3D-Suspension culture platform for high throughput screening of neurotoxic chemicals using LUHMES dopaminergic neurons.

Three-dimensional (3D) cell culture in vitro promises to improve representation of neuron physiology in vivo. This inspired development of a 3D culture platform for LUHMES (Lund Human Mesencephalic) dopaminergic neurons for high-throughput screening (HTS) of chemicals for neurotoxicity. Three culture platforms, adhesion (2D-monolayer), 3D-suspension, and 3D-shaken, were compared to monitor mRNA expression of seven neuronal marker genes, DCX, DRD2, ENO2, NEUROD4, SYN1, TH, and TUBB3. These seven marker genes reached similar maxima in all three formats, with the two 3D platforms showing similar kinetics, whereas several markers peaked earlier in 2D adhesion compared to both 3D culture platforms. The differentiated LUHMES (dLUHMES) neurons treated with ziram, methylmercury or thiram dynamically increased expression of metallothionein biomarker genes MT1G, MT1E and MT2A at 6 h. These gene expression increases were generally more dynamic in 2D adhesion cultures than in 3D cultures, but were generally comparable between 3D-suspension and 3D-u plate (low binding) platforms. Finally, we adapted 3D-suspension culture of dLUHMES and neural stem cells to 1536 well plates with a HTS cytotoxicity assay. This HTS assay revealed that cytotoxicity IC50 values were not significantly different between adhesion and 3D-suspension platforms for 31 of 34 (91%) neurotoxicants tested, whereas IC50 values were significantly different for at least two toxicants. In summary, the 3D-suspension culture platform for LUHMES dopaminergic neurons supported full differentiation and reproducible assay results, enabling quantitative HTS (qHTS) for cytotoxicity in 1536 well format with a Robust Z' score of 0.68.

Universal Toxicity Gene Signatures for Early Identification of Drug-Induced Tissue Injuries in Rats.

A new safety testing paradigm that relies on gene expression biomarker panels was developed to easily and quickly identify drug-induced injuries across tissues in rats prior to drug candidate selection. Here, we describe the development, qualification, and implementation of gene expression signatures that diagnose tissue degeneration/necrosis for use in early rat safety studies. Approximately 400 differentially expressed genes were first identified that were consistently regulated across 4 prioritized tissues (liver, kidney, heart, and skeletal muscle), following injuries induced by known toxicants. Hundred of these "universal" genes were chosen for quantitative PCR, and the most consistent and robustly responding transcripts selected, resulting in a final 22-gene set from which unique sets of 12 genes were chosen as optimal for each tissue. The approach was extended across 4 additional tissues (pancreas, gastrointestinal tract, bladder, and testes) where toxicities are less common. Mathematical algorithms were generated to convert each tissue's 12-gene expression values to a single metric, scaled between 0 and 1, and a positive threshold set. For liver, kidney, heart, and skeletal muscle, this was established using a training set of 22 compounds and performance determined by testing a set of approximately 100 additional compounds, resulting in 74%-94% sensitivity and 94%-100% specificity for liver, kidney, and skeletal muscle, and 54%-62% sensitivity and 95%-98% specificity for heart. Similar performance was observed across a set of 15 studies for pancreas, gastrointestinal tract, bladder, and testes. Bundled together, we have incorporated these tissue signatures into a 4-day rat study, providing a rapid assessment of commonly seen compound liabilities to guide selection of lead candidates without the necessity to perform time-consuming histopathologic analyses.

Stem Cell-Derived Endothelial Cell Model that Responds to Tobacco Smoke Like Primary Endothelial Cells.

