Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

Purinergic Receptor Transactivation by the β2-Adrenergic Receptor Increases Intracellular Ca2+ in Nonexcitable Cells

The β₂ adrenergic receptor (β₂AR) increases intracellular Ca²⁺ in a variety of cell types. By combining pharmacological and genetic manipulations, we reveal a novel mechanism through which the β₂AR promotes Ca²⁺ mobilization (pEC₅₀ = 7.32 ± 0.10) in nonexcitable human embryonic kidney (HEK)293S cells. Downregulation of Gs with sustained cholera toxin pretreatment and the use of Gs-null HEK293 (∆Gs-HEK293) cells generated using the clustered regularly interspaced short palindromic repeat-associated protein-9 nuclease (CRISPR/Cas9) system, combined with pharmacological modulation of cAMP formation, revealed a Gs-dependent but cAMP-independent increase in intracellular Ca²⁺ following β₂AR stimulation. The increase in cytoplasmic Ca²⁺ was inhibited by P2Y purinergic receptor antagonists as well as a dominant-negative mutant form of Gq, a Gq-selective inhibitor, and an inositol 1,4,5-trisphosphate (IP₃) receptor antagonist, suggesting a role for this Gq-coupled receptor family downstream of the β₂AR activation. Consistent with this mechanism, β₂AR stimulation promoted the extracellular release of ATP, and pretreatment with apyrase inhibited the β₂AR-promoted Ca²⁺ mobilization. Together, these data support a model whereby the β₂AR stimulates a Gs-dependent release of ATP, which transactivates Gq-coupled P2Y receptors through an inside-out mechanism, leading to a Gq- and IP₃-dependent Ca²⁺ mobilization from intracellular stores. Given that β₂AR and P2Y receptors are coexpressed in various tissues, this novel signaling paradigm could be physiologically important and have therapeutic implications. In addition, this study reports the generation and validation of HEK293 cells deleted of Gs using the CRISPR/Cas9 genome editing technology that will undoubtedly be powerful tools to study Gs-dependent signaling.

Transcriptional Perturbations in Graft Rejection

BACKGROUND

Understanding the regulatory interplay of relevant microRNAs (miRNAs) and messenger RNAs (mRNAs) in the rejecting allograft will result in a better understanding of the molecular pathophysiology of alloimmune injury.

METHODS

One hundred sixty-seven allograft biopsies, with (n = 47) and without (n = 120) central histology for Banff scored acute rejection (AR), were transcriptionally profiled for mRNA and miRNA by whole genome microarrays and multiplexed microfluidic quantitative polymerase chain reaction, respectively. A customized database was curated (GO-Elite) and used to identify AR-specific dysregulated mRNAs and the role of perturbations of their relevant miRNAs targets during AR.

RESULTS

The AR-specific changes in 1035 specific mRNAs were mirrored by AR-specific perturbations in 9 relevant miRNAs as predicted by Go-Elite and were regulated specifically by p53 and forkhead box P3. Infiltrating lymphocytes and the renal tubules drove the miRNA tissue pertubations in rejection, involving message degradation and transcriptional/translational activation. The expression of many of these miRNAs significantly associated with the intensity of the Banff-scored interstitial inflammation and tubulitis.

CONCLUSIONS

There is a highly regulated interplay between specific mRNA/miRNAs in allograft rejection that drive both immune-mediated injury and tissue repair during AR.

t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data

Microarray data analysis typically consists in identifying a list of differentially expressed genes (DEG), i.e., the genes that are differentially expressed between two experimental conditions. Variance shrinkage methods have been considered a better choice than the standard t-test for selecting the DEG because they correct the dependence of the error with the expression level. This dependence is mainly caused by errors in background correction, which more severely affects genes with low expression values. Here, we propose a new method for identifying the DEG that overcomes this issue and does not require background correction or variance shrinkage. Unlike current methods, our methodology is easy to understand and implement. It consists of applying the standard t-test directly on the normalized intensity data, which is possible because the probe intensity is proportional to the gene expression level and because the t-test is scale- and location-invariant. This methodology considerably improves the sensitivity and robustness of the list of DEG when compared with the t-test applied to preprocessed data and to the most widely used shrinkage methods, Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA). Our approach is useful especially when the genes of interest have small differences in expression and therefore get ignored by standard variance shrinkage methods.

hSETD1A regulates Wnt target genes and controls tumor growth of colorectal cancer cells

hSETD1A is a member of the trithorax (TrxG) family of histone methyltransferases (HMT) that methylate H3K4 at promoters of active genes. Although misregulation of mixed lineage leukemia (MLL) family proteins has been associated with acute leukemia, the role of hSETD1A in cancer remains unknown. In this study, we report that hSETD1A and its associated H3K4me3 are upregulated in human colorectal cancer cells and patient samples. Depletion of hSETD1A inhibits colorectal cancer cell growth, colony formation, and tumor engraftment. Genome-wide expression profiling of colorectal cancer cells reveals that approximately 50% of Wnt/β-catenin target genes are affected by the hSETD1A knockdown. We further demonstrate that hSETD1A is recruited to promoters of those Wnt signaling target genes through its interaction with β-catenin, a master regulator of the Wnt signaling pathway. The recruitment of the hSETD1A HMT complex confers promoter-associated H3K4me3 that leads to assembly of transcription preinitiation complex and transcriptional activation. Furthermore, the expression levels of hSETD1A are positively correlated with H3K4me3 enrichment at the promoters of Wnt/β-catenin target genes and the aberrant activation of these genes in human colorectal cancer. These results provide new biologic and mechanistic insights into the cooperative role of hSETD1A and β-catenin in regulation of Wnt target genes as well as in colorectal cancer cell growth in vitro and in vivo.

