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Next-generation risk assessment relies on mechanistic data from new approach methods, including transcriptome data. Various technologies, such as high-throughput targeted sequencing methods and microarray technologies based on hybridization with complementary probes, are used to determine differentially expressed genes (DEGs). The integration of data from different technologies requires a good understanding of the differences arising from the use of various technologies. To better understand the differences between the TempO-Seq platform and Affymetrix chip technology, whole-genome data for the volatile compound dimethylamine were compared. Selected DEGs were also confirmed using RTqPCR validation. Although the overlap of DEGs between TempO-Seq and Affymetrix was no higher than 37%, a comparison of the gene regulation in terms of log2fold changes revealed a very high concordance. RTqPCR confirmed the majority of DEGs from either platform in the examined dataset. Only a few conflicts were found (11%), while 22% were not confirmed, and 3% were not detected. Despite the observed differences between the two platforms, both can be validated using RTqPCR. Here we highlight some of the differences between the two platforms and discuss their applications in toxicology. [ABSTRACT FROM AUTHOR]

Abstract Next-generation risk assessment relies on mechanistic data from new approach methods, including transcriptome data. Various technologies, such as high-throughput targeted sequencing methods and microarray technologies based on hybridization with complementary probes, are used to determine differentially expressed genes (DEGs). The integration of data from different technologies requires a good understanding of the differences arising from the use of various technologies. To better understand the differences between the TempO-Seq platform and Affymetrix chip technology, whole-genome data for the volatile compound dimethylamine were compared. Selected DEGs were also confirmed using RTqPCR validation. Although the overlap of DEGs between TempO-Seq and Affymetrix was no higher than 37%, a comparison of the gene regulation in terms of log2fold changes revealed a very high concordance. RTqPCR confirmed the majority of DEGs from either platform in the examined dataset. Only a few conflicts were found (11%), while 22% were not confirmed, and 3% were not detected. Despite the observed differences between the two platforms, both can be validated using RTqPCR. Here we highlight some of the differences between the two platforms and discuss their applications in toxicology.

Dan-Lou tablets (DLT) effectively treat coronary heart disease (CHD). However, its pharmacological mechanism in CHD treatment requires further investigation. This study aimed to elucidate the underlying pharmacological mechanisms of DLT in the treatment of CHD through clinical trials, microarray research, bioinformatics analysis, and molecular mechanism research. In this study, DLT improved coagulation function, endothelial injury, and levels of lipids, metalloproteases, adhesion molecules, inflammatory mediators, and homocysteine. The results of molecular biology research demonstrated that DLT can increase the gene and protein expressions of meningioma expressed antigen 5 (MGEA5) and mouse doubleminute 2 (MDM2) and inhibited the gene and protein expressions of signal transcription and transcription activator 5 B (STAT5B), tropomyosin-1 (TPM1), and aromatic hydrocarbon receptor nuclear transpose (ARNT). The results indicate that DLT reduced the extent of vascular endothelial damage in CHD rats by reducing the expressions of STAT5B, TPM1, and MDM2; inhibiting the inflammatory reaction; and increasing the expressions of ARNT and MGEA5.

Motivation: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls.Results: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single- SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods.

Lung cancer is the leading cause of cancer death worldwide, and has a five-year survival rate of 18% [1]. MARK2 is a serine/threonine-protein kinase, and is a key component in the phosphorylation of microtubule-associated proteins [2,3]. A recent study published by Hubaux et al. found that microtubule affinity-regulating kinase 2 (MARK2) showed highly frequent DNA and RNA level disruption in lung cancer cell lines and independent non-small cell lung cancer (NSCLC) cohorts [4]. These alterations result in the acquisition of oncogenic properties in cell lines, such as increased viability and anchorage-independent growth. Furthermore, a microarray-based transcriptome analysis of three short hairpin RNA (shRNA)-mediated MARK2 knockdown lung adenocarcinoma cell lines (GEO#: GSE57966) revealed an association between MARK2 gene expression and cell cycle activation and DNA damage response. Here, we present a detailed description of transcriptome analysis to support the described role of MARK2 in promoting a malignant phenotype.

The multifocal nature of prostate cancer (PCa) creates a challenge to patients’ outcome prediction and their clinical management. An approach that scrutinizes every cancer focus is needed in order to generate a comprehensive evaluation of the disease, and by correlating to patients’ clinico-pathological information, specific prognostic biomarker can be identified. Our study utilized the Affymetrix SNP 6.0 Genome-wide assay to investigate forty-three fresh frozen PCa tissue foci from twenty-three patients. With a long clinical follow-up period that ranged from 2.0–9.7 (mean 5.4) years, copy number variation (CNV) data was evaluated for association with patients’ PSA status during follow-up. From our results, the loss of unique genes on 10q23.31 and 10q23.2–10q23.31 were identified to be significantly associated to PSA recurrence (p < 0.05). The implication of PTEN and FAS loss (10q23.31) support previous reports due to their critical roles in prostate carcinogenesis. Furthermore, we hypothesize that the PAPSS2 gene (10q23.2–10q23.31) may be functionally relevant in post-operative PSA recurrence because of its reported role in androgen biosynthesis. It is suggestive that the loss of the susceptible region on chromosome 10q, which implicates PTEN, FAS and PAPSS2 may serve as genetic predictors of PSA recurrence after radical prostatectomy.