To clarify how smoking leads to heart attack and stroke, we developed an endothelial cell model (iECs) generated from human induced Pluripotent Stem Cells (iPSC) and evaluated its responses to tobacco smoke. These iECs exhibited a uniform endothelial morphology, and expressed markers PECAM1/CD31, VWF/ von Willebrand Factor, and CDH5/VE-Cadherin. The iECs also exhibited tube formation and acetyl-LDL uptake comparable to primary endothelial cells (EC). RNA sequencing (RNA-Seq) revealed a robust correlation coefficient between iECs and EC (R = 0.76), whereas gene responses to smoke were qualitatively nearly identical between iECs and primary ECs (R = 0.86). Further analysis of transcriptional responses implicated 18 transcription factors in regulating responses to smoke treatment, and identified gene sets regulated by each transcription factor, including pathways for oxidative stress, DNA damage/repair, ER stress, apoptosis, and cell cycle arrest. Assays for 42 cytokines in HUVEC cells and iECs identified 23 cytokines that responded dynamically to cigarette smoke. These cytokines and cellular stress response pathways describe endothelial responses for lymphocyte attachment, activation of coagulation and complement, lymphocyte growth factors, and inflammation and fibrosis; EC-initiated events that collectively lead to atherosclerosis. Thus, these studies validate the iEC model and identify transcriptional response networks by which ECs respond to tobacco smoke. Our results systematically trace how ECs use these response networks to regulate genes and pathways, and finally cytokine signals to other cells, to initiate the diverse processes that lead to atherosclerosis and cardiovascular disease.

A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics.

Changes in gene expression can help reveal the mechanisms of disease processes and the mode of action for toxicities and adverse effects on cellular responses induced by exposures to chemicals, drugs and environment agents. The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in in vitro model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. Our approach is modular, applicable to any species, and facilitates a robust, quantitative evaluation of performance. In particular, we were able to perform gene selection such that the resulting set of "sentinel genes" adequately represents all known canonical pathways from Molecular Signature Database (MSigDB v4.0) and can be used to infer expression changes for the remainder of the transcriptome. The resulting computational model allowed us to choose a purely data-driven subset of 1500 sentinel genes, referred to as the S1500 set, which was then augmented using a knowledge-driven selection of additional genes to create the final S1500+ gene set. Our results indicate that the sentinel genes selected can be used to accurately predict pathway perturbations and biological relationships for samples under study.

The Toxmatrix: Chemo-Genomic Profiling Identifies Interactions That Reveal Mechanisms of Toxicity.

A chemical genomics "Toxmatrix" method was developed to elucidate mechanisms of cytotoxicity using neuronal models. Quantitative high-throughput screening (qHTS) was applied to systematically screen each toxicant against a panel of 70 modulators, drugs or chemicals that act on a known target, to identify interactions that either protect or sensitize cells to each toxicant. Thirty-two toxicants were tested at 10 concentrations for cytotoxicity to SH-SY5Y human neuroblastoma cells, with results fitted to the Hill equation to determine an IC50 for each toxicant. Thirty-three toxicant:modulator interactions were identified in SH-SY5Y cells for 14 toxicants, as modulators that shifted toxicant IC50 values lower or higher. The target of each modulator that sensitizes cells or protects cells from a toxicant suggests a mode of toxicant action or cellular adaptation. In secondary screening, we tested modulator-toxicant pairs identified from the SH-SY5Y primary screening for interactions in three differentiated neuronal human cell lines: dSH-SY5Y, conditionally immortalized dopaminergic neurons (LUHMES), and neural stem cells. Twenty toxicant-modulator pairs showed pronounced interactions in one or several differentiated cell models. Additional testing confirmed that several modulators acted through their primary targets. For example, several chelators protected differentiated LUHMES neurons from four toxicants by chelation of divalent cations and buthionine sulphoximine sensitized cells to 6-hydroxydopamine and 4-(methylamino)phenol hemisulfate by blocking glutathione synthesis. Such modulators that interact with multiple neurotoxicants suggest these may be vulnerable toxicity pathways in neurons. Thus, the Toxmatrix method is a systematic high-throughput approach that can identify mechanisms of toxicity and cellular adaptation.

A chemical-biological similarity-based grouping of complex substances as a prototype approach for evaluating chemical alternatives.

