Enhanced Survival of High-Risk Medulloblastoma-Bearing Mice after Multimodal Treatment with Radiotherapy, Decitabine, and Abacavir

Children with high-risk SHH/TP53-mut and Group 3 medulloblastoma (MB) have a 5-year overall survival of only 40%. Innovative approaches to enhance survival while preventing adverse effects are urgently needed. We investigated an innovative therapy approach combining irradiation (RT), decitabine (DEC), and abacavir (ABC) in a patient-derived orthotopic SHH/TP53-mut and Group 3 MB mouse model. MB-bearing mice were treated with DEC, ABC and RT. Mouse survival, tumor growth (BLI, MRT) tumor histology (H/E), proliferation (Ki-67), and endothelial (CD31) staining were analyzed. Gene expression was examined by microarray and RT-PCR (Ki-67, VEGF, CD31, CD15, CD133, nestin, CD68, IBA). The RT/DEC/ABC therapy inhibited tumor growth and enhanced mouse survival. Ki-67 decreased in SHH/TP53-mut MBs after RT, DEC, RT/ABC, and RT/DEC/ABC therapy. CD31 was higher in SHH/TP53-mut compared to Group 3 MBs and decreased after RT/DEC/ABC. Microarray analyses showed a therapy-induced downregulation of cell cycle genes. By RT-PCR, no therapy-induced effect on stem cell fraction or immune cell invasion/activation could be shown. We showed for the first time that RT/DEC/ABC therapy improves survival of orthotopic SHH/TP53-mut and Group 3 MB-bearing mice without inducing adverse effects suggesting the potential for an adjuvant application of this multimodal therapy approach in the human clinic.

Integrative transcriptomic analysis of pancreatic islets from patients with prediabetes/type 2 diabetes

AIM

To identify new transcriptomic alterations in pancreatic islets associated with metabolic dysfunctions in people with prediabetes (PD)/type 2 diabetes (T2D).

MATERIALS AND METHODS

We collected information from public data repositories T2D related microarray datasets from pancreatic islets. We identified Differential Expressed Genes (DEGs) in non-diabetic (ND) vs people with T2D in each study. To identify relevant DEGs in T2D, we selected those that varied consistently in the different studies for further meta-analysis and functional enrichment analysis. DEGs were also evaluated at the PD stage.

RESULTS

A total of seven microarray datasets were collected and analysed to find the DEGs in each study and meta-analysis was performed with 245 ND and 96 T2D cases. We identified 55 transcriptional alterations potentially associated with specific metabolic dysfunctions in T2D. Meta-analysis showed that 87% of transcripts identified as DEGs (48 out of 55) were confirmed as having statistically significant up- or down-modulation in T2D compared to ND. Notably, nine of these DEGs have not been previously reported as dysregulated in pancreatic islets from people with T2D. Consistently, the most significantly enriched pathways were related to the metabolism and/or development/maintenance of β-cells. Eighteen of the 48 selected DEGs (38%) showed an altered expression in islets from people with PD.

CONCLUSIONS

These results provide new evidence to interpret the pathogenesis of T2D and the transition from PD to T2D. Further studies are necessary to validate its potential use for the development/implementation of efficient new strategies for the prevention, diagnosis/prognosis and treatment of T2D.

High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery

The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on “-omics” have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.

Identification of differentially expressed genes in primary Sjögren's syndrome

Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease that affects exocrine glands. To study the molecular mechanism and identify crucial genes/pathways in pSS pathogenesis, the microarray-based whole-genome gene expression profiles from salivary glands of patients with pSS and non-sicca controls were retrieved. After normalization and subsequent batch effect adjustment, significance analysis of microarrays method was applied to five available datasets, and 379 differentially expressed genes (DEGs) were identified. The 300 upregulated DEGs were enriched in Gene Ontology terms of immune and inflammatory responses, including antigen processing and presentation, interferon-mediated signaling pathway, and chemotaxis. Previously reported pSS-associated genes, including HLA-DRA, TAP2, PRDM1, and IFI16, were found to be significantly upregulated. The downregulated DEGs were enriched in pathways of salivary secretion, carbohydrate digestion and absorption, and starch and sucrose metabolism, implying dysfunction of salivary glands during pathogenesis. Next, a protein-protein interaction network was constructed, and B2M, an upregulated DEG, was shown to be a hub, suggesting its potential involvement in pSS development. In summary, we found the activation of pSS-associated genes in pathogenesis, and provide clues for salivary glands dysfunction. Experimental investigation on the identified DEGs in this study will deepen our understanding on pSS.

Effects of cross-fostering and developmental exposure to mixtures of environmental contaminants on hepatic gene expression in prepubertal 21 days old and adult male Sprague-Dawley rats

The notion that adverse health effects produced by exposure to environmental contaminants (EC) may be modulated by the presence of non-chemical stressors is gaining attention. Previously, our lab demonstrated that cross-fostering (adoption of a litter at birth) acted as a non-chemical stressor that amplified the influence of developmental exposure to EC on the glucocorticoid stress-response in adult rats. Using liver from the same rats, the aim of the current study was to investigate whether cross-fostering might also modulate EC-induced alterations in hepatic gene expression profiles. During pregnancy and nursing, Sprague-Dawley dams were fed cookies laced with corn oil (control, C) or a chemical mixture (M) composed of polychlorinated biphenyls (PCB), organochlorine pesticides (OCP), and methylmercury (MeHg), at 1 mg/kg/day. This mixture simulated the contaminant profile reported in maternal human blood. At birth, some control and M treated litters were cross-fostered to form two additional groups with different biological/nursing mothers (CC and MM). The hepatic transcriptome was analyzed by DNA microarray in male offspring at postnatal days 21 and 78-86. Mixture exposure altered the expression of detoxification and energy metabolism genes in both age groups, but with different sets of genes affected at day 21 and 78-86. Cross-fostering modulated the effects of M on gene expression pattern (MM vs M), as well as expression of energy metabolism genes between control groups (CC vs C). In conclusion, while describing short and long-term effects of developmental exposure to EC on hepatic transcriptomes, these cross-fostering results further support the consideration of non-chemical stressors in EC risk assessments.

