Assessing affymetrix GeneChip microarray quality

Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs

BACKGROUND

Extracellular vesicle-derived (EV)-miRNAs have potential to serve as biomarkers for the diagnosis of various diseases. miRNA microarrays are widely used to quantify circulating EV-miRNA levels, and the preprocessing of miRNA microarray data is critical for analytical accuracy and reliability. Thus, although microarray data have been used in various studies, the effects of preprocessing have not been studied for Toray's 3D-Gene chip, a widely used measurement method. We aimed to evaluate batch effect, missing value imputation accuracy, and the influence of preprocessing on measured values in 18 different preprocessing pipelines for EV-miRNA microarray data from two cohorts with amyotrophic lateral sclerosis using 3D-Gene technology.

RESULTS

Eighteen different pipelines with different types and orders of missing value completion and normalization were used to preprocess the 3D-Gene microarray EV-miRNA data. Notable results were suppressed in the batch effects in all pipelines using the batch effect correction method ComBat. Furthermore, pipelines utilizing missForest for missing value imputation showed high agreement with measured values. In contrast, imputation using constant values for missing data exhibited low agreement.

CONCLUSIONS

This study highlights the importance of selecting the appropriate preprocessing strategy for EV-miRNA microarray data when using 3D-Gene technology. These findings emphasize the importance of validating preprocessing approaches, particularly in the context of batch effect correction and missing value imputation, for reliably analyzing data in biomarker discovery and disease research.

Meta-analysis of differential gene expression in lower motor neurons isolated by laser capture microdissection from post-mortem ALS spinal cords

Introduction

ALS is a fatal neurodegenerative disease for which underlying mechanisms are incompletely understood. The motor neuron is a central player in ALS pathogenesis but different transcriptome signatures have been derived from bulk analysis of post-mortem tissue and iPSC-derived motor neurons (iPSC-MNs).

Methods

This study performed a meta-analysis of six gene expression studies (microarray and RNA-seq) in which laser capture microdissection (LCM) was used to isolate lower motor neurons from post-mortem spinal cords of ALS and control (CTL) subjects. Differentially expressed genes (DEGs) with consistent ALS versus CTL expression differences across studies were identified.

Results

The analysis identified 222 ALS-increased DEGs (FDR <0.10, SMD >0.80) and 278 ALS-decreased DEGs (FDR <0.10, SMD < -0.80). ALS-increased DEGs were linked to PI3K-AKT signaling, innate immunity, inflammation, motor neuron differentiation and extracellular matrix. ALS-decreased DEGs were associated with the ubiquitin-proteosome system, microtubules, axon growth, RNA-binding proteins and synaptic membrane. ALS-decreased DEG mRNAs frequently interacted with RNA-binding proteins (e.g., FUS, HuR). The complete set of DEGs (increased and decreased) overlapped significantly with genes near ALS-associated SNP loci (p < 0.01). Transcription factor target motifs with increased proximity to ALS-increased DEGs were identified, most notably DNA elements predicted to interact with forkhead transcription factors (e.g., FOXP1) and motor neuron and pancreas homeobox 1 (MNX1). Some of these DNA elements overlie ALS-associated SNPs within known enhancers and are predicted to have genotype-dependent MNX1 interactions. DEGs were compared to those identified from SOD1-G93A mice and bulk spinal cord segments or iPSC-MNs from ALS patients. There was good correspondence with transcriptome changes from SOD1-G93A mice (r ≤ 0.408) but most DEGs were not differentially expressed in bulk spinal cords or iPSC-MNs and transcriptome-wide effect size correlations were weak (bulk tissue: r ≤ 0.207, iPSC-MN: r ≤ 0.037).

Conclusion

This study defines a robust transcriptome signature from LCM-based motor neuron studies of post-mortem tissue from ALS and CTL subjects. This signature differs from those obtained from analysis of bulk spinal cord segments and iPSC-MNs. Results provide insight into mechanisms underlying gene dysregulation in ALS and highlight connections between these mechanisms, ALS genetics, and motor neuron biology.

Transcriptomic Profiling of Influenza A Virus-Infected Mouse Lung at Recovery Stage Using RNA Sequencing

Influenza A virus (IAV) is known to cause mild to severe respiratory illness. Under some conditions, the infection can lead to pneumonia (viral or bacterial), acute respiratory distress syndrome, and other complications that can be fatal, especially in vulnerable populations such as the elderly, young children, and individuals with underlying health conditions. Despite previous studies, little is known about the host immune response and neuroimmune interactions in IAV infection. Using RNA sequencing, we performed transcriptomic analysis of murine lung tissue 21 days post infection (dpi) with IAV (H1N1) in order to find the differentially expression genes (DEGs) related to the host immune response and neuroimmune interactions inside the lung during recovery. Among 792 DEGs, 434 genes were up-regulated, whereas 358 genes were down-regulated. The most prominent molecular functions of the up-regulated genes were related to the immune response and tissue repair, whereas a large proportion of the down-regulated genes were associated with neural functions. Although further molecular/functional studies need to be performed for these DEGs, our results facilitate the understanding of the host response (from innate immunity to adaptive immunity) and neuroimmune interactions in infected lungs at the recovery stage of IAV infection. These genes might have potential uses as mechanistic/diagnostic biomarkers and represent possible targets for anti-IAV therapies.

A Short Review on Miniaturized Biosensors for the Detection of Nucleic Acid Biomarkers

Even today, most biomarker testing is executed in centralized, dedicated laboratories using bulky instruments, automated analyzers, and increased analysis time and expenses. The development of miniaturized, faster, low-cost microdevices is immensely anticipated for substituting for these conventional laboratory-oriented assays and transferring diagnostic results directly onto the patient's smartphone using a cloud server. Pioneering biosensor-based approaches might make it possible to test biomarkers with reliability in a decentralized setting, but there are still a number of issues and restrictions that must be resolved before the development and use of several biosensors for the proper understanding of the measured biomarkers of numerous bioanalytes such as DNA, RNA, urine, and blood. One of the most promising processes to address some of the issues relating to the growing demand for susceptible, quick, and affordable analysis techniques in medical diagnostics is the creation of biosensors. This article critically discusses a short review of biosensors used for detecting nucleic acid biomarkers, and their use in biomedical prognostics will be addressed while considering several essential characteristics.

Transcriptional Basis of Ca2+ Remodeling Reversal Induced by Polyamine Synthesis Inhibition in Colorectal Cancer Cells

Colorectal cancer (CRC) is associated with mutations in APC/Wnt leading to c-myc activation and the overexpression of ODC1, the limiting step in polyamine synthesis. CRC cells also display a remodeling of intracellular Ca²⁺ homeostasis that contributes to cancer hallmarks. As polyamines may modulate Ca²⁺ homeostasis during epithelial tissue repair, we investigated whether polyamine synthesis inhibition may reverse Ca²⁺ remodeling in CRC cells and, if so, the molecular basis for this reversal. To this end, we used calcium imaging and transcriptomic analysis in normal and CRC cells treated with DFMO, an ODC1 suicide inhibitor. We found that polyamine synthesis inhibition partially reversed changes in Ca²⁺ homeostasis associated with CRC, including a decrease in resting Ca²⁺ and SOCE along with an increased Ca²⁺ store content. We also found that polyamine synthesis inhibition reversed transcriptomic changes in CRC cells without affecting normal cells. Specifically, DFMO treatment enhanced the transcription of SOCE modulators CRACR2A; ORMDL3; and SEPTINS 6, 7, 8, 9, and 11, whereas it decreased SPCA2, involved in store-independent Orai1 activation. Therefore, DFMO treatment probably decreased store-independent Ca²⁺ entry and enhanced SOCE control. Conversely, DFMO treatment decreased the transcription of the TRP channels TRPC1 and 5, TRPV6, and TRPP1 while increasing TRPP2, thus probably decreasing Ca²⁺ entry through TRP channels. Finally, DFMO treatment enhanced the transcription of the PMCA4 Ca²⁺ pump and mitochondrial channels MCU and VDAC3 for enhanced Ca²⁺ extrusion through the plasma membrane and mitochondria. Collectively, these findings suggested the critical role of polyamines in Ca²⁺ remodeling in colorectal cancer.

