Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis

Background

Traumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge.

Methods and findings

The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. We then applied topology-based data-driven discovery to identify natural subgroups of patients, based on the TBI-CDEs collected. Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. TDA algorithms organized and mapped the data of TBI patients in multidimensional space, identifying a subset of mild TBI patients with a specific multivariate phenotype associated with unfavorable outcome at 3 and 6 months after injury. Further analyses revealed that this patient subset had high rates of post-traumatic stress disorder (PTSD), and enrichment in several distinct genetic polymorphisms associated with cellular responses to stress and DNA damage (PARP1), and in striatal dopamine processing (ANKK1, COMT, DRD2).

Conclusions

TDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. Machine learning methods such as TDA may provide a robust method for patient stratification and treatment planning targeting identified biomarkers in future clinical trials in TBI patients.

Trial Registration

ClinicalTrials.gov Identifier NCT01565551

Meditation and vacation effects have an impact on disease-associated molecular phenotypes

Meditation is becoming increasingly practiced, especially for stress-related medical conditions. Meditation may improve cellular health; however, studies have not separated out effects of meditation from vacation-like effects in a residential randomized controlled trial. We recruited healthy women non-meditators to live at a resort for 6 days and randomized to either meditation retreat or relaxing on-site, with both groups compared with 'regular meditators' already enrolled in the retreat. Blood drawn at baseline and post intervention was assessed for transcriptome-wide expression patterns and aging-related biomarkers. Highly significant gene expression changes were detected across all groups (the 'vacation effect') that could accurately predict (96% accuracy) between baseline and post-intervention states and were characterized by improved regulation of stress response, immune function and amyloid beta (Aβ) metabolism. Although a smaller set of genes was affected, regular meditators showed post-intervention differences in a gene network characterized by lower regulation of protein synthesis and viral genome activity. Changes in well-being were assessed post intervention relative to baseline, as well as 1 and 10 months later. All groups showed equivalently large immediate post-intervention improvements in well-being, but novice meditators showed greater maintenance of lower distress over time compared with those in the vacation arm. Regular meditators showed a trend toward increased telomerase activity compared with randomized women, who showed increased plasma Aβ42/Aβ40 ratios and tumor necrosis factor alpha (TNF-α) levels. This highly controlled residential study showed large salutary changes in gene expression networks due to the vacation effect, common to all groups. For those already trained in the practice of meditation, a retreat appears to provide additional benefits to cellular health beyond the vacation effect.

Acridine Derivatives as Inhibitors of the IRE1α-XBP1 Pathway Are Cytotoxic to Human Multiple Myeloma

Using a luciferase reporter-based high-throughput chemical library screen and topological data analysis, we identified N-acridine-9-yl-N',N'-dimethylpropane-1,3-diamine (DAPA) as an inhibitor of the inositol requiring kinase 1α (IRE1α)-X-box binding protein-1 (XBP1) pathway of the unfolded protein response. We designed a collection of analogues based on the structure of DAPA to explore structure-activity relationships and identified N(9)-(3-(dimethylamino)propyl)-N(3),N(3),N(6),N(6)-tetramethylacridine-3,6,9-triamine (3,6-DMAD), with 3,6-dimethylamino substitution on the chromophore, as a potent inhibitor. 3,6-DMAD inhibited both IRE1α oligomerization and in vitro endoribonuclease (RNase) activity, whereas the other analogues only blocked IRE1α oligomerization. Consistent with the inhibition of IRE1α-mediated XBP1 splicing, which is critical for multiple myeloma cell survival, these analogues were cytotoxic to multiple myeloma cell lines. Furthermore, 3,6-DMAD inhibited XBP1 splicing in vivo and the growth of multiple myeloma tumor xenografts. Our study not only confirmed the utilization of topological data analysis in drug discovery but also identified a class of compounds with a unique mechanism of action as potent IRE1α-XBP1 inhibitors in the treatment of multiple myeloma. Mol Cancer Ther; 15(9); 2055-65. ©2016 AACR.

Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury

Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis.

Innate and adaptive T cells in asthmatic patients: Relationship to severity and disease mechanisms

Background

Asthma is a chronic inflammatory disease involving diverse cells and mediators whose interconnectivity and relationships to asthma severity are unclear.

Objective

We performed a comprehensive assessment of T_H17 cells, regulatory T cells, mucosal-associated invariant T (MAIT) cells, other T-cell subsets, and granulocyte mediators in asthmatic patients.

