Testing for differentially expressed genes with microarray data

Comprehensive understanding of context-specific functions of PHF2 in lipid metabolic tissues

Adipose tissue and the liver are known to regulate lipid metabolism through the storage, synthesis, and breakdown of lipids. However, the shared molecular factors affecting lipid metabolism in both tissues remain unclear. Plant Homeodomain Finger 2 (PHF2), one of the histone lysine demethylase7 family, serves as an epigenetic regulator in adipose tissue and E3 ubiquitin ligase in liver cancer. This study uses bioinformatics to analyze the role of PHF2 in lipid metabolism, focusing on its functions in adipose tissue and the liver. We utilized cDNA-chip microarrays, public clinical data, and in vitro three-dimensional cell culture models, validating our bioinformatics findings. Consequently, our analyses showed that PHF2 is positively involved in histone demethylase activity and adipogenesis in patients with obesity and moderate liver disease. However, PHF2 suppressed de novo lipogenesis and tumor progression in patients with liver cancer, enhancing immune cell infiltration in liver cancer. Furthermore, it was experimentally confirmed that PHF2 increases lipid accumulation in adipocytes but acts as a tumor suppressor in liver cancer cells. Overall, this study provides a comprehensive overview of PHF2, highlighting its biological importance.

Insights into the Gene Expression Profile of Classical Hodgkin Lymphoma: A Study towards Discovery of Novel Therapeutic Targets

Classical Hodgkin lymphoma (cHL) is a common B-cell cancer and a significant health concern, especially in Western and Asian countries. Despite the effectiveness of chemotherapy, many relapse cases are being reported, highlighting the need for improved treatments. This study aimed to address this issue by discovering biomarkers through the analysis of gene expression data specific to cHL. Additionally, potential anticancer inhibitors were explored to target the discovered biomarkers. This study proceeded by retrieving microarray gene expression data from cHL patients, which was then analyzed to identify significant differentially expressed genes (DEGs). Functional and network annotation of the upregulated genes revealed the active involvement of matrix metallopeptidase 12 (MMP12) and C-C motif metallopeptidase ligand 22 (CCL22) genes in the progression of cHL. Additionally, the mentioned genes were found to be actively involved in cancer-related pathways, i.e., oxidative phosphorylation, complement pathway, myc_targets_v1 pathway, TNFA signaling via NFKB, etc., and showed strong associations with other genes known to promote cancer progression. MMP12, topping the list with a logFC value of +6.6378, was selected for inhibition using docking and simulation strategies. The known anticancer compounds were docked into the active site of the MMP12 molecular structure, revealing significant binding scores of -7.7 kcal/mol and -7.6 kcal/mol for BDC_24037121 and BDC_27854277, respectively. Simulation studies of the docked complexes further supported the effective binding of the ligands, yielding MMGBSA and MMPBSA scores of -78.08 kcal/mol and -82.05 kcal/mol for MMP12-BDC_24037121 and -48.79 kcal/mol and -49.67 kcal/mol for MMP12-BDC_27854277, respectively. Our findings highlight the active role of MMP12 in the progression of cHL, with known compounds effectively inhibiting its function and potentially halting the advancement of cHL. Further exploration of downregulated genes is warranted, as associated genes may play a role in cHL. Additionally, CCL22 should be considered for further investigation due to its significant role in the progression of cHL.

MSPJ: Discovering potential biomarkers in small gene expression datasets via ensemble learning

In transcriptomics, differentially expressed genes (DEGs) provide fine-grained phenotypic resolution for comparisons between groups and insights into molecular mechanisms underlying the pathogenesis of complex diseases or phenotypes. The robust detection of DEGs from large datasets is well-established. However, owing to various limitations (e.g., the low availability of samples for some diseases or limited research funding), small sample size is frequently used in experiments. Therefore, methods to screen reliable and stable features are urgently needed for analyses with limited sample size. In this study, MSPJ, a new machine learning approach for identifying DEGs was proposed to mitigate the reduced power and improve the stability of DEG identification in small gene expression datasets. This ensemble learning-based method consists of three algorithms: an improved multiple random sampling with meta-analysis, SVM-RFE (support vector machines-recursive feature elimination), and permutation test. MSPJ was compared with ten classical methods by 94 simulated datasets and large-scale benchmarking with 165 real datasets. The results showed that, among these methods MSPJ had the best performance in most small gene expression datasets, especially those with sample size below 30. In summary, the MSPJ method enables effective feature selection for robust DEG identification in small transcriptome datasets and is expected to expand research on the molecular mechanisms underlying complex diseases or phenotypes.

Microarray analysis of lipopolysaccharide-induced endotoxemia in the cochlea

Lipopolysaccharide (LPS)-induced endotoxemia alters intracochlear homeostasis and potentiates aminoglycoside-induced ototoxicity. However, the pathological mechanisms in the cochlea following systemic LPS-induced inflammation are unclear. In this study, three groups of mice received intraperitoneal injections [group A, saline control (n = 10); group B, 1 mg/kg LPS (n = 10); group C, 10 mg/kg LPS (n = 10)]. After 24 h, gene expression in cochlea samples was analyzed using DNA microarrays covering 28,853 genes in a duplicate manner. A total of 505 differentially expressed genes (DEGs) (≥2.0-fold change; p < 0.05) were identified. Interferon- and chemotaxis-related genes, including gbp2, gbp5, cxcl10, and Rnf125, were dose-dependently upregulated by LPS-induced endotoxemia. These results were verified by RT-qPCR. Upregulated DEGs were associated with inflammation, positive regulation of immune responses, and regulation of cell adhesion, while downregulated ones were associated with chemical synaptic transmission and the synaptic vesicle cycle. Protein-protein interaction included four functional clusters associated with interleukin-4, -10, and -13 and G protein-coupled receptor (GPCR) ligand binding; activation of matrix metalloproteinases and collagen degradation; recruitment of amyloid A proteins; and neutrophil degranulation. The findings of this study provide an additional basis on changes in the expression of genes in the cochlea in response to LPS-induced endotoxemia.

