwanted proteins under stress conditions, thereby maintaining cell integrity.

Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors-let us call that the silver bullet approach to cancer therapeutics-to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns-we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases "shrapnel gunshots" may become more effective than "silver bullets". Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.

Transcription-based drug repurposing for COVID-19

We have utilised the transcriptional response of lung epithelial cells following infection by the original Severe Acute Respiratory Syndrome coronavirus (SARS) to identify repurposable drugs for COVID-19. Drugs best able to recapitulate the infection profile are highly enriched for antiviral activity. Nine of these have been tested against SARS-2 and found to potently antagonise SARS-2 infection/replication, with a number now being considered for clinical trials. It is hoped that this approach may serve to broaden the spectrum of approved drugs that should be further assessed as potential anti-COVID-19 agents and may help elucidate how this seemingly disparate collection of drugs are able to inhibit SARS-2 infection/replication.

Aspirin Causes Lipid Accumulation and Damage to Cell Membrane by Regulating DCI1/OLE1 in Saccharomyces cerevisiae

Aspirin is one of the most commonly used nonsteroidal anti-inflammatory drugs. Various potential pharmacological effects of aspirin, such as anticancer, antibacterial activity, and prolonging life expectancy have been discovered. However, the mechanism of aspirin is not fully elucidated. Herein, the effects of aspirin on fatty acid metabolism in yeast cell model Saccharomyces cerevisiae were studied. The results showed that aspirin can induce lipid accumulation and reduce the unsaturated fat index in cells. The assessment of cell membrane integrity demonstrated that aspirin caused damage to the cell membrane. These effects of aspirin were attributed to the alterations of the expression of DCI1 and OLE1. Similarly, aspirin was able to cause lipid accumulation and damage to the cell membrane by interfering with the expression of OLE1 in Candida albicans. These findings are expected to improve current understanding of the mode of action of aspirin and provide a novel strategy for antifungal drug design. Graphical abstract [Figure: see text].

New directions in antimalarial target validation

Introduction: Malaria is one of the most prevalent human infections worldwide with over 40% of the world's population living in malaria-endemic areas. In the absence of an effective vaccine, emergence of drug-resistant strains requires urgent drug development. Current methods applied to drug target validation, a crucial step in drug discovery, possess limitations in malaria. These constraints require the development of techniques capable of simplifying the validation of Plasmodial targets.Areas covered: The authors review the current state of the art in techniques used to validate drug targets in malaria, including our contribution - the protein interference assay (PIA) - as an additional tool in rapid in vivo target validation.Expert opinion: Each technique in this review has advantages and disadvantages, implying that future validation efforts should not focus on a single approach, but integrate multiple approaches. PIA is a significant addition to the current toolset of antimalarial validation. Validation of aspartate metabolism as a druggable pathway provided proof of concept of how oligomeric interfaces can be exploited to control specific activity in vivo. PIA has the potential to be applied not only to other enzymes/pathways of the malaria parasite but could, in principle, be extrapolated to other infectious diseases.

Alzheimer's Disease Progression in the 5×FAD Mouse Captured with a Multiplex Gene Expression Array

BACKGROUND

Alzheimer's disease (AD) is an incurable complex neurodegenerative condition with no new therapies licensed in the past 20 years. AD progression is characterized by the up- and downregulation of distinct biological processes that can be followed through the expression level changes of associated genes and gene networks.

OBJECTIVE

Our study aims to establish a multiplex gene expression tracking platform to follow disease progression in an animal model facilitating the study of treatment paradigms.

METHODS

We have established a multiplex platform covering 47 key genes related to immunological, neuronal, mitochondrial, and autophagy cell types and processes that capture disease progression in the 5×FAD mouse model.

RESULTS

We show that the immunological response is the most pronounced change in aged 5×FAD mice (8 months and above), and in agreement with early stage human disease samples, observe an initial downregulation of microglial genes in one-month-old animals. The less dramatic downregulation of neuronal and mitochondrial gene sets is also reported.

CONCLUSION

This study provides the basis for a quantitative multi-dimensional platform to follow AD progression and monitor the efficacy of treatments in an animal model.