Methods

Sixty patients with mild-to-severe asthma and 24 control subjects underwent detailed clinical assessment and provided induced sputum, endobronchial biopsy, bronchoalveolar lavage, and blood samples. Adaptive and invariant T-cell subsets, cytokines, mast cells, and basophil mediators were analyzed.

Results

Significant heterogeneity of T-cell phenotypes was observed, with levels of IL-13–secreting T cells and type 2 cytokines increased at some, but not all, asthma severities. T_H17 cells and γδ-17 cells, proposed drivers of neutrophilic inflammation, were not strongly associated with asthma, even in severe neutrophilic forms. MAIT cell frequencies were strikingly reduced in both blood and lung tissue in relation to corticosteroid therapy and vitamin D levels, especially in patients with severe asthma in whom bronchoalveolar lavage regulatory T-cell numbers were also reduced. Bayesian network analysis identified complex relationships between pathobiologic and clinical parameters. Topological data analysis identified 6 novel clusters that are associated with diverse underlying disease mechanisms, with increased mast cell mediator levels in patients with severe asthma both in its atopic (type 2 cytokine–high) and nonatopic forms.

Conclusion

The evidence for a role for T_H17 cells in patients with severe asthma is limited. Severe asthma is associated with a striking deficiency of MAIT cells and high mast cell mediator levels. This study provides proof of concept for disease mechanistic networks in asthmatic patients with clusters that could inform the development of new therapies.

Topological data analysis of Escherichia coli O157:H7 and non-O157 survival in soils

Shiga toxin-producing E. coli O157:H7 and non-O157 have been implicated in many foodborne illnesses caused by the consumption of contaminated fresh produce. However, data on their persistence in soils are limited due to the complexity in datasets generated from different environmental variables and bacterial taxa. There is a continuing need to distinguish the various environmental variables and different bacterial groups to understand the relationships among these factors and the pathogen survival. Using an approach called Topological Data Analysis (TDA); we reconstructed the relationship structure of E. coli O157 and non-O157 survival in 32 soils (16 organic and 16 conventionally managed soils) from California (CA) and Arizona (AZ) with a multi-resolution output. In our study, we took a community approach based on total soil microbiome to study community level survival and examining the network of the community as a whole and the relationship between its topology and biological processes. TDA produces a geometric representation of complex data sets. Network analysis showed that Shiga toxin negative strain E. coli O157:H7 4554 survived significantly longer in comparison to E. coli O157:H7 EDL 933, while the survival time of E. coli O157:NM was comparable to that of E. coli O157:H7 EDL 933 in all of the tested soils. Two non-O157 strains, E. coli O26:H11 and E. coli O103:H2 survived much longer than E. coli O91:H21 and the three strains of E. coli O157. We show that there are complex interactions between E. coli strain survival, microbial community structures, and soil parameters.

Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets

Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptin^ob/ob mutation (Lep^ob). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein–protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

Alzheimer's disease and type 2 diabetes are two common aging-related diseases. Numerous studies have shown that the two diseases are associated. However, the mechanisms of such connection are not clear. Both diseases are complex diseases that are induced by multiple genetic factors and the environment. To understand the molecular network regulated by complex genetic factors causing type 2 diabetes, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant and diabetic mouse strains. We measured genotypes, clinical traits, and expression profiling in five tissues for each mouse. We then performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait, and developed a novel method for inferring key regulators for regulating plasma insulin. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

Common body mass index-associated variants confer risk of extreme obesity

To investigate the genetic architecture of severe obesity, we performed a genome-wide association study of 775 cases and 3197 unascertained controls at approximately 550,000 markers across the autosomal genome. We found convincing association to the previously described locus including the FTO gene. We also found evidence of association at a further six of 12 other loci previously reported to influence body mass index (BMI) in the general population and one of three associations to severe childhood and adult obesity and that cases have a higher proportion of risk-conferring alleles than controls. We found no evidence of homozygosity at any locus due to identity-by-descent associating with phenotype which would be indicative of rare, penetrant alleles, nor was there excess genome-wide homozygosity in cases relative to controls. Our results suggest that variants influencing BMI also contribute to severe obesity, a condition at the extreme of the phenotypic spectrum rather than a distinct condition.