A comparison of probe-level and probeset models for small-sample gene expression data

BACKGROUND

Statistical methods to tentatively identify differentially expressed genes in microarray studies typically assume larger sample sizes than are practical or even possible in some settings.

RESULTS

The performance of several probe-level and probeset models was assessed graphically and numerically using three spike-in datasets. Based on the Affymetrix GeneChip, a novel nested factorial model was developed and found to perform competitively on small-sample spike-in experiments.

CONCLUSIONS

Statistical methods with test statistics related to the estimated log fold change tend to be more consistent in their performance on small-sample gene expression data. For such small-sample experiments, the nested factorial model can be a useful statistical tool. This method is implemented in freely-available R code (affyNFM), available with a tutorial document at http://www.stat.usu.edu/~jrstevens.

Nonspecific hybridization scaling of microarray expression estimates: a physicochemical approach for chip-to-chip normalization

The problem of inferring accurate quantitative estimates of transcript abundances from gene expression microarray data is addressed. Particular attention is paid to correcting chip-to-chip variations arising mainly as a result of unwanted nonspecific background hybridization to give transcript abundances measured in a common scale. This study verifies and generalizes a model of the mutual dependence between nonspecific background hybridization and the sensitivity of the specific signal using an approach based on the physical chemistry of surface hybridization. We have analyzed GeneChip oligonucleotide microarray data taken from a set of five benchmark experiments including dilution, Latin Square, and "Golden spike" designs. Our analysis concentrates on the important effect of changes in the unwanted nonspecific background inherent in the technology due to changes in total RNA target concentration and/or composition. We find that incremental changes in nonspecific background entail opposite sign incremental changes in the effective specific binding constant. This effect, which we refer to as the "up-down" effect, results from the subtle interplay of competing interactions between the probes and specific and nonspecific targets at the chip surface and in bulk solution. We propose special rules for proper normalization of expression values considering the specifics of the up-down effect. Particularly for normalization one has to level the expression values of invariant expressed probes. Existing heuristic normalization techniques which do not exclude absent probes, level intensities instead of expression values, and/or use low variance criteria for identifying invariant sets of probes lead to biased results. Strengths and pitfalls of selected normalization methods are discussed. We also find that the extent of the up-down effect is modified if RNA targets are replaced by DNA targets, in that microarray sensitivity and specificity are improved via a decrease in nonspecific background, which effectively amplifies specific binding. The results emphasize the importance of physicochemical approaches for improving heuristic normalization algorithms to proceed toward quantitative microarray data analysis.

Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity

Background

To identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However, in addition to these recommendations, researchers also want to know which combinations enhance reproducibility.

Results

We compared eight conventional methods for ranking genes: weighted average difference (WAD), average difference (AD), fold change (FC), rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT) with six preprocessing algorithms (PLIER, VSN, FARMS, multi-mgMOS (mmgMOS), MBEI, and GCRMA). A total of 36 real experimental datasets was evaluated on the basis of the area under the receiver operating characteristic curve (AUC) as a measure for both sensitivity and specificity. We found that the RP method performed well for VSN-, FARMS-, MBEI-, and GCRMA-preprocessed data, and the WAD method performed well for mmgMOS-preprocessed data. Our analysis of the MicroArray Quality Control (MAQC) project's datasets showed that the FC-based gene ranking methods (WAD, AD, FC, and RP) had a higher level of reproducibility: The percentages of overlapping genes (POGs) across different sites for the FC-based methods were higher overall than those for the t-statistic-based methods (modT, samT, shrinkT, and ibmT). In particular, POG values for WAD were the highest overall among the FC-based methods irrespective of the choice of preprocessing algorithm.

Conclusion

Our results demonstrate that to increase sensitivity, specificity, and reproducibility in microarray analyses, we need to select suitable combinations of preprocessing algorithms and gene ranking methods. We recommend the use of FC-based methods, in particular RP or WAD.

A comprehensive re-analysis of the Golden Spike data: towards a benchmark for differential expression methods

Background

The Golden Spike data set has been used to validate a number of methods for summarizing Affymetrix data sets, sometimes with seemingly contradictory results. Much less use has been made of this data set to evaluate differential expression methods. It has been suggested that this data set should not be used for method comparison due to a number of inherent flaws.

Results

We have used this data set in a comparison of methods which is far more extensive than any previous study. We outline six stages in the analysis pipeline where decisions need to be made, and show how the results of these decisions can lead to the apparently contradictory results previously found. We also show that, while flawed, this data set is still a useful tool for method comparison, particularly for identifying combinations of summarization and differential expression methods that are unlikely to perform well on real data sets. We describe a new benchmark, AffyDEComp, that can be used for such a comparison.

Conclusion

We conclude with recommendations for preferred Affymetrix analysis tools, and for the development of future spike-in data sets.