Differential expression of DMD transcripts as a novel prognostic biomarker in histologically diverse mesotheliomas.

Background: The identification of prognostic biomarkers is crucial for guiding treatment strategies in mesothelioma patients. The Duchenne muscular dystrophy (DMD) gene and its specific transcripts have been associated with patient survival in various tumours. In this study, we aimed to investigate the prognostic potential of DMD gene expression and its transcripts in mesothelioma patients. Methods: We analysed The Cancer Genome Atlas (TCGA) mesothelioma RNAseq, mutation, and clinical data to assess the association between DMD gene expression and its transcripts (Dp427, Dp71 splice variants) and mesothelioma survival. We also evaluated the specific Dp71 transcript as a unique prognostic biomarker across mesothelioma subtypes. Additionally, we performed differential gene expression analysis between high and low DMD gene/transcript expression groups. Results: The analysis included 57 epithelioid, 23 biphasic, two sarcomatoid, and five not otherwise specified (NOS) histological subtypes of mesothelioma samples. Univariate analysis revealed that high expression of the DMD gene and its Dp71 transcript was significantly associated with shorter survival in mesothelioma patients (P=0.003 and P<0.001, respectively). In a multivariate analysis, the association between Dp71 expression and survival remained significant [hazard ratio (HR) 2.29, 95% confidence interval (CI): 1.24-4.23, P=0.008] across all mesothelioma patients, and also among patients with mesotheliomas without deep CDKN2A deletions (HR 3.58, 95% CI: 1.31-9.80, P=0.01). Pathway analysis revealed enrichment of cell cycle (P=3.01×10-4) and homologous recombination (P=0.01) pathways in differentially expressed genes (DEGs) between high and low Dp71 groups. Furthermore, there were correlations between Dp71 transcript expression and tumour microenvironment (TME) cells, including a weak positive correlation with macrophages (R=0.32, P=0.002) specifically M2 macrophages (R=0.34, P=0.001). Conclusions: Our findings indicate that the differential expression of specific DMD transcripts is associated with poor survival in mesothelioma patients. The specific Dp71 transcript can serve as a potential biomarker for predicting patient survival in diverse histological subtypes of mesothelioma. Further studies are needed to understand the role of specific dystrophin transcripts in cancer and TME cells, and their implications in the pathogenesis and progression of mesothelioma. Identifying patients at risk of poor survival based on DMD transcript expression can guide treatment strategies in mesothelioma, informing decisions regarding treatment intensity, follow-up schedules, eligibility for clinical trials, and ultimately, end-of-life care planning.

Early, precise, and safe clinical evaluation of the pharmacodynamic effects of novel agents in the intact human tumor microenvironment.

Introduction: Drug development is systemically inefficient. Research and development costs for novel therapeutics average hundreds of millions to billions of dollars, with the overall likelihood of approval estimated to be as low as 6.7% for oncology drugs. Over half of these failures are due to a lack of drug efficacy. This pervasive and repeated low rate of success exemplifies how preclinical models fail to adequately replicate the complexity and heterogeneity of human cancer. Therefore, new methods of evaluation, early in the development trajectory, are essential both to rule-in and rule-out novel agents with more rigor and speed, but also to spare clinical trial patients from the potentially toxic sequelae (high risk) of testing investigational agents that have a low likelihood of producing a response (low benefit). Methods: The clinical in vivo oncology (CIVO®) platform was designed to change this drug development paradigm. CIVO precisely delivers microdose quantities of up to 8 drugs or combinations directly into patient tumors 4-96 h prior to planned surgical resection. Resected tissue is then analyzed for responses at each site of intratumoral drug exposure. Results: To date, CIVO has been used safely in 6 clinical trials, including 68 subjects, with 5 investigational and 17 approved agents. Resected tissues were analyzed initially using immunohistochemistry and in situ hybridization assays (115 biomarkers). As technology advanced, the platform was paired with spatial biology analysis platforms, to successfully track anti-neoplastic and immune-modulating activity of the injected agents in the intact tumor microenvironment. Discussion: Herein we provide a report of the use of CIVO technology in patients, a depiction of the robust analysis methods enabled by this platform, and a description of the operational and regulatory mechanisms used to deploy this approach in synergistic partnership with pharmaceutical partners. We further detail how use of the CIVO platform is a clinically safe and scientifically precise alternative or complement to preclinical efficacy modeling, with outputs that inform, streamline, and de-risk drug development.

