Gene Expression-based Approach Research Articles

Preclinical side effect prediction through pathway engineering of protein interaction network models.

Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein-protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein-protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein-protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.

Identifying target organ location of Radix Achyranthis Bidentatae: a bioinformatics approach on active compounds and genes.

Background: Herbal medicines traditionally target organs for treatment based on medicinal properties, and this theory is widely used for prescriptions. However, the scientific evidence explaining how herbs act on specific organs by biological methods has been still limited. This study used bioinformatic tools to identify the target organ locations of Radix Achyranthis Bidentatae (RAB), a blood-activating herb that nourishes the liver and kidney, strengthens bones, and directs prescription to the lower body. Methods: RAB's active compounds and targets were collected and predicted using databases such as TCMSP, HIT2.0, and BATMAN-TCM. Next, the RAB's target list was analyzed based on two approaches to obtain target organ locations. DAVID and Gene ORGANizer enrichment-based approaches were used to enrich an entire gene list, and the BioGPS and HPA gene expression-based approaches were used to analyze the expression of core genes. Results: RAB's targets were found to be involved in whole blood, blood components, and lymphatic organs across all four tools. Each tool indicated a particular aspect of RAB's target organ locations: DAVID-enriched genes showed a predominance in blood, liver, and kidneys; Gene ORGANizer showed the effect on low body parts as well as bones and joints; BioGPS and HPA showed high gene expression in bone marrow, lymphoid tissue, and smooth muscle. Conclusion: Our bioinformatics-based target organ location prediction can serve as a modern interpretation tool for the target organ location theory of traditional medicine. Future studies should predict therapeutic target organ locations in complex prescriptions rather than single herbs and conduct experiments to verify predictions.

Identification of regulatory variants of carboxylesterase 1 (CES1): A proof-of-concept study for the application of the Allele-Specific Protein Expression (ASPE) assay in identifying cis-acting regulatory genetic polymorphisms.

It is challenging to study regulatory genetic variants as gene expression is affected by both genetic polymorphisms and non-genetic regulators. The mRNA allele-specific expression (ASE) assay has been increasingly used for the study of cis-acting regulatory variants because cis-acting variants affect gene expression in an allele-specific manner. However, poor correlations between mRNA and protein expressions were observed for many genes, highlighting the importance of studying gene expression regulation at the protein level. In the present study, we conducted a proof-of-concept study to utilize a recently developed allele-specific protein expression (ASPE) assay to identify the cis-acting regulatory variants of CES1 using a large set of human liver samples. The CES1 gene encodes for carboxylesterase 1 (CES1), the most abundant hepatic hydrolase in humans. Two cis-acting regulatory variants were found to be significantly associated with CES1 ASPE, CES1 protein expression, and its catalytic activity on enalapril hydrolysis in human livers. Compared to conventional gene expression-based approaches, ASPE demonstrated an improved statistical power to detect regulatory variants with small effect sizes since allelic protein expression ratios are less prone to the influence of non-genetic regulators (e.g., diseases and inducers). This study suggests that the ASPE approach is a powerful tool for identifying cis-regulatory variants.

Gene Expression-Based Diagnosis of Infections in Critically Ill Patients-Prospective Validation of the SepsisMetaScore in a Longitudinal Severe Trauma Cohort.