RORB and RORC associate with human islet dysfunction and inhibit insulin secretion in INS-1 cells

Little is known about the expression and function of Retinoic acid-related orphan receptors (RORA, B, and C) in pancreatic β cells. Here in, we utilized cDNA microarray and RNA sequencing approaches to investigate the expression pattern of ROR receptors in normal and diabetic human pancreatic islets. Possible correlations between RORs expression and HbA_1c levels as well as insulin secretory capacity in isolated human islets were evaluated. The impact of RORB and RORC expression on insulin secretion in INS-1 (832/13) cells was validated as well. While RORA was the highest expressed gene among the three RORs in human islet cells, RORC was the highest expressed in INS-1 cells (832/13) and while RORB was the lowest expressed gene in human islet cells, RORA was the highest expressed in INS-1 cells (832/13). The expression of RORB and RORC was significantly lower in diabetic/hyperglycemic donors as compared with non-diabetic counterparts. Furthermore, while the expression of RORB correlated positively with insulin secretion and negatively with HbA_1c, that of RORC correlated negatively with HbA_1c. The expression pattern of RORA did not correlate with either of the two parameters. siRNA silencing of RORB or RORC in INS-1 (832/13) cells resulted in a significant downregulation of insulin mRNA expression and insulin secretion. These findings suggest that RORB and RORC are part of the molecular cascade that regulates insulin secretion in pancreatic β cells; and insight that provides for further work on the potential therapeutic utility of RORB and RORC genes in β cell dysfunction in type 2 diabetes.

Key Immunological Functions Involved in the Progression of Epithelial Ovarian Serous Carcinoma Discovered by the Gene Ontology-Based Immunofunctionome Analysis

Serous carcinoma (SC) is the most common and lethal subtype of epithelial ovarian carcinoma; immunotherapy is a potential treatment for SC, however, the global immunological functions of SC as well as their change during the progression of SC have not been investigated in detail till now. We conducted a genome-wide integrative analysis to investigate the immunofunctionomes of SC at four tumor stages by quantifying the immunological functions defined by the Gene Ontology gene sets. DNA microarray gene expression profiles of 1100 SCs and 136 normal ovarian tissue controls were downloaded from the Gene Expression Omnibus database and converted to the functionome. Then the immunofunctionomes were reconstructed by extracting the offspring from the functionome for the four SC staging groups. The key immunological functions extracted from immunofunctionomes with a series of filters revealed that the immunopathy of SC consisted of a group of deregulated functions with the core members including B cell activation and differentiation, regulation of leukocyte chemotaxis/cellular extravasation, antigen receptor mediated signaling pathway, T helper mediated immunity and macrophage activation; and the auxiliary elements included leukocyte mediated immunity, regulation of inflammatory response, T cell differentiation, mononuclear cell migration, megakaryocyte differentiation, complement activation and cytokine production. These deregulated immunological functions reveal the candidates to target in the immunotherapy.

Identification of lncRNAs via microarray analysis for predicting HER2-negative breast cancer response to neoadjuvant chemotherapy

Mortality is high in patients with locally advanced HER2-negative breast cancer, especially those with residual tumor after neoadjuvant chemotherapy (NAC). Tissue-specific long non-coding RNAs (lncRNAs) are responsible for specific breast cancer subtypes. To identify the lncRNAs involved in residual cancer tissues (RCTs) and to evaluate their potential for predicting HER2-negative breast cancer response to NAC, we used three paired tissues to compare differences in gene expression between RCTs and remittent tissues (RTs) after NAC in human HER2-negative breast cancer. Subsequently, we detected expression of the top ten up-regulated and down-regulated lncRNAs in 11 paired tissues via quantitative RT-PCR analysis. Finally, we explored the potential function of these dysregulated lncRNAs through bioinformatics analysis. Our results indicate that 1348 mRNAs and 183 lncRNAs were differentially expressed in RCTs compared with RTs, and the expression levels of four novel lncRNAs (DSCAM-AS1, LINC01508, lnc-MGST1-2 and lnc-BTG2-2) were in agreement with the microarray analysis results. Furthermore, we found that the expression level of LINC01508 was significantly related to poor prognosis, suggesting that LINC01508 is a potential biomarker for predicting breast cancer response to NAC, which might be helpful in exploring potential diagnostic factors and therapeutic targets for chemo-resistant HER2-negative breast cancer.

Integrating the dysregulated inflammasome-based molecular functionome in the malignant transformation of endometriosis-associated ovarian carcinoma