Drug repurposing for Alzheimer's disease based on transcriptional profiling of human iPSC-derived cortical neurons

Alzheimer's disease is a complex disorder encompassing multiple pathological features with associated genetic and molecular culprits. However, target-based therapeutic strategies have so far proved ineffective. The aim of this study is to develop a methodology harnessing the transcriptional changes associated with Alzheimer's disease to develop a high content quantitative disease phenotype that can be used to repurpose existing drugs. Firstly, the Alzheimer's disease gene expression landscape covering severe disease stage, early pathology progression, cognitive decline and animal models of the disease has been defined and used to select a set of 153 drugs tending to oppose disease-associated changes in the context of immortalised human cancer cell lines. The selected compounds have then been assayed in the more biologically relevant setting of iPSC-derived cortical neuron cultures. It is shown that 51 of the drugs drive expression changes consistently opposite to those seen in Alzheimer's disease. It is hoped that the iPSC profiles will serve as a useful resource for drug repositioning within the context of neurodegenerative disease and potentially aid in generating novel multi-targeted therapeutic strategies.

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Predicting protein targets for drug-like compounds using transcriptomics

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.

Bioactive compounds often disrupt cellular gene expression in ways that are difficult to predict. While the correlation between a cellular response after treatment with a small molecule and the knockdown of its target protein should be simple to establish, in practice this goal has been difficult to achieve. The main challenges are that data are noisy, drugs are not intended to be active in all cell types, and signals from a bona fide target(s) may be obscured by correlations with knockdowns of other proteins in the same pathway(s). Here, we find that a random forest classification model can detect meaningful correlational patterns when gene expression profiles after compound treatment and gene knockdowns in four or more cell lines are compared. When this approach is enriched by a structure-based screen, novel drug-target interactions can be predicted. We then validated new ligand-protein interactions for four difficult targets. Although the initial compounds are not especially potent in vitro, they are capable of disrupting their target pathway in the cell to an extent that generates a significant and characteristic gene expression profile. Collectively, our studies provide insight on cell-level transcriptomic responses to pharmaceutical intervention and the use of these patterns for target identification. In addition, the method provides a novel drug discovery pipeline to test chemistries without a priori knowledge of their target(s).

Identification of Antifungal Targets Based on Computer Modeling

Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host⁻pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.

Methods to validate Hsp90 inhibitor specificity, to identify off-target effects, and to rethink approaches for further clinical development

The molecular chaperone Hsp90 is one component of a highly complex and interactive cellular proteostasis network (PN) that participates in protein folding, directs misfolded and damaged proteins for destruction, and participates in regulating cellular transcriptional responses to environmental stress, thus promoting cell and organismal survival. Over the last 20 years, it has become clear that various disease states, including cancer, neurodegeneration, metabolic disorders, and infection by diverse microbes, impact the PN. Among PN components, Hsp90 was among the first to be pharmacologically targeted with small molecules. While the number of Hsp90 inhibitors described in the literature has dramatically increased since the first such small molecule was described in 1994, it has become increasingly apparent that not all of these agents have been sufficiently validated for specificity, mechanism of action, and lack of off-target effects. Given the less than expected activity of Hsp90 inhibitors in cancer-related human clinical trials, a re-evaluation of potentially confounding off-target effects, as well as confidence in target specificity and mechanism of action, is warranted. In this commentary, we provide feasible approaches to achieve these goals and we discuss additional considerations to improve the clinical efficacy of Hsp90 inhibitors in treating cancer and other diseases.

CRISPR Approaches to Small Molecule Target Identification

A long-standing challenge in drug development is the identification of the mechanisms of action of small molecules with therapeutic potential. A number of methods have been developed to address this challenge, each with inherent strengths and limitations. We here provide a brief review of these methods with a focus on chemical-genetic methods that are based on systematically profiling the effects of genetic perturbations on drug sensitivity. In particular, application of these methods to mammalian systems has been facilitated by the recent advent of CRISPR-based approaches, which enable one to readily repress, induce, or delete a given gene and determine the resulting effects on drug sensitivity.

Calcineurin is an important factor involved in glucose uptake in human adipocytes

Calcineurin inhibitors are used in immunosuppressive therapy applied after transplantation, but they are associated with major metabolic side effects including the development of new onset diabetes. Previously, we have shown that the calcineurin inhibiting drugs tacrolimus and cyclosporin A reduce adipocyte and myocyte glucose uptakes by reducing the amount of glucose transporter type 4 (GLUT4) at the cell surface, due to an increased internalization rate. However, this happens without alteration in total protein and phosphorylation levels of key proteins involved in insulin signalling or in the total amount of GLUT4. The present study evaluates possible pathways involved in the altered internalization of GLUT4 and consequent reduction of glucose uptake provoked by calcineurin inhibitors in human subcutaneous adipose tissue. Short- and long-term treatments with tacrolimus, cyclosporin A or another CNI deltamethrin (herbicide) decreased basal and insulin-dependent glucose uptake in adipocytes, without any additive effects observed when added together. However, no tacrolimus effects were observed on glucose uptake when gene transcription and protein translation were inhibited. Investigation of genes potentially involved in GLUT4 trafficking showed only a small effect on ARHGEF11 gene expression (p < 0.05). In conlusion, the specific inhibition of calcineurin, but not that of protein phosphatases, decreases glucose uptake in human subcutaneous adipocytes, suggesting that calcineurin is an important regulator of glucose transport. This inhibitory effect is mediated via gene transcription or protein translation; however, expression of genes potentially involved in GLUT4 trafficking and endocytosis appears not to be involved in these effects.