Russian wheat aphid (Diuraphis noxia, Kurdjumov) feeding on susceptible Triticum aestivum L. leads to leaf rolling, chlorosis and plant death – symptoms not present in resistant lines. Although the effects of several D. noxia (Dn) resistance genes are known, none have been isolated or characterized. Wheat varieties expressing different Dn genes exhibit distinct modes of D. noxia resistance, such as antibiosis (Dn1), tolerance (Dn2), and antixenosis (Dn5). However, the mechanism whereby feeding aphids are perceived, and how subsequent transcriptional responses are partitioned into resistance categories, remains unclear. Here we report on downstream events in near-isogenic wheat lines containing different Dn genes after D. noxia biotype SA1 feeding. Transcripts involved in stress, signal transduction, photosynthesis, metabolism and gene regulation were differentially regulated during D. noxia feeding. Expression analyses using RT-qPCR and RNA hybridization, as well as enzyme activity profiling, provide evidence that the timing and intensity of pathways induced are critical in the development of particular modes of resistance. Pathways involved include the generation of kinase signalling cascades that lead to a sustained oxidative burst, and a hypersensitive response that is active during antibiosis. Tolerance is a passive resistance mechanism that acts through repair or de novo synthesis of photosystem proteins. Results further suggest that ethylene-mediated pathways are possibly involved in generating volatile compounds and cell wall fortification during the antixenosic response.

Tomato early blight is an important threat and it has capacity to reduce the production in all major tomato producing areas. Molecular mechanism underlying the resistance against this is not well known. Therefore we studied this system to search the possible mechanism of resistance, which includes pathogenesis related protein, and pathways and transcription factors, which are responsible for resistance against this pathogen using affymetrix gene chip for tomato. Their differential expressions have enhanced the biochemical and other related products, which have, direct or indirect role in stopping the penetration of mycelia in the host plant.

Atmospheric CO2 impacts all aspects of plant development. It has changed in the past and is predicted to change further on. Studies on the response of crop plants to low and elevated CO2 concerning growth, productivity and physiological processes are intense. In contrast, the molecular mechanisms of cellular CO2 exchange are still under discussion. At the same time it becomes more and more accepted that carbon dioxide is transported across cellular biomembranes by CO2 conducting aquaporins. Our recent study (Boudichevskaia et al., 2015) demonstrates that the lack of a single gene product – aquaporin AtPIP1;2 – resulted in massive transcriptional reprogramming in Arabidopsis as a consequence of reduced tissue CO2 diffusion rates. Therefore, the transcriptome data of the aquaporin AtPIP1;2 deficient line can be used in the comparative expression analyses for better understanding the role of aquaporins with regard to CO2 and water transport in plants. Here we describe a gene expression dataset generated for three biological replicates per genotype on Affymetrix platform. We provide detailed methods and analysis on microarray data which has been deposited in Gene Expression Omnibus (GEO): GSE62167. Additionally, we provide the R code for data preprocessing and quality control.

Astrocytes are considered to be an important contributor to central nervous system (CNS) disorders, particularly multiple sclerosis. The transcriptome of these cells is greatly affected by cytokines released by lymphocytes, penetrating the blood–brain barrier—in particular, the classical pro-inflammatory cytokine interferon-gamma (IFNγ). We report here the transcriptomal profiling of astrocytes treated using IFNγ and benztropine, a putative remyelinization agent. Our findings indicate that the expression of genes involved in antigen processing and presentation in astrocytes are significantly upregulated upon IFNγ exposure, emphasizing the critical role of this cytokine in the redirection of immune response towards self-antigens. Data reported herein support previous observations that the IFNγ-induced JAK-STAT signaling pathway may be regarded as a valuable target for pharmaceutical interventions. [ABSTRACT FROM AUTHOR]

Background: UK Biobank is a large prospective cohort study in the UK established by the Medical Research Council (MRC) and the Wellcome Trust to enable approved researchers to investigate the role of genetic factors, environmental exposures and lifestyle in the causes of major diseases of late and middle age. A wide range of phenotypic data has been collected at recruitment and has recently been enhanced by the UK Biobank Genotyping Project. All UK Biobank participants (500,000) have been genotyped on either the UK Biobank Axiom® Array or the Affymetrix UK BiLEVE Axiom® Array and the workflow for preparing samples for genotyping is described. The genetic data is hoped to provide further insight into the genetics of disease. All data, including the genetic data, is available for access to approved researchers. Data for two methods of DNA quantification (ultraviolet-visible spectroscopy [UV/Vis]) measured on the Trinean DropSenseTM 96 and PicoGreen®) were compared by two laboratories (UK Biobank and Affymetrix). Results: The sample processing workflow established at UK Biobank, for genotyping on the custom Affymetrix Axiom® array, resulted in high quality DNA (average DNA concentration 38.13 ng/μL, average 260/280 absorbance 1.91). The DNA generated high quality genotype data (average call rate 99.48% and pass rate 99.45%). The DNA concentration measured on the Trinean DropSenseTM 96 at UK Biobank correlated well with DNA concentration measured by PicoGreen® at Affymetrix (r = 0.85). Conclusions: The UK Biobank Genotyping Project demonstrated that the high throughput DNA extraction protocol described generates high quality DNA suitable for genotyping on the Affymetrix Axiom array. The correlation between DNA concentration derived from UV/Vis and PicoGreen® quantification methods suggests, in large-scale genetic studies involving two laboratories, it may be possible to remove the DNA quantification step in one laboratory without affecting downstream analyses. This would result in reductions in cost and time to complete the project, allowing generation of genetic data faster and cheaper. [ABSTRACT FROM AUTHOR]

Dan-Lou tablets (DLT) effectively treat coronary heart disease (CHD). However, its pharmacological mechanism in CHD treatment requires further investigation. This study aimed to elucidate the underlying pharmacological mechanisms of DLT in the treatment of CHD through clinical trials, microarray research, bioinformatics analysis, and molecular mechanism research. In this study, DLT improved coagulation function, endothelial injury, and levels of lipids, metalloproteases, adhesion molecules, inflammatory mediators, and homocysteine. The results of molecular biology research demonstrated that DLT can increase the gene and protein expressions of meningioma expressed antigen 5 (MGEA5) and mouse doubleminute 2 (MDM2) and inhibited the gene and protein expressions of signal transcription and transcription activator 5 B (STAT5B), tropomyosin-1 (TPM1), and aromatic hydrocarbon receptor nuclear transpose (ARNT). The results indicate that DLT reduced the extent of vascular endothelial damage in CHD rats by reducing the expressions of STAT5B, TPM1, and MDM2; inhibiting the inflammatory reaction; and increasing the expressions of ARNT and MGEA5., Competing Interests: All authors declare that they have no competing interests for this study.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Published by Elsevier Ltd.)