PTEN regulates adipose progenitor cell growth, differentiation, and replicative aging

The tumor suppressor phosphatase and tensin homolog (PTEN) negatively regulates the insulin signaling pathway. Germline PTEN pathogenic variants cause PTEN hamartoma tumor syndrome (PHTS), associated with lipoma development in children. Adipose progenitor cells (APCs) lose their capacity to differentiate into adipocytes during continuous culture, whereas APCs from lipomas of patients with PHTS retain their adipogenic potential over a prolonged period. It remains unclear which mechanisms trigger this aberrant adipose tissue growth. To investigate the role of PTEN in adipose tissue development, we performed functional assays and RNA-Seq of control and PTEN knockdown APCs. Reduction of PTEN levels using siRNA or CRISPR led to enhanced proliferation and differentiation of APCs. Forkhead box protein O1 (FOXO1) transcriptional activity is known to be regulated by insulin signaling, and FOXO1 was downregulated at the mRNA level while its inactivation through phosphorylation increased. FOXO1 phosphorylation initiates the expression of the lipogenesis-activating transcription factor sterol regulatory element-binding protein 1 (SREBP1). SREBP1 levels were higher after PTEN knockdown and may account for the observed enhanced adipogenesis. To validate this, we overexpressed constitutively active FOXO1 in PTEN CRISPR cells and found reduced adipogenesis, accompanied by SREBP1 downregulation. We observed that PTEN CRISPR cells showed less senescence compared with controls and the senescence marker CDKN1A (p21) was downregulated in PTEN knockdown cells. Cellular senescence was the most significantly enriched pathway found in RNA-Seq of PTEN knockdown versus control cells. These results provide evidence that PTEN is involved in the regulation of APC proliferation, differentiation, and senescence, thereby contributing to aberrant adipose tissue growth in patients with PHTS.

The Novel Phosphatidylinositol-3-Kinase (PI3K) Inhibitor Alpelisib Effectively Inhibits Growth of PTEN-Haploinsufficient Lipoma Cells

Germline mutations in the tumor suppressor gene PTEN cause PTEN Hamartoma Tumor Syndrome (PHTS). Pediatric patients with PHTS frequently develop lipomas. Treatment attempts with the mTORC1 inhibitor rapamycin were unable to reverse lipoma growth. Recently, lipomas associated with PIK3CA-related overgrowth syndrome were successfully treated with the novel PI3K inhibitor alpelisib. Here, we tested whether alpelisib has growth-restrictive effects and induces cell death in lipoma cells. We used PTEN-haploinsufficient lipoma cells from three patients and treated them with alpelisib alone or in combination with rapamycin. We tested the effect of alpelisib on viability, proliferation, cell death, induction of senescence, adipocyte differentiation, and signaling at 1-100 µM alpelisib. Alpelisib alone or in combination with rapamycin reduced proliferation in a concentration- and time-dependent manner. No cell death but an induction of senescence was detected after alpelisib incubation for 72 h. Alpelisib treatment led to a reduced phosphorylation of AKT, mTOR, and ribosomal protein S6. Rapamycin treatment alone led to increased AKT phosphorylation. This effect could be reversed by combining rapamycin with alpelisib. Alpelisib reduced the size of lipoma spheroids by attenuating adipocyte differentiation. Since alpelisib was well tolerated in first clinical trials, this drug alone or in combination with rapamycin is a potential new treatment option for PHTS-related adipose tissue overgrowth.

Computational deconvolution of transcriptomics data from mixed cell populations

Summary

Gene expression analyses of bulk tissues often ignore cell type composition as an important confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review, we highlight the importance and value of computational deconvolution methods to infer the abundance of different cell types and/or cell type-specific expression profiles in heterogeneous samples without performing physical cell sorting. We also explain the various deconvolution scenarios, the mathematical approaches used to solve them and the effect of data processing and different confounding factors on the accuracy of the deconvolution results.

Contact

katleen.depreter@ugent.be.

Supplementary information

Supplementary data are available at Bioinformatics online.

Weighted correlation network and differential expression analyses identify candidate genes associated with BRAF gene in melanoma

BACKGROUND

Primary cutaneous malignant melanoma is a cancer of the pigment cells of the skin, some of which are accompanied by BRAF mutation. Melanoma incidence and mortality rates have been rising around the world. As the current knowledge about pathogenesis, clinical and genetic features of cutaneous melanoma is not very clear, we aim to use bioinformatics to identify the potential key genes involved in the expression and mutation status of BRAF.

METHODS

Firstly, we used UCSC public hub datasets of melanoma (Lin et al., Cancer Res 68(3):664, 2008) to perform weighted genes co-expression network analysis (WGCNA) and differentially expressed genes analysis (DEGs), respectively. Secondly, overlapping genes between significant gene modules and DEGs were screened and validated at transcriptional levels and overall survival in TCGA and GTEx datasets. Lastly, the functional enrichment analysis was accomplished to find biological functions on the web-server database.

RESULTS

We performed weighted correlation network and differential expression analyses, using gene expression data in melanoma samples. We identified 20 genes whose expression was correlated with the mutation status of BRAF. For further validation, three of these genes (CYR61, DUSP1, and RNASE4) were found to have similar expression patterns in skin tumors from TCGA compared with normal skin samples from GTEx. We also found that weak expression of these three genes was associated with worse overall survival in the TCGA data. These three genes were involved in the nucleic acid metabolic process.

CONCLUSION

In this study, CYR61, DUSP1, and RNASE4 were identified as potential genes of interest for future molecular studies in melanoma, which would improve our understanding of its causes and underlying molecular events. These candidate genes may provide a promising avenue of future research for therapeutic targets in melanoma.