The coexistence of endometriosis (ES) with ovarian clear cell carcinoma (CCC) or endometrioid carcinoma (EC) suggested that malignant transformation of ES leads to endometriosis associated ovarian carcinoma (EAOC). However, there is still lack of an integrating data analysis of the accumulated experimental data to provide the evidence supporting the hypothesis of EAOC transformation. Herein we used a function-based analytic model with the publicly available microarray datasets to investigate the expression profiling between ES, CCC, and EC. We analyzed the functional regularity pattern of the three type of samples and hierarchically clustered the gene sets to identify key mechanisms regulating the malignant transformation of EAOC. We identified a list of 18 genes (NLRP3, AIM2, PYCARD, NAIP, Caspase-4, Caspase-7, Caspase-8, TLR1, TLR7, TOLLIP, NFKBIA, TNF, TNFAIP3, INFGR2, P2RX7, IL-1B, IL1RL1, IL-18) closely related to inflammasome complex, indicating an important role of inflammation/immunity in EAOC transformation. We next explore the association between these target genes and patient survival using Gene Expression Omnibus (GEO), and found significant correlation between the expression levels of the target genes and the progression-free survival. Interestingly, high expression levels of AIM2 and NLRP3, initiating proteins of inflammasomes, were significantly correlated with poor progression-free survival. Immunohistochemistry staining confirmed a correlation between high AIM2 and high Ki-67 in clinical EAOC samples, supporting its role in disease progression. Collectively, we established a bioinformatic platform of gene-set integrative molecular functionome to dissect the pathogenic pathways of EAOC, and demonstrated a key role of dysregulated inflammasome in modulating the malignant transformation of EAOC.

Molecular Elucidation of Biological Response to Mesoporous Silica Nanoparticles in Vitro and in Vivo

Biomedical applications of mesoporous silica nanoparticles (MSNs) require efficient cellular uptake and low toxicity. The purpose of this study is to investigate the cellular uptake and toxicity of MSNs with different sizes and charges (50, 100, and 250 nm with a positive surface charge and 100 nm with a negative surface charge) exposed to human monocyte-derived macrophages, lung epithelium BEAS-2B cells, and mice using genome-wide gene expression analysis and cellular/animal-level end point tests. We found that MSNs can be taken up into cells through endocytosis in a charge- and size-dependent manner, with positively charged and larger MSNs being more easily taken up into the cells by recruiting more types of endocytotic pathways for more cellular uptake. Moreover, the cytotoxicity of MSNs could be correlated with the amount of MSNs taken up by cells, which positively correlates to the particle size and dosage. Therefore, only positively charged and larger MSNs (≥100 nm) during higher treatment doses (≥500 μg mL^-1) resulted in a sufficient accumulation of internalized MSNs in cells to induce significant release of reactive oxygen species (ROS) and oxidative stress, inflammatory gene upregulation through NF-κB and AP-1, and eventually autophagy-mediated necrotic cell death. Furthermore, genome-wide gene expression analysis could reflect the above in vitro cellular damages and corresponding in vivo injuries in mice, indicating that specific gene expression footprints may be used for assessing the safety of nanoparticles. The present finding provides some insights into the rational design of effective MSN-based drug/gene delivery systems and biomedical applications.

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

Downregulation of genes related to immune and inflammatory response in IVF implantation failure cases under controlled ovarian stimulation

PROBLEM

Implantation failure (IF) even after the good-quality embryo transfer (ET) is main obstacle in in vitro fertilization (IVF). We aim to study the genomics of endometrial receptivity in IF patients under controlled ovarian stimulation (COS) during which ET is generally practised in IVF.

METHOD OF STUDY

Endometrial gene expression profiling in IF patients (n=10) and oocyte donors (n=8) were compared during window of implantation under COS by microarray. Enrichment analysis of microarray data was performed to determine dysregulated pathways. Microarray results were validated by real-time PCR. Localization of genes related to immune response (progestagen-associated endometrial protein (PAEP), leukaemia inhibitory factor (LIF), interleukin-6 signal transducer (IL6ST) was detected by immunohistochemistry.

RESULTS

The gene ontology, pathway analysis and enrichment mapping revealed significant downregulation in activation and regulation of immune and inflammation response in IF patients under COS. The lower expression of PAEP, LIF and IL6ST in cases compared to controls by real time and immunohistochemistry suggests the functional importance of these genes.

CONCLUSION

Importance of immune and inflammatory response in endometrial receptivity adds on to the current knowledge of gene expression profile in IF under COS. The panel of genes involved in these pathways would be useful in determining further line of treatment for IF during IVF.

Overexpression of CtCHS1 Increases Accumulation of Quinochalcone in Safflower

Carthami flos, the dried petal of safflower (Carthamus tinctorius L.) has been widely used in traditional Chinese medicine to treat cardiovascular and cerebrovascular diseases, in which quinochalcone glucosides such as hydrosafflower yellow A (HSYA), carthamin are uniquely present and have been identified as active compounds. In the present study, through sequencing of a safflower floret cDNA library and subsequent microarray analysis, we found 23 unigenes (5 PALs, 1 C4Hs, 5 4CLs, 6 CHSs, 2 CHIs, 2 DFRs, 2 FLSs) involved in flavonoid pathway, of which 4 were up-regulated differentially during quinochalcone glucosides accumulation with the floret developing stage. The up-regulated genes were verified by PCR methods. Considering chalcone synthase are entry enzyme in flavonoid biosynthesis, CHS1 was focused on target gene to verify its function furtherly. Bioinformation analysis showed that CHS1 shared 86.94% conserved residues with CHS from other plants. Subcellular localization showed that CtCHS1 was localized in cytoplasm in onion epidermal cells. The transgenic safflower plant with overexpression CtCHS1 by Agrobacterium-mediated pollen-tube pathway method was firstly generated. The results present that expression of PAL2, PAL3, CHS1, CHS4, CHS6 increased and expression of CHI1 and CHI2 decreased in the transgenic plant floret. Meanwhile, the accumulation of quinochalcone glucosides increased by ∼20-30% and accumulation of quercetin-3-β-D-glucoside and quercetin decreased by 48 and 63% in the transgenic plant floret. These results suggested that CtCHS1 played an important role in quinochalcone glucosides biosynthesis rather than flavonol biosynthesis. These results also demonstrated that the pollen-tube pathway method was an efficient method for gene transformation in safflower. Our study will provide a deep understanding of potential synthetic genes involved in quinochalcone biosynthetic pathway.