Trackable Intratumor Microdosing and Spatial Profiling Provide Early Insights into Activity of Investigational Agents in the Intact Tumor Microenvironment.

PURPOSE: Cancer drug development is currently limited by a paradigm of preclinical evaluation that does not adequately recapitulate the complexity of the intact human tumor microenvironment (TME). To overcome this, we combined trackable intratumor microdosing (CIVO) with spatial biology readouts to directly assess drug effects in patient tumors in situ. EXPERIMENTAL DESIGN: In a first-of-its-kind phase 0 clinical trial, we explored the effects of an investigational stage SUMOylation-activating enzyme (SAE) inhibitor, subasumstat (TAK-981) in 12 patients with head and neck carcinoma (HNC). Patients scheduled for tumor resection received percutaneous intratumor injections of subasumstat and vehicle control 1 to 4 days before surgery, resulting in spatially localized and graded regions of drug exposure (∼1,000-2,000 µm in diameter). Drug-exposed (n = 214) and unexposed regions (n = 140) were compared by GeoMx Digital Spatial Profiler, with evaluation at single-cell resolution in a subset of these by CosMx Spatial Molecular Imager. RESULTS: Localized regions of subasumstat exposure revealed SUMO pathway inhibition, elevation of type I IFN response, and inhibition of cell cycle across all tumor samples. Single-cell analysis by CosMx demonstrated cell-cycle inhibition specific to the tumor epithelium, and IFN pathway induction commensurate with a TME shift from immune-suppressive to immune-permissive. CONCLUSIONS: Pairing CIVO with spatial profiling enabled detailed investigation of response to subasumstat across a diverse sampling of native and intact TME. We demonstrate that drug mechanism of action can be directly evaluated in a spatially precise manner in the most translationally relevant setting: an in situ human tumor.

Altered gene expression in slc4a11-/- mouse cornea highlights SLC4A11 roles.

SLC4A11 is a H+/NH3/water transport protein, of corneal endothelial cells. SLC4A11 mutations cause congenital hereditary endothelial dystrophy and some cases of Fuchs endothelial corneal dystrophy. To probe SLC4A11's roles, we compared gene expression in RNA from corneas of 17-week-old slc4a11-/- (n = 3) and slc4a11+/+ mice (n = 3) and subjected to RNA sequencing. mRNA levels for a subset of genes were also assessed by quantitative real-time reverse transcription PCR (qRT RT-PCR). Cornea expressed 13,173 genes, which were rank-ordered for their abundance. In slc4a11-/- corneas, 100 genes had significantly altered expression. Abundant slc14a1 expression, encoding the urea transporter UT-A, suggests a significant role in the cornea. The set of genes with altered expression was subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, revealing that alterations clustered into extracellular region, cytoskeleton, cell adhesion and plasma membrane functions. Gene expression changes further clustered into classes (with decreasing numbers of genes): cell fate and development, extracellular matrix and cell adhesion, cytoskeleton, ion homeostasis and energy metabolism. Together these gene changes confirm earlier suggestions of a role of SLC4A11 in ion homeostasis, energy metabolism, cell adhesion, and reveal an unrecognized SLC4A11 role in cytoskeletal organization.

Sulforaphane reduces hepatic glucose production and improves glucose control in patients with type 2 diabetes.

A potentially useful approach for drug discovery is to connect gene expression profiles of disease-affected tissues ("disease signatures") to drug signatures, but it remains to be shown whether it can be used to identify clinically relevant treatment options. We analyzed coexpression networks and genetic data to identify a disease signature for type 2 diabetes in liver tissue. By interrogating a library of 3800 drug signatures, we identified sulforaphane as a compound that may reverse the disease signature. Sulforaphane suppressed glucose production from hepatic cells by nuclear translocation of nuclear factor erythroid 2-related factor 2 (NRF2) and decreased expression of key enzymes in gluconeogenesis. Moreover, sulforaphane reversed the disease signature in the livers from diabetic animals and attenuated exaggerated glucose production and glucose intolerance by a magnitude similar to that of metformin. Finally, sulforaphane, provided as concentrated broccoli sprout extract, reduced fasting blood glucose and glycated hemoglobin (HbA1c) in obese patients with dysregulated type 2 diabetes.

Gene expression elucidates functional impact of polygenic risk for schizophrenia.

Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.

Modeling RAS phenotype in colorectal cancer uncovers novel molecular traits of RAS dependency and improves prediction of response to targeted agents in patients.

