A statistical framework for consolidating "sibling" probe sets for Affymetrix GeneChip data

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.

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.

Transcriptional profiling identifies strain-specific effects of caloric restriction and opposite responses in human and mouse white adipose tissue

Caloric restriction (CR) has been extensively studied in rodents as an intervention to improve lifespan and healthspan. However, effects of CR can be strain- and species-specific. This study used publically available microarray data to analyze expression responses to CR in males from 7 mouse strains (C57BL/6J, BALB/c, C3H, 129, CBA, DBA, B6C3F1) and 4 tissues (epididymal white adipose tissue (eWAT), muscle, heart, cortex). In each tissue, the largest number of strain-specific CR responses was identified with respect to the C57BL/6 strain. In heart and cortex, CR responses in C57BL/6 mice were negatively correlated with responses in other strains. Strain-specific CR responses involved genes associated with olfactory receptors (Olfr1184, Olfr910) and insulin/IGF-1 signaling (Igf1, Irs2). In each strain, CR responses in eWAT were negatively correlated with those in human subcutaneous WAT (scWAT). In human scWAT, CR increased expression of genes associated with stem cell maintenance and vascularization. However, orthologous genes linked to these processes were down-regulated in mouse. These results identify strain-specific CR responses limiting generalization across mouse strains. Differential CR responses in mouse versus human WAT may be due to differences in the depots examined and/or the presence of “thrifty genes” in humans that resist adipose breakdown despite caloric deficit.

Genetic profiling of young and aged endothelial progenitor cells in hypoxia

Age is a major risk factor for diseases caused by ischemic hypoxia, such as stroke and coronary artery disease. Endothelial progenitor cells (EPCs) are the major cells respond to ischemic hypoxia through angiogenesis and vascular remodeling. However, the effect of aging on EPCs and their responses to hypoxia are not well understood. CD34⁺ EPCs were isolated from healthy volunteers and aged by replicative senescence, which was to passage cells until their doubling time was twice as long as the original cells. Young and aged CD34⁺ EPCs were exposed to a hypoxic environment (1% oxygen for 48hrs) and their gene expression profiles were evaluated using gene expression array. Gene array results were confirmed using quantitative polymerase chain reaction, Western blotting, and BALB/c female athymic nude mice hindlimb ischemia model. We identified 115 differentially expressed genes in young CD34⁺ EPCs, 54 differentially expressed genes in aged CD34⁺ EPCs, and 25 common genes between normoxia and hypoxia groups. Among them, the expression of solute carrier family 2 (facilitated glucose transporter), member 1 (SLC2A1) increased the most by hypoxia in young cells. Gene set enrichment analysis indicated the pathways affected by aging and hypoxia most, including genes “response to oxygen levels” in young EPCs and genes involved “chondroitin sulfate metabolic process” in aged cells. Our study results indicate the key factors that contribute to the effects of aging on response to hypoxia in CD34⁺ EPCs. With the potential applications of EPCs in cardiovascular and other diseases, our study also provides insight on the impact of ex vivo expansion might have on EPCs.

Psoriasis drug development and GWAS interpretation through in silico analysis of transcription factor binding sites

Background

Psoriasis is a cytokine-mediated skin disease that can be treated effectively with immunosuppressive biologic agents. These medications, however, are not equally effective in all patients and are poorly suited for treating mild psoriasis. To develop more targeted therapies, interfering with transcription factor (TF) activity is a promising strategy.

Methods

Meta-analysis was used to identify differentially expressed genes (DEGs) in the lesional skin from psoriasis patients (n = 237). We compiled a dictionary of 2935 binding sites representing empirically-determined binding affinities of TFs and unconventional DNA-binding proteins (uDBPs). This dictionary was screened to identify “psoriasis response elements” (PREs) overrepresented in sequences upstream of psoriasis DEGs.

Results

PREs are recognized by IRF1, ISGF3, NF-kappaB and multiple TFs with helix-turn-helix (homeo) or other all-alpha-helical (high-mobility group) DNA-binding domains. We identified a limited set of DEGs that encode proteins interacting with PRE motifs, including TFs (GATA3, EHF, FOXM1, SOX5) and uDBPs (AVEN, RBM8A, GPAM, WISP2). PREs were prominent within enhancer regions near cytokine-encoding DEGs (IL17A, IL19 and IL1B), suggesting that PREs might be incorporated into complex decoy oligonucleotides (cdODNs). To illustrate this idea, we designed a cdODN to concomitantly target psoriasis-activated TFs (i.e., FOXM1, ISGF3, IRF1 and NF-kappaB). Finally, we screened psoriasis-associated SNPs to identify risk alleles that disrupt or engender PRE motifs. This identified possible sites of allele-specific TF/uDBP binding and showed that PREs are disproportionately disrupted by psoriasis risk alleles.

