Correcting for intra-experiment variation in Illumina BeadChip data is necessary to generate robust gene-expression profiles

Transcriptomic and Proteomic Insights into Host Immune Responses in Pediatric Severe Malarial Anemia: Dysregulation in HSP60-70-TLR2/4 Signaling and Altered Glutamine Metabolism

Severe malarial anemia (SMA, Hb < 6.0 g/dL) is a leading cause of childhood morbidity and mortality in holoendemic Plasmodium falciparum transmission zones. This study explored the entire expressed human transcriptome in whole blood from 66 Kenyan children with non-SMA (Hb ≥ 6.0 g/dL, n = 41) and SMA (n = 25), focusing on host immune response networks. RNA-seq analysis revealed 6862 differentially expressed genes, with equally distributed up-and down-regulated genes, indicating a complex host immune response. Deconvolution analyses uncovered leukocytic immune profiles indicative of a diminished antigenic response, reduced immune priming, and polarization toward cellular repair in SMA. Weighted gene co-expression network analysis revealed that immune-regulated processes are central molecular distinctions between non-SMA and SMA. A top dysregulated immune response signaling network in SMA was the HSP60-HSP70-TLR2/4 signaling pathway, indicating altered pathogen recognition, innate immune activation, stress responses, and antigen recognition. Validation with high-throughput gene expression from a separate cohort of Kenyan children (n = 50) with varying severities of malarial anemia (n = 38 non-SMA and n = 12 SMA) confirmed the RNA-seq findings. Proteomic analyses in 35 children with matched transcript and protein abundance (n = 19 non-SMA and n = 16 SMA) confirmed dysregulation in the HSP60-HSP70-TLR2/4 signaling pathway. Additionally, glutamine transporter and glutamine synthetase genes were differentially expressed, indicating altered glutamine metabolism in SMA. This comprehensive analysis underscores complex immune dysregulation and novel pathogenic features in SMA.

A multi-omics dataset of human transcriptome and proteome stable reference

The development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. The transcriptome and proteome of most cell lines shift during culturing, which limits their applicability as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (r = 0.983~0.997) and proteome (r = 0.966~0.988 for data-dependent acquisition, r = 0.970~0.994 for data-independent acquisition) after 9 subculturing generations, which allows this steady standard sample to be consistently produced on an industrial scale in long term. Moreover, this stability was maintained across labs and platforms. In sum, our study provides omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.

Mitochondrial respiration variability and simulations in human skeletal muscle: The Gene SMART study

Mitochondrial respiration using the oxygraph‐2k respirometer (Oroboros) is widely used to estimate mitochondrial capacity in human skeletal muscle. Here, we measured mitochondrial respiration variability, in a relatively large sample, and for the first time, using statistical simulations, we provide the sample size required to detect meaningful respiration changes following lifestyle intervention. Muscle biopsies were taken from healthy, young men from the Gene SMART cohort, at multiple time points. We utilized samples for each measurement with two technical repeats using two respirometer chambers (n = 160 pairs of same muscle after removal of low‐quality samples). We measured the Technical Error of measurement (TE_M) and the coefficient of variation (CV) for each mitochondrial complex. There was a high correlation between measurements from the two chambers (R > 0.7 P < .001) for all complexes, but the TE_M was large (7.9‐27 pmol s⁻¹ mg⁻¹; complex dependent), and the CV was >15% for all complexes. We performed statistical simulations of a range of effect sizes at 80% power and found that 75 participants (with duplicate measurements) are required to detect a 6% change in mitochondrial respiration after an intervention, while for interventions with 11% effect size, ~24 participants are sufficient. The high variability in respiration suggests that the typical sample sizes in exercise studies may not be sufficient to capture exercise‐induced changes.

An airway epithelial IL-17A response signature identifies a steroid-unresponsive COPD patient subgroup

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a heterogeneous smoking-related disease characterized by airway obstruction and inflammation. This inflammation may persist even after smoking cessation and responds variably to corticosteroids. Personalizing treatment to biologically similar "molecular phenotypes" may improve therapeutic efficacy in COPD. IL-17A is involved in neutrophilic inflammation and corticosteroid resistance, and thus may be particularly important in a COPD molecular phenotype.

METHODS

We generated a gene expression signature of IL-17A response in bronchial airway epithelial brushings from smokers with and without COPD (n = 238), and validated it using data from 2 randomized trials of IL-17 blockade in psoriasis. This IL-17 signature was related to clinical and pathologic characteristics in 2 additional human studies of COPD: (a) SPIROMICS (n = 47), which included former and current smokers with COPD, and (b) GLUCOLD (n = 79), in which COPD participants were randomized to placebo or corticosteroids.