Isosorbide Fatty Acid Diesters Have Synergistic Anti-Inflammatory Effects in Cytokine-Induced Tissue Culture Models of Atopic Dermatitis

Atopic dermatitis (AD) is a chronic disease in which epidermal barrier disruption triggers Th2-mediated eruption of eczematous lesions. Topical emollients are a cornerstone of chronic management. This study evaluated efficacy of two plant-derived oil derivatives, isosorbide di-(linoleate/oleate) (IDL) and isosorbide dicaprylate (IDC), using AD-like tissue culture models. Treatment of reconstituted human epidermis with cytokine cocktail (IL-4 + IL-13 + TNF-α + IL-31) compromised the epidermal barrier, but this was prevented by co-treatment with IDL and IDC. Cytokine stimulation also dysregulated expression of keratinocyte (KC) differentiation genes whereas treatment with IDC or IDL + IDC up-regulated genes associated with early (but not late) KC differentiation. Although neither IDL nor IDC inhibited Th2 cytokine responses, both compounds repressed TNF-α-induced genes and IDL + IDC led to synergistic down-regulation of inflammatory (IL1B, ITGA5) and neurogenic pruritus (TRPA1) mediators. Treatment of cytokine-stimulated skin explants with IDC decreased lactate dehydrogenase (LDH) secretion by more than 50% (more than observed with cyclosporine) and in vitro LDH activity was inhibited by IDL and IDC. These results demonstrate anti-inflammatory mechanisms of isosorbide fatty acid diesters in AD-like skin models. Our findings highlight the multifunctional potential of plant oil derivatives as topical ingredients and support studies of IDL and IDC as therapeutic candidates.

Engineered bispecific antibodies targeting the interleukin-6 and -8 receptors potently inhibit cancer cell migration and tumor metastasis

Simultaneous inhibition of interleukin-6 (IL-6) and interleukin-8 (IL-8) signaling diminishes cancer cell migration, and combination therapy has recently been shown to synergistically reduce metastatic burden in a preclinical model of triple-negative breast cancer. Here, we have engineered two novel bispecific antibodies that target the IL-6 and IL-8 receptors to concurrently block the signaling activity of both ligands. We demonstrate that a first-in-class bispecific antibody design has promising therapeutic potential, with enhanced selectivity and potency compared with monoclonal antibody and small-molecule drug combinations in both cellular and animal models of metastatic triple-negative breast cancer. Mechanistic characterization revealed that our engineered bispecific antibodies have no impact on cell viability, but profoundly reduce the migratory potential of cancer cells; hence they constitute a true anti-metastatic treatment. Moreover, we demonstrate that our antibodies can be readily combined with standard-of-care anti-proliferative drugs to develop effective anti-cancer regimens. Collectively, our work establishes an innovative metastasis-focused direction for cancer drug development.

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.

Effect of EPOC on Serum MicroRNA Expression in Patients with Hypertension

In order to analyze the effect of EPOC on serum microRNA expression in patients with hypertension, we have carried out an experimental evaluation and presented a dedicated method to improve its effectiveness. For this purpose, 60 patients with hypertension treated in our hospital from January to December 2020 were selected and randomly divided into the EPOC group (30 cases) and the control group (30 cases). Patients in both groups received conventional hypertension medication according to the treatment guidelines, and education and guidance were strengthened to ensure patients' drug compliance. The EPOC group received EPOC training in addition to conventional medication. After the intervention, 6MWD, LVEF, and LVEDD in the control group and the EPOC group were significantly improved compared with that before treatment (P < 0.05). The improvement of cardiac function in the EPOC group was significantly better than that in the control group (P < 0.05). Physiological function, physiological characteristics, body pain, and general health of the control group and the EPOC group were significantly improved compared with that before treatment (P < 0.05). The improvement of quality of life in the EPOC group was significantly better than that in the control group (P < 0.05). The indexes of heart rate, oxygen pulse, minute ventilation, oxygen uptake in kg, and anaerobic threshold in the EPOC group were significantly better than those in the control group before and after treatment (P < 0.05). Full-sample real-time PCR verification of the 8 miRNAs with the highest expression differentiation showed that, compared with the control group, the expression levels of miR-20a-5p, miR-93-5p, and miR-1287-5p in the serum of the EPOC group were significantly increased, while the expression levels of miR-7706, miR-28-5p, and miR-125b-5p were significantly decreased. The expression of miR-20a-5p was the highest, while the expression of miR-125b-5p was the lowest, suggesting that miR-20a-5p and miR-125b-5p might be involved in the process of HIIT to improve hypertension. After EPOC rehabilitation treatment, patients' heart function indexes, lung function indexes, and quality of life have been significantly improved. EPOC rehabilitation can improve exercise tolerance, enhance cardiopulmonary function, and improve the prognosis of patients.

A Sensitization-Free Dimethyl Fumarate Prodrug, Isosorbide Di-(Methyl Fumarate), Provides a Topical Treatment Candidate for Psoriasis

Dimethyl fumarate (DMF) is an effective oral treatment for psoriasis administered in Europe for nearly 60 years. However, its potential has been limited by contact dermatitis that prohibits topical application. This paper characterizes a DMF derivative, isosorbide DMF (IDMF), which was designed to have antipsoriatic effects without skin-sensitizing properties. We show that IDMF exhibits neither genotoxicity nor radiation sensitivity in skin fibroblasts and is nonirritating and nonsensitizing in animal models (rat, rabbit, guinea pig). Microarray analysis of cytokine-stimulated keratinocytes showed that IDMF represses the expression of genes specifically upregulated in psoriatic skin lesions but not those of other skin diseases. IDMF also downregulated genes induced by IL-17A and TNF in keratinocytes as well as predicted targets of NF-κB and the antidifferentiation noncoding RNA (i.e., ANCR). IDMF further stimulated the transcription of oxidative stress response genes (NQO1, GPX2, GSR) with stronger NRF2/ARE activation compared to DMF. Finally, IDMF reduced erythema and scaling while repressing the expression of immune response genes in psoriasiform lesions elicited by topical application of imiquimod in mice. These data show that IDMF exhibits antipsoriatic activity that is similar or improved compared with that exhibited by DMF, without the harsh skin-sensitizing effects that have prevented topical delivery of the parent molecule.