Integrative genomics and drug development

The pharmaceutical industry faces unprecedented pressures based largely on the inability to bring sufficient new medicines to market. The high failure rate of drug candidates in clinical development highlights a need for new approaches to the study of disease mechanisms and drug discovery. We advocate an integrated approach based on the study of the entire organism leveraging the power of detailed phenotyping, high-throughput genomic technologies and mathematical modeling. Key to this paradigm is the realization that the systematic genetic perturbations that exist in populations provide an ideal structure for uncovering the interactions that define molecular networks or states. By linking molecular states to physiological states and in turn understanding how molecular states drive disease processes, the promise of truly rational drug-design with a high probability of success in clinical development can be realized.

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes and identification of involved pathways and networks.

Mapping the genetic architecture of gene expression in human liver

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process.

Genome-wide association studies seek to identify regions of the genome in which changes in DNA in a given population are correlated with disease, drug response, or other phenotypes of interest. However, changes in DNA that associate with traits like common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in the higher-order disease traits. Therefore, identifying molecular phenotypes that vary in response to changes in DNA that also associate with changes in disease traits can provide the functional information necessary to not only identify and validate the susceptibility genes directly affected by changes in DNA, but to understand as well the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. To enable this type of approach we profiled the expression levels of 39,280 transcripts and genotyped 782,476 SNPs in 427 human liver samples, identifying thousands of DNA variants that strongly associated with liver gene expression. These relationships were then leveraged by integrating them with genotypic and expression data from other human and mouse populations, leading to the direct identification of candidate susceptibility genes corresponding to genetic loci identified as key drivers of disease. Our analysis is able to provide much needed functional support for these candidate susceptibility genes.

Identifying changes in DNA that associate with changes in gene expression in human tissues elucidates the genetic architecture of gene expression in human populations and enables the direct identification of functionally supported candidate susceptibility genes in genomic regions associated with disease.

Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations

To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models.

Complex phenotypes such as common human diseases are caused by variations in DNA in many genes that interact in complex ways with a number of environmental factors. These multifactorial gene and environmental perturbations induce changes in molecular networks that in turn lead to phenotypic changes in the organism under study. The comprehensive monitoring of transcript abundances using gene expression microarrays in different tissues over a large number of individuals in a population can be used to reconstruct molecular networks that underlie higher-order phenotypes such as disease. The cost to generate these large-scale gene activity measurements over large numbers of individuals can be extreme. However, by integrating DNA variation and gene activity data monitored in each individual in a given population of interest, we demonstrate that the power to elucidate molecular networks that drive complex phenotypes can be significantly enhanced, without increasing the sample size. Using a biologically realistic simulation framework, we demonstrate that molecular networks reconstructed using the combined DNA variation and gene activity data are more accurate than molecular networks reconstructed from gene activity data alone, implying that adding DNA variation data might allow us to use fewer subjects to produce molecular networks that better explain complex phenotypes such as disease.

Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes

Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. Identifying susceptibility genes for these diseases using genetic and genomic technologies is accelerating, and the expectation over the next several years is that a number of genes will be identified for common diseases. However, the identification of single genes for disease has limited utility, given that diseases do not originate in complex systems from single gene changes. Further, the identification of single genes for disease may not lead directly to genes that can be targeted for therapeutic intervention. Therefore, uncovering single genes for disease in isolation of the broader network of molecular interactions in which they operate will generally limit the overall utility of such discoveries. Several integrative approaches have been developed and applied to reconstructing networks. Here we review several of these approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease. Networks reconstructed from these data provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.

Development of an approach for ab initio estimation of compound-induced liver injury based on global gene transcriptional profiles

Toxicity is a major cause of failure in drug development. A toxicogenomic approach may provide a powerful tool for better assessing the potential toxicity of drug candidates. Several approaches have been reported for predicting hepatotoxicity based on reference compounds with well-studied toxicity mechanisms. We developed a new approach for assessing compound-induced liver injury without prior knowledge of a compound's mechanism of toxicity. Using samples from rodents treated with 49 known liver toxins and 10 compounds without known liver toxicity, we derived a hepatotoxicity score as a single quantitative measurement for assessing the degree of induced liver damage. Combining the sensitivity of the hepatotoxicity score and the power of a machine learning algorithm, we then built a model to predict compound-induced liver injury based on 212 expression profiles. As estimated in an independent data set of 54 expression profiles, the built model predicted compound-induced liver damage with 90.9% sensitivity and 88.4% specificity. Our findings illustrate the feasibility of ab initio estimation of liver toxicity based on transcriptional profiles.