Hereditary non-polyposis colorectal cancer (HNPCC) is the commonest form of inherited colorectal cancer (CRC) predisposition and by definition describes families which conform to the Amsterdam Criteria or reiterations thereof. In ~50% of patients adhering to the Amsterdam criteria germline variants are identified in one of four DNA Mismatch repair (MMR) genes MLH1, MSH2, MSH6 and PMS2. Loss of function of any one of these genes results in a failure to repair DNA errors occurring during replication which can be most easily observed as DNA microsatellite instability (MSI)—a hallmark feature of this disease. The remaining 50% of patients without a genetic diagnosis of disease may harbour more cryptic changes within or adjacent to MLH1, MSH2, MSH6 or PMS2 or elsewhere in the genome. We used a high density cytogenetic array to screen for deletions or duplications in a series of patients, all of whom adhered to the Amsterdam/Bethesda criteria, to determine if genomic re-arrangements could account for a proportion of patients that had been shown not to harbour causative mutations as assessed by standard diagnostic techniques. The study has revealed some associations between copy number variants (CNVs) and HNPCC mutation negative cases and further highlights difficulties associated with CNV analysis.

Prostate cancer is widely observed to be biologically heterogeneous. Its heterogeneity is manifested histologically as multifocal prostate cancer, which is observed more frequently than unifocal prostate cancer. The clinical and prognostic significance of either focal cancer type is not fully established. To investigate prostate cancer heterogeneity, the genetic profiles of multifocal and unifocal prostate cancers were compared. Here, we report observations deduced from tumor-tumor comparison of copy number alteration data of both focal categories. Forty-one fresh frozen prostate cancer foci from 14 multifocal prostate cancers and eight unifocal prostate cancers were subjected to copy number variation analysis with the Affymetrix SNP 6.0 microarray tool. With the investigated cases, tumors obtained from a single prostate exhibited different genetic profiles of variable degrees. Further comparison identified no distinct genetic pattern or signatures specific to multifocal or unifocal prostate cancer. Our findings suggest that samples obtained from multiple sites of a single unifocal prostate cancer show as much genetic heterogeneity and variability as separate tumors obtained from a single multifocal prostate cancer.

Tomato early blight is an important threat due to its capacity to reduce the production in all major tomato producing areas. Molecular mechanisms underlying resistance to the causal organism are not well known. Therefore, we aimed to study tomato – Alternaria solani system to search the transcription factors and pathways which, are responsible for resistance to this fungi using, affymetrix gene chip for tomato. three hundred ninety five transcription factors were found to be differentially expressed at 24 h after inoculation with A. solani in the resistant genotype, EC-520,061, of tomato. Also, Zinc Finger Proteins, Ribosomal binding unit S4 and Auxin responsive transcription factors were found to play significant role in resistance. Their expression has enhanced the pathogenesis related proteins and also other proteins as well, which, have direct role in stopping the penetration of mycelia in host plant. [ABSTRACT FROM AUTHOR]

Recent reports suggest that aerobic exercise may boost the hypertrophic response to short-term resistance training. This study explored the effects of an acute aerobic exercise bout on the transcriptional response to subsequent resistance exercise. Ten moderately trained men performed ~45 min cycling on one leg followed by 4 x 7 maximal knee extensions for each leg, 15 min later. Thus, one limb performed aerobic and resistance exercise (AE + RE) while the opposing leg did resistance exercise only (RE). Biopsies were obtained from the vastus lateralis muscle of each leg 3 h after the resistance exercise bout. Using DNA microarray, we analyzed differences [≥1.5-fold, false discovery rate (FDR) ≤10%] in gene expression profiles for the two modes of exercise. There were 176 genes up (127)- or downregulated (49) by AE + RE compared with RE. Among the most significant differentially expressed genes were established markers for muscle growth and oxidative capacity, novel cytokines, transcription factors, and micro-RNAs (miRNAs). The most enriched functional categories were those linked to carbohydrate metabolism and transcriptional regulation. Upstream analysis revealed that vascular endothelial growth factor, cAMP-response element-binding protein, Tet methylcytosine dioxygenase, and mammalian target of rapamycin were regulators highly activated by AE + RE, whereas JnK, NF-κβ, MAPK, and several miRNAs were inhibited. Thus, aerobic exercise alters the skeletal muscle transcriptional signature of resistance exercise to initiate important gene programs promoting both myofiber growth and improved oxidative capacity. These results provide novel insight into human muscle adaptations to diverse exercise modes and offer the very first genomic basis explaining how aerobic exercise may augment, rather than compromise, muscle growth induced by resistance exercise. [ABSTRACT FROM AUTHOR]

Taking advantage of a progressive nonhuman primate model mimicking Parkinson's disease (PD) evolution, we monitored transcriptional fluctuations in the substantia nigra using Affymetrix microarrays in control (normal), saline-treated (normal), 6 days-treated (asymptomatic with 20% cell loss), 12 days-treated (asymptomatic with 40% cell loss) and 25 days-treated animals (fully parkinsonian with 85% cell loss). Two statistical methods were used to ascertain the regulation and real-time quantitative PCR was used to confirm their regulation. Surprisingly, the number of deregulated transcripts is limited at all time points and five clusters exhibiting different profiles were defined using a hierarchical clustering algorithm. Such profiles are likely to represent activation/deactivation of mechanisms of different nature. We briefly speculate about (i) the existence of yet unknown compensatory mechanisms is unraveled, (ii) the putative triggering of a developmental program in the mature brain in reaction to progressing degeneration and finally, (iii) the activation of mechanisms leading eventually to death in final stage. These data should help development of new therapeutic approaches either aimed at enhancing existing compensatory mechanisms or at protecting dopamine neurons.