Comparative assessment of potential human health impacts is a critical step in evaluating both chemical alternatives and existing products on the market. Most alternatives assessments are conducted on a chemical-by-chemical basis and it is seldom acknowledged that humans are exposed to complex products, not individual substances. Indeed, substances of Unknown or Variable composition, Complex reaction products, and Biological materials (UVCBs) are ubiquitous in commerce yet they present a major challenge for registration and health assessments. Here, we present a comprehensive experimental and computational approach to categorize UVCBs according to global similarities in their bioactivity using a suite of in vitro models. We used petroleum substances, an important group of UVCBs which are grouped for regulatory approval and read-across primarily on physico-chemical properties and the manufacturing process, and only partially based on toxicity data, as a case study. We exposed induced pluripotent stem cell-derived cardiomyocytes and hepatocytes to DMSO-soluble extracts of 21 petroleum substances from five product groups. Concentration-response data from high-content imaging in cardiomyocytes and hepatocytes, as well as targeted high-throughput transcriptomic analysis of the hepatocytes, revealed distinct groups of petroleum substances. Data integration showed that bioactivity profiling affords clustering of petroleum substances in a manner similar to the manufacturing process-based categories. Moreover, we observed a high degree of correlation between bioactivity profiles and physico-chemical properties, as well as improved groupings when chemical and biological data were combined. Altogether, we demonstrate how novel in vitro screening approaches can be effectively utilized in combination with physico-chemical characteristics to group complex substances and enable read-across. This approach allows for rapid and scientifically-informed evaluation of health impacts of both existing substances and their chemical alternatives.

Renal biomarker qualification submission: a dialog between the FDA-EMEA and Predictive Safety Testing Consortium.

The first formal qualification of safety biomarkers for regulatory decision making marks a milestone in the application of biomarkers to drug development. Following submission of drug toxicity studies and analyses of biomarker performance to the Food and Drug Administration (FDA) and European Medicines Agency (EMEA) by the Predictive Safety Testing Consortium's (PSTC) Nephrotoxicity Working Group, seven renal safety biomarkers have been qualified for limited use in nonclinical and clinical drug development to help guide safety assessments. This was a pilot process, and the experience gained will both facilitate better understanding of how the qualification process will probably evolve and clarify the minimal requirements necessary to evaluate the performance of biomarkers of organ injury within specific contexts.

Urinary biomarkers trefoil factor 3 and albumin enable early detection of kidney tubular injury.

The capacities of urinary trefoil factor 3 (TFF3) and urinary albumin to detect acute renal tubular injury have never been evaluated with sufficient statistical rigor to permit their use in regulated drug development instead of the current preclinical biomarkers serum creatinine (SCr) and blood urea nitrogen (BUN). Working with rats, we found that urinary TFF3 protein levels were markedly reduced, and urinary albumin were markedly increased in response to renal tubular injury. Urinary TFF3 levels did not respond to nonrenal toxicants, and urinary albumin faithfully reflected alterations in renal function. In situ hybridization localized TFF3 expression in tubules of the outer stripe of the outer medulla. Albumin outperformed either SCr or BUN for detecting kidney tubule injury and TFF3 augmented the potential of BUN and SCr to detect kidney damage. Use of urinary TFF3 and albumin will enable more sensitive and robust diagnosis of acute renal tubular injury than traditional biomarkers.

A panel of urinary biomarkers to monitor reversibility of renal injury and a serum marker with improved potential to assess renal function.

The Predictive Safety Testing Consortium's first regulatory submission to qualify kidney safety biomarkers revealed two deficiencies. To address the need for biomarkers that monitor recovery from agent-induced renal damage, we scored changes in the levels of urinary biomarkers in rats during recovery from renal injury induced by exposure to carbapenem A or gentamicin. All biomarkers responded to histologic tubular toxicities to varied degrees and with different kinetics. After a recovery period, all biomarkers returned to levels approaching those observed in uninjured animals. We next addressed the need for a serum biomarker that reflects general kidney function regardless of the exact site of renal injury. Our assay for serum cystatin C is more sensitive and specific than serum creatinine (SCr) or blood urea nitrogen (BUN) in monitoring generalized renal function after exposure of rats to eight nephrotoxicants and two hepatotoxicants. This sensitive serum biomarker will enable testing of renal function in animal studies that do not involve urine collection.

Diagnosis of drug-induced renal tubular toxicity using global gene expression profiles.