An integrative genomics approach to the reconstruction of gene networks in segregating populations

The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human diseases, but living systems more generally. Here we propose a novel gene network reconstruction algorithm, derived from classic Bayesian network methods, that utilizes naturally occurring genetic variations as a source of perturbations to elucidate the network. This algorithm incorporates relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of this novel algorithm is demonstrated via application to liver gene expression data from a segregating mouse population. We demonstrate that the network derived from these data using our novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.

Molecular requirements of the human nucleoside transporters hCNT1, hCNT2, and hENT1

Concentrative nucleoside transporters (CNTs) and equilibrative nucleoside transporters (ENTs) are important in physiological and pharmacological activity and disposition of nucleosides and nucleoside drugs. A better understanding of the structural requirements of inhibitors for these transporters will aid in designing therapeutic agents. To define the relative and unified structural requirements of nucleoside analogs for interaction with hCNT1, hCNT2, and hENT1, we applied an array of structure-activity techniques. Unique pharmacophore models for each respective nucleoside transporter were generated. These models reveal that hCNT2 affinity is dominated by hydrogen bonding features, whereas hCNT1 and hENT1 displayed mainly electrostatic and steric features. Hydrogen bond formation over 3'-OH is essential for all nucleoside transporters. Inhibition of nucleoside transporters by a series of uridine and adenosine analogs and a variety of drugs was analyzed by comparative molecular field analysis. Cross-validated r2 (q2) values were 0.65, 0.52, and 0.74 for hCNT1, hCNT2, and hENT1, respectively. The predictive quality of the models was further validated by successful prediction of the inhibition of a set of test compounds. Addition of a hydroxyl group around the 2-position of purine (or 3-position of pyrimidine) may increase inhibition to hCNT2 transporter; addition of hydroxyl group around the 2,7-position of purine (or the 3,5-position of pyrimidine) would increase the inhibition to hENT1 transporter. Utilization of these models should assist the design of high-affinity nucleoside transporter inhibitors and substrates for both anticancer and antiviral therapy.

A single glycine mutation in the equilibrative nucleoside transporter gene, hENT1, alters nucleoside transport activity and sensitivity to nitrobenzylthioinosine

The human equilibrative nucleoside transporter, hENT1, which is sensitive to inhibition by nitrobenzylthioinosine (NBMPR), is expressed in a wide variety of tissues. hENT1 is involved in the uptake of natural nucleosides, including regulation of the physiological effects of extracellular adenosine, and transports nucleoside drugs used in the treatment of cancer and viral diseases. Structure-function studies have revealed that transmembrane domains (TMD) 3 through 6 of hENT1 may be involved in binding of nucleosides. We have hypothesized that amino acid residues within TMD 3-6, which are conserved across equilibrative transporter sequences from several species, may have a critical role in the binding and transport of nucleosides. Therefore, we explored the role of point mutations of two conserved glycine residues, at positions 179 and 184 located in transmembrane domain 5 (TMD 5), using a GFP-tagged hENT1 in a yeast nucleoside transporter assay system. Mutations of glycine 179 to leucine, cysteine, or valine abolished transporter activity without affecting the targeting of the transporter to the plasma membrane, whereas more conservative mutations such as glycine to alanine or serine preserved both targeting to the plasma membrane and transport activity. Similar point mutations at glycine 184 resulted in poor targeting of hENT1 to the plasma membrane and little or no detectable functional activity. Uridine transport by G179A mutant was significantly lower (p < 0.05) and less sensitive (p < 0.05) to inhibition by NBMPR when compared to the wild-type transporter (IC(50) 7.7 +/- 0.8 nM versus 46 +/- 14.6 nM). Based on these data, we conclude that when hENT1 is expressed in yeast, glycine 179 is critical not only to the ability of hENT1 to transport uridine but also as a determinant of hENT1 sensitivity to NBMPR. In contrast, glycine 184 is likely important in targeting the transporter to the plasma membrane. This is the first identification and characterization of a critical amino acid residue of hENT1 that is important in both nucleoside transporter function and sensitivity to inhibition by NBMPR.

Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles

Microarray technology, which allows one to quantitate the expression of thousands of genes simultaneously, has begun to have a major impact on many different areas of drug discovery and development. The question remains of whether microarray analysis and gene expression signature profiles can be applied to the field of toxicology. To date, there are very few published studies showing the use of microarrays in toxicology and important questions remain regarding the predictability and accuracy of applying gene expression profiles to toxicology. To begin to address these questions, we have treated rats with 15 different known hepatotoxins, including allyl alcohol, amiodarone, Aroclor 1254, arsenic, carbamazepine, carbon tetrachloride, diethylnitrosamine, dimethylformamide, diquat, etoposide, indomethacin, methapyrilene, methotrexate, monocrotaline, and 3-methylcholanthrene. These agents cause a variety of hepatocellular injuries including necrosis, DNA damage, cirrhosis, hypertrophy, and hepatic carcinoma. Gene expression analysis was done on RNA from the livers of treated rats and was compared against vehicle-treated controls. The gene expression results were clustered and compared to the histopathology findings and clinical chemistry values. Our results show strong correlation between the histopathology, clinical chemistry, and gene expression profiles induced by the agents. In addition, genes were identified whose regulation correlated strongly with effects on clinical chemistry parameters. Overall, the results suggest that microarray assays may prove to be a highly sensitive technique for safety screening of drug candidates and for the classification of environmental toxins.

Experimental annotation of the human genome using microarray technology

The most important product of the sequencing of a genome is a complete, accurate catalogue of genes and their products, primarily messenger RNA transcripts and their cognate proteins. Such a catalogue cannot be constructed by computational annotation alone; it requires experimental validation on a genome scale. Using 'exon' and 'tiling' arrays fabricated by ink-jet oligonucleotide synthesis, we devised an experimental approach to validate and refine computational gene predictions and define full-length transcripts on the basis of co-regulated expression of their exons. These methods can provide more accurate gene numbers and allow the detection of mRNA splice variants and identification of the tissue- and disease-specific conditions under which genes are expressed. We apply our technique to chromosome 22q under 69 experimental condition pairs, and to the entire human genome under two experimental conditions. We discuss implications for more comprehensive, consistent and reliable genome annotation, more efficient, full-length complementary DNA cloning strategies and application to complex diseases.

Human intestinal es nucleoside transporter: molecular characterization and nucleoside inhibitory profiles

PURPOSE

To clone and sequence the equilibrative nitrobenzylthioinosine (NBMPR)-sensitive nucleoside transporter (es) from the human small intestine and to examine the capacities of nucleosides and nucleoside analogs to inhibit the uptake of uridine by this transporter.

METHODS

Using PCR, es was cloned from a cDNA library of the human small intestine. The uptake of 3H-uridine (10 microM) by the recombinant es, expressed in Xenopus oocytes, was measured in the presence (2 mM) and absence of nucleosides and nucleoside analogs.

RESULTS

The amino acid sequence of this es transporter was identical to that of the human placental es transporter. Uptake of 3H-uridine by this es transporter was inhibitable by 1 microM NBMPR. Removal of the oxygen from the 3' position or from both the 2' and 3' positions, but not from 2' or 5' position, resulted in a partial or total loss of the capacity of the nucleosides to inhibit 3H-uridine uptake. No modifications of the adenosine base or of the uridine base (except for 3 and 6 positions on uracil) affected nucleoside inhibitory capacity.

CONCLUSION

The es transporters of the human intestine and placenta are identical in their amino acid sequences. Moreover, the inhibitory profiles of various nucleoside analogs in inhibiting the uptake of uridine by the intestinal es transporter are similar to those obtained with the as-yet-uncloned human erythrocyte es transporter. Collectively, these findings suggest that the es transporter does not appear to be functionally variant in the human placenta, small intestine or erythrocytes.

Functional discovery via a compendium of expression profiles

Ascertaining the impact of uncharacterized perturbations on the cell is a fundamental problem in biology. Here, we describe how a single assay can be used to monitor hundreds of different cellular functions simultaneously. We constructed a reference database or "compendium" of expression profiles corresponding to 300 diverse mutations and chemical treatments in S. cerevisiae, and we show that the cellular pathways affected can be determined by pattern matching, even among very subtle profiles. The utility of this approach is validated by examining profiles caused by deletions of uncharacterized genes: we identify and experimentally confirm that eight uncharacterized open reading frames encode proteins required for sterol metabolism, cell wall function, mitochondrial respiration, or protein synthesis. We also show that the compendium can be used to characterize pharmacological perturbations by identifying a novel target of the commonly used drug dyclonine.