Early diagnosis of infections is pivotal in critically ill patients. Innovative gene expression-based approaches promise to deliver precise, fast, and clinically practicable diagnostic tools to bedside. This study aimed to validate the SepsisMetaScore, an 11-gene signature previously reported by our study group, in a representative longitudinal cohort of trauma patients. Prospective observational cohort study. Surgical ICUs of the University Medical Center Goettingen, Germany. Critically ill patients with severe traumatic injuries. None. Paired box gene (PAXgene) RNA blood tubes were drawn at predefined time points over the course of disease. The performance of the SepsisMetaScore was tested using targeted polymerase chain reaction and compared with Procalcitonin using area under the receiver operating characteristics analyses. The SepsisMetaScore showed significant differences between infected and noninfected patients (n = 52). It was able to accurately discriminate infectious from noninfectious acute inflammation with an area under the receiver operating characteristics of 0.92 (95% CI, 0.85-0.99) and significantly outperformed Procalcitonin (area under the receiver operating characteristics curve = 0.53; 95% CI, 0.42-0.64) early in the course of infection (p = 0.014). We demonstrated the clinical utility for diagnosis of infections with higher accuracy using the SepsisMetaScore compared with Procalcitonin in a prospective cohort of severe trauma patients. Future studies should assess whether the SepsisMetaScore may substantially improve clinical practice by accurate differentiation of infections from sterile inflammation and identification of patients at risk for sepsis. Our results support further investigation of the SepsisMetaScore for the development of tailored precision treatment of critically ill patients.

Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers.

Recently, a significant number of breast cancer (BC) patients have been diagnosed at an early stage. It is therefore critical to accurately predict the risk of recurrence and distant metastasis for better management of BC in this setting. Clinicopathologic patterns, particularly lymph node status, tumor size, and hormonal receptor status are routinely used to identify women at increased risk of recurrence. However, these factors have limitations regarding their predictive ability for late metastasis risk in patients with early BC. Emerging molecular signatures using gene expression-based approaches have improved the prognostic and predictive accuracy for this indication. However, the use of their based-scores for risk assessment has provided contradictory findings. Therefore, developing and using newly emerged alternative predictive and prognostic biomarkers for identifying patients at high- and low-risk is of great importance. The present review discusses some serum biomarkers and multigene profiling scores for predicting late recurrence and distant metastasis in early-stage BC based on recently published studies and clinical trials.

Targeted proteomics analysis revealed the imbalanced allele‐specific protein expression of carboxylesterase1 in human livers

Carboxylesterase 1 (CES1) is the primary hepatic hydrolase and is responsible for metabolizing many medications. Its clinical significance has been well demonstrated in various clinical trials with methylphenidate, clopidogrel, oseltamivir, and ACE inhibitors. CES1 expression varies markedly among individuals, which is a major contributing factor to the interindividual variability in the pharmacokinetics and pharmacodynamics of CES1 substrate drugs. Identifying genetic variants responsible for CES1 expression regulation could lead to the optimization of pharmacotherapy of medications metabolized by CES1. The analysis of allele‐specific expression is a powerful tool to determine regulatory genetic variants because this assay is less susceptible to the confounding effect of nongenetic factors (e.g. inducers) compared to those conventional gene expression‐based approaches (e.g. GWAS). In the present study, we developed a targeted proteomics method to measure the allele‐specific protein expression (ASPE) of CES1 in human livers using a heavy stable isotope‐labeled QconCAT internal standard. The CES1 nonsynonymous variant S75N (rs2307240) was utilized to differentiate the protein expressions of the two CES1 alleles. The QconCAT construct contained the sequences of both the wild type peptide (FTPPQPAEPW SFVK) and its S75N variant (FTPPQPAEPW NFVK), which ensures that the amounts of the two peptides released from the QconCAT internal standard are equal. Another essential feature of the QconCAT protein is that the 75S and 75N peptides are flanked with several native CES1 amino acids, such that the digestion efficiency of the QconCAT peptides will be the same of the native CES1. The method was successfully applied to the determination of the ASPE of CES1 in 32 S75N heterozygous liver samples. The ASPE ratios of 75S to 75N ranged from 0.80 to 1.47, indicating the presence of cis‐regulatory genetic variants of the CES1 gene. Further studies have been carried out to determine the variants responsible for the imbalanced ASPE of CES1 in human livers.Support or Funding InformationThis work was supported in part by the National Heart, Lung, and Blood Institute [Grant R01HL126969]

A host gene expression approach for identifying triggers of asthma exacerbations.

