Distribution of bacterial families within the gut microbiome of <i>Chd1</i> mutant and wild-type flies.

<p>Three sets of filtering parameters were used to analyse the data.</p

Chao1 and Shannon indices of <i>Chd1</i> mutant and wild-type sample replicates.

<p>Three sets of filtering parameters were used to analyse the data.</p

The microbiome composition in the gut of <i>Chd1</i>^<i>-/-</i> and <i>Chd1</i>^<i>WT/WT</i> flies is significantly different.

<p>(A) Principal Coordinate Analysis (PCoA) plot depicting β-diversity by jackknifed UniFrac distances (normalized, weighted UniFrac metric) based on 97% similarity OTU assignments. <i>Chd1</i>^<i>WT/WT</i> and <i>Chd1</i>^<i>-/-</i> replicates differ considerably for PC1, which explains 89.1% of the total variation. To estimate the statistical significance of the clustering a two-sample t-test based on distance matrices (distances within all replicates versus distances between all replicates) was performed (P = 4.709 e-5). (B) Rarefaction curves of 97% identity OTUs for <i>Chd1</i>^<i>WT/WT</i> and <i>Chd1</i>^<i>-/-</i> sample replicates show exhaustive sampling depth.</p

Time-dependent decrease in supplemented <i>L</i>. <i>plantarum</i> in <i>Chd1</i>^<i>-/-</i> but not <i>Chd1</i>^<i>WT/WT</i> flies.

<p>Flies were fed on <i>L</i>. <i>plantarum</i> overnight, transferred to fresh food vials and collected at the indicated days after inoculation. (A) <i>Lactobacillus</i> load was determined by plating fly homogenates on MRS agar; (B) <i>L</i>. <i>plantarum</i> was detected by real-time PCR. Signals were normalized against the <i>Drosophila</i> GAPDH gene and mean values ±SEM of three biological replicates are shown. Significant differences between the two fly lines were analyzed by t-test (level of significance set to p<0.05) and are marked by (*).</p

Age-related changes of bacterial load and species distribution in <i>Chd1</i> mutant and wild-type flies.

<p>(A) Flies of the indicated ages were surface-sterilized and homogenates were plated on Ace agar (left panel) to select for <i>Acetobacteraceae</i> or on MRS agar (right panel) to select for <i>Lactobacillaceae</i>. CFUs per fly were calculated and mean values from four biological replicates ±SEM were plotted. (B) Semiquantitative PCR was used to determine the relative amounts of <i>Acetobacter</i> “A” and <i>Pseudomonas</i> “P” species in guts of flies of the indicated ages. Band intensities were quantified and P/A ratios were calculated. Mean values ±SEM of three biological replicates are shown.</p

Loss of CHD1 causes decreased species diversity in the gut microbiome.

<p>(A) Relative abundance of bacterial families (97% similarity threshold) determined by 16S rDNA sequencing in <i>Chd1</i>^<i>WT/WT</i> and <i>Chd1</i>^<i>-/-</i> samples. Families present at levels less than 1.5% were summarized as “others“. (B) Heatmap of the 25 most abundant 97% identity OTUs within <i>Chd1</i>^<i>WT/WT</i> and <i>Chd1</i>^<i>-/-</i> guts. OTU classification down to the lowest level possible is shown. Color bars denote the relative abundance (log10 values) of each OTU within the respective sample. OTUs are clustered according to their average relative abundance. (C) Heatmap showing the abundance of <i>Acetobacter</i> and <i>Lactobacillus</i> species in <i>Chd1</i>^<i>WT/WT</i> and <i>Chd1</i>^<i>-/-</i> samples identified by alignment of sequencing reads to all respective sequences in the SILVA database at an identity threshold of 99%. All OTUs with fewer than 10 counts, were excluded. Color bars denote the relative abundance (log10 values) of each species within the respective sample.</p

Impact of the Chromatin Remodeling Factor CHD1 on Gut Microbiome Composition of <i>Drosophila melanogaster</i>