Molecular, functional and evolutionary characterization of the gene encoding HMG-CoA reductase in the fission yeast, Schizosaccharomyces pombe

The synthesis of mevalonate, a molecule required for both sterol and isoprene biosynthesis in eukaryotes, is catalysed by 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase. Using a gene dosage approach, we have isolated the gene encoding HMG-CoA reductase hmgl+, from the fission yeast Schizosaccharomyces pombe (Accession Number L76979). Specifically, hmgl+ was isolated on the basis of its ability to confer resistance to lovastatin, a competitive inhibitor of HMG-CoA reductase. Gene disruption analysis showed that hmgl+ was an essential gene. This result provided evidence that, unlike Saccharomyces cerevisiae, S. pombe contained only a single functional HMG-CoA reductase gene. The presence of a single HMG-CoA reductase gene was confirmed by genomic hybridization analysis. As observed for the S. cerevisiae HMGlp, the hmgl+ protein induced membrane proliferations known as karmellae. A previously undescribed 'feed-forward' regulation was observed in which elevated levels of HMG-CoA synthase, the enzyme catalysing the synthesis of the HMG-CoA reductase substrate, induced elevated levels of hmgl+ protein in the cell and conferred partial resistance to lovastatin. The amino acid sequences of yeast and human HMG-CoA reductase were highly divergent in the membrane domains, but were extensively conserved in the catalytic domains. We tested whether the gene duplication that produced the two functional genes in S. cerevisiae occurred before or after S. pombe and S. cerevisiae diverged by comparing the log likelihoods of trees specified by these hypotheses. We found that the tree specifying post-divergence duplication had significantly higher likelihood. Moreover, phylogenetic analyses of available HMG-CoA reductase sequences also suggested that the lineages of S. pombe and S. cerevisiae diverged approximately 420 million years ago but that the duplication event that produced two HMG-CoA reductase genes in the budding yeast occurred only approximately 56 million years ago. To date, S. pombe is the only unicellular eukaryote that has been found to contain a single HMG-CoA reductase gene. Consequently, S. pombe may provide important opportunities to study aspects of the regulation of sterol biosynthesis that have been difficult to address in other organisms and serve as a test organism to identify novel therapies for modulating cholesterol synthesis.

Degradation of HMG-CoA reductase-induced membranes in the fission yeast, Schizosaccharomyces pombe

Elevated levels of certain membrane proteins, including the sterol biosynthetic enzyme HMG-CoA reductase, induce proliferation of the endoplasmic reticulum. When the amounts of these proteins return to basal levels, the proliferated membranes are degraded, but the molecular details of this degradation remain unknown. We have examined the degradation of HMG-CoA reductase-induced membranes in the fission yeast, Schizosaccharomyces pombe. In this yeast, increased levels of the Saccharomyces cerevisiae HMG-CoA reductase isozyme encoded by HMG1 induced several types of membranes, including karmellae, which formed a cap of stacked membranes that partially surrounded the nucleus. When expression of HMG1 was repressed, the karmellae detached from the nucleus and formed concentric, multilayered membrane whorls that were then degraded. During the degradation process, CDCFDA-stained compartments distinct from preexisting vacuoles formed within the interior of the whorls. In addition to these compartments, particles that contained neutral lipids also formed within the whorl. As the thickness of the whorl decreased, the lipid particle became larger. When degradation was complete, only the lipid particle remained. Cycloheximide treatment did not prevent the formation of whorls. Thus, new protein synthesis was not needed for the initial stages of karmellae degradation. On the contrary, cycloheximide promoted the detachment of karmellae to form whorls, suggesting that a short lived protein may be involved in maintaining karmellae integrity. Taken together, these results demonstrate that karmellae membranes differentiated into self-degradative organelles. This process may be a common pathway by which ER membranes are turned over in cells.