Identification of Potential Key Genes and Pathways in Early-Onset Colorectal Cancer Through Bioinformatics Analysis

This study was designed to identify the potential key protein interaction networks, genes, and correlated pathways in early-onset colorectal cancer (CRC) via bioinformatics methods. We selected microarray data GSE4107 consisting 12 patient's colonic mucosa and 10 healthy control mucosa; initially, the GSE4107 were downloaded and analyzed using limma package to identify differentially expressed genes (DEGs). A total of 131 DEGs consisting of 108 upregulated genes and 23 downregulated genes of patients in early-onset CRC were selected by the criteria of adjusted P values <.01 and |log2 fold change (FC)| ≥ 2. The gene ontology functional enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were accomplished to view the biological process, cellular components, molecular function, and the KEGG pathways of DEGs. Finally, protein-protein interactions (PPIs) were constructed, and the hub protein module was identified. Genes such as ACTA2, ACTG2, MYH11, CALD1, MYL9, TPM2, and LMOD1 were strongly implicated in CRC. In summary, in this study, we indicated that molecular mechanisms were involved in muscle contraction and vascular smooth muscle contraction signaling pathway, which improve our understanding of CRC and could be used as new therapeutic targets for CRC.

A workflow for the integrative transcriptomic description of molecular pathology and the suggestion of normalizing compounds, exemplified by Parkinson's disease

The volume of molecular observations on human diseases in public databases is continuously increasing at accelerating rates. A bottleneck is their computational integration into a coherent description, from which researchers may derive new well-founded hypotheses. Also, the need to integrate data from different technologies (genetics, coding and regulatory RNA, proteomics) emerged in order to identify biomarkers for early diagnosis and prognosis of complex diseases and therefore facilitating the development of novel treatment approaches. We propose here a workflow for the integrative transcriptomic description of the molecular pathology in Parkinsons’s Disease (PD), including suggestions of compounds normalizing disease-induced transcriptional changes as a paradigmatic example. We integrated gene expression profiles, miRNA signatures, and publicly available regulatory databases to specify a partial model of the molecular pathophysiology of PD. Six genetic driver elements (2 genes and 4 miRNAs) and several functional network modules that are associated with PD were identified. Functional modules were assessed for their statistical significance, cellular functional homogeneity, literature evidence, and normalizing small molecules. In summary, our workflow for the joint regulatory analysis of coding and non-coding RNA, has the potential to yield clinically as well as biologically relevant information, as demonstrated here on PD data.

Radiation-Induced Gene Expression Changes in High and Low Grade Breast Cancer Cell Types

BACKGROUND

There is extensive scientific evidence that radiation therapy (RT) is a crucial treatment, either alone or in combination with other treatment modalities, for many types of cancer, including breast cancer (BC). BC is a heterogeneous disease at both clinical and molecular levels, presenting distinct subtypes linked to the hormone receptor (HR) status and associated with different clinical outcomes. The aim of this study was to assess the molecular changes induced by high doses of ionizing radiation (IR) on immortalized and primary BC cell lines grouped according to Human epidermal growth factor receptor (HER2), estrogen, and progesterone receptors, to study how HR status influences the radiation response. Our genomic approach using in vitro and ex-vivo models (e.g., primary cells) is a necessary first step for a translational study to describe the common driven radio-resistance features associated with HR status. This information will eventually allow clinicians to prescribe more personalized total doses or associated targeted therapies for specific tumor subtypes, thus enhancing cancer radio-sensitivity.

METHODS

Nontumorigenic (MCF10A) and BC (MCF7 and MDA-MB-231) immortalized cell lines, as well as healthy (HMEC) and BC (BCpc7 and BCpcEMT) primary cultures, were divided into low grade, high grade, and healthy groups according to their HR status. At 24 h post-treatment, the gene expression profiles induced by two doses of IR treatment with 9 and 23 Gy were analyzed by cDNA microarray technology to select and compare the differential gene and pathway expressions among the experimental groups.

RESULTS

We present a descriptive report of the substantial alterations in gene expression levels and pathways after IR treatment in both immortalized and primary cell cultures. Overall, the IR-induced gene expression profiles and pathways appear to be cell-line dependent. The data suggest that some specific gene and pathway signatures seem to be linked to HR status.

CONCLUSIONS

Genomic biomarkers and gene-signatures of specific tumor subtypes, selected according to their HR status and molecular features, could facilitate personalized biological-driven RT treatment planning alone and in combination with targeted therapies.

Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes

Microarray approaches are widely used high-throughput techniques to assess simultaneously the expression of thousands of genes under certain conditions and study the effects of certain treatments, diseases, and developmental stages. The traditional way to perform such experiments is to design oligonucleotide hybridization probes that correspond to specific genes and then measure the expression of the genes in order to determine which of them are up- or down-regulated compared to a condition that is used as a control. Hitherto, individual experiments cannot capture the bigger picture of how a biological system works and, therefore, data integration from multiple experimental studies and external data repositories is necessary to understand the function of genes and their expression patterns under certain conditions. Therefore, the development of methods for handling, integrating, comparing, interpreting and visualizing microarray data is necessary. The selection of an appropriate method for analysing microarray datasets is not an easy task. In this chapter, we provide an overview of the various methods developed for microarray data analysis, as well as suggestions for choosing the appropriate method for microarray meta-analysis.

Changes in microRNA expression in the whole hippocampus and hippocampal synaptoneurosome fraction following pilocarpine induced status epilepticus

MicroRNAs regulate protein synthesis by binding non-translated regions of mRNAs and suppressing translation and/or increasing mRNA degradation. MicroRNAs play an important role in the nervous system including controlling synaptic plasticity. Their expression is altered in disease states including stroke, head injury and epilepsy. To better understand microRNA expression changes that might contribute to the development of epilepsy, microRNA arrays were performed on rat hippocampus 4 hours, 48 hours and 3 weeks following an episode of pilocarpine induced status epilepticus. Eighty microRNAs increased at one or more of the time points. No microRNAs decreased at 4 hours, and only a few decreased at 3 weeks, but 188 decreased 48 hours after status epilepticus. The large number of microRNAs with altered expression following status epilepticus suggests that microRNA regulation of translation has the potential to contribute to changes in protein expression during epileptogenesis. We carried out a second set of array's comparing microRNA expression at 48 hours in synaptoneurosome and nuclear fractions of the hippocampus. In control rat hippocampi multiple microRNAs were enriched in the synaptoneurosomal fraction as compared to the nuclear fraction. In contrast, 48 hours after status epilepticus only one microRNA was enriched in the synaptoneurosome fraction. The loss of microRNAs enriched in the synaptoneurosomal fraction implies a dramatic change in translational regulation in synapses 48 hours after status epilepticus.