Transcriptome analysis of equine sarcoids

Equine sarcoids are the most commonly detected skin tumours in Equidae. In the present research, a comparative transcriptomic analysis was performed which aimed at looking inside a tumour biology and identification of the expression profile as a potential source of cancer specific genes useful as biomarkers. We have used Horse Gene Expression Microarray data from matched equine sarcoids and tumour-distant skin samples. In total, 901 significantly differentially expressed genes (DEGs) between lesional and healthy skin samples have been identified (fold change ≥ 2; P < 0.05). The large subset of DEGs, with decreased expression, was associated with a suppression of malignant transformation, whereas several overexpressed genes were involved in the processes associated with growth and progression of a tumour or immune system activity. Our results, as a first to date, showed comprehensive transcriptome analysis of skin tumour in horses and pinpointed significant pathways and genes related with oncogenesis processes.

Gene Set-Based Integrative Analysis Revealing Two Distinct Functional Regulation Patterns in Four Common Subtypes of Epithelial Ovarian Cancer

Clear cell (CCC), endometrioid (EC), mucinous (MC) and high-grade serous carcinoma (SC) are the four most common subtypes of epithelial ovarian carcinoma (EOC). The widely accepted dualistic model of ovarian carcinogenesis divided EOCs into type I and II categories based on the molecular features. However, this hypothesis has not been experimentally demonstrated. We carried out a gene set-based analysis by integrating the microarray gene expression profiles downloaded from the publicly available databases. These quantified biological functions of EOCs were defined by 1454 Gene Ontology (GO) term and 674 Reactome pathway gene sets. The pathogenesis of the four EOC subtypes was investigated by hierarchical clustering and exploratory factor analysis. The patterns of functional regulation among the four subtypes containing 1316 cases could be accurately classified by machine learning. The results revealed that the ERBB and PI3K-related pathways played important roles in the carcinogenesis of CCC, EC and MC; while deregulation of cell cycle was more predominant in SC. The study revealed that two different functional regulation patterns exist among the four EOC subtypes, which were compatible with the type I and II classifications proposed by the dualistic model of ovarian carcinogenesis.

A seven-gene signature predicts overall survival of patients with colorectal cancer

Colorectal cancer (CRC) is a major cause of global cancer mortality. Gene expression profiles can help predict prognosis of patients with CRC. In most of previous studies, disease recurrence was analyzed as the survival endpoint. Thus we aim to build a robust gene signature for prediction of overall survival (OS) in patients with CRC. Fresh frozen CRC tissues from 64 patients were analyzed using Affymetrix HG-U133plus 2.0 gene arrays. By performing univariate survival analysis, 6487 genes were found to be associated with the OS in our cohort. KEGG analysis revealed that these genes were mainly involved in pathways such as endocytosis, axon guidance, spliceosome, Wnt signalling and ubiquitin mediated proteolysis. A seven-gene signature was further selected by a robust likelihood-based survival modelling approach. The prognostic model of seven-gene signature (NHLRC3, ZDHHC21, PRR14L, CCBL1, PTPRB, PNPO, and PPIP5K2) was constructed and weighted by regression coefficient, which divided patients into high- and low-risk groups. The OS for patients in high-risk group was significantly poorer compared with patients in low-risk group. Moreover, all seven genes were found to be differentially expressed in CRC tissues as compared with adjacent normal tissues, indicating their potential role in CRC initiation and progression. This seven-gene signature was further validated as an independent prognostic marker for OS prediction in patients with CRC in other two independent cohorts. In short, we developed a robust seven-gene signature that can predict the OS for CRC patients, providing new insights into identification of CRC patients with high risk of mortality.

Gene Set-Based Functionome Analysis of Pathogenesis in Epithelial Ovarian Serous Carcinoma and the Molecular Features in Different FIGO Stages

Serous carcinoma (SC) is the most common subtype of epithelial ovarian carcinoma and is divided into four stages by the Federation of Gynecologists and Obstetrics (FIGO) staging system. Currently, the molecular functions and biological processes of SC at different FIGO stages have not been quantified. Here, we conducted a whole-genome integrative analysis to investigate the functions of SC at different stages. The function, as defined by the GO term or canonical pathway gene set, was quantified by measuring the changes in the gene expressional order between cancerous and normal control states. The quantified function, i.e., the gene set regularity (GSR) index, was utilized to investigate the pathogenesis and functional regulation of SC at different FIGO stages. We showed that the informativeness of the GSR indices was sufficient for accurate pattern recognition and classification for machine learning. The function regularity presented by the GSR indices showed stepwise deterioration during SC progression from FIGO stage I to stage IV. The pathogenesis of SC was centered on cell cycle deregulation and accompanied with multiple functional aberrations as well as their interactions.

Meta-analysis and meta-regression of transcriptomic responses to water stress in Arabidopsis

The large amounts of transcriptome data available for Arabidopsis thaliana make a compelling case for the need to generalize results across studies and extract the most robust and meaningful information possible from them. The results of various studies seeking to identify water stress-responsive genes only partially overlap. The aim of this work was to combine transcriptomic studies in a systematic way that identifies commonalities in response, taking into account variation among studies due to batch effects as well as sampling variation, while also identifying the effect of study-specific variables, such as the method of applying water stress, and the part of the plant the mRNA was extracted from. We used meta-analysis, the quantitative synthesis of independent research results, to summarize expression responses to water stress across studies, and meta-regression to model the contribution of covariates that may affect gene expression. We found that some genes with small but consistent differential responses become evident only when results are synthesized across experiments, and are missed in individual studies. We also identified genes with expression responses that are attributable to use of different plant parts and alternative methods for inducing water stress. Our results indicate that meta-analysis and meta-regression provide a powerful approach for identifying a robust gene set that is less sensitive to idiosyncratic results and for quantifying study characteristics that result in contrasting gene expression responses across studies. Combining meta-analysis with individual analyses may contribute to a richer understanding of the biology of water stress responses, and may prove valuable in other gene expression studies.