Differential gene expression analysis in ageing muscle and drug discovery perspectives

Identifying therapeutic target genes represents the key step in functional genomics-based therapies. Within this context, the disease heterogeneity, the exogenous factors and the complexity of genomic structure and function represent important challenges. The functional genomics aims to overcome such obstacles via identifying the gene functions and therefore highlight disease-causing genes as therapeutic targets. Genomic technologies promise to reshape the research on ageing muscle, exercise response and drug discovery. Herein, we describe the functional genomics strategies, mainly differential gene expression methods microarray, serial analysis of gene expression (SAGE), massively parallel signature sequence (MPSS), RNA sequencing (RNA seq), representational difference analysis (RDA), and suppression subtractive hybridization (SSH). Furthermore, we review these illustrative approaches that have been used to discover new therapeutic targets for some complex diseases along with the application of these tools to study the modulation of the skeletal muscle transcriptome.

A countermeasure development pipeline

We have developed an integrated pipeline for countermeasure discovery that, under the auspices of the National Institutes of Health Countermeasures Against Chemical Threats network, is one of the few efforts within academia that by design spans the spectrum from discovery to phase I. The successful implementation of this approach for cyanide would enable efficient proof-of-concept studies that would lay the foundation for a generalizable strategy for parallel mechanistic studies and accelerated countermeasure development in the face of new and emerging chemical threats.

Chemical genetics and strigolactone perception

Strigolactones (SLs) are a collection of related small molecules that act as hormones in plant growth and development. Intriguingly, SLs also act as ecological communicators between plants and mycorrhizal fungi and between host plants and a collection of parasitic plant species. In the case of mycorrhizal fungi, SLs exude into the soil from host roots to attract fungal hyphae for a beneficial interaction. In the case of parasitic plants, however, root-exuded SLs cause dormant parasitic plant seeds to germinate, thereby allowing the resulting seedling to infect the host and withdraw nutrients. Because a laboratory-friendly model does not exist for parasitic plants, researchers are currently using information gleaned from model plants like Arabidopsis in combination with the chemical probes developed through chemical genetics to understand SL perception of parasitic plants. This work first shows that understanding SL signaling is useful in developing chemical probes that perturb SL perception. Second, it indicates that the chemical space available to probe SL signaling in both model and parasitic plants is sizeable. Because these parasitic pests represent a major concern for food insecurity in the developing world, there is great need for chemical approaches to uncover novel lead compounds that perturb parasitic plant infections.

Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data

Gene expression is perturbated by drugs to different extent. Analyzing genes whose expression is frequently regulated by drugs would be useful for the screening of candidate therapeutic targets and genes implicated in side effect. Here, we obtained the differential expression number (DEN) for genes profiled in Affymetrix microarrays from the Connectivity Map project, and conducted systemic comparative computational analysis between high DEN genes and other genes. Results indicated that genes with higher down-/up-regulation number (down_h/up_h) tended to be clustered in genome, and have lower homologous gene number, higher SNP density and more disease-related SNP. Down_h and up_h were significantly enriched in cancer related pathways, while genes with lower down-/up-regulation number (down_l/up_l) were mainly involved in the development of nervous system diseases. Besides, up_h had lower interaction network degree, later developmental stage to express, higher tissue expression specificity than up_l, while down_h showed reversed tendency in comparison with down_l. Together, our analysis suggests that genes frequently regulated by drugs are more likely to be associated with disease-related functions, but the extensive activation of conserved and widely expressed genes by drugs is disfavored.

Determining the mode of action of anti-mycobacterial C17 diyne natural products using expression profiling: evidence for fatty acid biosynthesis inhibition

Background

The treatment of microbial infections is becoming increasingly challenging because of limited therapeutic options and the growing number of pathogenic strains that are resistant to current antibiotics. There is an urgent need to identify molecules with novel modes of action to facilitate the development of new and more effective therapeutic agents. The anti-mycobacterial activity of the C17 diyne natural products falcarinol and panaxydol has been described previously; however, their mode of action remains largely undetermined in microbes. Gene expression profiling was therefore used to determine the transcriptomic response of Mycobacterium smegmatis upon treatment with falcarinol and panaxydol to better characterize the mode of action of these C17 diynes.