Exploiting identifiability and intergene correlation for improved detection of differential expression

Accurate differential analysis of microarray data strongly depends on effective treatment of intergene correlation. Such dependence is ordinarily accounted for in terms of its effect on significance cutoffs. In this paper, it is shown that correlation can, in fact, be exploited to share information across tests and reorder expression differentials for increased statistical power, regardless of the threshold. Significantly improved differential analysis is the result of two simple measures: (i) adjusting test statistics to exploit information from identifiable genes (the large subset of genes represented on a microarray that can be classified a priori as nondifferential with very high confidence], but (ii) doing so in a way that accounts for linear dependencies among identifiable and nonidentifiable genes. A method is developed that builds upon the widely used two-sample t-statistic approach and uses analysis in Hilbert space to decompose the nonidentified gene vector into two components that are correlated and uncorrelated with the identified set. In the application to data derived from a widely studied prostate cancer database, the proposed method outperforms some of the most highly regarded approaches published to date. Algorithms in MATLAB and in R are available for public download.

Downregulation of miR-205 modulates cell susceptibility to oxidative and endoplasmic reticulum stresses in renal tubular cells

BACKGROUND

Oxidative stress and endoplasmic reticulum (ER) stress play a crucial role in tubular damage in both acute kidney injury (AKI) and chronic kidney disease (CKD). While the pathophysiological contribution of microRNAs (miRNA) to renal damage has also been highlighted, the effect of miRNA on renal damage under oxidative and ER stresses conditions remains elusive.

METHODS

We assessed changes in miRNA expression in the cultured renal tubular cell line HK-2 under hypoxia-reoxygenation-induced oxidative stress or ER stress using miRNA microarray assay and real-time RT-PCR. The pathophysiological effect of miRNA was evaluated by cell survival rate, intracellular reactive oxygen species (ROS) level, and anti-oxidant enzyme expression in miRNA-inhibited HK-2 or miRNA-overexpressed HK-2 under these stress conditions. The target gene of miRNA was identified by 3'-UTR-luciferase assay.

RESULTS

We identified 8 and 10 miRNAs whose expression was significantly altered by oxidative and ER stresses, respectively. Among these, expression of miR-205 was markedly decreased in both stress conditions. Functional analysis revealed that decreased miR-205 led to an increase in cell susceptibility to oxidative and ER stresses, and that this increase was associated with the induction of intracellular ROS and suppression of anti-oxidant enzymes. While increased miR-205 by itself made no change in cell growth or morphology, cell viability under oxidative or ER stress conditions was partially restored. Further, miR-205 bound to the 3'-UTR of the prolyl hydroxylase 1 (PHD1/EGLN2) gene and suppressed the transcription level of EGLN2, which modulates both intracellular ROS level and ER stress state.

CONCLUSIONS

miR-205 serves a protective role against both oxidative and ER stresses via the suppression of EGLN2 and subsequent decrease in intracellular ROS. miR-205 may represent a novel therapeutic target in AKI and CKD associated with oxidative or ER stress in tubules.

Nonlinear dependence in the discovery of differentially expressed genes

Microarray data are used to determine which genes are active in response to a changing cell environment. Genes are “discovered” when they are significantly differentially expressed in the microarray data collected under the differing conditions. In one prevalent approach, all genes are assumed to satisfy a null hypothesis, ℍ₀, of no difference in expression. A false discovery (type 1 error) occurs when ℍ₀ is incorrectly rejected. The quality of a detection algorithm is assessed by estimating its number of false discoveries, 𝔉. Work involving the second-moment modeling of the z-value histogram (representing gene expression differentials) has shown significantly deleterious effects of intergene expression correlation on the estimate of 𝔉. This paper suggests that nonlinear dependencies could likewise be important. With an applied emphasis, this paper extends the “moment framework” by including third-moment skewness corrections in an estimator of 𝔉. This estimator combines observed correlation (corrected for sampling fluctuations) with the information from easily identifiable null cases. Nonlinear-dependence modeling reduces the estimation error relative to that of linear estimation. Third-moment calculations involve empirical densities of 3 × 3 covariance matrices estimated using very few samples. The principle of entropy maximization is employed to connect estimated moments to 𝔉 inference. Model results are tested with BRCA and HIV data sets and with carefully constructed simulations.

Neurotrophin-3 mRNA a putative target of miR21 following status epilepticus

Status epilepticus induces a cascade of protein expression changes contributing to the subsequent development of epilepsy. By identifying the cascade of molecular changes that contribute to the development of epilepsy we hope to be able to design therapeutics for preventing epilepsy. MicroRNAs influence gene expression by altering mRNA stability and/or translation and have been implicated in the pathology of multiple diseases. MiR21 and its co-transcript miR21, microRNAs produced from either the 5' or 3' ends of the same precursor RNA strand, are increased in the hippocampus following status epilepticus. We have identified a miR21 binding site, in the 3' UTR of neurotrophin-3 that inhibits translation. Neurotrophin-3 mRNA levels decrease in the hippocampus following SE concurrent with the increase in miR21. MiR21 levels in cultured hippocampal neurons inversely correlate with neurotrophin-3 mRNA levels. Treatment of hippocampal neuronal cultures with excess K(+)Cl(-), a depolarizing agent mimicking the episode of status epilepticus, also results in an increase in miR21 and a decrease in neurotrophin-3 mRNA. MiR21 is a candidate for regulating neurotrophin-3 signaling in the hippocampus following status epilepticus.