Hsa-miR-3651 could serve as a novel predictor for in-breast recurrence via FRMD3

Background

MicroRNAs are small non-coding RNAs with pivotal regulatory functions in multiple cellular processes. Their significance as molecular predictors for breast cancer was demonstrated in the past 15 years. The aim of this study was to elucidate the role of hsa-miR-3651 for predicting of local control (LC) in early breast cancer.

Results

By means of high-throughput technology, hsa-miR-3651 was found to be differentially expressed between patients who experienced local relapse compared to those without (N  =  23; p  =  0.0035). This result could be validated in an independent cohort of 87 patients using RT-qPCR (p  <  0.0005). In a second analysis step with a chip-based microarray containing 70,523 probes of potential target molecules, FERM domain protein 3 (FRMD3) was found to be the most down-regulated protein (N  =  21; p  =  0.0016). Computational analysis employing different prediction algorithms revealed FRMD3 as a likely downstream target of hsa-miR-3651 with an 8mer binding site between the two molecules. This could be validated in an independent patient set (N  =  20, p  =  0.134).

Conclusion

The current study revealed that hsa-miR-3651 is a predictor of LC in early breast cancer via its putative target protein FRMD3. Since microRNAs interfere in multiple pathways, the results of this hypothesis generating study may contribute to the development of tailored therapies for breast cancer in the future.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12282-021-01308-y.

Tetrahexyldecyl Ascorbate (THDC) Degrades Rapidly under Oxidative Stress but Can Be Stabilized by Acetyl Zingerone to Enhance Collagen Production and Antioxidant Effects

Tetrahexyldecyl Ascorbate (THDC) is an L-ascorbic acid precursor with improved stability and ability to penetrate the epidermis. The stability and transdermal penetration of THDC, however, may be compromised by the oxidant-rich environment of human skin. In this study, we show that THDC is a poor antioxidant that degrades rapidly when exposed to singlet oxygen. This degradation, however, was prevented by combination with acetyl zingerone (AZ) as a stabilizing antioxidant. As a standalone ingredient, THDC led to unexpected activation of type I interferon signaling, but this pro-inflammatory effect was blunted in the presence of AZ. Moreover, the combination of THDC and AZ increased expression of genes associated with phospholipid homeostasis and keratinocyte differentiation, along with repression of MMP1 and MMP7 expression, inhibition of MMP enzyme activity, and increased production of collagen proteins by dermal fibroblasts. Lastly, whereas THDC alone reduced viability of keratinocytes exposed to oxidative stress, this effect was completely abrogated by the addition of AZ to THDC. These results show that AZ is an effective antioxidant stabilizer of THDC and that combination of these products may improve ascorbic acid delivery. This provides a step towards reaching the full potential of ascorbate as an active ingredient in topical preparations.

Horizontal meta-analysis identifies common deregulated genes across AML subgroups providing a robust prognostic signature

Advances in transcriptomics have improved our understanding of leukemic development and helped to enhance the stratification of patients. The tendency of transcriptomic studies to combine AML samples, regardless of cytogenetic abnormalities, could lead to bias in differential gene expression analysis because of the differential representation of AML subgroups. Hence, we performed a horizontal meta-analysis that integrated transcriptomic data on AML from multiple studies, to enrich the less frequent cytogenetic subgroups and to uncover common genes involved in the development of AML and response to therapy. A total of 28 Affymetrix microarray data sets containing 3940 AML samples were downloaded from the Gene Expression Omnibus database. After stringent quality control, transcriptomic data on 1534 samples from 11 data sets, covering 10 AML cytogenetically defined subgroups, were retained and merged with the data on 198 healthy bone marrow samples. Differentially expressed genes between each cytogenetic subgroup and normal samples were extracted, enabling the unbiased identification of 330 commonly deregulated genes (CODEGs), which showed enriched profiles of myeloid differentiation, leukemic stem cell status, and relapse. Most of these genes were downregulated, in accordance with DNA hypermethylation. CODEGs were then used to create a prognostic score based on the weighted sum of expression of 22 core genes (CODEG22). The score was validated with microarray data of 5 independent cohorts and by quantitative real time-polymerase chain reaction in a cohort of 142 samples. CODEG22-based stratification of patients, globally and into subpopulations of cytologically healthy and elderly individuals, may complement the European LeukemiaNet classification, for a more accurate prediction of AML outcomes.

A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices

One of the main problems in the analysis of real data is often related to the presence of anomalies. Namely, anomalous cases can both spoil the resulting analysis and contain valuable information at the same time. In both cases, the ability to detect these occurrences is very important. In the biomedical field, a correct identification of outliers could allow the development of new biological hypotheses that are not considered when looking at experimental biological data. In this work, we address the problem of detecting outliers in gene expression data, focusing on microarray analysis. We propose an ensemble approach for detecting anomalies in gene expression matrices based on the use of Hierarchical Clustering and Robust Principal Component Analysis, which allows us to derive a novel pseudo-mathematical classification of anomalies.

An integrated transcriptomics and metabolomics study of the immune response of newly hatched chicks to the cytosine-phosphate-guanine oligonucleotide stimulation

The immunological immaturity of the innate immune system during the first-week post-hatch enables pathogens to infect chickens, leading to the death of the animals. Current preventive solutions to improve the resistance of chicks to infections include vaccination, breeding, and sanitation. Other prophylactic solutions have been investigated, such as the stimulation of animal health with immunostimulants. Recent studies showed that administration of immune-modulators to one-day-old chicks, or in ovo, significantly reduces mortality in experimental bacterial or viral infection challenge models. Owing to a lack of molecular biomarkers required to evaluate chicken immune responses and assess the efficacy of vaccines or immune-modulators, challenge models are still used. One way to reduce challenge experiments is to define molecular signatures through omics approaches, resulting in new methodologies to rapidly screen candidate molecules or vaccines. This study aims at identifying a dual transcriptomics and metabolomics blood signature after administration of CpG-ODN (cytosine-phosphate-guanine oligodeoxynucleotides), a reference immune-stimulatory molecule. A clinical study was conducted with chicks and transcriptomics and metabolomics analyses were performed on whole-blood and plasma samples, respectively. Differentially expressed genes and metabolites with different abundance were identified in chicks treated with CpG-ODN. The results showed that CpG-ODN activated the innate immune system, within hours after administration, and its effect lasted over time, as metabolomics and transcriptomics profiles still varied 6 D after administration. In conclusion, through an integrated clinical omics approach, we deciphered in part the mode of action of CpG-ODN in post-hatch chicks.

MyoMiner: explore gene co-expression in normal and pathological muscle

BACKGROUND

High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-by-association approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type.

METHODS

To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition. Co-expression values were calculated for each category to create the MyoMiner database.

RESULTS

Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different.

CONCLUSIONS

These co-expression analyses will help investigators to delineate the tissue-, cell-, and pathology-specific elements of muscle protein interactions, cell signaling and gene regulation. Changes in co-expression between pathologic and healthy tissue may suggest new disease mechanisms and help define novel therapeutic targets. Thus, MyoMiner is a powerful muscle-specific database for the discovery of genes that are associated with related functions based on their co-expression. MyoMiner is freely available at https://www.sys-myo.com/myominer.

