A semi-parametric statistical model for integrating gene expression profiles across different platforms

Determining differentially expressed genes (DEGs) between biological samples is the key to understand how genotype gives rise to phenotype. RNA-seq and microarray are two main technologies for profiling gene expression levels. However, considerable discrepancy has been found between DEGs detected using the two technologies. Integration data across these two platforms has the potential to improve the power and reliability of DEG detection. We propose a rank-based semi-parametric model to determine DEGs using information across different sources and apply it to the integration of RNA-seq and microarray data. By incorporating both the significance of differential expression and the consistency across platforms, our method effectively detects DEGs with moderate but consistent signals. We demonstrate the effectiveness of our method using simulation studies, MAQC/SEQC data and a synthetic microRNA dataset. Our integration method is not only robust to noise and heterogeneity in the data, but also adaptive to the structure of data. In our simulations and real data studies, our approach shows a higher discriminate power and identifies more biologically relevant DEGs than eBayes, DEseq and some commonly used meta-analysis methods.     

Evolutionary rate and gene expression across different brain regions

Background

The evolutionary rate of a protein is a basic measure of evolution at the molecular level. Previous studies have shown that genes expressed in the brain have significantly lower evolutionary rates than those expressed in somatic tissues.

Results

We study the evolutionary rates of genes expressed in 21 different human brain regions. We find that genes highly expressed in the more recent cortical regions of the brain have lower evolutionary rates than genes highly expressed in subcortical regions. This may partially result from the observation that genes that are highly expressed in cortical regions tend to be highly expressed in subcortical regions, and thus their evolution faces a richer set of functional constraints. The frequency of mammal-specific and primate-specific genes is higher in the highly expressed gene sets of subcortical brain regions than in those of cortical brain regions. The basic inverse correlation between evolutionary rate and gene expression is significantly stronger in brain versus nonbrain tissues, and in cortical versus subcortical regions. Extending upon this cortical/subcortical trend, this inverse correlation is generally more marked for tissues that are located higher along the cranial vertical axis during development, giving rise to the possibility that these tissues are also more evolutionarily recent.

Conclusions

We find that cortically expressed genes are more conserved than subcortical ones, and that gene expression levels exert stronger constraints on sequence evolution in cortical versus subcortical regions. Taken together, these findings suggest that cortically expressed genes are under stronger selective pressure than subcortically expressed genes.     

A comparison of Affymetrix gene expression arrays

Background  

Affymetrix GeneChips™ are an important tool in many facets of biological research. Recently, notable design changes to the chips have been made. In this study, we use publicly available data from Affymetrix to gauge the performance of three human gene expression arrays: Human Genome U133 Plus 2.0 (U133), Human Exon 1.0 ST (HuEx) and Human Gene 1.0 ST (HuGene).     

Quantifying engineered nanomaterial toxicity: comparison of common cytotoxicity and gene expression measurements

Background

When evaluating the toxicity of engineered nanomaterials (ENMS) it is important to use multiple bioassays based on different mechanisms of action. In this regard we evaluated the use of gene expression and common cytotoxicity measurements using as test materials, two selected nanoparticles with known differences in toxicity, 5 nm mercaptoundecanoic acid (MUA)-capped InP and CdSe quantum dots (QDs). We tested the effects of these QDs at concentrations ranging from 0.5 to 160 µg/mL on cultured normal human bronchial epithelial (NHBE) cells using four common cytotoxicity assays: the dichlorofluorescein assay for reactive oxygen species (ROS), the lactate dehydrogenase assay for membrane viability (LDH), the mitochondrial dehydrogenase assay for mitochondrial function, and the Comet assay for DNA strand breaks.

Results

The cytotoxicity assays showed similar trends when exposed to nanoparticles for 24 h at 80 µg/mL with a threefold increase in ROS with exposure to CdSe QDs compared to an insignificant change in ROS levels after exposure to InP QDs, a twofold increase in the LDH necrosis assay in NHBE cells with exposure to CdSe QDs compared to a 50% decrease for InP QDs, a 60% decrease in the mitochondrial function assay upon exposure to CdSe QDs compared to a minimal increase in the case of InP and significant DNA strand breaks after exposure to CdSe QDs compared to no significant DNA strand breaks with InP. High-throughput quantitative real-time polymerase chain reaction (qRT-PCR) data for cells exposed for 6 h at a concentration of 80 µg/mL were consistent with the cytotoxicity assays showing major differences in DNA damage, DNA repair and mitochondrial function gene regulatory responses to the CdSe and InP QDs. The BRCA2, CYP1A1, CYP1B1, CDK1, SFN and VEGFA genes were observed to be upregulated specifically from increased CdSe exposure and suggests their possible utility as biomarkers for toxicity.

Conclusions

This study can serve as a model for comparing traditional cytotoxicity assays and gene expression measurements and to determine candidate biomarkers for assessing the biocompatibility of ENMs.

     

Analysis of matched mRNA measurements from two different microarray technologies

MOTVIATION: The existence of several technologies for measuring gene expression makes the question of cross-technology agreement of measurements an important issue. Cross-platform utilization of data from different technologies has the potential to reduce the need to duplicate experiments but requires corresponding measurements to be comparable. METHODS: A comparison of mRNA measurements of 2895 sequence-matched genes in 56 cell lines from the standard panel of 60 cancer cell lines from the National Cancer Institute (NCI 60) was carried out by calculating correlation between matched measurements and calculating concordance between cluster from two high-throughput DNA microarray technologies, Stanford type cDNA microarrays and Affymetrix oligonucleotide microarrays. RESULTS: In general, corresponding measurements from the two platforms showed poor correlation. Clusters of genes and cell lines were discordant between the two technologies, suggesting that relative intra-technology relationships were not preserved. GC-content, sequence length, average signal intensity, and an estimator of cross-hybridization were found to be associated with the degree of correlation. This suggests gene-specific, or more correctly probe-specific, factors influencing measurements differently in the two platforms, implying a poor prognosis for a broad utilization of gene expression measurements across platforms.     