The use of a constant fold-change to determine significant changes in gene expression has been widely accepted for its intuition and ease of use in microarray data analysis, but this concept has been increasingly criticized because it does not reflect signal intensity and can result in a substantial number of false positives and false negatives. To resolve this dilemma, we have analyzed 65 replicate Affymetrix chip-chip comparisons and determined a series of user adjustable signal-dependent thresholds which do not require replicates and offer a 95% confidence interval. Quantitative RT-PCR shows that such thresholds significantly improve the power to discriminate biological changes in mRNA from noise and reduce false calls compared to the traditional two-fold threshold. The user-friendly nature of this approach means that it can be easily applied by any user of microarray analysis, even those without any specialized knowledge of computational techniques or statistics. Noise is a function of signal intensity not only for Affymetrix data but also for cDNA array data, analysis of which may also be benefited by our methodology.

Abstract In spite of only a 1-2 per cent genomic DNA sequence difference, humans and chimpanzees differ considerably in behaviour and cognition. Affymetrix microarray technology provides a novel approach to addressing a long-term debate on whether the difference between humans and chimpanzees results from the alteration of gene expressions. Here, we used several statistical methods (distance method, two-sample t-tests, regularised t-tests, ANOVA and bootstrapping) to detect the differential expression pattern between humans and great apes. Our analysis shows that the pattern we observed before is robust against various statistical methods; that is, the pronounced expression changes occurred on the human lineage after the split from chimpanzees, and that the dramatic brain expression alterations in humans may be mainly driven by a set of genes with increased expression (up-regulated) rather than decreased expression (down-regulated).

Drought is one of the limiting environmental factors that affect crop production worldwide. Understanding the molecular mechanism of drought stress is the key to developing drought tolerant crop. In this experiment we performed expression profiling of tomato plants under water deficit conditions using microarray technology. The data set we generated (available in the NCBI/GEO database under GSE22304) has been analyzed to identify genes that are involved in the regulation of tomato's responses to drought.

Astrocytes are considered to be an important contributor to central nervous system (CNS) disorders, particularly multiple sclerosis. The transcriptome of these cells is greatly affected by cytokines released by lymphocytes, penetrating the blood–brain barrier—in particular, the classical pro-inflammatory cytokine interferon-gamma (IFNγ). We report here the transcriptomal profiling of astrocytes treated using IFNγ and benztropine, a putative remyelinization agent. Our findings indicate that the expression of genes involved in antigen processing and presentation in astrocytes are significantly upregulated upon IFNγ exposure, emphasizing the critical role of this cytokine in the redirection of immune response towards self-antigens. Data reported herein support previous observations that the IFNγ-induced JAK-STAT signaling pathway may be regarded as a valuable target for pharmaceutical interventions.

Background: Gene expression microarrays measure the levels of messenger ribonucleic acid (mRNA) in a sample using probe sequences that hybridize with transcribed regions. These probe sequences are designed using a reference genome for the relevant species. However, most model organisms and all humans have genomes that deviate from their reference. These variations, which include single nucleotide polymorphisms, insertions of additional nucleotides, and nucleotide deletions, can affect the microarray's performance. Genetic experiments comparing individuals bearing different population-associated single nucleotide polymorphisms that intersect microarray probes are therefore subject to systemic bias, as the reduction in binding efficiency due to a technical artifact is confounded with genetic differences between parental strains. This problem has been recognized for some time, and earlier methods of compensation have attempted to identify probes affected by genome variants using statistical models. These methods may require replicate microarray measurement of gene expression in the relevant tissue in inbred parental samples, which are not always available in model organisms and are never available in humans. Results: By using sequence information for the genomes of organisms under investigation, potentially problematic probes can now be identified a priori. However, there is no published software tool that makes it easy to eliminate these probes from an annotation. I present equalizer, a software package that uses genome variant data to modify annotation files for the commonly used Affymetrix IVT and Gene/Exon platforms. These files can be used by any microarray normalization method for subsequent analysis. I demonstrate how use of equalizer on experiments mapping germline influence on gene expression in a genetic cross between two divergent mouse species and in human samples significantly reduces probe hybridization-induced bias, reducing false positive and false negative findings. Conclusions: The equalizer package reduces probe hybridization bias from experiments performed on the Affymetrix microarray platform, allowing accurate assessment of germline influence on gene expression. [ABSTRACT FROM AUTHOR]

Background: The well-known Genome-Wide Association Studies (GWAS) had led to many scientific discoveries using SNP data. Even so, they were not able to explain the full heritability of complex diseases. Now, other structural variants like copy number variants or DNA inversions, either germ-line or in mosaicism events, are being studies. We present the R package affy2sv to pre-process Affymetrix CytoScan HD/750k array (also for Genome-Wide SNP 5.0/6.0 and Axiom) in structural variant studies. Results: We illustrate the capabilities of affy2sv using two different complete pipelines on real data. The first one performing a GWAS and a mosaic alterations detection study, and the other detecting CNVs and performing an inversion calling. Conclusion: Both examples presented in the article show up how affy2sv can be used as part of more complex pipelines aimed to analyze Affymetrix SNP arrays data in genetic association studies, where different types of structural variants are considered. [ABSTRACT FROM AUTHOR]