Results

Our analyses identified 704 and 907 transcripts that were differentially expressed in M. smegmatis after treatment with falcarinol and panaxydol respectively. Principal component analysis suggested that the C17 diynes exhibit a mode of action that is distinct to commonly used antimycobacterial drugs. Functional enrichment analysis and pathway enrichment analysis revealed that cell processes such as ectoine biosynthesis and cyclopropane-fatty-acyl-phospholipid synthesis were responsive to falcarinol and panaxydol treatment at the transcriptome level in M. smegmatis. The modes of action of the two C17 diynes were also predicted through Prediction of Activity Spectra of Substances (PASS). Based upon convergence of these three independent analyses, we hypothesize that the C17 diynes inhibit fatty acid biosynthesis, specifically phospholipid synthesis, in mycobacteria.

Conclusion

Based on transcriptomic responses, it is suggested that the C17 diynes act differently than other anti-mycobacterial compounds in M. smegmatis, and do so by inhibiting phospholipid biosynthesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2949-y) contains supplementary material, which is available to authorized users.

Investigating the Mechanistic Basis for Hepatic Toxicity Induced by an Experimental Chemokine Receptor 5 (CCR5) Antagonist Using a Compendium of Gene Expression Profiles

A compendium of hepatic gene expression signatures was used to identify a mechanistic basis for the hepatic toxicity of an experimental CCR5 antagonist (MrkA). Development of MrkA, a potential HIV therapeutic, was discontinued due to hepatotoxicity in preclinical studies. Rats were treated with MrkA at 3 dose levels (50, 250, and 500 mg/kg) for 1, 3, or 7 days. Hepatic toxicity (vacuolation, consistent with steatosis, and elevated serum transaminase levels) was observed at 250 and 500 mg/kg, but not at 50 mg/kg. Hepatic gene expression profiles were compared to a compendium of hepatic expression profiles. MrkA was similar to 3 β-oxidation inhibitors (valproate, cyclopropane carboxylate, pivalate), 8 PPARα agonists (fenofibrate, bezafibrate and 6 fibrate analogues), and 3 other diverse compounds (diethylnitrosamine, microcystin LR & actinomycin D). These data indicate MrkA to be a mitochondrial inhibitor, and activation of PPARα-regulated transcription was thought to be due to an accumulation of endogenous ligands. While mitochondrial inhibition was likely responsible for steatosis, canonical pathway analysis revealed that progression to liver injury may be mediated by activation of the innate immune system primarily through NF-kB pathways. These results demonstrate the utility of a gene expression response compendium in developing transcriptional biomarkers and identifying the mechanistic basis for toxicity.

The evolution of drug discovery: from phenotypes to targets, and back

Cumulative scientific and technological advances over the past two centuries have transformed drug discovery from a largely serendipitous process into the high tech pipelines of today.

Drug target prioritization by perturbed gene expression and network information

Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs’ targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects.

Microarray experiments and factors which affect their reliability

Oligonucleotide microarrays belong to the basic tools of molecular biology and allow for simultaneous assessment of the expression level of thousands of genes. Analysis of microarray data is however very complex, requiring sophisticated methods to control for various factors that are inherent to the procedures used. In this article we describe the individual steps of a microarray experiment, highlighting important elements and factors that may affect the processes involved and that influence the interpretation of the results. Additionally, we describe methods that can be used to estimate the influence of these factors, and to control the way in which they affect the expression estimates. A comprehensive understanding of the experimental protocol used in a microarray experiment aids the interpretation of the obtained results. By describing known factors which affect expression estimates this article provides guidelines for appropriate quality control and pre-processing of the data, additionally applicable to other transcriptome analysis methods that utilize similar sample handling protocols.

In-silico gene co-expression network analysis in Paracoccidioides brasiliensis with reference to haloacid dehalogenase superfamily hydrolase gene

CONTEXT

Paracoccidioides brasiliensis, a dimorphic fungus is the causative agent of paracoccidioidomycosis, a disease globally affecting millions of people. The haloacid dehalogenase (HAD) superfamily hydrolases enzyme in the fungi, in particular, is known to be responsible in the pathogenesis by adhering to the tissue. Hence, identification of novel drug targets is essential.

AIMS

In-silico based identification of co-expressed genes along with HAD superfamily hydrolase in P. brasiliensis during the morphogenesis from mycelium to yeast to identify possible genes as drug targets.

MATERIALS AND METHODS

In total, four datasets were retrieved from the NCBI-gene expression omnibus (GEO) database, each containing 4340 genes, followed by gene filtration expression of the data set. Further co-expression (CE) study was performed individually and then a combination these genes were visualized in the Cytoscape 2. 8.3.

