Classification of genes and putative biomarker identification using distribution metrics on expression profiles

Dynamic gene expression patterns in animal models of early and late heart failure reveal biphasic-bidirectional transcriptional activation of signaling pathways

Altered cardiac gene expression in heart failure (HF) has mostly been identified by single-point analysis of end-stage disease. This may miss earlier changes in gene expression that are transient and/or directionally opposite to those observed later. Myocardial datasets from the largest microarray data repository (Gene Expression Omnibus) yielded six HF studies with time-course data. Differentially expressed transcripts between nonfailing controls, early HF (<3 days after cardiac insult) and late HF (usually >2 wk) were determined, and analysis of KEGG pathways and predicted regulatory control elements performed. We found that gene expression followed varying patterns: Downregulation of metabolic pathways occurred early and was sustained into late-stage HF. In contrast, most signaling pathways undergo a complex biphasic pattern: Calcium signaling, p53, apoptosis, and MAPK pathways displayed a bidirectional response, declining early but rising late. These profiles were compatible with specific microRNA (miRNA) and transcription regulators: Estrogen-related receptor-α and myocyte-enhancer factor-2 binding sites were overrepresented in the promoter regions of downregulated transcripts. Concurrently, there were overrepresented binding sites for E2f and ETS family members (E-Twenty Six, including Gabp, Elf1, and Ets2), serum response and interferon regulated factor in biphasic-bidirectional and late-upregulated transcripts. Binding sites for miRNAs downregulated by HF were more common in upregulated transcripts (e.g., miRNA-22,-133a/b, and -150 in early HF and miRNA-1,-9,-499 in late HF). During the development of HF, gene expression is characterized by dynamic overlapping sets of transcripts controlled by specific interrelated regulatory mechanisms. While metabolic gene classes show early and sustained downregulation in HF, signaling pathways undergo a complex biphasic pattern with early down- and more pronounced late upregulation.

Protein Quantification by MRM for Biomarker Validation

In this chapter we describe how mass spectrometry-based quantitative protein measurements by multiple reaction monitoring (MRM) have opened up the opportunity for the assembly of large panels of candidate protein biomarkers that can be simultaneously validated in large clinical cohorts to identify diagnostic protein biomarker signatures. We outline a workflow in which candidate protein biomarker panels are initially assembled from multiple diverse sources of discovery data, including proteomics and transcriptomics experiments, as well as from candidates found in the literature. Subsequently, the individual candidates in these large panels may be prioritised by application of a range of bioinformatics tools to generate a refined panel for which MRM assays may be developed. We describe a process for MRM assay design and implementation, and illustrate how the data generated from these multiplexed MRM measurements of prioritised candidates may be subjected to a range of statistical tools to create robust biomarker signatures for further clinical validation in large patient sample cohorts. Through this overall approach MRM has the potential to not only support individual biomarker validation but also facilitate the development of clinically useful protein biomarker signatures.

MMP1 bimodal expression and differential response to inflammatory mediators is linked to promoter polymorphisms

Background

Identifying the functional importance of the millions of single nucleotide polymorphisms (SNPs) in the human genome is a difficult challenge. Therefore, a reverse strategy, which identifies functionally important SNPs by virtue of the bimodal abundance across the human population of the SNP-related mRNAs will be useful. Those mRNA transcripts that are expressed at two distinct abundances in proportion to SNP allele frequency may warrant further study. Matrix metalloproteinase 1 (MMP1) is important in both normal development and in numerous pathologies. Although much research has been conducted to investigate the expression of MMP1 in many different cell types and conditions, the regulation of its expression is still not fully understood.

Results

In this study, we used a novel but straightforward method based on agglomerative hierarchical clustering to identify bimodally expressed transcripts in human umbilical vein endothelial cell (HUVEC) microarray data from 15 individuals. We found that MMP1 mRNA abundance was bimodally distributed in un-treated HUVECs and showed a bimodal response to inflammatory mediator treatment. RT-PCR and MMP1 activity assays confirmed the bimodal regulation and DNA sequencing of 69 individuals identified an MMP1 gene promoter polymorphism that segregated precisely with the MMP1 bimodal expression. Chromatin immunoprecipation (ChIP) experiments indicated that the transcription factors (TFs) ETS1, ETS2 and GATA3, bind to the MMP1 promoter in the region of this polymorphism and may contribute to the bimodal expression.

Conclusions

We describe a simple method to identify putative bimodally expressed RNAs from transcriptome data that is effective yet easy for non-statisticans to understand and use. This method identified bimodal endothelial cell expression of MMP1, which appears to be biologically significant with implications for inflammatory disease. (271 Words)

Discovering Disease-specific Biomarker Genes for Cancer Diagnosis and Prognosis

The large amounts of microarray data provide us a great opportunity to identify gene expression profiles (GEPs) in different tissues or disease states. Disease-specific biomarker genes likely share GEPs that are distinct in disease samples as compared with normal samples. The similarity of the GEPs may be evaluated by Pearson Correlation Coefficient (PCC) and the distinctness of GEPs may be assessed by Kolmogorov-Smirnov distance (KSD). In this study, we used the PCC and KSD metrics for GEPs to identify disease-specific (cancer-specific) biomarkers. We first analyzed and compared GEPs using microarray datasets for smoking and lung cancer. We found that the number of genes with highly different GEPs between comparing groups in smoking dataset was much larger than that in lung cancer dataset; this observation was further verified when we compared GEPs in smoking dataset with prostate cancer datasets. Moreover, our Gene Ontology analysis revealed that the top ranked biomarker candidate genes for prostate cancer were highly enriched in molecular function categories such as ‘cytoskeletal protein binding’ and biological process categories such as ‘muscle contraction’. Finally, we used two genes, ACTC1 (encoding an actin subunit) and HPN (encoding hepsin), to demonstrate the feasibility of diagnosing and monitoring prostate cancer using the expression intensity histograms of marker genes. In summary, our results suggested that this approach might prove promising and powerful for diagnosing and monitoring the patients who come to the clinic for screening or evaluation of a disease state including cancer.