HES-Mediated Repression of Pten in Caenorhabditis elegans

The hairy/enhancer-of-split (HES) group of transcription factors controls embryonic development, often by acting downstream of the Notch signaling pathway; however, little is known about postembryonic roles of these proteins. In Caenorhabditis elegans, the six proteins that make up the REF-1 family are considered to be HES orthologs that act in both Notch-dependent and Notch-independent pathways to regulate embryonic events. To further our understanding of how the REF-1 family works to coordinate postembryonic cellular events, we performed a functional characterization of the REF-1 family member, HLH-25. We show that, after embryogenesis, hlh-25 expression persists throughout every developmental stage, including dauer, into adulthood. Like animals that carry loss-of-function alleles in genes required for normal cell-cycle progression, the phenotypes of hlh-25 animals include reduced brood size, unfertilized oocytes, and abnormal gonad morphology. Using gene expression microarray, we show that the HLH-25 transcriptional network correlates with the phenotypes of hlh-25 animals and that the C. elegans Pten ortholog, daf-18, is one major hub in the network. Finally, we show that HLH-25 regulates C. elegans lifespan and dauer recovery, which correlates with a role in the transcriptional repression of daf-18 activity. Collectively, these data provide the first genetic evidence that HLH-25 may be a functional ortholog of mammalian HES1, which represses PTEN activity in mice and human cells.

Dose-dependent benefits of quercetin on tumorigenesis in the C3(1)/SV40Tag transgenic mouse model of breast cancer

Breast cancer is the leading cause of cancer related death in women. Quercetin is a flavonol shown to have anti-carcinogenic actions. However, few studies have investigated the dose-dependent effects of quercetin on tumorigenesis and none have used the C3(1)/SV40 Tag breast cancer mouse model. At 4 weeks of age female C3(1)/SV40 Tag mice were randomized to one of four dietary treatments (n = 15-16/group): control (no quercetin), low-dose quercetin (0.02% diet), moderate-dose quercetin (0.2% diet), or high-dose quercetin (2% diet). Tumor number and volume was assessed twice a week and at sacrifice (20 wks). Results showed an inverted 'U' dose-dependent effect of dietary quercetin on tumor number and volume; at sacrifice the moderate dose was most efficacious and reduced tumor number 20% and tumor volume 78% compared to control mice (C3-Con: 9.0 ± 0.9; C3-0.2%: 7.3 ± 0.9) and (C3-Con: 2061.8 ± 977.0 mm(3); and C3-0.2%: 462.9 ± 75.9 mm(3)). Tumor volume at sacrifice was also reduced by the moderate dose compared to the high and low doses (C3-2%: 1163.2 ± 305.9 mm(3); C3-0.02%: 1401.5 ± 555.6 mm(3)), as was tumor number (C3-2%: 10.7 ± 1.3 mm(3); C3-0.02%: 8.1 ± 1.1 mm(3)). Gene expression microarray analysis performed on mammary glands from C3-Con and C3-0.2% mice determined that 31 genes were down-regulated and 9 genes were up-regulated more than 2-fold (P < 0.05) by quercetin treatment. We report the novel finding that there is a distinct dose-dependent effect of quercetin on tumor number and volume in a transgenic mouse model of human breast cancer, which is associated with a specific gene expression signature related to quercetin treatment.

Mineralocorticoid receptors are present in skeletal muscle and represent a potential therapeutic target

Early treatment with heart failure drugs lisinopril and spironolactone improves skeletal muscle pathology in Duchenne muscular dystrophy (DMD) mouse models. The angiotensin converting enzyme inhibitor lisinopril and mineralocorticoid receptor (MR) antagonist spironolactone indirectly and directly target MR. The presence and function of MR in skeletal muscle have not been explored. MR mRNA and protein are present in all tested skeletal muscles from both wild-type mice and DMD mouse models. MR expression is cell autonomous in both undifferentiated myoblasts and differentiated myotubes from mouse and human skeletal muscle cultures. To test for MR function in skeletal muscle, global gene expression analysis was conducted on human myotubes treated with MR agonist (aldosterone; EC50 1.3 nM) or antagonist (spironolactone; IC50 1.6 nM), and 53 gene expression differences were identified. Five differences were conserved in quadriceps muscles from dystrophic mice treated with spironolactone plus lisinopril (IC50 0.1 nM) compared with untreated controls. Genes down-regulated more than 2-fold by MR antagonism included FOS, ANKRD1, and GADD45B, with known roles in skeletal muscle, in addition to NPR3 and SERPINA3, bona fide targets of MR in other tissues. MR is a novel drug target in skeletal muscle and use of clinically safe antagonists may be beneficial for muscle diseases.

Integration of metabolic activation with a predictive toxicogenomics signature to classify genotoxic versus nongenotoxic chemicals in human TK6 cells

The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion article, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or nongenotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and nongenotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4 hr and collected 0 hr, 4 hr, and 20 hr postexposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24 hr. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid- and high concentrations at all three time points, whereas DEX was correctly classified as nongenotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24 hr, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells.

Integration of metabolic activation with a predictive toxicogenomics signature to classify genotoxic versus nongenotoxic chemicals in human TK6 cells

The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion article, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or nongenotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and nongenotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4 hr and collected 0 hr, 4 hr, and 20 hr postexposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24 hr. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid- and high concentrations at all three time points, whereas DEX was correctly classified as nongenotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24 hr, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells.