STATISTICAL ANALYSIS USED

Mean and standard deviation value of the HAD superfamily hydrolase gene was obtained from the expression data and this value was subsequently used for the CE calculation purpose by selecting specific correlation power and filtering threshold.

RESULTS

The 23 genes that were thus obtained are common with respect to the HAD superfamily hydrolase gene. A significant network was selected from the Cytoscape network visualization that contains total 7 genes out of which 5 genes, which do not have significant protein hits, obtained from gene annotation of the expressed sequence tags by BLAST X. For all the protein PSI-BLAST was performed against human genome to find the homology.

CONCLUSIONS

The gene co-expression network was obtained with respect to HAD superfamily dehalogenase gene in P. Brasiliensis.

A high-throughput microfluidic single-cell screening platform capable of selective cell extraction

Microfluidic devices and lab-on-a-chip technologies have been extensively used in high-throughput single-cell analysis applications using their capability to precisely manipulate cells as well as their microenvironment. Although significant technological advances have been made in single-cell capture, culture, and analysis techniques, most microfluidic systems cannot selectively retrieve samples off-chip for additional examinations. Being able to retrieve target cells of interest from large arrays of single-cell culture compartments is especially critical in achieving high-throughput single-cell screening applications, such as a mutant library screening. We present a high-throughput microfluidic single-cell screening platform capable of investigating cell properties, such as growth and biomolecule production, followed by selective extraction of particular cells showing desired traits to off-chip reservoirs for sampling or further analysis. The developed platform consists of 1024 single-cell trapping/culturing sites, where opening and closing of each trap can be individually controlled with a microfluidic OR logic gate. By opening only a specific site out of the 1024 trapping sites and applying backflow, particular cells of interest could be selectively released and collected off-chip. Using a unicellular microalga Chlamydomonas reinhardtii, single-cell capture and selective cell extraction capabilities of the developed platform were successfully demonstrated. The growth profile and intracellular lipid accumulation of the cells were also analyzed inside the platform, where 6-8 hours of doubling time and on-chip stained lipid bodies were successfully identified, demonstrating the compatibility of the system for cell culture and fluorescent tagging assays.

Application of nanomaterials in the bioanalytical detection of disease-related genes

In the diagnosis of genetic diseases and disorders, nanomaterials-based gene detection systems have significant advantages over conventional diagnostic systems in terms of simplicity, sensitivity, specificity, and portability. In this review, we describe the application of nanomaterials for disease-related genes detection in different methods excluding PCR-related method, such as colorimetry, fluorescence-based methods, electrochemistry, microarray methods, surface-enhanced Raman spectroscopy (SERS), quartz crystal microbalance (QCM) methods, and dynamic light scattering (DLS). The most commonly used nanomaterials are gold, silver, carbon and semiconducting nanoparticles. Various nanomaterials-based gene detection methods are introduced, their respective advantages are discussed, and selected examples are provided to illustrate the properties of these nanomaterials and their emerging applications for the detection of specific nucleic acid sequences.

AtCAST3.0 update: a web-based tool for analysis of transcriptome data by searching similarities in gene expression profiles

In transcriptome experiments, the experimental conditions (e.g. mutants and/or treatments) cause transcriptional changes. Identifying experimental conditions that induce similar or opposite transcriptional changes can be useful to identify experimental conditions that affect the same biological process. AtCAST (http://atpbsmd.yokohama-cu.ac.jp) is a web-based tool to analyze the relationship between experimental conditions among transcriptome data. Users can analyze 'user's transcriptome data' of a new mutant or a new chemical compound whose function remains unknown to generate novel biological hypotheses. This tool also allows for mining of related 'experimental conditions' from the public microarray data, which are pre-included in AtCAST. This tool extracts a set of genes (i.e. module) that show significant transcriptional changes and generates a network graph to present related transcriptome data. The updated AtCAST now contains data on >7,000 microarrays, including experiments on various stresses, mutants and chemical treatments. Gene ontology term enrichment (GOE) analysis is introduced to assist the characterization of transcriptome data. The new AtCAST supports input from multiple platforms, including the 'Arabisopsis gene 1.1 ST array', a new microarray chip from Affymetrix and RNA sequencing (RNA-seq) data obtained using next-generation sequencing (NGS). As a pilot study, we conducted microarray analysis of Arabidopsis under auxin treatment using the new Affymetrix chip, and then analyzed the data in AtCAST. We also analyzed RNA-seq data of the pifq mutant using AtCAST. These new features will facilitate analysis of associations between transcriptome data obtained using different platforms.