PURPOSE: KRAS wild-type status is an imperfect predictor of sensitivity to anti-EGF receptor (EGFR) monoclonal antibodies in colorectal cancer, motivating efforts to identify novel molecular aberrations driving RAS. This study aimed to build a quantitative readout of RAS pathway activity to (i) uncover molecular surrogates of RAS activity specific to colorectal cancer, (ii) improve the prediction of cetuximab response in patients, and (iii) suggest new treatment strategies. EXPERIMENTAL DESIGN: A model of RAS pathway activity was trained in a large colorectal cancer dataset and validated in three independent colorectal cancer patient datasets. Novel molecular traits were inferred from The Cancer Genome Atlas colorectal cancer data. The ability of the RAS model to predict resistance to cetuximab was tested in mouse xenografts and three independent patient cohorts. Drug sensitivity correlations between our model and large cell line compendiums were performed. RESULTS: The performance of the RAS model was remarkably robust across three validation datasets. (i) Our model confirmed the heterogeneity of the RAS phenotype in KRAS wild-type patients, and suggests novel molecular traits driving its phenotype (e.g., MED12 loss, FBXW7 mutation, MAP2K4 mutation). (ii) It improved the prediction of response and progression-free survival (HR, 2.0; P < 0.01) to cetuximab compared with KRAS mutation (xenograft and patient cohorts). (iii) Our model consistently predicted sensitivity to MAP-ERK kinase (MEK) inhibitors (P < 0.01) in two cell panel screens. CONCLUSIONS: Modeling the RAS phenotype in colorectal cancer allows for the robust interrogation of RAS pathway activity across cell lines, xenografts, and patient cohorts. It demonstrates clinical utility in predicting response to anti-EGFR agents and MEK inhibitors.

Dominant effects of the Huntington's disease HTT CAG repeat length are captured in gene-expression data sets by a continuous analysis mathematical modeling strategy.

In Huntington's disease (HD), the size of the expanded HTT CAG repeat mutation is the primary driver of the processes that determine age at onset of motor symptoms. However, correlation of cellular biochemical parameters also extends across the normal repeat range, supporting the view that the CAG repeat represents a functional polymorphism with dominant effects determined by the longer allele. A central challenge to defining the functional consequences of this single polymorphism is the difficulty of distinguishing its subtle effects from the multitude of other sources of biological variation. We demonstrate that an analytical approach based upon continuous correlation with CAG size was able to capture the modest (∼21%) contribution of the repeat to the variation in genome-wide gene expression in 107 lymphoblastoid cell lines, with alleles ranging from 15 to 92 CAGs. Furthermore, a mathematical model from an iterative strategy yielded predicted CAG repeat lengths that were significantly positively correlated with true CAG allele size and negatively correlated with age at onset of motor symptoms. Genes negatively correlated with repeat size were also enriched in a set of genes whose expression were CAG-correlated in human HD cerebellum. These findings both reveal the relatively small, but detectable impact of variation in the CAG allele in global data in these peripheral cells and provide a strategy for building multi-dimensional data-driven models of the biological network that drives the HD disease process by continuous analysis across allelic panels of neuronal cells vulnerable to the dominant effects of the HTT CAG repeat.

A systems biology framework identifies molecular underpinnings of coronary heart disease.

OBJECTIVE: Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene-disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD. APPROACH AND RESULTS: We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. Twenty-four coexpression modules were identified, including 1 case-specific and 1 control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with gene expression-associated single-nucleotide polymorphisms and with results of genome-wide association studies of CHD and its risk factors, the control-specific DM was implicated as CHD causal based on its significant enrichment for both CHD and lipid expression-associated single-nucleotide polymorphisms. This causal DM was further integrated with tissue-specific Bayesian networks and protein-protein interaction networks to identify regulatory key driver genes. Multitissue key drivers (SPIB and TNFRSF13C) and tissue-specific key drivers (eg, EBF1) were identified. CONCLUSIONS: Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk.

Lung eQTLs to help reveal the molecular underpinnings of asthma.