microRNA-210 is upregulated in hypoxic cardiomyocytes through Akt- and p53-dependent pathways and exerts cytoprotective effects

microRNA-210 (miR-210) is upregulated in hypoxia, but its function in cardiomyocytes and its regulation in response to hypoxia are not well characterized. The purpose of this study was to identify upstream regulators of miR-210, as well as to characterize miR-210's function in cardiomyocytes. We first showed miR-210 is upregulated through both hypoxia-inducible factor (HIF)-dependent and -independent pathways, since aryl hydrocarbon nuclear translocator (ARNT) knockout mouse embryonic fibroblasts (MEF), lacking intact HIF signaling, still displayed increased miR-210 levels in hypoxia. To determine the mechanism for HIF-independent regulation of miR-210, we focused on p53 and protein kinase B (Akt). Overexpression of p53 in wild-type MEFs induced miR-210, whereas p53 overexpression in ARNT knockout MEFs did not, suggesting p53 regulates miR-210 in a HIF-dependent mechanism. Akt inhibition reduced miR-210 induction by hypoxia, whereas Akt overexpression increased miR-210 levels in both wild-type and ARNT knockout MEFs, indicating Akt regulation of miR-210 is HIF-independent. We then studied the effects of miR-210 in cardiomyocytes. Overexpression of miR-210 reduced cell death in response to oxidative stress and reduced reactive oxygen species (ROS) production both at baseline and after treatment with antimycin A. Furthermore, downregulation of miR-210 increased ROS after hypoxia-reoxygenation. To determine a mechanism for the cytoprotective effects of miR-210, we focused on the predicted target, apoptosis-inducing factor, mitochondrion-associated 3 (AIFM3), known to induce cell death. Although miR-210 reduced AIFM3 levels, overexpression of AIFM3 in the presence of miR-210 overexpression did not reduce cellular viability either at baseline or after hydrogen peroxide treatment, suggesting AIFM3 does not mediate miR-210's cytoprotective effects. Furthermore, HIF-3α, a negative regulator of HIF signaling, is targeted by miR-210, but miR-210 does not modulate HIF activity. In conclusion, we demonstrate a novel role for p53 and Akt in regulating miR-210 and demonstrate that, in cardiomyocytes, miR-210 exerts cytoprotective effects, potentially by reducing mitochondrial ROS production.

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.

Comparative genomic analysis of light-regulated transcripts in the Solanaceae

Background

Plants use different light signals to adjust their growth and development to the prevailing environmental conditions. Studies in the model species Arabidopsis thaliana and rice indicate that these adjustments are mediated by large changes in the transcriptome. Here we compared transcriptional responses to light in different species of the Solanaceae to investigate common as well as species-specific changes in gene expression.

Results

cDNA microarrays were used to identify genes regulated by a transition from long days (LD) to short days (SD) in the leaves of potato and tobacco plants, and by phytochrome B (phyB), the photoreceptor that represses tuberization under LD in potato. We also compared transcriptional responses to photoperiod in Nicotiana tabacum Maryland Mammoth (MM), which flowers only under SD, with those of Nicotiana sylvestris, which flowers only under LD conditions. Finally, we identified genes regulated by red compared to far-red light treatments that promote germination in tomato.

Conclusion

Most of the genes up-regulated in LD were associated with photosynthesis, the synthesis of protective pigments and the maintenance of redox homeostasis, probably contributing to the acclimatization to seasonal changes in irradiance. Some of the photoperiodically regulated genes were the same in potato and tobacco. Others were different but belonged to similar functional categories, suggesting that conserved as well as convergent evolutionary processes are responsible for physiological adjustments to seasonal changes in the Solanaceae. A β-ZIP transcription factor whose expression correlated with the floral transition in Nicotiana species with contrasting photoperiodic responses was also regulated by photoperiod and phyB in potato, and is a candidate gene to act as a general regulator of photoperiodic responses. Finally, GIGANTEA, a gene that controls flowering time in Arabidopsis thaliana and rice, was regulated by photoperiod in the leaves of potato and tobacco and by red compared to far-light treatments that promote germination in tomato seeds, suggesting that a conserved light signaling cascade acts across developmental contexts and species.

A permutation two one-sided tests procedure to detect minimal fold changes of gene expression levels

Current approaches to identifying differentially expressed genes are based either on the fold changes or on the traditional hypotheses of equality. However, the fold changes do not take into consideration the variation in estimation of the average expression. In addition, the use of fold changes is not in the frame of hypothesis testing, and hence the probability associated with errors for decision making of identification of differentially expressed genes cannot be quantified and evaluated. On the other hand, the traditional hypothesis of equality fails to take into consideration the magnitudes of the biologically meaningful fold changes that truly differentiate the genes between populations. Because of the large number of genes tested and small number of samples available for microarray experiments, the false positive rate for differentially expressed genes is quite high and requires further multiplicity adjustments, or use of an arbitrary cutoff for the p-values. However, all these adjustments do not have any biological justification. Hence, based on the interval hypothesis, Liu and Chow proposed a two one-sided tests procedure by consideration of both the minimal biologically meaningful fold changes and statistical significance simultaneously. To incorporate the correlation structure of expression levels among different genes and possible violation of normality assumption, we propose to apply a permutation method to the two one-sided tests procedure. A simulation study is conducted to empirically compare the type I error rate and power of the procedures based on the traditional hypothesis and the proposed permutation two one-sided tests procedure based on the interval hypothesis under various combinations of fold changes, variability, and sample sizes. Simulation results show that the proposed permutation two one-sided tests procedure based on the interval hypothesis not only can control the type I error rate at the nominal level but also provide adequate power to detect differentially expressed genes. Numerical data from public domains illustrate the proposed methods.