RationaleAsthma exacerbations often occur due to infectious triggers, but determining whether infection is present and whether it is bacterial or viral remains clinically challenging. A diagnostic strategy that clarifies these uncertainties could enable personalized asthma treatment and mitigate antibiotic overuse.ObjectivesTo explore the performance of validated peripheral blood gene expression signatures in discriminating bacterial, viral, and noninfectious triggers in subjects with asthma exacerbations.MethodsSubjects with suspected asthma exacerbations of various etiologies were retrospectively selected for peripheral blood gene expression analysis from a pool of subjects previously enrolled in emergency departments with acute respiratory illness. RT-PCR quantified 87 gene targets, selected from microarray-based studies, followed by logistic regression modeling to define bacterial, viral, or noninfectious class. The model-predicted class was compared to clinical adjudication and procalcitonin.ResultsOf 46 subjects enrolled, 7 were clinically adjudicated as bacterial, 18 as viral, and 21 as noninfectious. Model prediction was congruent with clinical adjudication in 15/18 viral and 13/21 noninfectious cases, but only 1/7 bacterial cases. None of the adjudicated bacterial cases had confirmatory microbiology; the precise etiology in this group was uncertain. Procalcitonin classified only one subject in the cohort as bacterial. 47.8% of subjects received antibiotics.ConclusionsOur model classified asthma exacerbations by the underlying bacterial, viral, and noninfectious host response. Compared to clinical adjudication, the majority of discordances occurred in the bacterial group, due to either imperfect adjudication or model misclassification. Bacterial infection was identified infrequently by all classification schemes, but nearly half of subjects were prescribed antibiotics. A gene expression-based approach may offer useful diagnostic information in this population and guide appropriate antibiotic use.

Abstract P3-07-31: Immune activation signatures identify a subset of ER+ breast cancers with increased pathologic complete response to neoadjuvant chemotherapy

Abstract Background: Proliferation is the strongest predictor of response to neoadjuvant chemotherapy in estrogen receptor-positive (ER+) breast cancer. Evidence of immune activation has also been associated with improved response to neoadjuvant chemotherapy in breast cancer. We hypothesized that immune signatures may be associated with response independent of proliferation in ER+ breast cancers. Approach: We compiled microarray expression data from breast cancer biopsies obtained prior to neoadjuvant chemotherapy on 465 ER-positive/HER2-negative patients by reported pathologic receptor status. We evaluated the association of 118 published gene expression signatures with response to neoadjuvant chemotherapy, based on study-defined pathologic complete response (pCR) versus residual disease (RD). Results: Overall, 42 of 118 signatures were significantly associated with response to neoadjuvant chemotherapy in ER+ breast cancer (FDR-corrected p<0.05, simple logistic regression). Of those signatures that achieved significance, 52% (22/42) of signatures were proliferation-associated based on correlation to the 11-gene PAM50 proliferation index (Pearson's R2>0.30, p<1e-10). Among signatures that were NOT proliferation-associated, 50% (10/20) were immune-related. Using unsupervised hierarchical clustering of all 118 signatures, these ten immune signatures formed a distinct cluster. Of the 10 signatures, nine were designed to reflect "immune activation" and were highly correlated with each other in ER+ tumors (R2>0.4, p<0.001). The mean of each of these nine signatures was significantly higher in patients with pCR versus RD (FDR-corrected p<0.05, t-test). Patients with higher "immune activation" signatures had increased likelihood of pCR within multiple subgroups of ER+ breast cancer, including luminal B and non-luminal PAM50 subgroups, as well as intermediate- and high-proliferation ER+ breast cancers. For luminal A or low-proliferation breast cancers, "immune activation" signatures were not significantly associated with response, though very few patients achieved pCR in these two subgroups. Conclusions: Gene expression signatures associated with "immune activation" identify a subset of ER+ breast cancers with higher rates of pCR to neoadjuvant chemotherapy. These "immune activation" signatures appear to be proliferation-independent and may provide additional predictive information to existing gene expression-based approaches for ER+ breast cancer. Citation Format: Stover DG, Waks AG, Erica ML, Brugge JS, Winer EP, Selfors LM. Immune activation signatures identify a subset of ER+ breast cancers with increased pathologic complete response to neoadjuvant chemotherapy. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-07-31.