Using RNA Sequencing to Characterize the Tumor Microenvironment

RNA sequencing (RNA-seq) is an integral tool in immunogenomics, allowing for interrogation of the transcriptome of a tumor and its microenvironment. Analytical methods to deconstruct the genomics data can then be applied to infer gene expression patterns associated with the presence of various immunocyte populations. High quality RNA-seq is possible from formalin-fixed, paraffin-embedded (FFPE), fresh-frozen, and fresh tissue, with a wide variety of sequencing library preparation methods, sequencing platforms, and downstream bioinformatics analyses currently available. Selection of an appropriate library preparation method is largely determined by tissue type, quality of RNA, and quantity of RNA. Downstream of sequencing, many analyses can be applied to the data, including differential gene expression analysis, immune gene signature analysis, gene pathway analysis, T/B-cell receptor inference, HLA inference, and viral transcript quantification. In this chapter, we will describe our workflow for RNA-seq from bulk tissue to evaluable data, including extraction of RNA, library preparation methods, sequencing of libraries, alignment and quality assurance of data, and initial downstream analyses of RNA-seq data to extract relevant immunogenomics features. Systems biology methods that draw additional insights by integrating these features are covered further in Chapters 28 - 30 .

High EMSY expression defines a BRCA-like subgroup of high-grade serous ovarian carcinoma with prolonged survival and hypersensitivity to platinum

BACKGROUND

Approximately half of high-grade serous ovarian carcinomas (HGSOCs) demonstrate homologous recombination repair (HR) pathway defects, resulting in a distinct clinical phenotype comprising hypersensitivity to platinum, superior clinical outcome, and greater sensitivity to poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitors. EMSY, which is known to be amplified in breast and ovarian cancers, encodes a protein reported to bind and inactivate BRCA2. Thus, EMSY overexpression may mimic BRCA2 mutation, resulting in HR deficiency. However, to our knowledge, the phenotypic consequences of EMSY overexpression in HGSOC patients has not been explored.

METHODS

Here we investigate the impact of EMSY expression on clinical outcome and sensitivity to platinum-based chemotherapy using available data from transcriptomically characterized HGSOC cohorts.

RESULTS

High EMSY expression was associated with better clinical outcome in a cohort of 265 patients with HGSOC from Edinburgh (overall survival multivariable hazard ratio, 0.58 [95% CI, 0.38-0.88; P = .011] and progression-free survival multivariable hazard ratio, 0.62 [95% CI, 0.40-0.96; P = .030]). Superior outcome also was demonstrated in the Medical Research Council ICON7 clinical trial and multiple publicly available data sets. Patients within the Edinburgh cohort who had high EMSY expression were found to demonstrate greater rates of complete response to multiple platinum-containing chemotherapy regimens (radiological complete response rate of 44.4% vs 12.5% at second exposure; P = .035) and corresponding prolonged time to disease progression (median, 151.5 days vs 60.5 days after third platinum exposure; P = .004).

CONCLUSIONS

Patients with HGSOCs demonstrating high EMSY expression appear to experience prolonged survival and greater platinum sensitivity, reminiscent of BRCA-mutant cases. These data are consistent with the notion that EMSY overexpression may render HGSOCs HR deficient.

Targeting the RNA m6A Reader YTHDF2 Selectively Compromises Cancer Stem Cells in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive clonal disorder of hematopoietic stem cells (HSCs) and primitive progenitors that blocks their myeloid differentiation, generating self-renewing leukemic stem cells (LSCs). Here, we show that the mRNA m6A reader YTHDF2 is overexpressed in a broad spectrum of human AML and is required for disease initiation as well as propagation in mouse and human AML. YTHDF2 decreases the half-life of diverse m6A transcripts that contribute to the overall integrity of LSC function, including the tumor necrosis factor receptor Tnfrsf2, whose upregulation in Ythdf2-deficient LSCs primes cells for apoptosis. Intriguingly, YTHDF2 is not essential for normal HSC function, with YTHDF2 deficiency actually enhancing HSC activity. Thus, we identify YTHDF2 as a unique therapeutic target whose inhibition selectively targets LSCs while promoting HSC expansion.

Muscle Gene Sets: a versatile methodological aid to functional genomics in the neuromuscular field

BACKGROUND

The approach of building large collections of gene sets and then systematically testing hypotheses across these collections is a powerful tool in functional genomics, both in the pathway analysis of omics data and to uncover the polygenic effects associated with complex diseases in genome-wide association study. The Molecular Signatures Database includes collections of oncogenic and immunologic signatures enabling researchers to compare transcriptional datasets across hundreds of previous studies and leading to important insights in these fields, but such a resource does not currently exist for neuromuscular research. In previous work, we have shown the utility of gene set approaches to understand muscle cell physiology and pathology.

METHODS

Following a systematic survey of public muscle data, we passed gene expression profiles from 4305 samples through a robust pre-processing and standardized data analysis pipeline. Two hundred eighty-two samples were discarded based on a battery of rigorous global quality controls. From among the remaining studies, 578 comparisons of interest were identified by a combination of text mining and manual curation of the study meta-data. For each comparison, significantly dysregulated genes (FDR adjusted p < 0.05) were identified.

RESULTS

Lists of dysregulated genes were divided between upregulated and downregulated to give 1156 Muscle Gene Sets (MGS). This resource is available for download ( www.sys-myo.com/muscle_gene_sets ) and is accessible through three commonly used functional genomics platforms (GSEA, EnrichR, and WebGestalt). Basic guidance and recommendations are provided for the use of MGS through these platforms. In addition, consensus muscle gene sets were created to capture the overlap between the results of similar studies, and analysis of these highlighted the potential for novel disease-relevant findings.

CONCLUSIONS

The MGS resource can be used to investigate the behaviour of any list of genes across previous comparisons of muscle conditions, to compare previous studies to one another, and to explore the functional relationship of muscle dysregulation to the Gene Ontology. Its major intended use is in enrichment testing for functional genomics analysis.

Upper Airway Cell Transcriptomics Identify a Major New Immunological Phenotype with Strong Clinical Correlates in Young Children with Acute Wheezing

Asthma exacerbations are triggered by rhinovirus infections. We employed a systems biology approach to delineate upper-airway gene network patterns underlying asthma exacerbation phenotypes in children. Cluster analysis unveiled distinct IRF7^hi versus IRF7^lo molecular phenotypes, the former exhibiting robust upregulation of Th1/type I IFN responses and the latter an alternative signature marked by upregulation of cytokine and growth factor signaling and downregulation of IFN-γ. The two phenotypes also produced distinct clinical phenotypes. For IRF7^lo children, symptom duration prior to hospital presentation was more than twice as long from initial symptoms (p = 0.011) and nearly three times as long for cough (p < 0.001), the odds ratio of admission to hospital was increased more than 4-fold (p = 0.018), and time to recurrence was shorter (p = 0.015). In summary, our findings demonstrate that asthma exacerbations in children can be divided into IRF7^hi versus IRF7^lo phenotypes with associated differences in clinical phenotypes.

Transcriptomic profiles of tissues from rats treated with anticancer drug combinations

To achieve therapeutic goals, many cancer chemotherapeutics are used at doses close to their maximally tolerated doses. Thus, it may be expected that when therapies are combined at therapeutic doses, toxicity profiles may change. In many ways, prediction of synergistic toxicities for drug combinations is similar to predicting synergistic efficacy, and is dependent upon building hypotheses from molecular mechanisms of drug toxicity. The key objective of this initiative was to generate and make publicly available key high-content data sets for mechanistic hypothesis generation as it pertains to a unique toxicity profile of a drug pair for several anticancer drug combinations. The expectation is that tissue-based genomic information that are derived from target tissues will also facilitate the generation and testing of mechanistic hypotheses. The view is that availability of these data sets for bioinformaticians and other scientists will contribute to analysis of these data and evaluation of the approach.