Background: In recent years, the use of genomic information in livestock species for genetic improvement, association studies and many other fields has become routine. In order to accommodate different market requirements in terms of genotyping cost, manufacturers of single nucleotide polymorphism (SNP) arrays, private companies and international consortia have developed a large number of arrays with different content and different SNP density. The number of currently available SNP arrays differs among species: ranging from one for goats to more than ten for cattle, and the number of arrays available is increasing rapidly. However, there is limited or no effort to standardize and integrate array- specific (e.g. SNP IDs, allele coding) and species-specific (i.e. past and current assemblies) SNP information. Results: Here we present SNPchiMp v.3, a solution to these issues for the six major livestock species (cow, pig, horse, sheep, goat and chicken). Original data was collected directly from SNP array producers and specific international genome consortia, and stored in a MySQL database. The database was then linked to an open-access web tool and to public databases. SNPchiMp v.3 ensures fast access to the database (retrieving within/across SNP array data) and the possibility of annotating SNP array data in a user-friendly fashion. Conclusions: This platform allows easy integration and standardization, and it is aimed at both industry and research. It also enables users to easily link the information available from the array producer with data in public databases, without the need of additional bioinformatics tools or pipelines. In recognition of the open-access use of Ensembl resources, SNPchiMp v.3 was officially credited as an Ensembl E!mpowered tool. Availability at http://bioinformatics.tecnoparco.org/ SNPchimp. [ABSTRACT FROM AUTHOR]

The multifocal nature of prostate cancer (PCa) creates a challenge to patients' outcome prediction and their clinical management. An approach that scrutinizes every cancer focus is needed in order to generate a comprehensive evaluation of the disease, and by correlating to patients' clinico-pathological information, specific prognostic biomarker can be identified. Our study utilized the Affymetrix SNP 6.0 Genome-wide assay to investigate forty-three fresh frozen PCa tissue foci from twenty-three patients. With a long clinical follow-up period that ranged from 2.0-9.7 (mean 5.4) years, copy number variation (CNV) data was evaluated for association with patients' PSA status during follow-up. From our results, the loss of unique genes on 10q23.31 and 10q23.2-10q23.31 were identified to be significantly associated to PSA recurrence (p < 0.05). The implication of PTEN and FAS loss (10q23.31) support previous reports due to their critical roles in prostate carcinogenesis. Furthermore, we hypothesize that the PAPSS2 gene (10q23.2-10q23.31) may be functionally relevant in post-operative PSA recurrence because of its reported role in androgen biosynthesis. It is suggestive that the loss of the susceptible region on chromosome 10q, which implicates PTEN, FAS and PAPSS2 may serve as genetic predictors of PSA recurrence after radical prostatectomy. [ABSTRACT FROM AUTHOR]

Background/aim: Despite the rise in type 2 diabetes prevalence worldwide, we do not have a method for early risk prediction. The predictive ability of genetic models has been found to be little or negligible so far. In this study, we aimed to develop a better early risk prediction method for type 2 diabetes. Materials and methods: We used phenotypic and genotypic data from the Nurses' Health Study and Health Professionals' Follow-up Study cohorts and analyzed them by using binary logistic regression. Results: Phenotypic variables yielded 70.7% overall correctness and an area under the curve (AUC) of 0.77. With regard to genotype, 798 single nucleotide polymorphisms with P-values of lower than 1.0E-3 yielded 90.0% correctness and an AUC of 0.965. This is the highest score in the literature, even including the scores obtained with phenotypic variables. The additive contributions of phenotype and genotype increased the overall correctness to 92.9% and the AUC to 0.980. Conclusion: Our results showed that genotype could be used to obtain a higher score, which could enable early risk prediction. These findings present new possibilities for genome-wide association study analysis in terms of discovering missing heritability. These results should be confirmed by follow-up studies. [ABSTRACT FROM AUTHOR]

Background Microarrays have revolutionized breast cancer (BC) research by enabling studies of gene expression on a transcriptome-wide scale. Recently, RNA-Sequencing (RNA-Seq) has emerged as an alternative for precise readouts of the transcriptome. To date, no study has compared the ability of the two technologies to quantify clinically relevant individual genes and microarray-derived gene expression signatures (GES) in a set of BC samples encompassing the known molecular BC's subtypes. To accomplish this, the RNA from 57 BCs representing the four main molecular subtypes (triple negative, HER2 positive, luminal A, luminal B), was profiled with Affymetrix HG-U133 Plus 2.0 chips and sequenced using the Illumina HiSeq 2000 platform. The correlations of three clinically relevant BC genes, six molecular subtype classifiers, and a selection of 21 GES were evaluated. Results 16,097 genes common to the two platforms were retained for downstream analysis. Genewise comparison of microarray and RNA-Seq data revealed that 52% had a Spearman's correlation coefficient greater than 0.7 with highly correlated genes displaying significantly higher expression levels. We found excellent correlation between microarray and RNA-Seq for the estrogen receptor (ER; rs =0.973; 95%CI: 0.971-0.975), progesterone receptor (PgR; rs =0.95; 0.947-0.954), and human epidermal growth factor receptor 2 (HER2; rs =0.918; 0.912- 0.923), while a few discordances between ER and PgR quantified by immunohistochemistry and RNA-Seq/microarray were observed. All the subtype classifiers evaluated agreed well (Cohen's kappa coefficients >0.8) and all the proliferation-based GES showed excellent Spearman correlations between microarray and RNA-Seq (all rs >0.965). Immune-, stromaand pathway-based GES showed a lower correlation relative to prognostic signatures (all rs >0.6). Conclusions To our knowledge, this is the first study to report a systematic comparison of RNA-Seq to microarray for the evaluation of single genes and GES clinically relevant to BC. According to our results, the vast majority of single gene biomarkers and well-established GES can be reliably evaluated using the RNA-Seq technology. [ABSTRACT FROM AUTHOR]