Conclusions

We identified new TF/uDBP candidates and developed an approach that (i) connects transcriptome informatics to cdODN drug development and (ii) enhances our ability to interpret GWAS findings. Disruption of PRE motifs by psoriasis risk alleles may contribute to disease susceptibility.

Electronic supplementary material

The online version of this article (doi:10.1186/s40169-015-0054-5) contains supplementary material, which is available to authorized users.

A generalized dSpliceType framework to detect differential splicing and differential expression events using RNA-Seq

Transcriptomes are routinely compared in term of a list of differentially expressed genes followed by functional enrichment analysis. Due to the technology limitations of microarray, the molecular mechanisms of differential expression is poorly understood. Using RNA-seq data, we propose a generalized dSpliceType framework to systematically investigate the synergistic and antagonistic effects of differential splicing and differential expression. We applied the method to two public RNA-seq data sets and compared the transcriptomes between treatment and control conditions. The generalized dSpliceType detects and prioritizes a list of genes that are differentially expressed and/or spliced. In particular, the multivariate dSpliceType is among the fist to utilize sequential dependency of normalized base-wise read coverage signals and capture biological variability among replicates using a multivariate statistical model. We compared dSpliceType with two other methods in terms of five most common types of differential splicing events between two conditions using RNA-Seq. dSpliceType is free, available from http://dsplicetype.sourceforge.net/.

Analysis of discordant Affymetrix probesets casts serious doubt on idea of microarray data reutilization

Background

Affymetrix microarray technology allows one to investigate expression of thousands of genes simultaneously upon a variety of conditions. In a popular U133A microarray platform, the expression of 37% of genes is measured by more than one probeset. The discordant expression observed for two different probesets that match the same gene is a widespread phenomenon which is usually underestimated, ignored or disregarded.

Results

Here we evaluate the prevalence of discordant expression in data collected using Affymetrix HG-U133A microarray platform. In U133A, about 30% of genes annotated by two different probesets demonstrate a substantial correlation between independently measured expression values. To our surprise, sorting the probesets according to the nature of the discrepancy in their expression levels allowed the classification of the respective genes according to their fundamental functional properties, including observed enrichment by tissue-specific transcripts and alternatively spliced variants. On another hand, an absence of discrepancies in probesets that simultaneously match several different genes allowed us to pinpoint non-expressed pseudogenes and gene groups with highly correlated expression patterns. Nevertheless, in many cases, the nature of discordant expression of two probesets that match the same transcript remains unexplained. It is possible that these probesets report differently regulated sets of transcripts, or, in best case scenario, two different sets of transcripts that represent the same gene.

Conclusion

The majority of absolute gene expression values collected using Affymetrix microarrays may not be suitable for typical interpretative downstream analysis.

Mixture models for gene expression experiments with two species

Cross-species research in drug development is novel and challenging. A bivariate mixture model utilizing information across two species was proposed to solve the fundamental problem of identifying differentially expressed genes in microarray experiments in order to potentially improve the understanding of translation between preclinical and clinical studies for drug development. The proposed approach models the joint distribution of treatment effects estimated from independent linear models. The mixture model posits up to nine components, four of which include groups in which genes are differentially expressed in both species. A comprehensive simulation to evaluate the model performance and one application on a real world data set, a mouse and human type II diabetes experiment, suggest that the proposed model, though highly structured, can handle various configurations of differential gene expression and is practically useful on identifying differentially expressed genes, especially when the magnitude of differential expression due to different treatment intervention is weak. In the mouse and human application, the proposed mixture model was able to eliminate unimportant genes and identify a list of genes that were differentially expressed in both species and could be potential gene targets for drug development.

SCOREM: statistical consolidation of redundant expression measures

Many platforms for genome-wide analysis of gene expression contain ‘redundant’ measures for the same gene. For example, the most highly utilized platforms for gene expression microarrays, Affymetrix GeneChip® arrays, have as many as ten or more probe sets for some genes. Occasionally, individual probe sets for the same gene report different trends in expression across experimental conditions, a situation that must be resolved in order to accurately interpret the data. We developed an algorithm, SCOREM, for determining the level of agreement between such probe sets, utilizing a statistical test of concordance, Kendall's W coefficient of concordance, and a graph-searching algorithm for the identification of concordant probe sets. We also present methods for consolidating concordant groups into a single value for its corresponding gene and for post hoc analysis of discordant groups. By combining statistical consolidation with sequence analysis, SCOREM possesses the unique ability to identify biologically meaningful discordant behaviors, including differing behaviors in alternate RNA isoforms and tissue-specific patterns of expression. When consolidating concordant behaviors, SCOREM outperforms other methods in detecting both differential expression and overrepresented functional categories.