Chronic low-dose γ-irradiation of Drosophila melanogaster larvae induces gene expression changes and enhances locomotive behavior

Although radiation effects have been extensively studied, the biological effects of low-dose radiation (LDR) are controversial. This study investigates LDR-induced alterations in locomotive behavior and gene expression profiles of Drosophila melanogaster. We measured locomotive behavior using larval pupation height and the rapid iterative negative geotaxis (RING) assay after exposure to 0.1 Gy γ-radiation (dose rate of 16.7 mGy/h). We also observed chronic LDR effects on development (pupation and eclosion rates) and longevity (life span). To identify chronic LDR effects on gene expression, we performed whole-genome expression analysis using gene-expression microarrays, and confirmed the results using quantitative real-time PCR. The pupation height of the LDR-treated group at the first larval instar was significantly higher (∼2-fold increase in PHI value, P < 0.05). The locomotive behavior of LDR-treated male flies (∼3 - 5 weeks of age) was significantly increased by 7.7%, 29% and 138%, respectively (P < 0.01), but pupation and eclosion rates and life spans were not significantly altered. Genome-wide expression analysis identified 344 genes that were differentially expressed in irradiated larvae compared with in control larvae. We identified several genes belonging to larval behavior functional groups such as locomotion (1.1%), oxidation reduction (8.0%), and genes involved in conventional functional groups modulated by irradiation such as defense response (4.9%), and sensory and perception (2.5%). Four candidate genes were confirmed as differentially expressed genes in irradiated larvae using qRT-PCR (>2-fold change). These data suggest that LDR stimulates locomotion-related genes, and these genes can be used as potential markers for LDR.

Molecular profiling of tumor-specific TH1 cells activated in vivo

The central role of tumor-specific T_H1 cells in anticancer immune responses is becoming increasingly appreciated. However, little is known about how these cells are generated in vivo. Here, we used flow cytometry and gene expression microarrays to characterize the primary activation and T_H1 differentiation of naïve tumor-specific CD4⁺ T cells in a mouse model of cancer immunosurveillance. We took advantage of T-cell receptor-transgenic mice in which CD4⁺ T cells recognize a tumor-specific antigen secreted by MHC class II-negative MOPC315 myeloma cells. Cancer cells were injected subcutaneously and T-cell activation was analyzed in draining lymph nodes and at the incipient tumor site 8 d later. Upon activation and migration to incipient tumor sites, tumor-specific CD4⁺ T cells exhibited the upregulation of 29 cell-surface molecules (CD2, CD5, CD11a, CD18, CD25, CD28, CD44, CD45, CD49d, CD51, CD54, CD69, CD71, CD83, CD86, CD90, CD95, CD102, CD122, CD153, CD166, CD200, CD249, CD254, CD274, CD279, Ly6C, MHC class I and CCR7) and the downregulation of five (CD27, CD31, CD45RB, CD62L and CD126). Activated CD4⁺ T cells produced interferon γ, a cytokine consistent with a T_H1-polarized response, tumor necrosis factor α as well as interleukin (IL)-2, IL-3 and IL-10. The activation of naïve tumor-specific CD4⁺ T cells in draining lymph nodes resulted in the upregulation of 609 genes and the downregulation of 284 genes. The bioinformatic analysis of differentially expressed genes identified functional pathways related to tumor-specific T_H1 cell activation. This study may represent a useful resource to guide the development of T_H1-based immunotherapies against cancer.

Gene expression profiling for targeted cancer treatment

INTRODUCTION

There is certain degree of frustration and discontent in the area of microarray gene expression data analysis of cancer datasets. It arises from the mathematical problem called 'curse of dimensionality,' which is due to the small number of samples available in training sets, used for calculating transcriptional signatures from the large number of differentially expressed (DE) genes, measured by microarrays. The new generation of causal reasoning algorithms can provide solutions to the curse of dimensionality by transforming microarray data into activity of a small number of cancer hallmark pathways. This new approach can make feature space dimensionality optimal for mathematical signature calculations.

AREAS COVERED

The author reviews the reasons behind the current frustration with transcriptional signatures derived from DE genes in cancer. He also provides an overview of the novel methods for signature calculations based on differentially variable genes and expression regulators. Furthermore, the authors provide perspectives on causal reasoning algorithms that use prior knowledge about regulatory events described in scientific literature to identify expression regulators responsible for the differential expression observed in cancer samples.

EXPERT OPINION

The author advocates causal reasoning methods to calculate cancer pathway activity signatures. The current challenge for these algorithms is in ensuring quality of the knowledgebase. Indeed, the development of cancer hallmark pathway collections, together with statistical algorithms to transform activity of expression regulators into pathway activity, are necessary for causal reasoning to be used in cancer research.

Target genes of recurrent chromosomal amplification and deletion in urothelial carcinoma

BACKGROUND

Urothelial carcinoma (UC) is characterized by multiple recurrent chromosomal changes on a background of increasing genomic instability. To define target genes of recurrent deletions and amplifications, we explored which gene alterations are common in UC, in two recently established cell lines, BC44 and BC61.

MATERIALS AND METHODS

Genes located in regions of gain or deletion in the cell lines were identified by array comparative genomic hybridization (aCGH). Six published microarray datasets were analyzed for genes differentially expressed between urothelial tumor vs. normal tissues. Gene expression and chromosomal changes were compared in BC61 cells.

RESULTS

The cell lines share homozygous deletions at 9p21 around CDKN2A and amplifications at 11q13.2 around CCND1. In both cell lines 11 genes were commonly lost and 115 gained. Across UC in general, 230 genes were expressed stronger and 349 weaker; a subset displaying corresponding genetic changes in the cell lines. The commonly affected subset contains well-investigated genes like E2F1 and CCNE1, but also several genes not yet sufficiently investigated in UC.