RESULTS

The IL-17 signature was associated with an inflammatory profile characteristic of an IL-17 response, including increased airway neutrophils and macrophages. In SPIROMICS the signature was associated with increased airway obstruction and functional small airways disease on quantitative chest CT. In GLUCOLD the signature was associated with decreased response to corticosteroids, irrespective of airway eosinophilic or type 2 inflammation.

CONCLUSION

These data suggest that a gene signature of IL-17 airway epithelial response distinguishes a biologically, radiographically, and clinically distinct COPD subgroup that may benefit from personalized therapy.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01969344.

FUNDING

Primary support from the NIH, grants K23HL123778, K12HL11999, U19AI077439, DK072517, U01HL137880, K24HL137013 and R01HL121774 and contracts HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C and HHSN268200900020C.

Alternative empirical Bayes models for adjusting for batch effects in genomic studies

Background

Combining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies.

Results

Here we contribute multiple methods and software tools for improved combination and analysis of data from multiple batches. In particular, we provide batch effect solutions for cases where the severity of the batch effects is not extreme, and for cases where one high-quality batch can serve as a reference, such as the training set in a biomarker study. We illustrate our approaches and software in both simulated and real data scenarios.

Conclusions

We demonstrate the value of these new contributions compared to currently established approaches in the specified batch correction situations.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2263-6) contains supplementary material, which is available to authorized users.

Re-evaluating data quality of dog mitochondrial, Y chromosomal, and autosomal SNPs genotyped by SNP array

Quality deficiencies in single nucleotide polymorphism (SNP) analyses have important implications. We used missingness rates to investigate the quality of a recently published dataset containing 424 mitochondrial, 211 Y chromosomal, and 160 432 autosomal SNPs generated by a semicustom Illumina SNP array from 5 392 dogs and 14 grey wolves. Overall, the individual missingness rate for mitochondrial SNPs was ~43.8%, with 980 (18.1%) individuals completely missing mitochondrial SNP genotyping (missingness rate=1). In males, the genotype missingness rate was ~28.8% for Y chromosomal SNPs, with 374 males recording rates above 0.96. These 374 males also exhibited completely failed mitochondrial SNPs genotyping, indicative of a batch effect. Individual missingness rates for autosomal markers were greater than zero, but less than 0.5. Neither mitochondrial nor Y chromosomal SNPs achieved complete genotyping (locus missingness rate=0), whereas 5.9% of autosomal SNPs had a locus missingness rate=1. The high missingness rates and possible batch effect show that caution and rigorous measures are vital when genotyping and analyzing SNP array data for domestic animals. Further improvements of these arrays will be helpful to future studies.

Tumour sampling method can significantly influence gene expression profiles derived from neoadjuvant window studies

Patient-matched transcriptomic studies using tumour samples before and after treatment allow inter-patient heterogeneity to be controlled, but tend not to include an untreated comparison. Here, Illumina BeadArray technology was used to measure dynamic changes in gene expression from thirty-seven paired diagnostic core and surgically excised breast cancer biopsies obtained from women receiving no treatment prior to surgery, to determine the impact of sampling method and tumour heterogeneity. Despite a lack of treatment and perhaps surprisingly, consistent changes in gene expression were identified during the diagnosis-surgery interval (48 up, 2 down; Siggenes FDR 0.05) in a manner independent of both subtype and sampling-interval length. Instead, tumour sampling method was seen to directly impact gene expression, with similar effects additionally identified in six published breast cancer datasets. In contrast with previous findings, our data does not support the concept of a significant wounding or immune response following biopsy in the absence of treatment and instead implicates a hypoxic response following the surgical biopsy. Whilst sampling-related gene expression changes are evident in treated samples, they are secondary to those associated with response to treatment. Nonetheless, sampling method remains a potential confounding factor for neoadjuvant study design.

Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data

Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data.