DiOC6 staining reveals organelle structure and dynamics in living yeast cells

When present at low concentrations, the fluorescent lipophilic dye, DiOC6, stains mitochondria in living yeast cells [Pringle et al.: Methods in Cell Biol. 31:357-435, 1989; Weisman et al.: Proc. Natl. Acad. Sci. U.S.A. 87:1076-1080, 1990]. However, we found that the nuclear envelope and endoplasmic reticulum were specifically stained if the dye concentration was increased or if certain respiratory-deficient yeast strains were examined. The quality of nuclear envelope staining with DiOC6 was sufficiently sensitive to reveal alterations in the nuclear envelope known as karmellae. These membranes were previously apparent only by electron microscopy. At the high dye concentrations required to stain the nuclear envelope, wild-type cells could no longer grow on non-fermentable carbon sources. In spite of this effect on mitochondrial function, the presence of high dye concentration did not adversely affect cell viability or general growth characteristics when strains were grown under standard conditions on glucose. Consequently, time-lapse confocal microscopy was used to examine organelle dynamics in living yeast cells stained with DiOC6. These in vivo observations correlated very well with previous electron microscopic studies, including analyses of mitochondria, karmellae, and mitosis. For example, cycles of mitochondrial fusion and division, as well as the changes in nuclear shape and position that occur during mitosis, were readily imaged in time-lapse studies of living DiOC6-stained cells. This technique also revealed new aspects of nuclear disposition and interactions with other organelles. For example, the nucleus and vacuole appeared to form a structurally coupled unit that could undergo coordinated movements. Furthermore, unlike the general view that nuclear movements occur only in association with division, the nucleus/vacuole underwent dramatic migrations around the cell periphery as cells exited from stationary phase. In addition to the large migrations or rotations of the nucleus/vacuole, DiOC6 staining also revealed more subtle dynamics, including the forces of the spindle on the nuclear envelope during mitosis. This technique should have broad application in analyses of yeast cell structure and function.

Primary role of adipokinetic hormone in the formation of low density lipophorin in locusts

It was demonstrated that the primary action of adipokinetic hormone (AKH) is to stimulate calcium ion uptake into the fat body cell, subsequently causing the formation of diacylglycerol from triacylglycerol. Furthermore, it was also shown that AKH is not directly responsible for increased diacylglycerol uptake by lipophorin from the fat body. The diacylglycerol level of the fat body was found to increase by an average of 2.4-fold after 90 min of incubation in the presence of AKH. Calcium ion was also found to be essential in the action of AKH on the fat body. Supporting this is the observation that calcium ionophore mimics the AKH action in vivo and in vitro; injection of calcium ionophore into adult locusts as well as incubation of hemolymph with fat body and ionophore caused the transformation of high density lipophorin to low density lipophorin. When the fat body, preincubated with or without AKH, was reincubated with hemolymph, diacylglycerol uptake by lipophorin occurred for both incubations. In some sets of experiments, low density lipophorin particles were formed even in the hemolymph that was incubated with fat body preincubated without AKH, indicating that AKH is not directly responsible for its formation. Calcium ion was found not to be necessary for the diacylglycerol uptake process to occur.

Trehalose, the insect blood sugar, inhibits loading of diacylglycerol by lipophorin from the fat body in locusts

Trehalose, the insect blood sugar, was found to inhibit diacylglycerol uptake by lipophorin from the fat body in vitro. Trehalose inhibited diacylglycerol uptake by about 40%-50% at various physiological concentrations. This suggests that trehalose may play a dual role in the hemolymph, i.e. serving as the insect's fuel and as a regulator in lipid transport.

Induction of cytochrome P-448 activity as exemplified by the O-deethylation of ethoxyresorufin. Effects of dose, sex, tissue and animal species

The effects of tissue, sex, animal species and dose on the induction of cytochrome P-448 activity by various inducing agents were investigated using O-ethoxyresorufin as a model substrate. The liver was by far more effective in catalysing the O-deethylation of ethoxyresorufin (EROD) than the lung and kidney. The extent of induction was also highest in the liver, with the exception of benzo(a)pyrene and 3-methylcholanthrene where inducibility was more pronounced in the kidney. The benzo(a)pyrene-induced hepatic EROD activity in the rat decayed to reach control levels four days after a single administration. Rat hepatic EROD activity was induced in both sexes but tended to be higher in the male. Marked species differences in the inducibility of hepatic EROD activity by various chemicals was observed, the rat being always more responsive when compared to the hamster or mouse. The induction of rat hepatic EROD activity by benzo(a)pyrene, 2-acetylaminofluorene and safrole was dose-dependent, maximum induction being achieved with single doses of 5, 2 and 5 mg/kg, respectively.