Cell type-specific transcriptome profiling in mammalian brains.

A mammalian brain contains numerous types of cells. Advances in neuroscience in the past decade allow us to identify and isolate neural cells of interest from mammalian brains. Recent developments in high-throughput technologies, such as microarrays and next-generation sequencing (NGS), provide detailed information on gene expression in pooled cells on a genomic scale. As a result, many novel genes have been found critical in cell type-specific transcriptional regulation. These differentially expressed genes can be used as molecular signatures, unique to a particular class of neural cells. Use of this gene expression-based approach can further differentiate neural cell types into subtypes, potentially linking some of them with neurological diseases. In this article, experimental techniques used to purify neural cells are described, followed by a review on recent microarray- or NGS-based transcriptomic studies of common neural cell types. The future prospects of cell type-specific research are also discussed.

Genome-driven integrated classification of breast cancer validated in over 7,500 samples.

BackgroundIntClust is a classification of breast cancer comprising 10 subtypes based on molecular drivers identified through the integration of genomic and transcriptomic data from 1,000 breast tumors and validated in a further 1,000. We present a reliable method for subtyping breast tumors into the IntClust subtypes based on gene expression and demonstrate the clinical and biological validity of the IntClust classification.ResultsWe developed a gene expression-based approach for classifying breast tumors into the ten IntClust subtypes by using the ensemble profile of the index discovery dataset. We evaluate this approach in 983 independent samples for which the combined copy-number and gene expression IntClust classification was available. Only 24 samples are discordantly classified. Next, we compile a consolidated external dataset composed of a further 7,544 breast tumors. We use our approach to classify all samples into the IntClust subtypes. All ten subtypes are observable in most studies at comparable frequencies. The IntClust subtypes are significantly associated with relapse-free survival and recapitulate patterns of survival observed previously. In studies of neo-adjuvant chemotherapy, IntClust reveals distinct patterns of chemosensitivity. Finally, patterns of expression of genomic drivers reported by TCGA (The Cancer Genome Atlas) are better explained by IntClust as compared to the PAM50 classifier.ConclusionsIntClust subtypes are reproducible in a large meta-analysis, show clinical validity and best capture variation in genomic drivers. IntClust is a driver-based breast cancer classification and is likely to become increasingly relevant as more targeted biological therapies become available.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0431-1) contains supplementary material, which is available to authorized users.

Protein-Protein Interaction Detection: Methods and Analysis

Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.

Genome-driven integrated classification of breast cancer validated in over 7,500 samples

IntClust is a classification of breast cancer comprising 10 subtypes based on molecular drivers identified through the integration of genomic and transcriptomic data from 1,000 breast tumors and validated in a further 1,000. We present a reliable method for subtyping breast tumors into the IntClust subtypes based on gene expression and demonstrate the clinical and biological validity of the IntClust classification. We developed a gene expression-based approach for classifying breast tumors into the ten IntClust subtypes by using the ensemble profile of the index discovery dataset. We evaluate this approach in 983 independent samples for which the combined copy-number and gene expression IntClust classification was available. Only 24 samples are discordantly classified. Next, we compile a consolidated external dataset composed of a further 7,544 breast tumors. We use our approach to classify all samples into the IntClust subtypes. All ten subtypes are observable in most studies at comparable frequencies. The IntClust subtypes are significantly associated with relapse-free survival and recapitulate patterns of survival observed previously. In studies of neo-adjuvant chemotherapy, IntClust reveals distinct patterns of chemosensitivity. Finally, patterns of expression of genomic drivers reported by TCGA (The Cancer Genome Atlas) are better explained by IntClust as compared to the PAM50 classifier. IntClust subtypes are reproducible in a large meta-analysis, show clinical validity and best capture variation in genomic drivers. IntClust is a driver-based breast cancer classification and is likely to become increasingly relevant as more targeted biological therapies become available.