A Regulatory T-Cell Gene Signature Is a Specific and Sensitive Biomarker to Identify Children With New-Onset Type 1 Diabetes

Type 1 diabetes (T1D) is caused by immune-mediated destruction of insulin-producing β-cells. Insufficient control of autoreactive T cells by regulatory T cells (Tregs) is believed to contribute to disease pathogenesis, but changes in Treg function are difficult to quantify because of the lack of Treg-exclusive markers in humans and the complexity of functional experiments. We established a new way to track Tregs by using a gene signature that discriminates between Tregs and conventional T cells regardless of their activation states. The resulting 31-gene panel was validated with the NanoString nCounter platform and then measured in sorted CD4(+)CD25(hi)CD127(lo) Tregs from children with T1D and age-matched control subjects. By using biomarker discovery analysis, we found that expression of a combination of six genes, including TNFRSF1B (CD120b) and FOXP3, was significantly different between Tregs from subjects with new-onset T1D and control subjects, resulting in a sensitive (mean ± SD 0.86 ± 0.14) and specific (0.78 ± 0.18) biomarker algorithm. Thus, although the proportion of Tregs in peripheral blood is similar between children with T1D and control subjects, significant changes in gene expression can be detected early in disease process. These findings provide new insight into the mechanisms underlying the failure to control autoimmunity in T1D and might lead to a biomarker test to monitor Tregs throughout disease progression.

Comparative Analysis of Matrix Metalloproteinase Family Members Reveals That MMP9 Predicts Survival and Response to Temozolomide in Patients with Primary Glioblastoma

Background

Glioblastoma multiform (GBM) is the most common malignant primary brain tumor in adults. Radiotherapy plus concomitant and adjuvant TMZ chemotherapy is the current standard of care for patients with GBM. Matrix metalloproteinases (MMPs), a family of zinc-dependent endopeptidases, are key modulators of tumor invasion and metastasis due to their ECM degradation capacity. The aim of the present study was to identify the most informative MMP member in terms of prognostic and predictive ability for patients with primary GBM.

Method

The mRNA expression profiles of all MMP genes were obtained from the Chinese Glioma Genome Atlas (CGGA), the Repository for Molecular Brain Neoplasia Data (REMBRANDT) and the GSE16011 dataset. MGMT methylation status was also examined by pyrosequencing. The correlation of MMP9 expression with tumor progression was explored in glioma specimens of all grades. Kaplan–Meier analysis and Cox proportional hazards regression models were used to investigate the association of MMP9 expression with survival and response to temozolomide.

Results

MMP9 was the only significant prognostic factor in three datasets for primary glioblastoma patients. Our results indicated that MMP9 expression is correlated with glioma grade (p<0.0001). Additionally, low expression of MMP9 was correlated with better survival outcome (OS: p = 0.0012 and PFS: p = 0.0066), and MMP9 was an independent prognostic factor in primary GBM (OS: p = 0.027 and PFS: p = 0.032). Additionally, the GBM patients with low MMP9 expression benefited from temozolomide (TMZ) chemotherapy regardless of the MGMT methylation status.

Conclusions

Patients with primary GBMs with low MMP9 expression may have longer survival and may benefit from temozolomide chemotherapy.

Integration of flow studies for robust selection of mechanoresponsive genes

Blood flow is an essential contributor to plaque growth, composition and initiation. It is sensed by endothelial cells, which react to blood flow by expressing > 1000 genes. The sheer number of genes implies that one needs genomic techniques to unravel their response in disease. Individual genomic studies have been performed but lack sufficient power to identify subtle changes in gene expression. In this study, we investigated whether a systematic meta-analysis of available microarray studies can improve their consistency. We identified 17 studies using microarrays, of which six were performed in vivo and 11 in vitro. The in vivo studies were disregarded due to the lack of the shear profile. Of the in vitro studies, a cross-platform integration of human studies (HUVECs in flow cells) showed high concordance (> 90 %). The human data set identified > 1600 genes to be shear responsive, more than any other study and in this gene set all known mechanosensitive genes and pathways were present. A detailed network analysis indicated a power distribution (e. g. the presence of hubs), without a hierarchical organisation. The average cluster coefficient was high and further analysis indicated an aggregation of 3 and 4 element motifs, indicating a high prevalence of feedback and feed forward loops, similar to prokaryotic cells. In conclusion, this initial study presented a novel method to integrate human-based mechanosensitive studies to increase its power. The robust network was large, contained all known mechanosensitive pathways and its structure revealed hubs, and a large aggregate of feedback and feed forward loops.

Endothelial inflammatory transcriptional responses to an altered plasma exposome following inhalation of diesel emissions

BACKGROUND

Air pollution, especially emissions derived from traffic sources, is associated with adverse cardiovascular outcomes. However, it remains unclear how inhaled factors drive extrapulmonary pathology.

