26 research outputs found

    Differential Co-Expression between Ī±-Synuclein and IFN-Ī³ Signaling Genes across Development and in Parkinsonā€™s Disease

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    <div><p>Expression patterns of the alpha-synuclein gene (SNCA) were studied across anatomy, development, and disease to better characterize its role in the brain. In this postmortem study, negative spatial co-expression between SNCA and 73 interferon-Ī³ (IFN-Ī³) signaling genes was observed across many brain regions. Recent animal studies have demonstrated that IFN-Ī³ induces loss of dopamine neurons and nigrostriatal degeneration. This opposing pattern between SNCA and IFN-Ī³ signaling genes increases with age (rhoā€Š=ā€Šāˆ’0.78). In contrast, a meta-analysis of four microarray experiments representing 126 substantia nigra samples reveals a switch to positive co-expression in Parkinsonā€™s disease (p<0.005). Use of genome-wide testing demonstrates this relationship is specific to SNCA (p<0.002). This change in co-expression suggests an immunomodulatory role of SNCA that may provide insight into neurodegeneration. Genes showing similar co-expression patterns have been previously linked to Alzheimerā€™s (ANK1) and Parkinsonā€™s disease (UBE2E2, PCMT1, HPRT1 and RIT2).</p></div

    Specialization of Gene Expression during Mouse Brain Development

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    <div><p>The transcriptome of the brain changes during development, reflecting processes that determine functional specialization of brain regions. We analyzed gene expression, measured using in situ hybridization across the full developing mouse brain, to quantify functional specialization of brain regions. Surprisingly, we found that during the time that the brain becomes anatomically regionalized in early development, transcription specialization actually decreases reaching a low, ā€œneurotypicā€, point around birth. This decrease of specialization is brain-wide, and mainly due to biological processes involved in constructing brain circuitry. Regional specialization rises again during post-natal development. This effect is largely due to specialization of plasticity and neural activity processes. Post-natal specialization is particularly significant in the cerebellum, whose expression signature becomes increasingly different from other brain regions. When comparing mouse and human expression patterns, the cerebellar post-natal specialization is also observed in human, but the regionalization of expression in the human Thalamus and Cortex follows a strikingly different profile than in mouse.</p></div

    Changes in dissimilarity across individual brain regions.

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    <p>Embedding of all regions onto a 2D plane using multidimensional scaling. Each circle corresponds to a brain region, with a size that corresponds to the within-region expression standard deviation and a color that corresponds to its embryonic origins. Red: forebrain, telencephalon; pink: forebrain, diencephalon; cyan: midbrain; blue: hindbrain. Rhombomere 1 and Isthmus in the developing post-natal time points are and the cerebellar cortex and cerebellar nuclei at P56 are marked with a black arrow.</p

    Differential co-expression between IFN-Ī³ genes and SNCA across age.

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    <p>A) Scatterplot of SNCA and IFN-Ī³-mediated-signaling pathway median expression in prenatal (red triangles, nā€Š=ā€Š232) and postnatal samples (blue circles, nā€Š=ā€Š345). High rank corresponds to high expression. An outlier prenatal sample with low SNCA expression of 16524 was excluded to improve plotting. B) Boxplots showing spatial correlation of the 73 IFN-Ī³ genes against SNCA within each BrainSpan donor. The 25 pcw (postconception weeks) fetus has only two sampled brain regions which results in a Spearman correlation of āˆ’1 or 1.</p

    Scatterplot of SNCA co-expression with IFN-Ī³ genes in five Parkinsonā€™s studies.

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    <p>The first four studies assayed post-mortem substantia nigra samples with the final plot <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115029#pone.0115029-Scherzer1" target="_blank">[14]</a> showing co-expression results from blood. Genes switching from negative co-expression in normal controls to positive correlation in PD cases are highlighted in the shaded quadrant. The red point marks the IFNGR1 gene, which has the strongest change in co-expression in the substantia nigra datasets.</p

    Comparison with human data.

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    <p>(A) Cross correlation between mouse and human gene expression. The black line is taken from known developmental timeline of the two species based on anchor events <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003185#pcbi.1003185-Clancy1" target="_blank">[27]</a>. Region-specific dissimilarity curves of four brain regions in mouse and human. (B) mouse thalamus, (C) human mediodorsal nucleus of the thalamus, (D) mouse dorsal pallium, (E) human cortical regions, (F) mouse striatum, (G) human striatum, (H) mouse rhombomere 1 and isthmus and (I) human cerebellar cortex. Error bars denote standard deviation across regions.</p

    Functional characterization of hourglass shape.

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    <p>(A), (B) Clusters of gene profiles that are functionally enriched. Each profile is a measure of contribution to dissimilarity D (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003185#s4" target="_blank">Methods</a>). Black bold curve is the mean of the cluster. Blue lines - all the genes in the cluster; red lines - genes that are in the cluster and in the category; grey lines - genes that are not in the cluster even though belong to the category. (A) <i>Neuron migration</i> shows decreasing dissimilarity (B) <i>Learning or memory</i> shows a post-natal increase in dissimilarity. (C) Spatial expression of the genes <i>Neurog1</i> and <i>Neurog2</i> at E11.5 in 11 coarse regions, selected as <i>neuron differentiation</i> genes with highly similar sequence.</p

    Inter-region distances are minimized around birth.

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    <p>(A) The data: ISH for each gene was performed at eight time points during development. Shown here are mid-sagittal slices for the gene <i>Hmgn2</i>, taken with permission from Allen Institute for Brain Science. Allen Mouse Brain Atlas [Internet] Available from: <a href="http://mouse.brain-map.org/" target="_blank">http://mouse.brain-map.org/</a><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003185#pcbi.1003185-Lein1" target="_blank">[17]</a> (B) Mean pair-wise dissimilarities between the regions. The curve is a second-order polynomial which minimizes the squared error of the fit to the data. Error bars encompass data within 1.5 times the inter-quartile range, and the boxes show the lower and upper quartiles together with the median. (C) The hourglass shape is robust throughout the dataset: Inter-region distance curve was calculated for the data withholding top k most variable genes for each time point. Error bars represent standard error between brain regions. (D) The hourglass shape is robust throughout the brain. Inter-region distance curve was calculated for the data withholding one region at a time. The blue curve is the mean across brain regions, error bars represent standard deviations from mean. (E) The dissimilarity curve using sets of regions taken from different levels of the reference atlas regional ontology tree, starting from the leaf regions (level 1).</p

    Differential co-expression between SNCA and IFN-Ī³ genes in Parkinsonā€™s gene expression studies.

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    <p>The last dataset (GSE20159 ā€“ Zheng) assayed gene expression in cases of mild PD related Lewy body neuropathology. Paired Wilcoxon and empirical permutation tests that shuffled disease labels were used to test differential co-expression. The ā€œInversionsā€ column shows the proportion of IFN-Ī³ genes that switch from negative co-expression with SNCA in normal control samples to positive in Parkinsonā€™s cases.</p><p>Differential co-expression between SNCA and IFN-Ī³ genes in Parkinsonā€™s gene expression studies.</p

    Mean contribution values of GO categories at E11.5 and P28.

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    <p>The contribution of each GO categories C to inter-region dissimilarity was computed as the mean contribution of all genes assigned to C (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003185#s4" target="_blank">Methods</a>).</p
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