Application of “omics” to Prion Biomarker Discovery

The advent of genomics and proteomics has been a catalyst for the discovery of biomarkers able to discriminate biological processes such as the pathogenesis of complex diseases. Prompt detection of prion diseases is particularly desirable given their transmissibility, which is responsible for a number of human health risks stemming from exogenous sources of prion protein. Diagnosis relies on the ability to detect the biomarker PrPSc, a pathological isoform of the host protein PrPC, which is an essential component of the infectious prion. Immunochemical detection of PrPSc is specific and sensitive enough for antemortem testing of brain tissue, however, this is not the case in accessible biological fluids or for the detection of recently identified novel prions with unique biochemical properties. A complementary approach to the detection of PrPSc itself is to identify alternative, “surrogate” gene or protein biomarkers indicative of disease. Biomarkers are also useful to track the progress of disease, especially important in the assessment of therapies, or to identify individuals “at risk”. In this review we provide perspective on current progress and pitfalls in the use of “omics” technologies to screen body fluids and tissues for biomarker discovery in prion diseases

MicroRNA 146a (miR-146a) Is Over-Expressed during Prion Disease and Modulates the Innate Immune Response and the Microglial Activation State

Increasing evidence supports the involvement of microRNAs (miRNAs) in inflammatory and immune processes in prion neuropathogenesis. MiRNAs are small, non-coding RNA molecules which are emerging as key regulators of numerous cellular processes. We established miR-146a over-expression in prion-infected mouse brain tissues concurrent with the onset of prion deposition and appearance of activated microglia. Expression profiling of a variety of central nervous system derived cell-lines revealed that miR-146a is preferentially expressed in cells of microglial lineage. Prominent up-regulation of miR-146a was evident in the microglial cell lines BV-2 following TLR2 or TLR4 activation and also EOC 13.31 via TLR2 that reached a maximum 24–48 hours post-stimulation, concomitant with the return to basal levels of transcription of induced cytokines. Gain- and loss-of-function studies with miR-146a revealed a substantial deregulation of inflammatory response pathways in response to TLR2 stimulation. Significant transcriptional alterations in response to miR-146a perturbation included downstream mediators of the pro-inflammatory transcription factor, nuclear factor-kappa B (NF-κB) and the JAK-STAT signaling pathway. Microarray analysis also predicts a role for miR-146a regulation of morphological changes in microglial activation states as well as phagocytic mediators of the oxidative burst such as CYBA and NOS3. Based on our results, we propose a role for miR-146a as a potent modulator of microglial function by regulating the activation state during prion induced neurodegeneration

A miRNA Signature of Prion Induced Neurodegeneration

MicroRNAs (miRNAs) are small, non-coding RNA molecules which are emerging as key regulators of numerous cellular processes. Compelling evidence links miRNAs to the control of neuronal development and differentiation, however, little is known about their role in neurodegeneration. We used microarrays and RT-PCR to profile miRNA expression changes in the brains of mice infected with mouse-adapted scrapie. We determined 15 miRNAs were de-regulated during the disease processes; miR-342-3p, miR-320, let-7b, miR-328, miR-128, miR-139-5p and miR-146a were over 2.5 fold up-regulated and miR-338-3p and miR-337-3p over 2.5 fold down-regulated. Only one of these miRNAs, miR-128, has previously been shown to be de-regulated in neurodegenerative disease. De-regulation of a unique subset of miRNAs suggests a conserved, disease-specific pattern of differentially expressed miRNAs is associated with prion–induced neurodegeneration. Computational analysis predicted numerous potential gene targets of these miRNAs, including 119 genes previously determined to be also de-regulated in mouse scrapie. We used a co-ordinated approach to integrate miRNA and mRNA profiling, bioinformatic predictions and biochemical validation to determine miRNA regulated processes and genes potentially involved in disease progression. In particular, a correlation between miRNA expression and putative gene targets involved in intracellular protein-degradation pathways and signaling pathways related to cell death, synapse function and neurogenesis was identified

Analysis of genes dysregulated upon over-expression, or knock-down, of miR-146a in stimulated EOC 13.31 cells using either miRNA mimics or anti-miRs.