Application of a cDNA microarray for profiling the gene expression of Echinococcus granulosus protoscoleces treated with albendazole and artemisinin

Cystic echinoccocosis (CE) is a neglected zoonosis that is caused by the dog-tapeworm Echinococcus granulosus. The disease is endemic worldwide. There is an urgent need for searching effective drug for the treatment of the disease. In this study, we sequenced a cDNA library constructed using RNA isolated from oncospheres, protoscoleces, cyst membrane and adult worms of E. granulosus. A total of 9065 non-redundant or unique sequences were obtained and spotted on chips as uniEST probes to profile the gene expression in protoscoleces of E. granulosus treated with the anthelmintic drugs albendazole and artemisinin, respectively. The results showed that 7 genes were up-regulated and 38 genes were down-regulated in the protoscoleces treated with albendazole. Gene analysis showed that these genes are responsible for energy metabolism, cell cycle and assembly of cell structure. We also identified 100 genes up-regulated and 6 genes down-regulated in the protoscoleces treated with artemisinin. These genes play roles in the transduction of environmental signals, and metabolism. Albendazole appeared its drug efficacy in damaging cell structure, while artemisinin was observed to increase the formation of the heterochromatin in protoscolex cells. Our results highlight the utility of using cDNA microarray methods to detect gene expression profiles of E. granulosus and, in particular, to understand the pharmacologic mechanism of anti-echinococcosis drugs.

Yeast as a model system to screen purine derivatives against human CDK1 and CDK2 kinases

Cyclin-dependent kinases (Cdk) play crucial roles in cell cycle progression. Aberrant activation of Cdk1 has been observed in a number of primary tumors and Cdk2 is deregulated in various malignancies. The therapeutic value of targeting Cdk1 and Cdk2 has been explored in a number of experimental systems. In the present study, taking advantage of the fact that deletion of the yeast CDC28 gene is functionally complemented by human CDK1 or CDK2, we set up an in vivo screen system to evaluate the inhibitory potency of purine derivatives against these two human Cdks. We constructed three isogenic strains highly sensitive to small molecules and harboring genes CDK1, CDK2 or CDC28, under the control of the CDC28 promoter. In a proof of principle assay, we determined the inhibitory effect of 82 purine derivatives on the growth rate of these strains. Thirty-three of them were revealed to be able to inhibit the Cdk1- or Cdk2-harboring strains but not the Cdc28-harboring strain, suggesting a specific inhibitory effect on human Cdks. Our data demonstrate that the yeast-based assay is an efficient system to identify potential specific inhibitors that should be preferentially selected for further investigation in cultured human cell lines.

Mitochondrial targets for pharmacological intervention in human disease

Over the past several years, mitochondrial dysfunction has been linked to an increasing number of human illnesses, making mitochondrial proteins (MPs) an ever more appealing target for therapeutic intervention. With 20% of the mitochondrial proteome (312 of an estimated 1500 MPs) having known interactions with small molecules, MPs appear to be highly targetable. Yet, despite these targeted proteins functioning in a range of biological processes (including induction of apoptosis, calcium homeostasis, and metabolism), very few of the compounds targeting MPs find clinical use. Recent work has greatly expanded the number of proteins known to localize to the mitochondria and has generated a considerable increase in MP 3D structures available in public databases, allowing experimental screening and in silico prediction of mitochondrial drug targets on an unprecedented scale. Here, we summarize the current literature on clinically active drugs that target MPs, with a focus on how existing drug targets are distributed across biochemical pathways and organelle substructures. Also, we examine current strategies for mitochondrial drug discovery, focusing on genetic, proteomic, and chemogenomic assays, and relevant model systems. As cell models and screening techniques improve, MPs appear poised to emerge as relevant targets for a wide range of complex human diseases, an eventuality that can be expedited through systematic analysis of MP function.

In vitro extinction learning in Hermissenda: involvement of conditioned inhibition molecules