<div><p>The composition of the intestinal microbiota of <i>Drosophila</i> has been studied in some detail in recent years. Environmental, developmental and host-specific genetic factors influence microbiome composition in the fly. Our previous work has indicated that intestinal bacterial load can be affected by chromatin-targeted regulatory mechanisms. Here we studied a potential role of the conserved chromatin assembly and remodeling factor CHD1 in the shaping of the gut microbiome in <i>Drosophila melanogaster</i>. Using high-throughput sequencing of 16S rRNA gene amplicons, we found that <i>Chd1</i> deletion mutant flies exhibit significantly reduced microbial diversity compared to rescued control strains. Specifically, although <i>Acetobacteraceae</i> dominated the microbiota of both <i>Chd1</i> wild-type and mutant guts, <i>Chd1</i> mutants were virtually monoassociated with this bacterial family, whereas in control flies other bacterial taxa constituted ~20% of the microbiome. We further show age-linked differences in microbial load and microbiota composition between <i>Chd1</i> mutant and control flies. Finally, diet supplementation experiments with <i>Lactobacillus plantarum</i> revealed that, in contrast to wild-type flies, <i>Chd1</i> mutant flies were unable to maintain higher <i>L</i>. <i>plantarum</i> titres over time. Collectively, these data provide evidence that loss of the chromatin remodeler CHD1 has a major impact on the gut microbiome of <i>Drosophila melanogaster</i>.</p></div

Deregulated miRNA-mRNA regulatory network to “Immune system process” in MSA mice in disease pre-motor stage.

<p>Differentially expressed miRNAs with predicted negatively correlated differentially expressed mRNA targets are visualized by employing Cytoscape (version 3.2.1). Round nodes show mRNA and triangle nodes miRNA. Node size is proportional to its degree. Fold change (log₂ transformed) for each node is ranging from red (negative) to green (positive). Interaction arrow thickness is proportional to the number of algorithms predicting the miRNA-mRNA target 3’ UTR interaction, ranging from one to four. Differential expression of genes, in striatum and SN, such as <i>Anln</i>, <i>Car2</i>, <i>Cd59a</i>, <i>Hba-a1</i> and <i>Rps17</i>, is visualized by color corresponding to the mean fold change (exact values can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150705#pone.0150705.s007" target="_blank">S2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150705#pone.0150705.s008" target="_blank">S3</a> Tables).</p

Changes in the miRNA-mRNA Regulatory Network Precede Motor Symptoms in a Mouse Model of Multiple System Atrophy: Clinical Implications - Fig 7

<p><b>Deregulated miRNA-mRNA regulatory network in the striatum of MSA mice in pre-motor stage of disease:</b> Modules “Protein handling” (A) and “Metabolism” (B). Differentially expressed miRNAs with predicted negatively correlated differentially expressed mRNA targets assigned to the indicated GO-terms (light blue rectangles) are visualized by employing Cytoscape (version 3.2.1). Round nodes designate mRNA and triangle nodes miRNA. Node size is proportional to its degree. Fold change (log₂ transformed) for each node is ranging from -0.75 (red) to 1 (green). The shade of blue color of the interaction arrows indicates the degree (range -1.00–0.00) of negative correlation between miRNA-mRNA target 3’ UTR interaction. Interaction arrow thickness is proportional to the number of algorithms predicting the miRNA-mRNA target 3’ UTR interaction, ranging from one to four.</p

Differential expression of mRNAs in a mouse model of pre-motor stage MSA.

<p>(A) Heatmaps represent significantly differentially expressed genes of RNA-seq (striatum, SN) and microarray (SN) analyses. For each gene (row), the log2-transformed change of the expression value in each sample to the average expression value over all samples is shown. Columns represent individual replicates grouped into MSA and control (WT) samples indicated by the blue (MSA) and grey (WT) bars at the top of the heatmaps. The color gradient indicates the expression change from negative to positive. The asterisks following gene names indicate overlapping genes between microarray and RNA-seq analyses in SN. (B) Venn diagram illustrating the number of overlapping differentially expressed mRNAs between SN and striatum tissue in MSA mice. (C) Heatmap highlights log₂-transformed fold changes of mRNAs overlapping between striatum und SN. Down-regulated mRNAs are indicated by a blue color gradient, whereas up-regulated miRNAs are indicated by an orange color gradient. mRNA with expression signals below background in the microarray experiment are highlighted in gray. From left to right, microarray and RNA-seq analysis results of SN and RNA-seq analysis of striatum are shown. Differential expression analysis of control versus transgenic MSA mice of both, striatum and SN samples, was performed by employing the DESeq2 package with predefined parameters [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150705#pone.0150705.ref037" target="_blank">37</a>]. Genes with an adjusted p-value below 0.1 after multiple testing corrections were considered statistically significant [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150705#pone.0150705.ref038" target="_blank">38</a>]. For microarray data differential gene expression was tested by a moderated t-test using the <i>limma</i> package [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150705#pone.0150705.ref039" target="_blank">39</a>]. For both methods genes with an adjusted p-value < 0.1 after multiple testing corrections were considered statistically significant [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150705#pone.0150705.ref038" target="_blank">38</a>].</p