DISCUSSION

Our approach highlights genes involved in cell cycle regulation, apoptosis and signal transduction as commonly deregulated across UC. Nevertheless, many chromosomal regions undergoing recurrent changes harbor several commonly deregulated genes that may act jointly in UC development and progression.

Disturbed protein-protein interaction networks in metastatic melanoma are associated with worse prognosis and increased functional mutation burden

For disseminated melanoma, new prognostic biomarkers and therapeutic targets are urgently needed. The organization of protein-protein interaction networks was assessed via the transcriptomes of four independent studies of metastatic melanoma and related to clinical outcome and MAP-kinase pathway mutations (BRAF/NRAS). We also examined patient outcome-related differences in a predicted network of microRNAs and their targets. The 32 hub genes with the most reproducible survival-related disturbances in co-expression with their protein partner genes included oncogenes and tumor suppressors, previously known correlates of prognosis, and other proteins not previously associated with melanoma outcome. Notably, this network-based gene set could classify patients according to clinical outcomes with 67-80% accuracy among cohorts. Reproducibly disturbed networks were also more likely to have a higher functional mutation burden than would be expected by chance. The disturbed regions of networks are therefore markers of clinically relevant, selectable tumor evolution in melanoma which may carry driver mutations.

Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes

While Illumina microarrays can be used successfully for detecting small gene expression changes due to their high degree of technical replicability, there is little information on how different normalization and differential expression analysis strategies affect outcomes. To evaluate this, we assessed concordance across gene lists generated by applying different combinations of normalization strategy and analytical approach to two Illumina datasets with modest expression changes. In addition to using traditional statistical approaches, we also tested an approach based on combinatorial optimization. We found that the choice of both normalization strategy and analytical approach considerably affected outcomes, in some cases leading to substantial differences in gene lists and subsequent pathway analysis results. Our findings suggest that important biological phenomena may be overlooked when there is a routine practice of using only one approach to investigate all microarray datasets. Analytical artefacts of this kind are likely to be especially relevant for datasets involving small fold changes, where inherent technical variation-if not adequately minimized by effective normalization-may overshadow true biological variation. This report provides some basic guidelines for optimizing outcomes when working with Illumina datasets involving small expression changes.

Differential effect of exogenous interleukin-10 versus glucocorticoids on gene expression and pro-inflammatory cytokine release by polymorphonuclear leukocytes and monocytes of the newly born

Bronchopulmonary dysplasia (BPD) is one of the most common causes of mortality and morbidity in neonatal intensive care units. Persistent inflammation, with an abnormal influx of polymorphonuclear leukocytes (PMNs) followed by monocytes (MONOs), occurs early in the pathogenesis of BPD. Anti-inflammatory therapy with better efficacy and safety than dexamethasone (DEX) is needed. In the present study we determined cell-specific gene expression and cytokine release in response to glucocorticoids versus interleukin-10 (IL-10). Subsequently, we hypothesized that the insensitivity of MONOs to DEX was associated with a failure of the glucocorticoid receptor to translocate to the nucleus. PMNs and MONOs were isolated from umbilical cord blood at birth, and pretreated with PBS vehicle, IL-10 or glucocorticoids prior to endotoxin (LPS)-stimulation for 4 and 18h. Genome-wide gene expressions were determined by microarray and validated by RT-qPCR. Interleukin 8 release in cell culture supernatant was measured by ELISA. To examine the mechanism of monocyte insensitivity to glucocorticoids, nuclear translocation of the glucocorticoid receptor was determined by Western blots. MONOs had 6 times the number of genes changing expression with IL-10 compared to PMNs at 4h. DEX at the therapeutic level for neonates with BPD had no effect on gene expression in MONOs. The order of potency for inhibition of interleukin-8 release from MONOs was IL-10 >betamethasone >dexamethasone and hydrocortisone. Glucocorticoid potency in MONOs was directly related to glucocorticoid receptor translocation to nucleus. Gene expression profiling for IL-10 versus glucocorticoids indicates there may be major differences in therapeutic efficacy for BPD.

An integrated functional genomics approach identifies the regulatory network directed by brachyury (T) in chordoma

Chordoma is a rare malignant tumour of bone, the molecular marker of which is the expression of the transcription factor, brachyury. Having recently demonstrated that silencing brachyury induces growth arrest in a chordoma cell line, we now seek to identify its downstream target genes. Here we use an integrated functional genomics approach involving shRNA-mediated brachyury knockdown, gene expression microarray, ChIP-seq experiments, and bioinformatics analysis to achieve this goal. We confirm that the T-box binding motif of human brachyury is identical to that found in mouse, Xenopus, and zebrafish development, and that brachyury acts primarily as an activator of transcription. Using human chordoma samples for validation purposes, we show that brachyury binds 99 direct targets and indirectly influences the expression of 64 other genes, thereby acting as a master regulator of an elaborate oncogenic transcriptional network encompassing diverse signalling pathways including components of the cell cycle, and extracellular matrix components. Given the wide repertoire of its active binding and the relative specific localization of brachyury to the tumour cells, we propose that an RNA interference-based gene therapy approach is a plausible therapeutic avenue worthy of investigation.

TNBCtype: A Subtyping Tool for Triple-Negative Breast Cancer

Motivation:

Triple-negative breast cancer (TNBC) is a heterogeneous breast cancer group, and identification of molecular subtypes is essential for understanding the biological characteristics and clinical behaviors of TNBC as well as for developing personalized treatments. Based on 3,247 gene expression profiles from 21 breast cancer data sets, we discovered six TNBC subtypes from 587 TNBC samples with unique gene expression patterns and ontologies. Cell line models representing each of the TNBC subtypes also displayed different sensitivities to targeted therapeutic agents. Classification of TNBC into subtypes will advance further genomic research and clinical applications.