Extinction of a conditioned association is typically viewed as the establishment of new learning rather than the erasure of the original memory. However, recent research in the nudibranch, Hermissenda crassicornis (H.c.) demonstrated that extinction training (using repeated light-alone presentations) given 15 min, but not 23 h, after memory acquisition reversed both the cellular correlates of learning (enhanced Type B cell excitability) and the behavioral changes (reduced phototaxis) produced by associative conditioning (pairings of light, CS, and rotation, US). Here, we investigated the putative molecular signaling pathways that underlie this extinction in H.c. by using a novel in vitro protocol combined with pharmacological manipulations. After intact H.c. received either light-rotation pairings (Paired), random presentations of light and rotation (Random), or no stimulation (Untrained), B cells from isolated CNSs were recorded from during exposure to extinction training consisting of two series of 15 consecutive light-steps (LSs). When in vitro extinction was administered shortly (2 h, but not 24 h) after paired training, B cells from Paired animals showed progressive and robust declines in spike frequency by the 30th LS, while control cells (Random and Untrained) did not. We found that several molecules implicated in H.c. conditioned inhibitory (CI) learning, protein phosphatase 1 (PP1) and arachidonic acid (AA)/12-lipoxygenase (12-LOX) metabolites, also contributed to the spike frequency decreases produced by in vitro extinction. Protein phosphatase 2B (PP2B) also appeared to play a role. Calyculin A (PP1 inhibitor), cyclosporin A (PP2B inhibitor), and baicalein (a 12-LOX inhibitor) all blocked the spike frequency declines in Paired B cells produced by 30 LSs. Conversely, injection of catalytically-active PP1 (caPP1) or PP2B (caPP2B) into Untrained B cells partially mimicked the spike frequency declines observed in Paired cells, as did bath-applied AA, and occluded additional LS-produced reductions in spiking in Paired cells.

DNA fluorescence shift sensor: a rapid method for the detection of DNA hybridization using silver nanoclusters

DNA-templated silver nanoclusters (AgNC) are a class of subnanometer sized fluorophores with good photostability and brightness. It has been applied as a diagnostic tool mainly for deoxyribonucleic acid (DNA) detection. Integration of DNA oligomers to generate AgNCs is interesting as varying DNA sequences can result in different fluorescence spectra. This allows a simple fluorescence shifting effect to occur upon DNA hybridization with the hybridization efficiency being a pronominal factor for successful shifting. The ability to shift the fluorescence spectra as a result of hybridization overcomes the issue of background intensities in most fluorescent based assays. Here we describe an optimized method for the detection of single-stranded and double-stranded synthetic forkhead box P3 (FOXP3) target by hybridization with the DNA fluorescence shift sensor. The system forms a three-way junction by successful hybridization of AgNC, G-rich strand (G-rich) to the target DNA, which generated a shift in fluorescence spectra with a marked increase in fluorescence intensity. The DNA fluorescence shift sensor presents a rapid and specific alternative to conventional DNA detection.

SPIEDw: a searchable platform-independent expression database web tool

Background

SPIEDw is a web tool designed to facilitate fast and simple quantitative querying of publically available gene expression data. The resource is motivated by the observation that transcriptional profiles can serve as effective means of comparing biological states across a wide set of experiments.

Results

Gene expression data for over 200,000 experiments across multiple species and platforms have been collected into a searchable database. The new resource is a development of the previously published SPIED, which was designed to be downloaded and queried locally with SPIED software. SPIEDw features three significant improvements over the original version. Firstly, the number of experiments covered has been doubled and now includes Agilent and Illumina technologies. Secondly, SPIEDw has an enhanced search algorithm for speedy web-based querying and lastly an abridged dataset comprising the most regulated genes has been included for a speedier search and searching for enrichment of gene sets.

Conclusions

SPIEDw is simple to use, not requiring any expertise in microarray analysis, and the output straightforward to interpret. It is hoped that this will open up gene expression data mining to the wider research community.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-765) contains supplementary material, which is available to authorized users.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Target identification and mechanism of action in chemical biology and drug discovery

Target-identification and mechanism-of-action studies have important roles in small-molecule probe and drug discovery. Biological and technological advances have resulted in the increasing use of cell-based assays to discover new biologically active small molecules. Such studies allow small-molecule action to be tested in a more disease-relevant setting at the outset, but they require follow-up studies to determine the precise protein target or targets responsible for the observed phenotype. Target identification can be approached by direct biochemical methods, genetic interactions or computational inference. In many cases, however, combinations of approaches may be required to fully characterize on-target and off-target effects and to understand mechanisms of small-molecule action.

Cell sorting in cancer research--diminishing degree of cell heterogeneity

Increasing evidence of intratumor heterogeneity and its augmentation due to selective pressure of microenvironment and recent achievements in cancer therapeutics lead to the need to investigate and track the tumor subclonal structure. Cell sorting of heterogeneous subpopulations of tumor and tumor-associated cells has been a long established strategy in cancer research. Advancement in lasers, computer technology and optics has led to a new generation of flow cytometers and cell sorters capable of high-speed processing of single cell suspensions. Over the last several years cell sorting was used in combination with molecular biological methods, imaging and proteomics to characterize primary and metastatic cancer cell populations, minimal residual disease and single tumor cells. It was the principal method for identification and characterization of cancer stem cells. Analysis of single cancer cells may improve early detection of tumors, monitoring of circulating tumor cells, evaluation of intratumor heterogeneity and chemotherapeutic treatments. The aim of this review is to provide an overview of major cell sorting applications and approaches with new prospective developments such as microfluidics and microchip technologies.