Human papillomavirus 16 (HPV16) is causative in human cancer. The E2 protein regulates transcription from and replication of the viral genome; the role of E2 in regulating the host genome has been less well studied. We have expressed HPV16 E2 (E2) stably in U2OS cells; these cells tolerate E2 expression well and gene expression analysis identified 74 genes showing differential expression specific to E2. Analysis of published gene expression data sets during cervical cancer progression identified 20 of the genes as being altered in a similar direction as the E2 specific genes. In addition, E2 altered the splicing of many genes implicated in cancer and cell motility. The E2 expressing cells showed no alteration in cell growth but were altered in cell motility, consistent with the E2 induced altered splicing predicted to affect this cellular function. The results present a model system for investigating E2 regulation of the host genome. [ABSTRACT FROM AUTHOR]

Background A large number of single nucleotide polymorphisms (SNPs) have been identified in common carp (Cyprinus carpio) but, as yet, no \high-throughput genotyping platform is available for this species. C. carpio is an important aquaculture species that accounts for nearly 14% of freshwater aquaculture production worldwide. We have developed an array for C. carpio with 250,000 SNPs and evaluated its performance using samples from various strains of C. carpio. Results The SNPs used on the array were selected from two resources: the transcribed sequences from RNA-seq data of four strains of C. carpio, and the genome re-sequencing data of five strains of C. carpio. The 250,000 SNPs on the resulting array are distributed evenly across the reference C.carpio genome with an average spacing of 6.6 kb. To evaluate the SNP array, 1,072 C. carpio samples were collected and tested. Of the 250,000 SNPs on the array, 185,150 (74.06%) were found to be polymorphic sites. Genotyping accuracy was checked using genotyping data from a group of full-siblings and their parents, and over 99.8% of the qualified SNPs were found to be reliable. Analysis of the linkage disequilibrium on all samples and on three domestic C.carpio strains revealed that the latter had the longer haplotype blocks. We also evaluated our SNP array on 80 samples from eight species related to C. carpio, with from 53,526 to 71,984 polymorphic SNPs. An identity by state analysis divided all the samples into three clusters; most of the C. carpio strains formed the largest cluster. Conclusions The Carp SNP array described here is the first high-throughput genotyping platform for C. carpio. Our evaluation of this array indicates that it will be valuable for farmed carp and for genetic and population biology studies in C. carpio and related species. [ABSTRACT FROM AUTHOR]

Domestic broiler chickens rapidly accumulate fat and are naturally hyperglycemic and insulin resistant, making them an attractive model for studies of human obesity. We previously demonstrated that short-term (5 h) fasting rapidly upregulates pathways of fatty acid oxidation in broiler chickens and proposed that activation of these pathways may promote leanness. The objective of the current study was to characterize adipose tissue from relatively lean and fatty lines of chickens and determine if heritable leanness in chickens is associated with activation of some of the same pathways induced by fasting. We compared adipose gene expression and metabolite profiles in white adipose tissue of lean Leghorn and Fayoumi breeds to those of fattier commercial broiler chickens. Both lipolysis and expression of genes involved in fatty acid oxidation were upregulated in lean chickens compared with broilers. Although there were strong similarities between the lean lines compared with broilers, distinct expression signatures were also found between Fayoumi and Leghorn, including differences in adipogenic genes. Similarities between genetically lean and fasted chickens suggest that fatty acid oxidation in white adipose tissue is adaptively coupled to lipolysis and plays a role in heritable differences in fatness. Unique signatures of leanness in Fayoumi and Leghorn lines highlight distinct pathways that may provide insight into the basis for leanness in humans. Collectively, our results provide a number of future directions through which to fully exploit chickens as unique models for the study of human obesity and adipose metabolism. [ABSTRACT FROM AUTHOR]

Genomic DNA-based probe selection by using high density oligonucleotide arrays has recently been applied to heterologous species (Xspecies). With the advent of this new approach, researchers are able to study the genome and transcriptome of a non-model or an underutilised crop species through current state-of-the-art microarray platforms. However, a software package with a graphical user interface (GUI) to analyse and parse the oligonucleotide probe pair level data is still lacking when an experiment is designed on the basis of this cross species approach. A novel computer program called Pigeons has been developed for customised array data analysis to allow the user to import and analyse Affymetrix GeneChip® probe level data through XSpecies. One can determine empirical boundaries for removing poor probes based on genomic hybridisation of the test species to the Xspecies array, followed by making a species-specific Chip Description File (CDF) file for transcriptomics in the heterologous species, or Pigeons can be used to examine an experimental design to identify potential Single-Feature Polymorphisms (SFPs) at the DNA or RNA level. Pigeons is also focused around visualization and interactive analysis of the datasets. The software with its manual (the current release number version 1.2.1) is freely available at the website of the Nottingham Arabidopsis Stock Centre (NASC). [ABSTRACT FROM AUTHOR]