Evaluation of time profile reconstruction from complex two-color microarray designs

BACKGROUND

As an alternative to the frequently used "reference design" for two-channel microarrays, other designs have been proposed. These designs have been shown to be more profitable from a theoretical point of view (more replicates of the conditions of interest for the same number of arrays). However, the interpretation of the measurements is less straightforward and a reconstruction method is needed to convert the observed ratios into the genuine profile of interest (e.g. a time profile). The potential advantages of using these alternative designs thus largely depend on the success of the profile reconstruction. Therefore, we compared to what extent different linear models agree with each other in reconstructing expression ratios and corresponding time profiles from a complex design.

RESULTS

On average the correlation between the estimated ratios was high, and all methods agreed with each other in predicting the same profile, especially for genes of which the expression profile showed a large variance across the different time points. Assessing the similarity in profile shape, it appears that, the more similar the underlying principles of the methods (model and input data), the more similar their results. Methods with a dye effect seemed more robust against array failure. The influence of a different normalization was not drastic and independent of the method used.

CONCLUSION

Including a dye effect such as in the methods lmbr_dye, anovaFix and anovaMix compensates for residual dye related inconsistencies in the data and renders the results more robust against array failure. Including random effects requires more parameters to be estimated and is only advised when a design is used with a sufficient number of replicates. Because of this, we believe lmbr_dye, anovaFix and anovaMix are most appropriate for practical use.

Key aspects of analyzing microarray gene-expression data

One major challenge with the use of microarray technology is the analysis of massive amounts of gene-expression data for various applications. This review addresses the key aspects of the microarray gene-expression data analysis for the two most common objectives: class comparison and class prediction. Class comparison mainly aims to select which genes are differentially expressed across experimental conditions. Gene selection is separated into two steps: gene ranking and assigning a significance level. Class prediction uses expression profiling analysis to develop a prediction model for patient selection, diagnostic prediction or prognostic classification. Development of a prediction model involves two components: model building and performance assessment. It also describes two additional data analysis methods: gene-class testing and multiple ordering criteria.

A new paradigm for profiling testicular gene expression during normal and disturbed human spermatogenesis

The aim of this study was to identify gene expression patterns of the testis that correlate with the appearance of distinct stages of male germ cells. We avoided the pitfalls of mixed pathological phenotypes of the testis and circumvented the inapplicability of using the first spermatogenic wave as done previously on rodents. This was accomplished by using 28 samples showing defined and highly homogeneous pathologies selected from 578 testicular biopsies obtained from 289 men with azoospermia (two biopsies each). The molecular signature of the different developmental stages correlated with the morphological preclassification of the testicular biopsies, as shown by resampling-based hierarchical clustering using different measures of variability. By using analysis of variance (ANOVA) and extensive permutation analysis, we filtered 1181 genes that exhibit exceptional statistical significance in testicular expression and grouped subsets with transcriptional changes within the pre-meiotic (348 genes), post-meiotic (81 genes) and terminal differentiation (38 genes) phase. Several distinct molecular classes, metabolic pathways and transcription factor binding sites are involved, depending on the transcriptional profile of the gene clusters that were built using a novel clustering procedure based on not only similarity but also statistical significance.

Dissimilarity in gene expression profiles of lung adenocarcinoma in Japanese men and women

BACKGROUND

Although clinical differences in lung cancer between men and women have been noted, few studies have examined the sex dissimilarity using gene expression analysis.

OBJECTIVE

The purpose of this study was to determine the different molecular carcinogenic mechanisms involved in lung cancers in Japanese men and women.

METHODS

Patients who received surgery for stage I lung adenocarcinoma were included. RNA was extracted from cancerous and normal tissue, and gene expression was then examined with oligonucleotide microarray analysis. A quantitative polymerase chain reaction assay was performed.

RESULTS

In a microarray analysis of tissue from 13 men and 6 women, 12 genes were under-expressed and 24 genes were overexpressed in lung adenocarcinoma in women compared with men. Genes related to cell cycle were present in underexpressed genes, and genes related to apoptosis, ubiquitination, and metabolism were observed in overexpressed genes. Of interest among the selected genes were WAP four-disulfide core domain 2 (WFDC2) and major histocompatibility complex, class II, DM alpha (HLA-DMA); these genes were classified into 2 groups by hierarchical clustering analysis. Expression of WFDC2 in nonsmokers was significantly higher than that in smokers (P=0.023). However, there was no significant difference in HLA-DMA expression between smokers and nonsmokers.

CONCLUSION

Thirty-six genes that characterize lung adenocarcinoma by sex were selected. This information may contribute to the development of novel diagnostic techniques and treatment modalities that consider sex differences in lung adenocarcinoma.

Selection of differentially expressed genes in microarray data analysis

One common objective in microarray experiments is to identify a subset of genes that express differentially among different experimental conditions, for example, between drug treatment and no drug treatment. Often, the goal is to determine the underlying relationship between poor versus good gene signatures for identifying biological functions or predicting specific therapeutic outcomes. Because of the complexity in studying hundreds or thousands of genes in an experiment, selection of a subset of genes to enhance relationships among the underlying biological structures or to improve prediction accuracy of clinical outcomes has been an important issue in microarray data analysis. Selection of differentially expressed genes is a two-step process. The first step is to select an appropriate test statistic and compute the P-value. The genes are ranked according to their P-values as evidence of differential expression. The second step is to assign a significance level, that is, to determine a cutoff threshold from the P-values in accordance with the study objective. In this paper, we consider four commonly used statistics, t-, S- (SAM), U-(Mann-Whitney) and M-statistics to compute the P-values for gene ranking. We consider the family-wise error and false discovery rate false-positive error-controlled procedures to select a limited number of genes, and a receiver-operating characteristic (ROC) approach to select a larger number of genes for assigning the significance level. The ROC approach is particularly useful in genomic/genetic profiling studies. The well-known colon cancer data containing 22 normal and 40 tumor tissues are used to illustrate different gene ranking and significance level assignment methods for applications to genomic/genetic profiling studies. The P-values computed from the t-, U- and M-statistics are very similar. We discuss the common practice that uses the P-value, false-positive error probability, as the primary criterion, and then uses the fold-change as a surrogate measure of biological significance for gene selection. The P-value and the fold-change can be pictorially shown simultaneously in a volcano plot. We also address several issues on gene selection.