Result:

We developed a web-based subtyping tool TNBCtype for candidate TNBC samples using our gene expression meta data and classification methods. Given a gene expression data matrix, this tool will display for each candidate sample the predicted subtype, the corresponding correlation coefficient, and the permutation P-value. We offer a user-friendly web interface to predict the subtypes for new TNBC samples that may facilitate diagnostics, biomarker selection, drug discovery, and the more tailored treatment of breast cancer.

Gene expression profile of the regeneration epithelium during axolotl limb regeneration

Urodele amphibians are unique among adult vertebrates in their ability to regenerate missing limbs. The process of limb regeneration requires several key tissues including a regeneration-competent wound epidermis called the regeneration epithelium (RE). We used microarray analysis to profile gene expression of the RE in the axolotl, a Mexican salamander. A list of 125 genes and expressed sequence tags (ESTs) showed a ≥1.5-fold expression in the RE than in a wound epidermis covering a lateral cuff wound. A subset of the RE ESTs and genes were further characterized for expression level changes over the time-course of regeneration. This study provides the first large scale identification of specific gene expression in the RE.

Effective feature selection framework for cluster analysis of microarray data

The microarray technique has become a standard means in simultaneously examining expression of all genes measured in different circumstances. As microarray data are typically characterized by high dimensional features with a small number of samples, feature selection needs to be incorporated to identify a subset of genes that are meaningful for biological interpretation and accountable for the sample variation. In this article, we present a simple, yet effective feature selection framework suitable for two-dimensional microarray data. Our correlation-based, nonparametric approach allows compact representation of class-specific properties with a small number of genes. We evaluated our method using publicly available experimental data and obtained favorable results.

Exploratory Visual Analysis of statistical results from microarray experiments comparing high and low grade glioma

The biological interpretation of gene expression microarray results is a daunting challenge. For complex diseases such as cancer, wherein the body of published research is extensive, the incorporation of expert knowledge provides a useful analytical framework. We have previously developed the Exploratory Visual Analysis (EVA) software for exploring data analysis results in the context of annotation information about each gene, as well as biologically relevant groups of genes. We present EVA as a flexible combination of statistics and biological annotation that provides a straightforward visual interface for the interpretation of microarray analyses of gene expression in the most commonly occurring class of brain tumors, glioma. We demonstrate the utility of EVA for the biological interpretation of statistical results by analyzing publicly available gene expression profiles of two important glial tumors. The results of a statistical comparison between 21 malignant, high-grade glioblastoma multiforme (GBM) tumors and 19 indolent, low-grade pilocytic astrocytomas were analyzed using EVA. By using EVA to examine the results of a relatively simple statistical analysis, we were able to identify tumor class-specific gene expression patterns having both statistical and biological significance. Our interactive analysis highlighted the potential importance of genes involved in cell cycle progression, proliferation, signaling, adhesion, migration, motility, and structure, as well as candidate gene loci on a region of Chromosome 7 that has been implicated in glioma. Because EVA does not require statistical or computational expertise and has the flexibility to accommodate any type of statistical analysis, we anticipate EVA will prove a useful addition to the repertoire of computational methods used for microarray data analysis. EVA is available at no charge to academic users and can be found at http://www.epistasis.org.

Feature selection for predicting tumor metastases in microarray experiments using paired design

Among the major issues in gene expression profile classification, feature selection is an important and necessary step in achieving and creating good classification rules given the high dimensionality of microarray data. Although different feature selection methods have been reported, there has been no method specifically proposed for paired microarray experiments. In this paper, we introduce a simple procedure based on a modified t-statistic for feature selection to microarray experiments using the popular matched case-control design and apply to our recent study on tumor metastasis in a low-malignant group of breast cancer patients for selecting genes that best predict metastases. Gene or feature selection is optimized by thresholding in a leaving one-pair out cross-validation. Model comparison through empirical application has shown that our method manifests improved efficiency with high sensitivity and specificity.

Whole genome microarray analysis of gene expression in Prader-Willi syndrome

Prader-Willi syndrome (PWS) is caused by loss of function of paternally expressed genes in the 15q11-q13 region and a paucity of data exists on transcriptome variation. To further characterize genetic alterations in this classic obesity syndrome using whole genome microarrays to analyze gene expression, microarray and quantitative RT-PCR analysis were performed using RNA isolated from lymphoblastoid cells from PWS male subjects (four with 15q11-q13 deletion and three with UPD) and three age and cognition matched nonsyndromic comparison males. Of more than 47,000 probes examined in the microarray, 23,383 were detectable and 323 had significantly different expression in the PWS lymphoblastoid cells relative to comparison cells, 14 of which were related to neurodevelopment and function. As expected, there was no evidence of expression of paternally expressed genes from the 15q11-q13 region (e.g., SNRPN) in the PWS cells. Alterations in expression of serotonin receptor genes (e.g., HTR2B) and genes involved in eating behavior and obesity (ADIPOR2, MC2R, HCRT, OXTR) were noted. Other genes of interest with reduced expression in PWS subjects included STAR (a key regulator of steroid synthesis) and SAG (an arrestin family member which desensitizes G-protein-coupled receptors). Quantitative RT-PCR for SAG, OXTR, STAR, HCRT, and HTR2B using RNA isolated from their lymphoblastoid cells and available brain tissue (frontal cortex) from separate individuals with PWS and control subjects and normalized to GAPD gene expression levels validated our microarray gene expression data. Our analysis identified previously unappreciated changes in gene expression which may contribute to the clinical manifestations seen in PWS.