OBJECTIVES

Previously, we found that canonical inflammatory response transcripts were elevated in cultured endothelial cells treated with plasma obtained after exposure compared with pre-exposure samples or filtered air (sham) exposures. While the findings confirmed the presence of bioactive factor(s) in the plasma after diesel inhalation, we wanted to better examine the complete genomic response to investigate (1) major responsive transcripts and (2) collected response pathways and ontogeny that may help to refine this method and inform the pathogenesis.

METHODS

We assayed endothelial RNA with gene expression microarrays, examining the responses of cultured endothelial cells to plasma obtained from six healthy human subjects exposed to 100 μg/m(3) diesel exhaust or filtered air for 2 h on separate occasions. In addition to pre-exposure baseline samples, we investigated samples obtained immediately-post and 24 h-post exposure.

RESULTS

Microarray analysis of the coronary artery endothelial cells challenged with plasma identified 855 probes that changed over time following diesel exhaust exposure. Over-representation analysis identified inflammatory cytokine pathways were upregulated both at the 2 and 24 h conditions. Novel pathways related to FOXO transcription factors and secreted extracellular factors were also identified in the microarray analysis.

CONCLUSIONS

These outcomes are consistent with our recent findings that plasma contains bioactive and inflammatory factors following pollutant inhalation and provide a novel pathway to explain the well-reported extrapulmonary toxicity of ambient air pollutants.

Immune-Signatures for Lung Cancer Diagnostics: Evaluation of Protein Microarray Data Normalization Strategies

New minimal invasive diagnostic methods for early detection of lung cancer are urgently needed. It is known that the immune system responds to tumors with production of tumor-autoantibodies. Protein microarrays are a suitable highly multiplexed platform for identification of autoantibody signatures against tumor-associated antigens (TAA). These microarrays can be probed using 0.1 mg immunoglobulin G (IgG), purified from 10 µL of plasma. We used a microarray comprising recombinant proteins derived from 15,417 cDNA clones for the screening of 100 lung cancer samples, including 25 samples of each main histological entity of lung cancer, and 100 controls. Since this number of samples cannot be processed at once, the resulting data showed non-biological variances due to “batch effects”. Our aim was to evaluate quantile normalization, “distance-weighted discrimination” (DWD), and “ComBat” for their effectiveness in data pre-processing for elucidating diagnostic immune-signatures. “ComBat” data adjustment outperformed the other methods and allowed us to identify classifiers for all lung cancer cases versus controls and small-cell, squamous cell, large-cell, and adenocarcinoma of the lung with an accuracy of 85%, 94%, 96%, 92%, and 83% (sensitivity of 0.85, 0.92, 0.96, 0.88, 0.83; specificity of 0.85, 0.96, 0.96, 0.96, 0.83), respectively. These promising data would be the basis for further validation using targeted autoantibody tests.

Endocrine Resistance in Breast Cancer

Around 70% of all breast cancers are estrogen receptor alpha positive and hence their development is highly dependent on estradiol. While the invention of endocrine therapies has revolusioned the treatment of the disease, resistance to therapy eventually occurs in a large number of patients. This paper seeks to illustrate and discuss the complexity and heterogeneity of the mechanisms which underlie resistance and the approaches proposed to combat them. It will also focus on the use and development of methods for predicting which patients are likely to develop resistance.

Volcano plots in analyzing differential expressions with mRNA microarrays

A volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log(10)(p-value) from the t-test). We review the basic and interactive use of the volcano plot and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide a unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility of applying volcano plots to other fields beyond microarray.

Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis

Background

Affymetrix GeneChips and Illumina BeadArrays are the most widely used commercial single channel gene expression microarrays. Public data repositories are an extremely valuable resource, providing array-derived gene expression measurements from many thousands of experiments. Unfortunately many of these studies are underpowered and it is desirable to improve power by combining data from more than one study; we sought to determine whether platform-specific bias precludes direct integration of probe intensity signals for combined reanalysis.

Results

Using Affymetrix and Illumina data from the microarray quality control project, from our own clinical samples, and from additional publicly available datasets we evaluated several approaches to directly integrate intensity level expression data from the two platforms. After mapping probe sequences to Ensembl genes we demonstrate that, ComBat and cross platform normalisation (XPN), significantly outperform mean-centering and distance-weighted discrimination (DWD) in terms of minimising inter-platform variance. In particular we observed that DWD, a popular method used in a number of previous studies, removed systematic bias at the expense of genuine biological variability, potentially reducing legitimate biological differences from integrated datasets.

Conclusion

Normalised and batch-corrected intensity-level data from Affymetrix and Illumina microarrays can be directly combined to generate biologically meaningful results with improved statistical power for robust, integrated reanalysis.