Nitrogen-dependent calcineurin activation in the yeast Hansenula polymorpha

Non-preferred nitrogen sources, unlike preferred ones, raised total cell Ca(2+) content and expression of ENA1, a very well-known calcineurin-regulated gene. This indicates calcineurin activation is regulated by nitrogen source. Nitrogen catabolite repression (NCR) and nitrate induction mechanisms, both regulating nitrate assimilation in Hansenula polymorpha, are controlled by calcineurin. Concerning NCR, lack of calcineurin (cnb1 mutant) decreased nitrate-assimilation gene expression, levels of the transcription factor Gat1 and growth in several nitrogen sources. We found that the role of calcineurin in NCR was mediated by Crz1 via Gat1. Regarding nitrate induction, calcineurin also affects the levels of transcription factors Gat2 and Yna2 involved in this process. We conclude that Ca(2+) and calcineurin play a central role in nitrogen signalling and assimilation. Thus, the nitrogen source modulates Ca(2+) content and calcineurin activation. Calcineurin in turn regulates nitrogen assimilation genes.

Forward chemical genetics in yeast for discovery of chemical probes targeting metabolism

The many virtues that made the yeast Saccharomyces cerevisiae a dominant model organism for genetics and molecular biology, are now establishing its role in chemical genetics. Its experimental tractability (i.e., rapid doubling time, simple culture conditions) and the availability of powerful tools for drug-target identification, make yeast an ideal organism for high-throughput phenotypic screening. It may be especially applicable for the discovery of chemical probes targeting highly conserved cellular processes, such as metabolism and bioenergetics, because these probes would likely inhibit the same processes in higher eukaryotes (including man). Importantly, changes in normal cellular metabolism are associated with a variety of diseased states (including neurological disorders and cancer), and exploiting these changes for therapeutic purposes has accordingly gained considerable attention. Here, we review progress and challenges associated with forward chemical genetic screening in yeast. We also discuss evidence supporting these screens as a useful strategy for discovery of new chemical probes and new druggable targets related to cellular metabolism.

Chemoinformatics and chemical genomics: potential utility of in silico methods

Computational life sciences and informatics are inseparably intertwined and they lie at the heart of modern biology, predictive quantitative modeling and high-performance computing. Two of the applied biological disciplines that are poised to benefit from such progress are pharmacology and toxicology. This review will describe in silico chemoinformatics methods such as (quantitative) structure-activity relationship modeling and will overview how chemoinformatic technologies are considered in applied regulatory research. Given the post-genomics era and large-scale repositories of omics data that are available, this review will also address potential applications of in silico techniques in chemical genomics. Chemical genomics utilizes small molecules to explore the complex biological phenomena that may not be not amenable to straightforward genetic approach. The reader will gain the understanding that chemoinformatics stands at the interface of chemistry and biology with enabling systems for mapping, statistical modeling, pattern recognition, imaging and database tools. The great potential of these technologies to help address complex issues in the toxicological sciences is appreciated with the applied goal of the protection of public health.

Chemical genetics: budding yeast as a platform for drug discovery and mapping of genetic pathways

The budding yeast Saccharomyces cerevisiae is a widely used model organism, and yeast genetic methods are powerful tools for discovery of novel functions of genes. Recent advancements in chemical-genetics and chemical-genomics have opened new avenues for development of clinically relevant drug treatments. Systematic mapping of genetic networks by high-throughput chemical-genetic screens have given extensive insight in connections between genetic pathways. Here, I review some of the recent developments in chemical-genetic techniques in budding yeast.

Sparse Methods for Biomedical Data

Following recent technological revolutions, the investigation of massive biomedical data with growing scale, diversity, and complexity has taken a center stage in modern data analysis. Although complex, the underlying representations of many biomedical data are often sparse. For example, for a certain disease such as leukemia, even though humans have tens of thousands of genes, only a few genes are relevant to the disease; a gene network is sparse since a regulatory pathway involves only a small number of genes; many biomedical signals are sparse or compressible in the sense that they have concise representations when expressed in a proper basis. Therefore, finding sparse representations is fundamentally important for scientific discovery. Sparse methods based on the [Formula: see text] norm have attracted a great amount of research efforts in the past decade due to its sparsity-inducing property, convenient convexity, and strong theoretical guarantees. They have achieved great success in various applications such as biomarker selection, biological network construction, and magnetic resonance imaging. In this paper, we review state-of-the-art sparse methods and their applications to biomedical data.