Significance of gene ranking for classification of microarray samples

Many methods for classification and gene selection with microarray data have been developed. These methods usually give a ranking of genes. Evaluating the statistical significance of the gene ranking is important for understanding the results and for further biological investigations, but this question has not been well addressed for machine learning methods in existing works. Here, we address this problem by formulating it in the framework of hypothesis testing and propose a solution based on resampling. The proposed r-test methods convert gene ranking results into position p-values to evaluate the significance of genes. The methods are tested on three real microarray data sets and three simulation data sets with support vector machines as the method of classification and gene selection. The obtained position p-values help to determine the number of genes to be selected and enable scientists to analyze selection results by sophisticated multivariate methods under the same statistical inference paradigm as for simple hypothesis testing methods.

Expression profiles of 10,422 genes at early stage of low nitrogen stress in rice assayed using a cDNA microarray

Development of crop varieties with high nitrogen use efficiency (NUE) is imperative for sustainable agriculture. Understanding how plant genes respond to low N stress is essential for formulating approaches to manipulating genes for improving NUE. In this study we analyzed the expression profiles of an indica rice cultivar Minghui 63 at seedling stage at 20 min, 1 and 2 h after low N stress with the normal N as the control, using a microarray of 11,494 rice ESTs representing 10,422 unique genes. While no significant difference was detected in the leaf tissue, a total of 471 ESTs were detected as responsive to low N stress in the root tissue with 115 ESTs showing up-regulation and 358 ESTs showing down-regulation. The analysis of expression profiles after low N stress identified following patterns: (1) the genes involved in photosynthesis and energy metabolism were down-regulated rapidly; (2) many of the genes involved in early responses to biotic and abiotic stresses were up-regulated while many other stress responsive genes were down-regulated; (3) regulatory genes including transcription factors and ones involved in signal transduction were both up- and down-regulated; and (4) the genes known to be involved in N uptake and assimilation showed little response to the low N stress. The challenges for future studies are to characterize the functional roles of the low N stress responsive genes in N metabolisms, including the large number of genes presently with unknown functions.

Development of Ewing's sarcoma from primary bone marrow-derived mesenchymal progenitor cells

Ewing's sarcoma is a member of Ewing's family tumors (EFTs) and the second most common solid bone and soft tissue malignancy of children and young adults. It is associated in 85% of cases with the t(11;22)(q24:q12) chromosomal translocation that generates fusion of the 5' segment of the EWS gene with the 3' segment of the ETS family gene FLI-1. The EWS-FLI-1 fusion protein behaves as an aberrant transcriptional activator and is believed to contribute to EFT development. However, EWS-FLI-1 induces growth arrest and apoptosis in normal fibroblasts, and primary cells that are permissive for its putative oncogenic properties have not been discovered, hampering basic understanding of EFT biology. Here, we show that EWS-FLI-1 alone can transform primary bone marrow-derived mesenchymal progenitor cells and generate tumors that display hallmarks of Ewing's sarcoma, including a small round cell phenotype, expression of EFT-associated markers, insulin like growth factor-I dependence, and induction or repression of numerous EWS-FLI-1 target genes. These observations provide the first identification of candidate primary cells from which EFTs originate and suggest that EWS-FLI-1 expression may constitute the initiating event in EFT pathogenesis.

Synthesis of quantum dot-tagged submicrometer polystyrene particles by miniemulsion polymerization

Submicrometer fluorescent polystyrene (PS) particles have been synthesized via miniemulsion polymerization using CdSe/ZnS core-shell quantum dots (QDs). The influence of QD concentration, QD coating (either trioctylphosphine oxide (TOPO)-coated or vinyl-functionalized), and surfactant concentration on the polymerization kinetics and the photoluminescence properties of the prepared particles has been analyzed. Polymerization kinetics were not altered by the presence of QDs, whatever their surface coating. Latexes exhibited particle sizes ranging from 100 to 350 nm, depending on surfactant concentration, and a narrow particle size distribution was obtained in all cases. The fluorescence signal of the particles increased with the number of incorporated TOPO-coated QDs. The slight red shift of the emission maximum was correlated with phase separation between PS and QDs, which occurred during the polymerization, locating the QDs in the vicinity of the particle/water interface. QD-tagged particles displayed higher fluorescence intensity with TOPO-coated QDs compared to those with the vinyl moiety. The obtained fluorescent particles open up new opportunities for a variety of applications in biotechnology.

Gene selection from microarray data for cancer classification--a machine learning approach

A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification of cancers. Cancer microarray data normally contains a small number of samples which have a large number of gene expression levels as features. To select relevant genes involved in different types of cancer remains a challenge. In order to extract useful gene information from cancer microarray data and reduce dimensionality, feature selection algorithms were systematically investigated in this study. Using a correlation-based feature selector combined with machine learning algorithms such as decision trees, naïve Bayes and support vector machines, we show that classification performance at least as good as published results can be obtained on acute leukemia and diffuse large B-cell lymphoma microarray data sets. We also demonstrate that a combined use of different classification and feature selection approaches makes it possible to select relevant genes with high confidence. This is also the first paper which discusses both computational and biological evidence for the involvement of zyxin in leukaemogenesis.