Hereditary non-polyposis colorectal cancer (HNPCC) is the commonest form of inherited colorectal cancer (CRC) predisposition and by definition describes families which conform to the Amsterdam Criteria or reiterations thereof. In ~50% of patients adhering to the Amsterdam criteria germline variants are identified in one of four DNA Mismatch repair (MMR) genes MLH1, MSH2, MSH6 and PMS2. Loss of function of any one of these genes results in a failure to repair DNA errors occurring during replication which can be most easily observed as DNA microsatellite instability (MSI)--a hallmark feature of this disease. The remaining 50% of patients without a genetic diagnosis of disease may harbour more cryptic changes within or adjacent to MLH1, MSH2, MSH6 or PMS2 or elsewhere in the genome. We used a high density cytogenetic array to screen for deletions or duplications in a series of patients, all of whom adhered to the Amsterdam/Bethesda criteria, to determine if genomic re-arrangements could account for a proportion of patients that had been shown not to harbour causative mutations as assessed by standard diagnostic techniques. The study has revealed some associations between copy number variants (CNVs) and HNPCC mutation negative cases and further highlights difficulties associated with CNV analysis. [ABSTRACT FROM AUTHOR]

Background: High-throughput profiling of human tissues typically yield as results the gene lists comprised of a mix of relevant molecular entities with multiple false positives that obstruct the translation of such results into mechanistic hypotheses. From general probabilistic considerations, gene lists distilled for the mechanistically relevant components can be far more useful for subsequent experimental design or data interpretation. Results: The input candidate gene lists were processed into different tiers of evidence consistency established by enrichment analysis across subsets of the same experiments and across different experiments and platforms. The cut-offs were established empirically through ontological and semantic enrichment; resultant shortened gene list was re-expanded by Ingenuity Pathway Assistant tool. The resulting sub-networks provided the basis for generating mechanistic hypotheses that were partially validated by literature search. This approach differs from previous consistency-based studies in that the cut-off on the Receiver Operating Characteristic of the true-false separation process is optimized by flexible selection of the consistency building procedure. The gene list distilled by this analytic technique and its network representation were termed Compact Disease Model (CDM). Here we present the CDM signature for the study of early-stage Alzheimer’s disease. The integrated analysis of this gene signature allowed us to identify the protein traffic vesicles as prominent players in the pathogenesis of Alzheimer’s. Considering the distances and complexity of protein trafficking in neurons, it is plausible that spontaneous protein misfolding along with a shortage of growth stimulation result in neurodegeneration. Several potentially overlapping scenarios of early-stage Alzheimer pathogenesis have been discussed, with an emphasis on the protective effects of AT-1 mediated antihypertensive response on cytoskeleton remodeling, along with neuronal activation of oncogenes, luteinizing hormone signaling and insulin-related growth regulation, forming a pleiotropic model of its early stages. Alignment with emerging literature confirmed many predictions derived from early-stage Alzheimer’s disease’ CDM. Conclusions: A flexible approach for high-throughput data analysis, the Compact Disease Model generation, allows extraction of meaningful, mechanism-centered gene sets compatible with instant translation of the results into testable hypotheses. [ABSTRACT FROM AUTHOR]

Prostate cancer is widely observed to be biologically heterogeneous. Its heterogeneity is manifested histologically as multifocal prostate cancer, which is observed more frequently than unifocal prostate cancer. The clinical and prognostic significance of either focal cancer type is not fully established. To investigate prostate cancer heterogeneity, the genetic profiles of multifocal and unifocal prostate cancers were compared. Here, we report observations deduced from tumor-tumor comparison of copy number alteration data of both focal categories. Forty-one fresh frozen prostate cancer foci from 14 multifocal prostate cancers and eight unifocal prostate cancers were subjected to copy number variation analysis with the Affymetrix SNP 6.0 microarray tool. With the investigated cases, tumors obtained from a single prostate exhibited different genetic profiles of variable degrees. Further comparison identified no distinct genetic pattern or signatures specific to multifocal or unifocal prostate cancer. Our findings suggest that samples obtained from multiple sites of a single unifocal prostate cancer show as much genetic heterogeneity and variability as separate tumors obtained from a single multifocal prostate cancer. [ABSTRACT FROM AUTHOR]

Background: Many gene expression normalization algorithms exist for Affymetrix GeneChip microarrays. The most popular of these is RMA, primarily due to the precision and low noise produced during the process. A significant strength of this and similar approaches is the use of the entire set of arrays during both normalization and model-based estimation of signal. However, this leads to differing estimates of expression based on the starting set of arrays, and estimates can change when a single, additional chip is added to the set. Additionally, outlier chips can impact the signals of other arrays, and can themselves be skewed by the majority of the population. Results: We developed an approach, termed IRON, which uses the best-performing techniques from each of several popular processing methods while retaining the ability to incrementally renormalize data without altering previously normalized expression. This combination of approaches results in a method that performs comparably to existing approaches on artificial benchmark datasets (i.e. spike-in) and demonstrates promising improvements in segregating true signals within biologically complex experiments. Conclusions: By combining approaches from existing normalization techniques, the IRON method offers several advantages. First, IRON normalization occurs pair-wise, thereby avoiding the need for all chips to be normalized together, which can be important for large data analyses. Secondly, the technique does not require similarity in signal distribution across chips for normalization, which can be important for maintaining biologically relevant differences in a heterogeneous background. Lastly, IRON introduces fewer post-processing artifacts, particularly in data whose behavior violates common assumptions. Thus, the IRON method provides a practical solution to common needs of expression analysis. A software implementation of IRON is available at [http://gene.moffitt.org/ libaffy/]. [ABSTRACT FROM AUTHOR]