Prognostic Implications of Heterogeneity in Intra-tumoral Immune Composition for Recurrence in Early Stage Lung Cancer

Background: Studies in the past have identified selected immune cells that associate with different clinical outcomes in non-small cell lung cancer (NSCLC). Considering the fact that immune responses are heterogenous and that the clinical outcome could be influenced by the interplay of various immune cell types, it is imperative to evaluate multiple intra-tumoral immune cell types in the same set of patients. Objective: To evaluate the individual and combined effects of diverse intra-tumoral immune cell types on recurrence after complete surgical resection in early stage lung adenocarcinoma. Methods: We obtained NCBI GEO datasets for lung adenocarcinoma, the most prevalent histological subtype of NSCLC and re-analyzed the gene expression data of 292 patients with early stage cancer (IA/IB). CIBERSORT was used to resolve 22 immune cell types from the tumor transcriptomes. Survival analysis was carried out to assess the effect of immune cell types and genes associated with recurrence. Results: Out of the 22 cell types, a high proportion of Tregs and monocyte-macrophages in the tumors were associated with significantly increased probability of recurrence. Conversely, increased proportion of non-Treg CD4+ T cells and plasma cells were associated with a lower probability of recurrence. The higher expression of CCL20 (which can direct the migration of cells of B cell lineage), XCL1 (associated with prototypical Th1 responses) and the immunoglobulin chains IGHV4.34 and IGLV6.57 were associated with a significantly lower probability of recurrence. Importantly, the intra-tumoral immune phenotype comprising these four cell types varied among patients and differentially associated with recurrence depending on net levels of positive and negative prognostic factors. Despite a high level of intra-tumoral plasma cells, a concomitant high level of monocyte-macrophages reduced the freedom from recurrence from ~80 to ~50% at 80 months (p < 0.05). Furthermore, stratification of the patients on the basis of a score estimated from the levels of four cell types enabled the identification of patients with significantly increased probability of recurrence (~50%) after surgery. Significance: Our analysis suggests that concomitant levels of macrophages and plasma cells, in addition to the T regs and non-TregCD4+ T cells in tumors can identify patients with early stage lung cancer at greater risk of recurrence.

A Gene Expression Signature Predicts Bladder Cancer Cell Line Sensitivity to EGFR Inhibition

Background:

Bladder cancer remains a cancer type in need of novel and alternative therapies. While multiple inhibitors of EGFR have been evaluated for efficacy in bladder cancer, the results have largely been disappointing with few patients responding to these therapies. Yet, there is a subset of patients that positively responds to EGFR inhibition with tumor shrinkage, indicating it is an effective treatment for a targeted set of bladder tumors.

Objective:

To derive a gene expression signature capable of predicting the response to EGFR inhibition in bladder cancer cell lines.

Methods:

he response to cetuximab for 68 colorectal cancer patients was used as training data to generate a gene expression signature. We applied this signature to bladder cancer cell lines and predictions were compared to the responses to seven EGFR inhibitors.

Results:

A novel 67-gene signature derived from colorectal cancer was able to significantly identify bladder cancer cell lines by their response to several EGFR inhibitors.

Conclusions:

The 67-gene signature can determine bladder cancer cell line sensitivity to EGFR inhibition. This work demonstrates a preclinical strategy to identify bladder cancer cell lines for EGFR-targeted therapy.

Data mining of pediatric medulloblastoma microarray expression reveals a novel potential subdivision of the Group 4 molecular subgroup

Medulloblastoma is the most common type of solid brain tumor in children. This type of embryonic tumor is highly heterogeneous and has been classified into 4 molecular subgroups based on their gene expression profiles: WNT, SHH, Group 3 (G3) and Group 4 (G4). WNT and SHH tumors exhibit the specific dysregulation of genes and pathways, whereas G3 and G4 tumors, two of the more frequent subtypes, are the least characterized. Thus, novel markers to aid in the diagnosis, prognosis and management of medulloblastoma are required. In the present study, microarray gene expression data was downloaded from the Gene Expression Omnibus database, including data from the 4 subgroups of medulloblastoma and healthy cerebellum tissue (CT). The data was utilized in an in silico analysis to characterize each subgroup at a transcriptomic level. Using Partek Genomics Suite software, the data were visualized via hierarchical clustering and principal component analysis. The differentially expressed genes were uploaded to the MetaCore portal to perform enrichment analysis using CT gene expression as baseline, with fold change thresholds of <-5 and >5 for differential expression. The data mining analysis of microarray gene expression data enabled the identification of a range of dysregulated molecules associated with each subgroup of medulloblastoma. G4 is the most heterogeneous subgroup, as no definitive pathway defines its pathogenesis; analysis of the gene expression profiles were associated with the G4α and G4β subcategories. TOX high mobility group box family member 3, synuclein α interacting protein and, potassium voltage-gated channel interacting protein 4 were identified as three novel potential markers for distinguishing the α and β subcategories of G4. These genes may be associated with medulloblastoma pathogenesis, and thus may provide a basis for researching novel targeted treatment strategies for G4 medulloblastoma.

The Pathway Coexpression Network: Revealing pathway relationships

A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer’s Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/.

Genes do not function alone, but interact within pathways to carry out specific biological processes. Pathways, in turn, interact at a higher level to affect major cellular activities such as motility, growth and development. We present a pathway coexpression network (PCxN) that systematically maps and quantifies these high-level interactions and establishes a unifying reference for pathway relationships. The method uses 3,207 human microarrays from 72 normal human tissues and 1,330 of the most well established pathway annotations to describe global relationships between pathways. PCxN accounts for shared genes to estimate correlations between pathways with related functions rather than with redundant pathway definitions. PCxN can be used to discover and explore pathways correlated with a pathway of interest. We applied PCxN to identify key processes related to Alzheimer’s disease (AD), interpreting a mixed genetic association and experimental derived set of disease genes in the context of gene co-expression. We expand the known relationships between pathways identified by gene set enrichment analysis in brain tissues affected with AD. PCxN provides a high-level overview of pathway relationships. PCxN is available as a webtool at http://pcxn.org/, and as a Bioconductor package at http://bioconductor.org/packages/pcxn/.