Comparison of label-free methods for quantifying human proteins by shotgun proteomics

Measurements of mass spectral peak intensities and spectral counts are promising methods for quantifying protein abundance changes in shotgun proteomic analyses. We describe Serac, software developed to evaluate the ability of each method to quantify relative changes in protein abundance. Dynamic range and linearity using a three-dimensional ion trap were tested using standard proteins spiked into a complex sample. Linearity and good agreement between observed versus expected protein ratios were obtained after normalization and background subtraction of peak area intensity measurements and correction of spectral counts to eliminate discontinuity in ratio estimates. Peak intensity values useful for protein quantitation ranged from 10(7) to 10(11) counts with no obvious saturation effect, and proteins in replicate samples showed variations of less than 2-fold within the 95% range (+/-2sigma) when >or=3 peptides/protein were shared between samples. Protein ratios were determined with high confidence from spectral counts when maximum spectral counts were >or=4 spectra/protein, and replicates showed equivalent measurements well within 95% confidence limits. In further tests, complex samples were separated by gel exclusion chromatography, quantifying changes in protein abundance between different fractions. Linear behavior of peak area intensity measurements was obtained for peptides from proteins in different fractions. Protein ratios determined by spectral counting agreed well with those determined from peak area intensity measurements, and both agreed with independent measurements based on gel staining intensities. Overall spectral counting proved to be a more sensitive method for detecting proteins that undergo changes in abundance, whereas peak area intensity measurements yielded more accurate estimates of protein ratios. Finally these methods were used to analyze differential changes in protein expression in human erythroleukemia K562 cells stimulated under conditions that promote cell differentiation by mitogen-activated protein kinase pathway activation. Protein changes identified with p<0.1 showed good correlations with parallel measurements of changes in mRNA expression.

Sample size for identifying differentially expressed genes in microarray experiments

Microarray technology allows simultaneous comparison of expression levels of thousands of genes under each condition. This paper concerns sample size calculation in the identification of differentially expressed genes between a control and a treated sample. In a typical experiment, only a fraction of genes (altered genes) is expected to be differentially expressed between two samples. Sample size determination depends on a number of factors including the specified significance level (alpha), the desired statistical power (1-beta), the fraction (eta) of truly altered genes out of the total g genes studied, and the effect sizes (Delta) for the altered genes. This paper proposes a method to calculate the number of arrays required to detect at least 100lambda % (where 0 < lambda < or = 1) of the truly altered genes under the model of an equal effect size for all altered genes. The required numbers of arrays are tabulated for various values of alpha, beta, Delta, eta, and lambda for the one-sample and two-sample t-tests for g = 10,000. Based on the proposed approach, to identify up to 90% of truly altered genes among the unknown number of truly altered genes, the estimated numbers of arrays needed appear to be manageable. For instance, when the standardized effect size is at least 2.0, the number of arrays needed is less than or equal to 14 for the two-sample t-test and is less than or equal to 10 for the one-sample t-test. As the cost per array declines, such array numbers become practical. The proposed method offers a simple, intuitive, and practical way to determine the number of arrays needed in microarray experiments in which the true correlation structure among the genes under investigation cannot be reasonably assumed. An example dataset is used to illustrate the use of the proposed approach to plan microarray experiments.

QA/QC: challenges and pitfalls facing the microarray community and regulatory agencies

The scientific community has been enthusiastic about DNA microarray technology for pharmacogenomic and toxicogenomic studies in the hope of advancing personalized medicine and drug development. The US Food and Drug Administration has been proactive in promoting the use of pharmacogenomic data in drug development and has issued a draft guidance for the pharmaceutical industry on data submissions. However, many challenges and pitfalls are facing the microarray community and regulatory agencies before microarray data can be reliably applied to support regulatory decision making. Four types of factors (i.e., technical, instrumental, computational and interpretative) affect the outcome of a microarray study, and a major concern about microarray studies has been the lack of reproducibility and accuracy. Intralaboratory data consistency is the foundation of reliable knowledge extraction and meaningful crosslaboratory or crossplatform comparisons; unfortunately, it has not been seriously evaluated and demonstrated in every study. Profound problems in data quality have been observed from analyzing published data sets, and many laboratories have been struggling with technical troubleshooting rather than generating reliable data of scientific significance. The microarray community and regulatory agencies must work together to establish a set of consensus quality assurance and quality control criteria for assessing and ensuring data quality, to identify critical factors affecting data quality, and to optimize and standardize microarray procedures so that biologic interpretation and decision-making are not based on unreliable data. These fundamental issues must be adequately addressed before microarray technology can be transformed from a research tool to clinical practices.

Overview of bioinformatics and its application to oral genomics

The "informatics revolution" in both bioinformatics and dental informatics will eventually change the way we practice dentistry. This convergence will play a pivotal role in creating a bridge of opportunity by integrating scientific and clinical specialties to promote the advances in treatment, risk assessment, diagnosis, therapeutics, and oral health-care outcome. Bioinformatics has been an emerging field in the biomedical research community and has been gaining momentum in dental medicine. This area has created a steady stream of large and complex genomic data, which has transformed the way a clinical or basic science researcher approaches genomic research. This application to dental medicine, termed "oral genomics", can aid in the molecular understanding of the genes and proteins, their interactions, pathways, and networks that are responsible for the development and progression of oral diseases and disorders. As the result of the Human Genome Project, new advances have prompted high-throughput technologies, such as DNA microarrays, which have become accepted tools in the biomedical research community. This manuscript reviews the two most commonly used microarray technologies, basic microarray data analysis, and the results from several ongoing oral cancer genomic studies.

Identification and handling of artifactual gene expression profiles emerging in microarray hybridization experiments

Mathematical methods of analysis of microarray hybridizations deal with gene expression profiles as elementary units. However, some of these profiles do not reflect a biologically relevant transcriptional response, but rather stem from technical artifacts. Here, we describe two technically independent but rationally interconnected methods for identification of such artifactual profiles. Our diagnostics are based on detection of deviations from uniformity, which is assumed as the main underlying principle of microarray design. Method 1 is based on detection of non-uniformity of microarray distribution of printed genes that are clustered based on the similarity of their expression profiles. Method 2 is based on evaluation of the presence of gene-specific microarray spots within the slides' areas characterized by an abnormal concentration of low/high differential expression values, which we define as 'patterns of differentials'. Applying two novel algorithms, for nested clustering (method 1) and for pattern detection (method 2), we can make a dual estimation of the profile's quality for almost every printed gene. Genes with artifactual profiles detected by method 1 may then be removed from further analysis. Suspicious differential expression values detected by method 2 may be either removed or weighted according to the probabilities of patterns that cover them, thus diminishing their input in any further data analysis.