Prognostic Impact of Ikaros (IKZF1) Gene Alterations In Childhood ALL Treated with ALL IC-BFM 2002 Protocol: A Comparison of Gene Expression and Genomic-Based Methods.

AbstractAbstract 1656 Introduction:Recently, Ikaros (IKZF1) gene alterations were found to predict poor prognosis in childhood acute lymphoblastic leukemia (ALL). Thus, the implementation of IKZF1 status into the risk group stratification is discussed. So far, limited data are available concerning both IKZF1 importance in different treatment protocols for Ph-negative ALL and the choice of the best diagnostic method. In this study, we compared two methods based on either genomic DNA examination or gene expression analysis, and their prognostic impact within a treatment protocol for childhood ALL. Methods:Gene expression of functional (IK1, IK2) and non-DNA binding (IK4, IK6, IK8) IKZF1 isoforms was semi-quantitatively evaluated using Lab-on-a-chip (Agilent) electrophoresis and reported either as an absolute level or relatively to the total isoform signal. The thresholds for abnormal gene expression were set based on the analysis of peripheral blood (PB) of healthy donors, remission bone marrow (BM) samples of children with ALL, and sorted B- and T-cell precursor subpopulations. MLPA (multiplex ligation-dependent probe amplification) reaction including probes for Ikaros exons 1 to 8 was performed on BM DNA samples and the products were analyzed with the Coffalyser v9.4 software. Results:Results of both gene expression and MLPA analysis in the BM were available for 120 of 193 children diagnosed with ALL between 2002 and 2005. MLPA analysis revealed a deletion of at least one exon of IKZF1 gene in 17/120 (14%) patients. Of note, two patients with this deletion underwent a lineage switch (LS) from ALL to AML during the induction treatment. The entire IKZF1 gene was mononoallelically deleted in 3 patients. The ratio between non-DNA binding (IK4, IK4del, IK4A, IK6, Ik6del, IK8) and functional (IK1 and IK2) isoform expression was significantly elevated (non-DNA binding isoforms>70%) in 21 of 120 (18%) patients. The expression of a dominant-negative IK6 isoform was significantly elevated (>20% of total) in 7 of 120 (6%) patients. Surprisingly, gene deletion on one allele was not accompanied by decrease of total IKZF1 gene expression. On the contrary, patients with IKZF1 deletion had higher total IKZF1 transcript level than those without deletion (p=0.008, Mann Whitney), but it was not possible to set any reliable expression threshold for the prediction of gene deletion. The change on a DNA level was not always reflected in relative gene expression: Of 17 patients with gene deletion, only 7 had significantly altered short/long isoform ratio and 5 patients had an increased expression of IK6. Conversely, of 7 patients with IK6 overexpression, two patients had no DNA alteration, suggesting a different mechanism of altered gene expression. We next evaluated the prognostic impact of IKZF1 alterations in 113 patients treated with ALL IC-BFM 2002 protocol (5 patients were excluded due to Ph-positive ALL with imatinib-based treatment and 2 patients due to treatment change after LS). Patients with IKZF1 gene deletion had significantly worse relapse-free survival (RFS) than other patients (5-year RFS 50.0±14.4% vs. 90.8±2.9%, p=0.0002). The presence of IKZF1 deletion did not correlate with minimal residual disease (MRD) during induction treatment (days 8, 15, 33), neither in BM nor in PB. Patients with IK6 overexpression had 5-year RFS 50.0±20.4% compared to 88.4± 3.2% in those with low IK6 expression (p=0.004). The elevated short/long isoform ratio (>70%) had no prognostic impact. The best prediction of relapse was achieved via combining two factors: the presence of IKZF1 deletion detected by MLPA or the relative IK6 overexpression (>50% of total isoform signal). The 5-year RFS was 50.0±13.4% for this group (14 pts, 7 relapses) compared to 91.6±2.8% for other patients (99 pts, 8 relapses, p<0.0001). Conclusion:This study confirmed that the presence of Ikaros gene alterations was connected with a high risk of relapse also in a BFM-based protocol for Ph-negative childhood ALL treatment. The deletion within IKZF1 locus did not necessarily correlate with an altered Ikaros gene expression. Ideally, both genomic and gene expression-based approach should be applied together for the evaluation of prognosis. However, if this is not possible, the examination of DNA changes by MLPA identifies more patients who subsequently relapse than the gene expression-based approach. Support:VZ MSM 0021620813, P301/10/1877, IGA NS/10472-3 Disclosures:No relevant conflicts of interest to declare.