Background Genomic complexity can predict the clinical course of patients affected by chronic lymphocytic leukemia (CLL) with a normal FISH. However, large studies are still lacking. Here, we analyzed a large series of CLL patients and also carried out the so far largest comparison of FISH versus single-nucleotide polymorphism (SNP) array in this disease. Patients and methods SNP-array data were derived from a previously reported dataset. Results Seventy-seven of 329 CLL patients (23%) presented with a normal FISH. At least one large (>5 Mb) genomic aberration was detected by SNP array in 17 of 77 patients (22%); this finding significantly affected TTT. There was no correlation with the presence of TP53 mutations. In multivariate analysis, including age, Binet stage, IGHV genes mutational status and large genomic lesion, the latter three factors emerged as independent prognosticators. The concordance between FISH and SNP array varied between 84 and 97%, depending on the specific genomic locus investigated. Conclusions SNP array detected additional large genomic aberrations not covered by the standard FISH panel predicting the outcome of CLL patients. [ABSTRACT FROM AUTHOR]

Background Standard 3' Affymetrix gene expression arrays have contributed a significantly higher volume of existing gene expression data than other microarray platforms. These arrays were designed to identify differentially expressed genes, but not their alternatively spliced transcript forms. No resource can currently identify expression pattern of specific mRNA forms using these microarray data, even though it is possible to do this. Results We report a web server for expression profiling of alternatively spliced transcripts using microarray data sets from 31 standard 3' Affymetrix arrays for human, mouse and rat species. The tool has been experimentally validated for mRNAs transcribed or not-detected in a human disease condition (non-obstructive azoospermia, a male infertility condition). About 4000 gene expression datasets were downloaded from a public repository. 'Good probes' with complete coverage and identity to latest reference transcript sequences were first identified. Using them, 'Transcript specific probe-clusters' were derived for each platform and used to identify expression status of possible transcripts. The web server can lead the user to datasets corresponding to specific tissues, conditions via identifiers of the microarray studies or hybridizations, keywords, official gene symbols or reference transcript identifiers. It can identify, in the tissues and conditions of interest, about 40% of known transcripts as 'transcribed', 'not-detected' or 'differentially regulated'. Corresponding additional information for probes, genes, transcripts and proteins can be viewed too. We identified the expression of transcripts in a specific clinical condition and validated a few of these transcripts by experiments (using reverse transcription followed by polymerase chain reaction). The experimental observations indicated higher agreements with the web server results, than contradictions. The tool is accessible at http://resource.ibab.ac.in/TIPMaP. Conclusion The newly developed online tool forms a reliable means for identification of alternatively spliced transcript-isoforms that may be differentially expressed in various tissues, cell types or physiological conditions. Thus, by making better use of existing data, TIPMaP avoids the dependence on precious tissue-samples, in experiments with a goal to establish expression profiles of alternative splice forms - at least in some cases. [ABSTRACT FROM AUTHOR]

The development of microarray technology for highthroughput measurement of gene expressions is proving a powerful means for studying the transcriptome on a genomic scale and across diverse biological systems and experimental designs. The technology has grown rapidly in academia, medicine and the pharmaceutical, biotechnology, agrochemical and food industries. The technology confers the freedom to conduct experiments in its multiple platforms and attends to the extensive molecular surveillance of cells and tissues. Despite a large number of studies available in the literature comparing results between the existing platforms remains a challenging task in microarray technology. In this direction, we have developed an approach that allows integration of microarray experiments between two of its platforms, i.e. Affymetrix ® and cDNA microarrays. In this communication, we elaborate on our approach and present our validation. In addition, we have also examined our approach to evaluate where it stands amid a few existing methods. [ABSTRACT FROM AUTHOR]

The microarray-based analysis of gene expression has become a workhorse for biomedical research. Managing the amount and diversity of data that such experiments produce is a task that must be supported by appropriate software tools, which led to the creation of literally hundreds of systems. In consequence, choosing the right tool for a given project is difficult even for the expert. We report on the results of a survey encompassing 78 of such tools, of which 22 were inspected in detail and seven were tested hands-on. We report on our experiences with a focus on completeness of functionality, ease-of-use, and necessary effort for installation and maintenance. Thereby, our survey provides a valuable guideline for any project considering the use of a microarray data management system. It reveals which tasks are covered by mature tools and also shows that important requirements, especially in the area of integrated analysis of different experimental data, are not yet met satisfyingly by existing systems. [ABSTRACT FROM PUBLISHER]

The use of microarray technology to measure gene expression on a genome-wide scale has been well established for more than a decade. Methods to process and analyse the vast quantity of expression data generated by a typical microarray experiment are similarly well-established. The Affymetrix Exon 1.0 ST array is a relatively new type of array, which has the capability to assess expression at the individual exon level. This allows a more comprehensive analysis of the transcriptome, and in particular enables the study of alternative splicing, a gene regulation mechanism important in both normal conditions and in diseases. Some aspects of exon array data analysis are shared with those for standard gene expression data but others present new challenges that have required development of novel tools. Here, I will introduce the exon array and present a detailed example tutorial for analysis of data generated using this platform. [ABSTRACT FROM PUBLISHER]

Abstract: Pseudotrisomy 13 syndrome is characterised by holoprosencephaly with or without polydactyly, but with a normal karyotype. The genetic cause of this syndrome remains unclear, but it is thought to be autosomal recessive. In order to identify possible candidate genes, we identified regions of homozygosity in the DNA of an affected foetus, which was the seventh pregnancy of a healthy non-consanguineous Cook Island Maori couple; this ethnic group derives from a small founder population. Several large regions of homozygosity were identified using a high density array. We excluded two candidate genes that lay within these regions, and suggest that Pseudotrisomy 13 syndrome might not be monogenic and that a larger cohort of patients should be analysed using high density dosage/SNP arrays as well as whole exome sequencing in order to clarify the genetic underpinning of this rare syndrome. [Copyright &y& Elsevier]