Crowd-Sourced Verification of Computational Methods and Data in Systems Toxicology: A Case Study with a Heat-Not-Burn Candidate Modified Risk Tobacco Product

Systems toxicology intends to quantify the effect of toxic molecules in biological systems and unravel their mechanisms of toxicity. The development of advanced computational methods is required for analyzing and integrating high throughput data generated for this purpose as well as for extrapolating predictive toxicological outcomes and risk estimates. To ensure the performance and reliability of the methods and verify conclusions from systems toxicology data analysis, it is important to conduct unbiased evaluations by independent third parties. As a case study, we report here the results of an independent verification of methods and data in systems toxicology by crowdsourcing. The sbv IMPROVER systems toxicology computational challenge aimed to evaluate computational methods for the development of blood-based gene expression signature classification models with the ability to predict smoking exposure status. Participants created/trained models on blood gene expression data sets including smokers/mice exposed to 3R4F (a reference cigarette) or noncurrent smokers/Sham (mice exposed to air). Participants applied their models on unseen data to predict whether subjects classify closer to smoke-exposed or nonsmoke exposed groups. The data sets also included data from subjects that had been exposed to potential modified risk tobacco products (MRTPs) or that had switched to a MRTP after exposure to conventional cigarette smoke. The scoring of anonymized participants' predictions was done using predefined metrics. The top 3 performers' methods predicted class labels with area under the precision recall scores above 0.9. Furthermore, although various computational approaches were used, the crowd's results confirmed our own data analysis outcomes with regards to the classification of MRTP-related samples. Mice exposed directly to a MRTP were classified closer to the Sham group. After switching to a MRTP, the confidence that subjects belonged to the smoke-exposed group decreased significantly. Smoking exposure gene signatures that contributed to the group separation included a core set of genes highly consistent across teams such as AHRR, LRRN3, SASH1, and P2RY6. In conclusion, crowdsourcing constitutes a pertinent approach, in complement to the classical peer review process, to independently and unbiasedly verify computational methods and data for risk assessment using systems toxicology.

A comprehensive hybridization model allows whole HERV transcriptome profiling using high density microarray

Background

Human endogenous retroviruses (HERVs) have received much attention for their implications in the etiology of many human diseases and their profound effect on evolution. Notably, recent studies have highlighted associations between HERVs expression and cancers (Yu et al., Int J Mol Med 32, 2013), autoimmunity (Balada et al., Int Rev Immunol 29:351–370, 2010) and neurological (Christensen, J Neuroimmune Pharmacol 5:326–335, 2010) conditions. Their repetitive nature makes their study particularly challenging, where expression studies have largely focused on individual loci (De Parseval et al., J Virol 77:10414–10422, 2003) or general trends within families (Forsman et al., J Virol Methods 129:16–30, 2005; Seifarth et al., J Virol 79:341–352, 2005; Pichon et al., Nucleic Acids Res 34:e46, 2006).

Methods

To refine our understanding of HERVs activity, we introduce here a new microarray, HERV-V3. This work was made possible by the careful detection and annotation of genomic HERV/MaLR sequences as well as the development of a new hybridization model, allowing the optimization of probe performances and the control of cross-reactions.

Results

HERV-V3 offers an almost complete coverage of HERVs and their ancestors (mammalian apparent LTR-retrotransposons, MaLRs) at the locus level along with four other repertoires (active LINE-1 elements, lncRNA, a selection of 1559 human genes and common infectious viruses). We demonstrate that HERV-V3 analytical performances are comparable with commercial Affymetrix arrays, and that for a selection of tissue/pathological specific loci, the patterns of expression measured on HERV-V3 is consistent with those reported in the literature.

Conclusions

Given its large HERVs/MaLRs coverage and additional repertoires, HERV-V3 opens the door to multiple applications such as enhancers and alternative promoters identification, biomarkers identification as well as the characterization of genes and HERVs/MaLRs modulation caused by viral infection.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3669-7) contains supplementary material, which is available to authorized users.

Transcriptome study and identification of potential marker genes related to the stable expression of recombinant proteins in CHO clones

BACKGROUND

Chinese hamster ovary (CHO) cells have become the host of choice for the production of recombinant proteins, due to their capacity for correct protein folding, assembly, and posttranslational modifications. The most widely used system for recombinant proteins is the gene amplification procedure that uses the CHO-Dhfr expression system. However, CHO cells are known to have a very unstable karyotype. This is due to chromosome rearrangements that can arise from translocations and homologous recombination, especially when cells with the CHO-Dhfr expression system are treated with methotrexate hydrate. The present method used in the industry for testing clones for their long-term stability of recombinant protein production is empirical, and it involves their cultivation over extended periods of time prior to the selection of the most suitable clone for further bioprocess development. The aim of the present study was the identification of marker genes that can predict stable expression of recombinant genes in particular clones early in the development stage.

RESULTS

The transcriptome profiles of CHO clones with stable and unstable recombinant protein production were investigated over 10-weeks of cultivation, using a DNA microarray. We identified 14 genes that were differentially expressed between the stable and unstable clones already at 2 weeks from the beginning of the cultivation. Their expression was validated by reverse-transcription quantitative real-time PCR (RT-qPCR). Furthermore, the k-nearest neighbour algorithm approach shows that the combination of the gene expression patterns of only five of these 14 genes is sufficient to predict stable recombinant protein production in clones in the early phases of cell-line development.

CONCLUSIONS

The exact molecular mechanisms that cause unstable recombinant protein production are not fully understood. However, the expression profiles of some genes in clones with stable and unstable recombinant protein production allow prediction of such instability early in the cell-line development stage. We have thus developed a proof-of-concept for a novel approach to eliminate unstable clones in the CHO-Dhfr expression system, which saves time and labour-intensive work in cell-line development.

Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge

Microarrays can provide large amounts of data for genetic relative expression in illnesses of interest such as cancer in short time. These data, however, are stored and often times abandoned when new experimental technologies arrive. This work reexamines lung cancer microarray data with a novel multiple criteria optimization‐based strategy aiming to detect highly differentially expressed genes. This strategy does not require any adjustment of parameters by the user and is capable to handle multiple and incommensurate units across microarrays. In the analysis, groups of samples from patients with distinct smoking habits (never smoker, current smoker) and different gender are contrasted to elicit sets of highly differentially expressed genes, several of which are already associated to lung cancer and other types of cancer. The list of genes is provided with a discussion of their role in cancer, as well as the possible research directions for each of them.

Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis

Gene expression signatures are commonly used to create cancer prognosis and diagnosis methods, yet only a small number of them are successfully deployed in the clinic since many fail to replicate performance on subsequent validation. A primary reason for this lack of reproducibility is the fact that these signatures attempt to model the highly variable and unstable genomic behavior of cancer. Our group recently introduced gene expression anti-profiles as a robust methodology to derive gene expression signatures based on the observation that while gene expression measurements are highly heterogeneous across tumors of a specific cancer type relative to the normal tissue, their degree of deviation from normal tissue expression in specific genes involved in tissue differentiation is a stable tumor mark that is reproducible across experiments and cancer types. Here we show that constructing gene expression signatures based on variability and the anti-profile approach yields classifiers capable of successfully distinguishing benign growths from cancerous growths based on deviation from normal expression. We then show that this same approach generates stable and reproducible signatures that predict probability of relapse and survival based on tumor gene expression. These results suggest that using the anti-profile framework for the discovery of genomic signatures is an avenue leading to the development of reproducible signatures suitable for adoption in clinical settings.

Distribution and function of 3',5'-Cyclic-AMP phosphodiesterases in the human ovary

The concentration of the important second messenger cAMP is regulated by phosphodiesterases (PDEs) and hence an attractive drug target. However, limited human data are available about the PDEs in the ovary. The aim of the present study was to describe and characterise the PDEs in the human ovary. Results were obtained by analysis of mRNA microarray data from follicles and granulosa cells (GCs), combined RT-PCR and enzymatic activity analysis in GCs, immunohistochemical analysis of ovarian sections and by studying the effect of PDE inhibitors on progesterone production from cultured GCs. We found that PDE3, PDE4, PDE7 and PDE8 are the major families present while PDE11A was not detected. PDE8B was differentially expressed during folliculogenesis. In cultured GCs, inhibition of PDE7 and PDE8 increased basal progesterone secretion while PDE4 inhibition increased forskolin-stimulated progesterone secretion. In conclusion, we identified PDE3, PDE4, PDE7 and PDE8 as the major PDEs in the human ovary.