Genome-wide association studies (GWAS) have identified loci reproducibly associated with pulmonary diseases; however, the molecular mechanism underlying these associations are largely unknown. The objectives of this study were to discover genetic variants affecting gene expression in human lung tissue, to refine susceptibility loci for asthma identified in GWAS studies, and to use the genetics of gene expression and network analyses to find key molecular drivers of asthma. We performed a genome-wide search for expression quantitative trait loci (eQTL) in 1,111 human lung samples. The lung eQTL dataset was then used to inform asthma genetic studies reported in the literature. The top ranked lung eQTLs were integrated with the GWAS on asthma reported by the GABRIEL consortium to generate a Bayesian gene expression network for discovery of novel molecular pathways underpinning asthma. We detected 17,178 cis- and 593 trans- lung eQTLs, which can be used to explore the functional consequences of loci associated with lung diseases and traits. Some strong eQTLs are also asthma susceptibility loci. For example, rs3859192 on chr17q21 is robustly associated with the mRNA levels of GSDMA (P = 3.55 × 10(-151)). The genetic-gene expression network identified the SOCS3 pathway as one of the key drivers of asthma. The eQTLs and gene networks identified in this study are powerful tools for elucidating the causal mechanisms underlying pulmonary disease. This data resource offers much-needed support to pinpoint the causal genes and characterize the molecular function of gene variants associated with lung diseases.

Resolving candidate genes of mouse skeletal muscle QTL via RNA-Seq and expression network analyses.

BACKGROUND: We have recently identified a number of Quantitative Trait Loci (QTL) contributing to the 2-fold muscle weight difference between the LG/J and SM/J mouse strains and refined their confidence intervals. To facilitate nomination of the candidate genes responsible for these differences we examined the transcriptome of the tibialis anterior (TA) muscle of each strain by RNA-Seq. RESULTS: 13,726 genes were expressed in mouse skeletal muscle. Intersection of a set of 1061 differentially expressed transcripts with a mouse muscle Bayesian Network identified a coherent set of differentially expressed genes that we term the LG/J and SM/J Regulatory Network (LSRN). The integration of the QTL, transcriptome and the network analyses identified eight key drivers of the LSRN (Kdr, Plbd1, Mgp, Fah, Prss23, 2310014F06Rik, Grtp1, Stk10) residing within five QTL regions, which were either polymorphic or differentially expressed between the two strains and are strong candidates for quantitative trait genes (QTGs) underlying muscle mass. The insight gained from network analysis including the ability to make testable predictions is illustrated by annotating the LSRN with knowledge-based signatures and showing that the SM/J state of the network corresponds to a more oxidative state. We validated this prediction by NADH tetrazolium reductase staining in the TA muscle revealing higher oxidative potential of the SM/J compared to the LG/J strain (p<0.03). CONCLUSION: Thus, integration of fine resolution QTL mapping, RNA-Seq transcriptome information and mouse muscle Bayesian Network analysis provides a novel and unbiased strategy for nomination of muscle QTGs.

Secreted frizzled-related protein 4 reduces insulin secretion and is overexpressed in type 2 diabetes.

A plethora of candidate genes have been identified for complex polygenic disorders, but the underlying disease mechanisms remain largely unknown. We explored the pathophysiology of type 2 diabetes (T2D) by analyzing global gene expression in human pancreatic islets. A group of coexpressed genes (module), enriched for interleukin-1-related genes, was associated with T2D and reduced insulin secretion. One of the module genes that was highly overexpressed in islets from T2D patients is SFRP4, which encodes secreted frizzled-related protein 4. SFRP4 expression correlated with inflammatory markers, and its release from islets was stimulated by interleukin-1ß. Elevated systemic SFRP4 caused reduced glucose tolerance through decreased islet expression of Ca(2+) channels and suppressed insulin exocytosis. SFRP4 thus provides a link between islet inflammation and impaired insulin secretion. Moreover, the protein was increased in serum from T2D patients several years before the diagnosis, suggesting that SFRP4 could be a potential biomarker for islet dysfunction in T2D.

Predictive genes in adjacent normal tissue are preferentially altered by sCNV during tumorigenesis in liver cancer and may rate limiting.