Classification across gene expression microarray studies

Background  

The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.     

A plasmid toolbox for controlled gene expression across the Proteobacteria

Controlled gene expression is fundamental for the study of gene function and our ability to engineer bacteria. However, there is currently no easy-to-use genetics toolbox that enables controlled gene expression in a wide range of diverse species. To facilitate the development of genetics systems in a fast, easy, and standardized manner, we constructed and tested a plasmid assembly toolbox that will enable the identification of well-regulated promoters in many Proteobacteria and potentially beyond. Each plasmid is composed of four categories of genetic parts (i) the origin of replication, (ii) resistance marker, (iii) promoter-regulator and (iv) reporter. The plasmids can be efficiently assembled using ligation-independent cloning, and any gene of interest can be easily inserted in place of the reporter. We tested this toolbox in nine different Proteobacteria and identified regulated promoters with over fifty-fold induction range in eight of these bacteria. We also constructed variant libraries that enabled the identification of promoter-regulators with varied expression levels and increased inducible fold change relative to the original promoter. A selection of over 50 plasmids, which contain all of the toolbox''s genetic parts, are available for community use and will enable easy construction and testing of genetics systems in both model and non-model bacteria.     

Analysis of adiponectin gene and comparison of its expression in two different pig breeds

Adiponectin, an adipokine secreted from adipose tissue (AT), exerts beneficial pleiotropic effects on obesity-related metabolic diseases. We have analyzed the adiponectin gene (ACDC) and its expression in two genetically different breeds of pigs, lean type, large white (LW) and fat type, Casertana (CE). DNA, RNA, and protein extracts from 10 LW and 10 CE pigs were analyzed by sequence analysis, enzyme-linked immunosorbent assay (ELISA), fast protein liquid chromatography, and northern and western blotting. Sequence analysis revealed an identity of 100% between the ACDC gene from the two breeds, but the expression of the adiponectin protein was higher in LW than in CE pigs. We identified sexual dimorphism of adiponectin in both breeds, namely a balanced distribution of the low isoforms ( approximately 50 kDa), whereas the middle isoforms ( approximately 75-150 kDa) were increased in sows. In conclusion, in this study, we demonstrate that adiponectin is produced and secreted differently in the two breeds of pig, namely adiponectin is more abundant in LW than in CE. Moreover, the visceral AT of LW expresses more adiponectin than the subcutaneous AT. This relationship is absent in CE. These observations provided the first evidence that adiponectin expression is correlated with the "fat" phenotype in pig.     

Transport measurements across Caco-2 monolayers of different organic and inorganic selenium

The transport and uptake of the most common Se compounds, selenate (SeO ₄ ²⁻ ), selenite (SeO ₃ ²⁻ ), selenomethionine, and selenocystine, were investigated using confluent monolayers of Caco-2 cells, a human carcinoma cell line. Comparative measurements were performed in the absorptive (apical to basolateral side) and exsorptive (basolateral to apical side) directions. Apparent permeability coefficients (P _app), calculated from transport experiments in the absorptive direction, showed increasing values in the following rank order: about 1×10⁻⁶ cm/s ≤ mannitol ≤ SeO ₃ ²⁻ ≤ selenocystine < selenomethionine < SeO ₄ ²⁻ ≤ about 16×10⁻⁶ cm/s. The ratios of the P _app measured in the absorptive versus exsorptive directions indicated that only the organic forms presented a net polarized transport (P _app ratio ≫1), suggesting the presence of a transcellular pathway. No significant excretion was observed. The transport of selenomethionine was inhibited by its sulfur analog, methionine, suggesting a common transport mechanism. In contrast, an inhibition of the transport of selenocystine by cysteine was not observed. From the two substrates tested, sulfate and thiosulfate, only thiosulfate inhibited the transport of SeO ₄ ²⁻ . This effect was also observed for SeO ₃ ²⁻ (i.e., was unspecific), which questioned the assertion of a common transport for sulfate and SeO ₄ ²⁻ and may confirm the paracellular pathway of SeO ₄ ²⁻ suggested by the P _app ratio of about 1. The addition of glutathione (GSH) in large excess had no consequence on the passage of SeO ₃ ²⁻ but strongly increased the uptake (about fourfold). The liquid chromatography — mass spectrometry (LC-MS) data showed that, in the ionic condition of incubation medium, GSH promptly reduced SeO ₃ ²⁻ (≤2 min) in its elemental form Se⁰, which cannot ascribe to selenodiglutathione a direct role in the effect of GSH.     

Indirect measurements of differential gene expression with cDNA microarrays

The use of universal RNA reference sets is an increasingly common approach to molecular classification studies with cDNA microarrays. Here we evaluated the reliability of indirect measurements of fluorescence ratios with a common RNA reference as a means of identifying differentially expressed genes. Comparisons of direct and indirect measures of differential gene expression showed a strong overall correlation in fluorescence ratio measurements but also a high degree of false positives in our indirect measurements. These results indicated that the application of more stringent ratio filters may be required when assessing differential gene expression utilizing a common RNA reference in classification studies.