Alkoxyresorufin O-dealkylases: association with the murine Ah locus

The hepatic microsomal dealkylation of a series of alkoxyresorufins and the oxidation of phenoxazone to resorufin were investigated in C57BL/6 and DBA/2 mice of both sexes. In both strains of mice and in both sexes the dealkylation rate decreased with increasing length of the alkyl chain. With all alkoxyresorufins the dealkylation rates were higher in the C57BL mice than the DBA mice, whereas the rate of phenoxazone hydroxylation was higher in the latter. In the C57BL mice, and to a lesser extent in the DBA mice, females were more efficient in dealkylating the resorufin ethers. Treatment with 3-methylcholanthrene (3MC) enhanced the rates of dealkylation of all alkoxyresorufins in the C57BL mice but not in the DBA mice, the extent of stimulation being highest for the propoxy- and butoxyresorufins and least for pentoxy-, heptoxy- and benzyloxyresorufins. The same treatment had no effect on the oxidation of phenoxazone in either strain of mice. It is concluded that the dealkylation of alkoxyresorufins, not the oxidation of phenoxazone, is associated with the murine Ah locus.

Foetal and neonatal development of cytochrome P-450 and cytochrome P-448 catalysed mixed function oxidases in the rat: induction by 3-methylcholanthrene

Benzphetamine N-demethylase (cytochrome P-450) and ethoxyresorufin O-deethylase activities (cytochrome P-448) were determined in the growing neonate and foetus of control and 3-methylcholanthrene-pretreated rats. Ethoxyresorufin O-deethylase activity was highest in the 1-2-week-old animals and then decreased with age. The inducibility of this activity by 3-methylcholanthrene was low in the young animals, but increased with age. In contrast, benzphetamine N-demethylase activity in the control animals was low at birth and increased with age, and was not induced by 3-methylcholanthrene. In the foetal liver, ethoxyresorufin O-deethylase was the only activity present at higher levels than in the maternal liver. Transplacental administration of 3-methylcholanthrene failed to induce the foetal activities while the maternal liver showed the expected response. These observations demonstrate that cytochrome P-448 may be a predominant hepatic form in the foetus and neonate but cytochrome P-450 becomes a major form as the animal grows. The implications of these findings in chemical toxicity are discussed.

Determination of cytochrome P-448 activity in biological tissues

Three enzymes used for the determination of cytochrome P-448 activity, namely aryl hydrocarbon hydroxylase, biphenyl 2-hydroxylase and ethoxyresorufin O-de-ethylase, were evaluated with respect to their specificity, sensitivity and inducibility. Purified cytochrome P-448 (LM4), but not cytochrome P-450 (LM2), catalysed the O-de-ethylation of ethoxyresorufin in a reaction that was markedly inhibited by 9-hydroxyellipticine. After the administration of 3-methylcholanthrene to rats all three activities were induced, the extent of induction being highest for ethoxyresorufin O-de-ethylase. Administration of very small doses of benzo[a]pyrene (50 micrograms/kg) to rats to induce cytochrome P-448 specifically increased only the O-de-ethylation of ethoxyresorufin. 3-Hydroxybenzo[a]pyrene, the major metabolite determined by the aryl hydrocarbon hydroxylase assay, undergoes further NADPH-dependent oxygenation leading to loss of fluorescence. On the basis of these observations and those by other workers, we conclude that ethoxyresorufin O-de-ethylase provides the most specific, sensitive and reproducible means of determining cytochrome P-448 activity.

Cytochrome P-448 and the activation of toxic chemicals and carcinogens

The metabolic activation of carcinogens and some toxic chemicals appears to involve oxygenation in conformationally hindered positions in the chemical molecules. Oxygenation of xenobiotics in hindered positions is effected by cytochrome P-448 (LM4) but not by cytochrome P-450 (LM2). Substrate-interaction spectra show that cytochrome P-448 has an active site with a conformation different from that of cytochrome P-450. Induction of cytochrome P-448, as specifically measured by ethoxyresorufin O-deethylase activity, occurs in rat liver, kidney and lung after administration of the carcinogens, 3-methylcholanthrene, Aroclor 1254, 2-anthramine, safrole, 7,12-dimethylbenz[a]anthracene, MNNG and 2-acetamidofluorene. The doubtful carcinogens, saccharin, DDT and aldrin, resulted in no significant induction. The drugs paracetamol, antipyrine, imipramine and rifampicin resulted in diminished enzyme activity, indicating the absence of any induction of cytochrome P-448. In studies with the matched pairs of carcinogens and non-carcinogens, benzo[a]pyrene and benzo[e]pyrene, and 1,2,5,6-dibenzanthracene and anthracene, only the carcinogenic analogue resulted in induction of cytochrome P-448. With alpha- and beta-naphthylamine, both resulted in marked induction of cytochrome P-448 in liver, kidney and lung, indicating that both isomers might be carcinogenic.