Variation of RNA Quality and Quantity Are Major Sources of Batch Effects in Microarray Expression Data

The great utility of microarrays for genome-scale expression analysis is challenged by the widespread presence of batch effects, which bias expression measurements in particular within large data sets. These unwanted technical artifacts can obscure biological variation and thus significantly reduce the reliability of the analysis results. It is largely unknown which are the predominant technical sources leading to batch effects. We here quantitatively assess the prevalence and impact of several known technical effects on microarray expression results. Particularly, we focus on important factors such as RNA degradation, RNA quantity, and sequence biases including multiple guanine effects. We find that the common variation of RNA quality and RNA quantity can not only yield low-quality expression results, but that both factors also correlate with batch effects and biological characteristics of the samples.

CellNet: network biology applied to stem cell engineering

Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering.

Robust selection of cancer survival signatures from high-throughput genomic data using two-fold subsampling

Identifying relevant signatures for clinical patient outcome is a fundamental task in high-throughput studies. Signatures, composed of features such as mRNAs, miRNAs, SNPs or other molecular variables, are often non-overlapping, even though they have been identified from similar experiments considering samples with the same type of disease. The lack of a consensus is mostly due to the fact that sample sizes are far smaller than the numbers of candidate features to be considered, and therefore signature selection suffers from large variation. We propose a robust signature selection method that enhances the selection stability of penalized regression algorithms for predicting survival risk. Our method is based on an aggregation of multiple, possibly unstable, signatures obtained with the preconditioned lasso algorithm applied to random (internal) subsamples of a given cohort data, where the aggregated signature is shrunken by a simple thresholding strategy. The resulting method, RS-PL, is conceptually simple and easy to apply, relying on parameters automatically tuned by cross validation. Robust signature selection using RS-PL operates within an (external) subsampling framework to estimate the selection probabilities of features in multiple trials of RS-PL. These probabilities are used for identifying reliable features to be included in a signature. Our method was evaluated on microarray data sets from neuroblastoma, lung adenocarcinoma, and breast cancer patients, extracting robust and relevant signatures for predicting survival risk. Signatures obtained by our method achieved high prediction performance and robustness, consistently over the three data sets. Genes with high selection probability in our robust signatures have been reported as cancer-relevant. The ordering of predictor coefficients associated with signatures was well-preserved across multiple trials of RS-PL, demonstrating the capability of our method for identifying a transferable consensus signature. The software is available as an R package rsig at CRAN (http://cran.r-project.org).

A robust estimation of exon expression to identify alternative spliced genes applied to human tissues and cancer samples

BACKGROUND

Accurate analysis of whole-gene expression and individual-exon expression is essential to characterize different transcript isoforms and identify alternative splicing events in human genes. One of the omic technologies widely used in many studies on human samples are the exon-specific expression microarray platforms.

RESULTS

Since there are not many validated comparative analyses to identify specific splicing events using data derived from these types of platforms, we have developed an algorithm (called ESLiM) to detect significant changes in exon use, and applied it to a reference dataset of 270 human genes that show alternative expression in different tissues. We compared the results with three other methodological approaches and provided the R source code to be applied elsewhere. The genes positively detected by these analyses also provide a verified subset of human genes that present tissue-regulated isoforms. Furthermore, we performed a validation analysis on human patient samples comparing two different subtypes of acute myeloid leukemia (AML) and we experimentally validated the splicing in several selected genes that showed exons with highly significant signal change.

CONCLUSIONS

The comparative analyses with other methods using a fair set of human genes that show alternative splicing and the validation on clinical samples demonstrate that the proposed novel algorithm is a reliable tool for detecting differential splicing in exon-level expression data.

Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays

MOTIVATION

The recently released Infinium HumanMethylation450 array (the '450k' array) provides a high-throughput assay to quantify DNA methylation (DNAm) at ∼450 000 loci across a range of genomic features. Although less comprehensive than high-throughput sequencing-based techniques, this product is more cost-effective and promises to be the most widely used DNAm high-throughput measurement technology over the next several years.

RESULTS

Here we describe a suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. The software is structured to easily adapt to future versions of the technology. We include methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. We show how our software provides a powerful and flexible development platform for future methods. We also illustrate how our methods empower the technology to make discoveries previously thought to be possible only with sequencing-based methods.

AVAILABILITY AND IMPLEMENTATION

http://bioconductor.org/packages/release/bioc/html/minfi.html.

CONTACT

khansen@jhsph.edu; rafa@jimmy.harvard.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

IQRray, a new method for Affymetrix microarray quality control, and the homologous organ conservation score, a new benchmark method for quality control metrics

MOTIVATION

Microarray results accumulated in public repositories are widely reused in meta-analytical studies and secondary databases. The quality of the data obtained with this technology varies from experiment to experiment, and an efficient method for quality assessment is necessary to ensure their reliability.

RESULTS

The lack of a good benchmark has hampered evaluation of existing methods for quality control. In this study, we propose a new independent quality metric that is based on evolutionary conservation of expression profiles. We show, using 11 large organ-specific datasets, that IQRray, a new quality metrics developed by us, exhibits the highest correlation with this reference metric, among 14 metrics tested. IQRray outperforms other methods in identification of poor quality arrays in datasets composed of arrays from many independent experiments. In contrast, the performance of methods designed for detecting outliers in a single experiment like Normalized Unscaled Standard Error and Relative Log Expression was low because of the inability of these methods to detect datasets containing only low-quality arrays and because the scores cannot be directly compared between experiments.

AVAILABILITY AND IMPLEMENTATION

The R implementation of IQRray is available at: ftp://lausanne.isb-sib.ch/pub/databases/Bgee/general/IQRray.R.

CONTACT

Marta.Rosikiewicz@unil.ch

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Determinants of expression variability

The amount of tissue-specific expression variability (EV) across individuals is an essential characteristic of a gene and believed to have evolved, in part, under functional constraints. However, the determinants and functional implications of EV are only beginning to be investigated. Our analyses based on multiple expression profiles in 41 primary human tissues show that a gene’s EV is significantly correlated with a number of features pertaining to the genomic, epigenomic, regulatory, polymorphic, functional, structural and network characteristics of the gene. We found that (i) EV of a gene is encoded, in part, by its genomic context and is further influenced by the epigenome; (ii) strong promoters induce less variable expression; (iii) less variable gene loci evolve under purifying selection against copy number polymorphisms; (iv) genes that encode inherently disordered or highly interacting proteins exhibit lower variability; and (v) genes with less variable expression are enriched for house-keeping functions, while genes with highly variable expression tend to function in development and extra-cellular response and are associated with human diseases. Thus, our analysis reveals a number of potential mediators as well as functional and evolutionary correlates of EV, and provides new insights into the inherent variability in eukaryotic gene expression.