Individual variation and longitudinal pattern of genome-wide DNA methylation from birth to the first two years of life

Prenatal development and early childhood are critical periods for establishing the tissue-specific epigenome, and may have a profound impact on health and disease in later life. However, epigenomic profiles at birth and in early childhood remain largely unexplored. The focus of this report is to examine the individual variation and longitudinal pattern of genome-wide DNA methylation levels from birth through the first two years of life in 105 Black children (59 males and 46 females) enrolled at the Boston Medical Center. We performed epigenomic mapping of cord blood at birth and venous blood samples from the same set of children within the first two years of life using Illumina Infinium Humanmethylation27 BeadChip. We observed a wide range of inter-individual variations in genome-wide methylation at each time point including lower levels at CpG islands, TSS200, 5'UTR and 1st Exon locations, but significantly higher levels in CpG shores, shelves, TSS1500, gene body and 3'UTR. We identified CpG sites with significant intra-individual longitudinal changes in the first two years of life throughout the genome. Specifically, we identified 159 CpG sites in males and 149 CpG sites in females with significant longitudinal changes defined by both statistical significance and magnitude of changes. These significant CpG sites appeared to be located within genes with important biological functions including immunity and inflammation. Further studies are needed to replicate our findings, including analysis by specific cell types, and link those individual variations and longitudinal changes with specific health outcomes in early childhood and later life.

Dexamethasone stimulated gene expression in peripheral blood is a sensitive marker for glucocorticoid receptor resistance in depressed patients

Although gene expression profiles in peripheral blood in major depression are not likely to identify genes directly involved in the pathomechanism of affective disorders, they may serve as biomarkers for this disorder. As previous studies using baseline gene expression profiles have provided mixed results, our approach was to use an in vivo dexamethasone challenge test and to compare glucocorticoid receptor (GR)-mediated changes in gene expression between depressed patients and healthy controls. Whole genome gene expression data (baseline and following GR-stimulation with 1.5 mg dexamethasone p.o.) from two independent cohorts were analyzed to identify gene expression pattern that would predict case and control status using a training (N=18 cases/18 controls) and a test cohort (N=11/13). Dexamethasone led to reproducible regulation of 2670 genes in controls and 1151 transcripts in cases. Several genes, including FKBP5 and DUSP1, previously associated with the pathophysiology of major depression, were found to be reliable markers of GR-activation. Using random forest analyses for classification, GR-stimulated gene expression outperformed baseline gene expression as a classifier for case and control status with a correct classification of 79.1 vs 41.6% in the test cohort. GR-stimulated gene expression performed best in dexamethasone non-suppressor patients (88.7% correctly classified with 100% sensitivity), but also correctly classified 77.3% of the suppressor patients (76.7% sensitivity), when using a refined set of 19 genes. Our study suggests that in vivo stimulated gene expression in peripheral blood cells could be a promising molecular marker of altered GR-functioning, an important component of the underlying pathology, in patients suffering from depressive episodes.

Relative impact of key sources of systematic noise in Affymetrix and Illumina gene-expression microarray experiments

BACKGROUND

Systematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA.

RESULTS

A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependent upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content.

CONCLUSIONS

The magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies.

BeadArray expression analysis using bioconductor

Illumina whole-genome expression BeadArrays are a popular choice in gene profiling studies. Aside from the vendor-provided software tools for analyzing BeadArray expression data (GenomeStudio/BeadStudio), there exists a comprehensive set of open-source analysis tools in the Bioconductor project, many of which have been tailored to exploit the unique properties of this platform. In this article, we explore a number of these software packages and demonstrate how to perform a complete analysis of BeadArray data in various formats. The key steps of importing data, performing quality assessments, preprocessing, and annotation in the common setting of assessing differential expression in designed experiments will be covered.

Biomarkers and patient selection for PI3K/Akt/mTOR targeted therapies: current status and future directions

The phosphatidylinositol 3-kinase (PI3K)/Akt/ mammalian target of rapamycin (mTOR) pathway regulates a broad spectrum of physiologic and pathologic processes. In breast cancer mutation, amplification, deletion, methylation, and posttranslational modifications lead to significant dysregulation of this pathway leading to more aggressive and potentially drug-resistant disease. Multiple novel agents, targeting different nodes within the pathway are currently under development by both commercial and academic partners. The key to the successful validation of these markers is selection of the appropriate patient groups using biomarkers. This article reviews current progress in this area, highlighting the key molecular alterations described in genes within the PI3K/Akt/mTOR pathway that may have an effect on response to current and future therapeutic interventions. Herein, gaps in current knowledge are highlighted and suggestions for future research directions given that may facilitate biomarker development in partnership with current drug development.

Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods

The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by “batch effects,” the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.

Functional genomic and proteomic analysis reveals disruption of myelin-related genes and translation in a mouse model of early life neglect

Early life neglect is an important public health problem which can lead to lasting psychological dysfunction. Good animal models are necessary to understand the mechanisms responsible for the behavioral and anatomical pathology that results. We recently described a novel model of early life neglect, maternal separation with early weaning (MSEW), that produces behavioral changes in the mouse that persist into adulthood. To begin to understand the mechanism by which MSEW leads to these changes we applied cDNA microarray, next-generation RNA-sequencing (RNA-seq), label-free proteomics, multiple reaction monitoring (MRM) proteomics, and methylation analysis to tissue samples obtained from medial prefrontal cortex to determine the molecular changes induced by MSEW that persist into adulthood. The results show that MSEW leads to dysregulation of markers of mature oligodendrocytes and genes involved in protein translation and other categories, an apparent downward biasing of translation, and methylation changes in the promoter regions of selected dysregulated genes. These findings are likely to prove useful in understanding the mechanism by which early life neglect affects brain structure, cognition, and behavior.

Validation of the use of peripheral blood mononuclear cells as surrogate model for skeletal muscle tissue in nutrigenomic studies

Peripheral blood mononuclear cells (PBMCs) offer a significant promise for gene expression analyses as a substitute for tissues that are not easily accessible. The objective of this study was to validate the use of PBMCs for gene expression analysis as a marker of nutritional intervention as an alternative to skeletal muscle tissue (SMT) biopsies. We performed a transcriptome comparison of PBMCs versus SMT after an 8-week supplementation with n-3 polyunsaturated fatty acid (PUFA) in 16 obese and insulin-resistant subjects. Expression levels of 48,803 transcripts were assessed by the Human-6 v3 Expression BeadChips (Illumina, San Diego, CA). In SMT, 36,738 (75%) transcripts were detected, whereas 34,182 (70%) transcripts were detected in PBMCs. Further, 88% (32,341) of these transcripts were coexpressed in both tissues. Importantly, a strong correlation (r = 0.84, p < 0.0001) was observed between transcript expression levels of PBMCs and SMT after n-3 PUFA supplementation. In conclusion, PBMCs express the majority of transcripts expressed in SMT subsequent to n-3 PUFA supplementation and their expression levels are comparable. In the interest of practicalities and cost, these results support the use of PBMCs as a surrogate model for SMT gene expression in nutrigenomic studies. Further research on PBMC and SMT gene expression in response to other nutritional exposures is warranted.

Gene expression profiling of response to mTOR inhibitor everolimus in pre-operatively treated post-menopausal women with oestrogen receptor-positive breast cancer

There is growing evidence that uncontrolled activation of the PI3K/Akt/mTOR pathway contributes to the development and progression of breast cancer. Inhibition of this pathway has antitumour effects in preclinical studies and efficacy in combination with other agents in breast cancer patients. The aim of this study is to characterise the effects of pre-operative everolimus treatment in primary breast cancer patients and to identify potential molecular predictors of response. Twenty-seven patients with oestrogen receptor (ER)-positive breast cancer completed 11-14 days of neoadjuvant treatment with 5-mg everolimus. Core biopsies were taken before and after treatment and analysed using Illumina HumanRef-8 v2 Expression BeadChips. Changes in proliferation (Ki67) and phospho-AKT were measured on diagnostic core biopsies/resection samples embedded in paraffin by immunohistochemistry to determine response to treatment. Patients that responded to everolimus treatment with significant reductions in proliferation (fall in % Ki67 positive cells) also had significant decreases in the expression of genes involved in cell cycle (P = 8.70E-09) and p53 signalling (P = 0.01) pathways. Highly proliferating tumours that have a poor prognosis exhibited dramatic reductions in the expression of cell cycle genes following everolimus treatment. The genes that most clearly separated responding from non-responding pre-treatment tumours were those involved with protein modification and dephosphorylation, including DYNLRB2, ERBB4, PTPN13, ULK2 and DUSP16. The majority of ER-positive breast tumours treated with everolimus showed a significant reduction in genes involved with proliferation, these may serve as markers of response and predict which patients will derive most benefit from mTOR inhibition.