BACKGROUND: In hepatocellular carcinoma (HCC) genes predictive of survival have been found in both adjacent normal (AN) and tumor (TU) tissues. The relationships between these two sets of predictive genes and the general process of tumorigenesis and disease progression remains unclear. METHODOLOGY/PRINCIPAL FINDINGS: Here we have investigated HCC tumorigenesis by comparing gene expression, DNA copy number variation and survival using ∼250 AN and TU samples representing, respectively, the pre-cancer state, and the result of tumorigenesis. Genes that participate in tumorigenesis were defined using a gene-gene correlation meta-analysis procedure that compared AN versus TU tissues. Genes predictive of survival in AN (AN-survival genes) were found to be enriched in the differential gene-gene correlation gene set indicating that they directly participate in the process of tumorigenesis. Additionally the AN-survival genes were mostly not predictive after tumorigenesis in TU tissue and this transition was associated with and could largely be explained by the effect of somatic DNA copy number variation (sCNV) in cis and in trans. The data was consistent with the variance of AN-survival genes being rate-limiting steps in tumorigenesis and this was confirmed using a treatment that promotes HCC tumorigenesis that selectively altered AN-survival genes and genes differentially correlated between AN and TU. CONCLUSIONS/SIGNIFICANCE: This suggests that the process of tumor evolution involves rate-limiting steps related to the background from which the tumor evolved where these were frequently predictive of clinical outcome. Additionally treatments that alter the likelihood of tumorigenesis occurring may act by altering AN-survival genes, suggesting that the process can be manipulated. Further sCNV explains a substantial fraction of tumor specific expression and may therefore be a causal driver of tumor evolution in HCC and perhaps many solid tumor types.

Identification of genes and networks driving cardiovascular and metabolic phenotypes in a mouse F2 intercross.

To identify the genes and pathways that underlie cardiovascular and metabolic phenotypes we performed an integrated analysis of a mouse C57BL/6JxA/J F2 (B6AF2) cross by relating genome-wide gene expression data from adipose, kidney, and liver tissues to physiological endpoints measured in the population. We have identified a large number of trait QTLs including loci driving variation in cardiac function on chromosomes 2 and 6 and a hotspot for adiposity, energy metabolism, and glucose traits on chromosome 8. Integration of adipose gene expression data identified a core set of genes that drive the chromosome 8 adiposity QTL. This chromosome 8 trans eQTL signature contains genes associated with mitochondrial function and oxidative phosphorylation and maps to a subnetwork with conserved function in humans that was previously implicated in human obesity. In addition, human eSNPs corresponding to orthologous genes from the signature show enrichment for association to type II diabetes in the DIAGRAM cohort, supporting the idea that the chromosome 8 locus perturbs a molecular network that in humans senses variations in DNA and in turn affects metabolic disease risk. We functionally validate predictions from this approach by demonstrating metabolic phenotypes in knockout mice for three genes from the trans eQTL signature, Akr1b8, Emr1, and Rgs2. In addition we show that the transcriptional signatures for knockout of two of these genes, Akr1b8 and Rgs2, map to the F2 network modules associated with the chromosome 8 trans eQTL signature and that these modules are in turn very significantly correlated with adiposity in the F2 population. Overall this study demonstrates how integrating gene expression data with QTL analysis in a network-based framework can aid in the elucidation of the molecular drivers of disease that can be translated from mice to humans.

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.

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.

Differentially regulated functional gene clusters identified during ischemia and reperfusion in isolated cardiac myocytes using coverslip hypoxia.

INTRODUCTION: Coverslip hypoxia (CSH) is a recently described method for producing rapid and severe ischemia derived from the metabolic activity of synchronously contracting isolated neonatal rat ventricular myocytes (NRVMs). While the effect of acute ischemia produced by CSH is documented, the contribution of reperfusion to cell viability has not been fully studied. METHODS: We therefore used fluorescence microscopy and expression profiling by microarray to determine the morphological and genetic effects in NRVMs of both the ischemic and reperfusion events of CSH. RESULTS: Fluorescence microscopy studies in coverslipped NRVMs showed cell death at 1 h as previously reported. Matched samples coverslipped for up to 2 h and then reperfused 18 h showed myocyte recovery prior to but not beyond 1 h upon post-staining, suggesting a limited window of recovery. Expression profiling of more than 30,000 genes using total RNA collected from NRVMs subjected to varying periods of ischemia and reperfusion revealed 103 genes regulated at least 2-fold at p<10(-7). These genes fall into discrete functional groups including apoptosis, metabolism, and hypoxia/acidosis. The regulation of a subset of genes from these groups was confirmed by RT-PCR. Interestingly, the hypoxia/acidosis gene BNip3 (a Bcl-2 family member implicated in hypoxia/acidosis-associated cell death) was upregulated early during ischemia and persisted throughout reperfusion. In addition, other hypoxia/acidosis genes such as heme oxygenase 1, pyruvate dehydrogenase kinase 1, prolyl hydroxylases, and hypoxia-inducible protein 2 were upregulated. DISCUSSION: These data suggest that the ischemic and reperfusion events created by CSH induce gene regulation within distinct functional groups related to in vivo ischemia.