<p><b>A.</b> Venn diagram to show the intersection between genes down-regulated in LPS stimulated EOC 13.31 cells by miR-146a over-expression, and those up-regulated upon miR-146a knock-down. Also shown are those targets bioinformatically predicted using the TargetScan 5.1 program and IPA software. <b>B.</b> Schematic showing alterations in expression in key inflammatory response-related genes in LPS stimulated EOC 13.31 cells following miR-146a over-expression. Colored green are those down-regulated genes, colored red are up-regulated genes, while those highlighted in blue are bioinformatically predicted targets of miR-146a using the TargetScan 5.1 program and IPA software.</p

MiR-146a induction in microglia.

<p><b>A.</b> LPS at concentrations ranging from 0.1–100 ng/ml was used to stimulate BV-2 cells. After 8 hours, RNA was collected and TaqMan® qRT-PCR was used to determine miR-146a expression relative to PBS treated (unstimulated) control cells. The experiment was performed in triplicate and the average fold induction is shown. <b>B.</b> EOC 13.31 cells were stimulated with 100 ng/ml semi-pure LPS, 100 ng ultra-pure LPS or PBS alone (unstimulated). RNA was collected after 8 hours and miR-146a expression was measured by TaqMan® qRT-PCR. Fold induction relative to untreated cells is shown. The experiment was performed in triplicate and the average fold change is shown. <b>C.</b> EOC 13.31 cells were incubated with increasing concentrations of an anti-TLR2 antibody for 30 minutes prior to stimulation with 100 ng/ml LPS. MiR-146a expression relative to mock-treated control cells was measured by TaqMan® qRT-PCR. Inhibition of miR-146a expression following anti-TLR2 antibody treatment was significant at all concentrations; 10 and 50 ng/ml * p<0.01, 100 ng/ml ** p<0.005. Treatment of BV-2 cells with 100 ng/ml anti-TLR-2 antibody prior to LPS stimulation failed to inhibit miR-146a induction. The experiment was performed in triplicate and the average fold change is shown. <b>D.</b> EOC 13.31 and BV-2 cells were stimulated with with 10⁸ heat-killed <i>Listeria monocytogenes</i> (HKLM) cells/ml. RNA was collected at various time-points over 72 hours and miR-146a expression measured by TaqMan® qRT-PCR. Fold induction relative to unstimulated cells (US) is shown. The experiment was performed in triplicate and the average fold change is shown.</p

EOC 13.31 cells were stimulated with 100 ng/ml semi-pure LPS and temporal changes in cytokine gene expression relative to mock-treated control were measured by microarray analysis.

<p>MiR-146a expression relative to mock-treated control was measured by TaqMan® qRT-PCR and expression is shown as grey bars.</p

Hierarchical cluster plot generated from miRNA expression profiling of a repertoire of a variety of CNS cell lineages (neuronal, microglia, astrocytes).

<p>A microglia specific cluster of miRNAs is indicated. Red indicates high levels of miRNA expression, green low levels and gray indicates expression that was undetected by the microarray used.</p

Analysis of genes dysregulated by over-expression, or knock-down of miR-146a in resting EOC 13.31 cells using either miRNA mimics or anti-miRs.

<p><b>A.</b> Venn diagram to show the intersection between genes down-regulated by miR-146a over-expression, up-regulated on miR-146a knock-down and those targets bioinformatically predicted using the TargetScan 5.1 program and IPA software. <b>B.</b> Networks showing the interactions between several predicted miR-146a target genes. Shaded grey are those genes also bioinformatically predicted using the TargetScan 5.1 program and IPA software.</p

Analysis of genes dysregulated by over-expression, or knock-down, in resting EOC 13.31 cells using either miR-146a mimics or anti-miRs.

<p>Genes listed in bold are those that are bioinformatically predicted to be targets of miR-146a using the TargetScan 5.1 program and IPA software.</p