Transcriptome analysis reveals differentially expressed genes associated with the mantled homeotic flowering abnormality in oil palm (Elaeis guineensis)

Micropropagation of oil palm by somatic embryogenesis produces a proportion of off-type individuals (approximately 5% overall) displaying a homeotically modified flower structure known as mantled. Transformation of the fertile or sterile androecium into carpel-like structures is observed in staminate and pistillate mantled flowers, respectively, resulting in lower oil yields in affected plantations. Given the epigenetic nature of the mantled condition, a gene expression-based approach was used rather than a genetic one to investigate its molecular basis. Suppression subtractive hybridisation (SSH) and macroarray hybridisation were used to compare transcriptome patterns between normal and mantled inflorescences. Two SSH libraries, enriched for complementary deoxyribonucleic acids (cDNAs) of either true-to-type or somaclonal variant material, were generated. Bioinformatic analysis of these two libraries allowed the identification of 1,350 unique sequences and their annotation by a gene ontology-based approach. Macroarray hybridisation was used to compare gene expression between normal and mantled inflorescences, and 32 genes were found to be differentially expressed. The temporal expression patterns of six genes were further investigated in more detail in relation to male and female inflorescence development. Full-length cDNAs were isolated and characterised for two of these genes, EgFB1 and EgRING1, both of which are down-regulated in the mantled inflorescences and both of which encode proteins associated with proteolytic signalling complexes. Our data shed light on gene expression changes associated with the mantled phenotype and have provided novel transcriptome markers which can help to distinguish the abnormal and wild-type inflorescences.

Sepsis-induced myocardial depression is associated with transcriptional changes in energy metabolism and contractile related genes: A physiological and gene expression-based approach*

Increased nitric oxide production and altered mitochondrial function have been implicated in sepsis-induced cardiac dysfunction. The molecular mechanisms underlying myocardial depression in sepsis and the contribution of nitric oxide in this process however, are incompletely understood. To assess the transcriptional profile associated with sepsis-induced myocardial depression in a clinically relevant mouse model, and specifically test the hypothesis that critical transcriptional changes are inducible nitric oxide synthase-dependent. Laboratory investigation. University affiliated research laboratory. C57/BL6 wild type and congenic B6 129P2-Nos2tm1Lau/J (iNOS) mice. Assessment of myocardial function after 48 hrs of induction of polymicrobial sepsis by caecal ligation and perforation. We compared the myocardial transcriptional profile in C57/BL6 wild type mice and congenic B6 129P2-Nos2tm1Lau/J litter mates after 48 hrs of polymicrobial sepsis induced by caecal ligation and perforation. Profiling of 22,690 expressed sequence tags by gene set enrichment analysis demonstrated that inducible nitric oxide synthase -/- failed to down regulate critical bioenergy and metabolism related genes including the gene for peroxisome proliferator-activated receptor gamma coactivator 1. Bioinformatics analysis identified a striking concordance in down regulation of transcriptional activity of proliferator-activated receptor gamma coactivator 1-related transcription factors resulting in sepsis associated myocardial remodeling as shown by isoform switching in the expression of contractile protein myosin heavy chain. In inducible nitric oxide synthase -/- deficient mice, contractile depression was minimal, and the transcriptional switch was absent. Metabolic and myosin isoform gene expression switch in sepsis-induced myocardial depression is inducible nitric oxide synthase-dependent. Furthermore, we suggest that the molecular switch favoring the expression of fetal isoforms of contraction related proteins is associated with regulation of proliferator-activated receptor gamma coactivator 1 and related transcription factors in an inducible nitric oxide synthase-dependent manner.