Integrating multiple microarray datasets on oral squamous cell carcinoma to reveal dysregulated networks

BACKGROUND

Oral squamous cell carcinoma (OSCC) is the sixth most common type of carcinoma worldwide. The pathogenic pathways involved in this cancer are mostly unknown; therefore, a better characterization of the OSCC gene expression profile would represent a considerable advance. The public availability of gene expression datasets was meant to obtain new insights on biological processes.

METHODS

We integrated 4 public microarray datasets on OSCC to evaluate the degree of consistency among the biological results obtained in these different studies and to identify common regulatory pathways that could be responsible for tumor growth.

RESULTS

Twelve altered cellular pathways implicated in OSCC and 4 genes altered in the extracellular matrix (ECM) receptor pathway were validated by quantitative real-time polymerase chain reaction (qRT-PCR).

CONCLUSION

Using 4 expression array datasets, we have developed a robust method for analyzing pathways altered in OSCC.

Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures

Comparing independent high-throughput gene-expression experiments can generate hypotheses about which gene-expression programs are shared between particular biological processes. Current techniques to compare expression profiles typically involve choosing a fixed differential expression threshold to summarize results, potentially reducing sensitivity to small but concordant changes. We present a threshold-free algorithm called Rank–rank Hypergeometric Overlap (RRHO). This algorithm steps through two gene lists ranked by the degree of differential expression observed in two profiling experiments, successively measuring the statistical significance of the number of overlapping genes. The output is a graphical map that shows the strength, pattern and bounds of correlation between two expression profiles. To demonstrate RRHO sensitivity and dynamic range, we identified shared expression networks in cancer microarray profiles driving tumor progression, stem cell properties and response to targeted kinase inhibition. We demonstrate how RRHO can be used to determine which model system or drug treatment best reflects a particular biological or disease response. The threshold-free and graphical aspects of RRHO complement other rank-based approaches such as Gene Set Enrichment Analysis (GSEA), for which RRHO is a 2D analog. Rank–rank overlap analysis is a sensitive, robust and web-accessible method for detecting and visualizing overlap trends between two complete, continuous gene-expression profiles. A web-based implementation of RRHO can be accessed at http://systems.crump.ucla.edu/rankrank/.

Integrating multiple genome annotation databases improves the interpretation of microarray gene expression data

Background

The Affymetrix GeneChip is a widely used gene expression profiling platform. Since the chips were originally designed, the genome databases and gene definitions have been considerably updated. Thus, more accurate interpretation of microarray data requires parallel updating of the specificity of GeneChip probes. We propose a new probe remapping protocol, using the zebrafish GeneChips as an example, by removing nonspecific probes, and grouping the probes into transcript level probe sets using an integrated zebrafish genome annotation. This genome annotation is based on combining transcript information from multiple databases. This new remapping protocol, especially the new genome annotation, is shown here to be an important factor in improving the interpretation of gene expression microarray data.

Results

Transcript data from the RefSeq, GenBank and Ensembl databases were downloaded from the UCSC genome browser, and integrated to generate a combined zebrafish genome annotation. Affymetrix probes were filtered and remapped according to the new annotation. The influence of transcript collection and gene definition methods was tested using two microarray data sets. Compared to remapping using a single database, this new remapping protocol results in up to 20% more probes being retained in the remapping, leading to approximately 1,000 more genes being detected. The differentially expressed gene lists are consequently increased by up to 30%. We are also able to detect up to three times more alternative splicing events. A small number of the bioinformatics predictions were confirmed using real-time PCR validation.

Conclusions

By combining gene definitions from multiple databases, it is possible to greatly increase the numbers of genes and splice variants that can be detected in microarray gene expression experiments.

Consistency analysis of redundant probe sets on affymetrix three-prime expression arrays and applications to differential mRNA processing

Affymetrix three-prime expression microarrays contain thousands of redundant probe sets that interrogate different regions of the same gene. Differential expression analysis methods rarely consider probe redundancy, which can lead to inaccurate inference about overall gene expression or cause investigators to overlook potentially valuable information about differential regulation of variant mRNA products. We investigated the behaviour and consistency of redundant probe sets in a publicly-available data set containing samples from mouse brain amygdala and hippocampus and asked how applying filtering methods to the data affected consistency of results obtained from redundant probe sets. A genome-based filter that screens and groups probe sets according to their overlapping genomic alignments significantly improved redundant probe set consistency. Screening based on qualitative Present-Absent calls from MAS5 also improved consistency. However, even after applying these filters, many redundant probe sets showed significant fold-change differences relative to each other, suggesting differential regulation of alternative transcript production. Visual inspection of these loci using an interactive genome visualization tool (igb.bioviz.org) exposed thirty putative examples of differential regulation of alternative splicing or polyadenylation across brain regions in mouse. This work demonstrates how P/A-call and genome-based filtering can improve consistency among redundant probe sets while at the same time exposing possible differential regulation of RNA processing pathways across sample types.