Contribution of CBX4 to cumulus oophorus cell phenotype in mice and attendant effects in cumulus cell cloned embryos

Cumulus oophorus cells play an essential role in oocyte development. They are also widely employed as donor cells for cloning by somatic cell nuclear transfer. Our previous studies revealed that Cbx4 mRNA was overexpressed in cloned two-cell embryos. These data indicated that CBX4 may regulate normal cumulus cell differentiation and that its overexpression in clones could contribute to aberrant gene regulation. We used siRNA-mediated knockdown of Cbx4 to assess its role in determining cumulus cell phenotype and compared the effects of this knockdown to published data for aberrant gene regulation in cloned embryos. We observed widespread effects on the expression of genes related to diverse processes in cultured cumulus cells, including cell assembly/proliferation and DNA replication/repair, endocrine function, carbohydrate and lipid metabolism, inflammation, and cell morphology, with apparent effects of CBX4 in promoting cumulus cell proliferation and survival and inhibiting differentiation. Overall, the data implicate CBX4 as a key component in the pathway integrating endocrine signals, intraovarian paracrine factors, and oocyte-derived factors in the control of cumulus cell functions. We also observed altered expression of 25 cumulus cell markers of oocyte quality, indicating an important role of CBX4 in production of high quality oocytes. Finally, we found that about one-quarter of the genes showing aberrant transcription in cloned embryos are sensitive to Cbx4 knockdown in cumulus cells, consistent with a role for aberrant Cbx4 regulation in elaborating abnormal cloned embryo characteristics.

The Gene Expression Barcode 3.0: improved data processing and mining tools

The Gene Expression Barcode project, http://barcode.luhs.org, seeks to determine the genes expressed for every tissue and cell type in humans and mice. Understanding the absolute expression of genes across tissues and cell types has applications in basic cell biology, hypothesis generation for gene function and clinical predictions using gene expression signatures. In its current version, this project uses the abundant publicly available microarray data sets combined with a suite of single-array preprocessing, quality control and analysis methods. In this article, we present the improvements that have been made since the previous version of the Gene Expression Barcode in 2011. These include a variety of new data mining tools and summaries, estimated transcriptomes and curated annotations.

Plasticity regulators modulate specific root traits in discrete nitrogen environments

Plant development is remarkably plastic but how precisely can the plant customize its form to specific environments? When the plant adjusts its development to different environments, related traits can change in a coordinated fashion, such that two traits co-vary across many genotypes. Alternatively, traits can vary independently, such that a change in one trait has little predictive value for the change in a second trait. To characterize such “tunability” in developmental plasticity, we carried out a detailed phenotypic characterization of complex root traits among 96 accessions of the model Arabidopsis thaliana in two nitrogen environments. The results revealed a surprising level of independence in the control of traits to environment – a highly tunable form of plasticity. We mapped genetic architecture of plasticity using genome-wide association studies and further used gene expression analysis to narrow down gene candidates in mapped regions. Mutants in genes implicated by association and expression analysis showed precise defects in the predicted traits in the predicted environment, corroborating the independent control of plasticity traits. The overall results suggest that there is a pool of genetic variability in plants that controls traits in specific environments, with opportunity to tune crop plants to a given environment.

Plants can dramatically alter their development in order to cope with new environmental conditions. Such plasticity is especially evident in the root system since it adopts a particular architecture under one condition, but can change architecture by altering the extent of lateral root branching in a different condition. To explore the extent of root plasticity to the critical nutrient nitrogen we analyzed a natural population of the model plant Arabidopsis in both nitrogen-limiting and nitrogen-rich environments. This revealed that root architecture plasticity appears to be the combined effect of many individual root responses to the environment that are independently modulated. Each aspect, such as lateral root length, number, or density seems to be turned on or off separately, giving the whole system flexibility. We then identified specific genes that control these individual component responses by exploring the genetic variation across the natural population in combination with analyzing which genes respond to nitrogen. Together the results help us gain insights into how the environment shapes plant development. This knowledge can be used to better understand how the growth of our existing crop species might change as the climate varies, and identify new crop varieties that will be robust to such variation.

Quality assessment metrics for whole genome gene expression profiling of paraffin embedded samples

BACKGROUND

Formalin fixed, paraffin embedded tissues are most commonly used for routine pathology analysis and for long term tissue preservation in the clinical setting. Many institutions have large archives of Formalin fixed, paraffin embedded tissues that provide a unique opportunity for understanding genomic signatures of disease. However, genome-wide expression profiling of Formalin fixed, paraffin embedded samples have been challenging due to RNA degradation. Because of the significant heterogeneity in tissue quality, normalization and analysis of these data presents particular challenges. The distribution of intensity values from archival tissues are inherently noisy and skewed due to differential sample degradation raising two primary concerns; whether a highly skewed array will unduly influence initial normalization of the data and whether outlier arrays can be reliably identified.

FINDINGS

Two simple extensions of common regression diagnostic measures are introduced that measure the stress an array undergoes during normalization and how much a given array deviates from the remaining arrays post-normalization. These metrics are applied to a study involving 1618 formalin-fixed, paraffin-embedded HER2-positive breast cancer samples from the N9831 adjuvant trial processed with Illumina's cDNA-mediated Annealing Selection extension and Ligation assay.

CONCLUSION

Proper assessment of array quality within a research study is crucial for controlling unwanted variability in the data. The metrics proposed in this paper have direct biological interpretations and can be used to identify arrays that should either be removed from analysis all together or down-weighted to reduce their influence in downstream analyses.

Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity

Background

RNA and microarray quality assessment form an integral part of gene expression analysis and, although methods such as the RNA integrity number (RIN) algorithm reliably asses RNA integrity, the relevance of RNA integrity in gene expression analysis as well as analysis methods to accommodate the possible effects of degradation requires further investigation. We investigated the relationship between RNA integrity and array quality on the commonly used Affymetrix Gene 1.0 ST array platform using reliable within-array and between-array quality assessment measures. The possibility of a transcript specific bias in the apparent effect of RNA degradation on the measured gene expression signal was evaluated after either excluding quality-flagged arrays or compensation for RNA degradation at different steps in the analysis.

Results

Using probe-level and inter-array quality metrics to assess 34 Gene 1.0 ST array datasets derived from historical, paired tumour and normal primary colorectal cancer samples, 7 arrays (20.6%), with a mean sample RIN of 3.2 (SD = 0.42), were flagged during array quality assessment while 10 arrays from samples with RINs < 7 passed quality assessment, including one sample with a RIN < 3. We detected a transcript length bias in RNA degradation in only 5.8% of annotated transcript clusters (p-value 0.05, FC ≥ |2|), with longer and shorter than average transcripts under- and overrepresented in quality-flagged samples respectively. Applying compensatory measures for RNA degradation performed at least as well as excluding quality-flagged arrays, as judged by hierarchical clustering, gene expression analysis and Ingenuity Pathway Analysis; importantly, use of these compensatory measures had the significant benefit of enabling lower quality array data from irreplaceable clinical samples to be retained in downstream analyses.

Conclusions

Here, we demonstrate an effective array-quality assessment strategy, which will allow the user to recognize lower quality arrays that can be included in the analysis once appropriate measures are applied to account for known or unknown sources of variation, such as array quality- and batch- effects, by implementing ComBat or Surrogate Variable Analysis. This approach of quality control and analysis will be especially useful for clinical samples with variable and low RNA qualities, with RIN scores ≥ 2.