Genomic approaches to small molecule discovery

With the sequencing of the human genome and the development of new genomic technologies, biomedical discovery has been transformed. The applications of these new approaches are ever-expanding from disease classification, to identification of new targets, to outcome prediction. A logical next step is the integration of genomic approaches into small molecule discovery. This review will focus on the application of genomics to compound discovery, with an emphasis on the hematological malignancies. It will focus on the use of genomic tools to discover cancer targets and the development and application of both cell-based and in silico gene expression-based approaches to small molecule discovery.

Signature-Based Small Molecule Screening Identifies Cytosine Arabinoside as an EWS/FLI Modulator in Ewing Sarcoma

BackgroundThe presence of tumor-specific mutations in the cancer genome represents a potential opportunity for pharmacologic intervention to therapeutic benefit. Unfortunately, many classes of oncoproteins (e.g., transcription factors) are not amenable to conventional small-molecule screening. Despite the identification of tumor-specific somatic mutations, most cancer therapy still utilizes nonspecific, cytotoxic drugs. One illustrative example is the treatment of Ewing sarcoma. Although the EWS/FLI oncoprotein, present in the vast majority of Ewing tumors, was characterized over ten years ago, it has never been exploited as a target of therapy. Previously, this target has been intractable to modulation with traditional small-molecule library screening approaches. Here we describe a gene expression–based approach to identify compounds that induce a signature of EWS/FLI attenuation. We hypothesize that screening small-molecule libraries highly enriched for FDA-approved drugs will provide a more rapid path to clinical application.Methods and FindingsA gene expression signature for the EWS/FLI off state was determined with microarray expression profiling of Ewing sarcoma cell lines with EWS/FLI-directed RNA interference. A small-molecule library enriched for FDA-approved drugs was screened with a high-throughput, ligation-mediated amplification assay with a fluorescent, bead-based detection. Screening identified cytosine arabinoside (ARA-C) as a modulator of EWS/FLI. ARA-C reduced EWS/FLI protein abundance and accordingly diminished cell viability and transformation and abrogated tumor growth in a xenograft model. Given the poor outcomes of many patients with Ewing sarcoma and the well-established ARA-C safety profile, clinical trials testing ARA-C are warranted.ConclusionsWe demonstrate that a gene expression–based approach to small-molecule library screening can identify, for rapid clinical testing, candidate drugs that modulate previously intractable targets. Furthermore, this is a generic approach that can, in principle, be applied to the identification of modulators of any tumor-associated oncoprotein in the rare pediatric malignancies, but also in the more common adult cancers.

Gene expression-based approaches to beef quality research

Advances in mammalian genomics have permitted the application of gene expression profiling approaches to gene discovery for meat quality traits in cattle. The first custom cDNA microarray based on the transcriptome of bovine muscle and fat tissue was developed and applied to animal experimentation and cell culture experimentation between 1999 and 2005. Complementary DNA microarray tools for beef quality research were developed in parallel with bioinformatics tools that permit the analysis of microarray data obtained from complex experimental designs commonly encountered in large animal research. In addition, tools were designed to link gene expression data with gene function in the bovine, such as in vitro models of bovine adipogenesis and bioinformatics tools to map gene networks from expression data. The application of these genomics tools to the study of beef quality has yielded novel knowledge of genes and molecules involved in the processes of intramuscular adipogenesis and protein turnover. This review summarises the current state of knowledge and important lessons derived from bovine genomics initiatives in Australia and around the world.