Toxicogenomics can measure the expression of thousands of genes to identify changes associated with drug induced toxicities. It is expected that toxicogenomics can be an alternative or complementary approach in preclinical drug safety evaluation to identify or predict drug induced toxicities. One of the major concerns in applying toxicogenomics to diagnose or predict drug induced organ toxicity, is how generalizable the statistical classification model is when derived from small datasets? Here we presented that a diagnosis of kidney proximal tubule toxicity, measured by pathology, can successfully be achieved even with a study design of limited number of training studies or samples. We selected a total of ten kidney toxicants, designed the in life study with multiple dose and multiple time points to cover samples at doses and time points with or without concurrent toxicity. We employed SVM (Support Vector Machine) as the classification algorithm for the toxicogenomic diagnosis of kidney proximal tubule toxicity. Instead of applying cross validation methods, we used an independent testing set by dividing the studies or samples into independent training and testing sets to evaluate the diagnostic performance. We achieved a Sn (sensitivity) = 88% and a Sp (specificity) = 91%. The diagnosis performance underscores the potential application of toxicogenomics in a preclinical lead optimization process of drugs entering into development.

Better therapeutics through microarrays.

DNA microarrays are an integral part of the process for therapeutic discovery, optimization and clinical validation. At an early stage, investigators use arrays to prioritize a few genes as potential therapeutic targets on the basis of various criteria. Subsequently, gene expression analysis assists in drug discovery and toxicology by eliminating poor compounds and optimizing the selection of promising leads. Integral to this process is the use of sophisticated statistics, mathematics and bioinformatics to define statistically valid observations and to deduce complex patterns of phenotypes and biological pathways. In short, microarrays are redefining the drug discovery process by providing greater knowledge at each step and by illuminating the complex workings of biological systems.

Dorsal horn-enriched genes identified by DNA microarray, in situ hybridization and immunohistochemistry.

BACKGROUND: Neurons in the dorsal spinal cord play important roles in nociception and pain. These neurons receive input from peripheral sensory neurons and then transmit the signals to the brain, as well as receive and integrate descending control signals from the brain. Many molecules important for pain transmission have been demonstrated to be localized to the dorsal horn of the spinal cord. Further understanding of the molecular interactions and signaling pathways in the dorsal horn neurons will require a better knowledge of the molecular neuroanatomy in the dorsal spinal cord. RESULTS: A large scale screening was conducted for genes with enriched expression in the dorsal spinal cord using DNA microarray and quantitative real-time PCR. In addition to genes known to be specifically expressed in the dorsal spinal cord, other neuropeptides, receptors, ion channels, and signaling molecules were also found enriched in the dorsal spinal cord. In situ hybridization and immunohistochemistry revealed the cellular expression of a subset of these genes. The regulation of a subset of the genes was also studied in the spinal nerve ligation (SNL) neuropathic pain model. In general, we found that the genes that are enriched in the dorsal spinal cord were not among those found to be up-regulated in the spinal nerve ligation model of neuropathic pain. This study also provides a level of validation of the use of DNA microarrays in conjunction with our novel analysis algorithm (SAFER) for the identification of differences in gene expression. CONCLUSION: This study identified molecules that are enriched in the dorsal horn of the spinal cord and provided a molecular neuroanatomy in the spinal cord, which will aid in the understanding of the molecular mechanisms important in nociception and pain.

Gene expression profile of adipocyte differentiation and its regulation by peroxisome proliferator-activated receptor-gamma agonists.

PPAR gamma is an adipocyte-specific nuclear hormone receptor. Agonists of PPAR gamma, such as thiazolidinediones (TZDs), promote adipocyte differentiation and have insulin-sensitizing effects in animals and diabetic patients. Affymetrix oligonucleotide arrays representing 6347 genes were employed to profile the gene expression responses of mature 3T3-L1 adipocytes and differentiating preadipocytes to a TZD PPAR gamma agonist in vitro. The expression of 579 genes was significantly up- or down-regulated by more than 1.5-fold during differentiation and/or by treatment with TZD, and these genes were organized into 32 clusters that demonstrated concerted changes in expression of genes controlling cell growth or lipid metabolism. Quantitative PCR was employed to further characterize gene expression and led to the identification of beta-catenin as a new PPAR gamma target gene. Both mRNA and protein levels for beta-catenin were down-regulated in 3T3-L1 adipocytes compared with fibroblasts and were further decreased by treatment of adipocytes with PPAR gamma agonists. Treatment of db/db mice with a PPAR gamma agonist also resulted in reduction of beta-catenin mRNA levels in adipose tissue. These results suggest that beta-catenin plays an important role in the regulation of adipogenesis. Thus, the transcriptional patterns revealed in this study further the understanding of adipogenesis process and the function of PPAR gamma activation.