Suppressed RNA-Polymerase 1 Pathway Is Associated with Benign Multiple Sclerosis

<div><p>Benign multiple sclerosis (BMS) occurs in about 15% of patients with relapsing-remitting multiple sclerosis (RRMS) that over time do not develop significant neurological disability. The molecular events associated with BMS are not clearly understood. This study sought to underlie the biological mechanisms associated with BMS. Blood samples obtained from a cohort of 31 patients with BMS and 36 patients with RRMS were applied for gene expression microarray analysis using HG-U133A-2 array (Affymetrix). Data were analyzed by Partek and pathway reconstruction was performed by Ingenuity for the most informative genes (MIGs). We identified a differing gene expression signature of 406 MIGs between BMS patients, mean±SE age 44.5±1.5 years, 24 females, 7 males, EDSS 1.9±0.2, disease duration 17.0±1.3 years, and RRMS patients, age 40.3±1.8 years, 24 females, 12 males, EDSS 3.5±0.2, disease duration 10.9±1.4 years. The signature was enriched by genes related RNA polymerase I (POL-1) transcription, general inflammatory response and activation of cell death. The most significant under-expressed pathway operating in BMS was the POL-1 pathway (p = 4.0*10⁻⁵) known while suppressed to activate P53 dependent apoptosis and to suppress NFκB induced inflammation. In accordance, of the 30 P53 target genes presented within the BMS signature, 19 had expression direction consistent with P53 activation. The transcripts within the pathway include POL-1 transcription factor 3 (RRN3, p = 4.8*10⁻⁵), POL-1 polypeptide D (POLR1D, p = 2.2*10⁻⁴), leucine-rich PPR-motif containing protein (LRPPRC p = 2.3*10⁻⁵⁾, followed by suppression of the downstream family of ribosomal genes like RPL3, 6,13,22 and RPS6. In accordance POL-1 transcript and release factor PTRF that terminates POL-1 transcription, was over-expressed (p = 4.4*10⁻³). Verification of POL-1 pathway key genes was confirmed by qRT-PCR, and RRN3 silencing resulted in significant increase in the apoptosis level of PBMC sub-populations in RRMS patients. Our findings demonstrate that suppression of POL-1 pathway induce the low disease activity of BMS.</p> </div

Suppressed POL-1 pathway activity in BMS.

<p>A schematic model demonstrating the suppressed POL-1 pathway activity in BMS leading to activation of P53 dependent apoptosis. Over-expressed genes are depicted in red, down-expressed genes in green.</p

POL-1 pathway key genes verification by qRT-PCR.

<p>White dots represent BMS patients (N = 20), black dots represent RRMS patients (N = 15). Data are presented as relative quantification values using ΔCT method. The house keeping gene GAPDH expression levels were used as internal control for sample normalization. Low level of the POL-1 pathway key genes POLR1D (p = 0.001), RRN3 (p = 0.03) and LRPPRC (p = 0.03) is demonstrated in BMB patients as compared with RRMS patients.</p

The extent of ribosome queuing in budding yeast

<div><p>Ribosome queuing is a fundamental phenomenon suggested to be related to topics such as genome evolution, synthetic biology, gene expression regulation, intracellular biophysics, and more. However, this phenomenon hasn't been quantified yet at a genomic level. Nevertheless, methodologies for studying translation (e.g. ribosome footprints) are usually calibrated to capture only single ribosome protected footprints (mRPFs) and thus limited in their ability to detect ribosome queuing. On the other hand, most of the models in the field assume and analyze a certain level of queuing. Here we present an experimental-computational approach for studying ribosome queuing based on sequencing of RNA footprints extracted from pairs of ribosomes (dRPFs) using a modified ribosome profiling protocol. We combine our approach with traditional ribosome profiling to generate a detailed profile of ribosome traffic. The data are analyzed using computational models of translation dynamics. The approach was implemented on the <i>Saccharomyces cerevisiae</i> transcriptome. Our data shows that ribosome queuing is more frequent than previously thought: the measured ratio of ribosomes within dRPFs to mRPFs is 0.2–0.35, suggesting that at least one to five translating ribosomes is in a traffic jam; these queued ribosomes cannot be captured by traditional methods. We found that specific regions are enriched with queued ribosomes, such as the 5’-end of ORFs, and regions upstream to mRPF peaks, among others. While queuing is related to higher density of ribosomes on the transcript (characteristic of highly translated genes), we report cases where traffic jams are relatively more severe in lowly expressed genes and possibly even selected for. In addition, our analysis demonstrates that higher adaptation of the coding region to the intracellular tRNA levels is associated with lower queuing levels. Our analysis also suggests that the <i>Saccharomyces cerevisiae</i> transcriptome undergoes selection for eliminating traffic jams. Thus, our proposed approach is an essential tool for high resolution analysis of ribosome traffic during mRNA translation and understanding its evolution.</p></div

Differential expression between BMS and RRMS.

<p>A. Volcano plot based on all microarray transcripts demonstrates global p value and Log2 fold change for each gene in differentiating between PBMC gene expression of patients with BMS and patients with RRMS. Red dots display over-expressed genes, blue dots display down-expressed genes. 406 MIGs, 171 gene over-expressed and 235 down-expressed, with p<0.01 and a log fold change between -3.1 to 3.3, are demonstrated above the black horizontal line. B. Principal component analysis (PCA) plot for microarray data showing the difference between BMS and RRMS blood gene expression. The three first principal components PC1, PC2 and PC3 are the linear combinations of the expressions of 406 MIGs plotted with the proportion of variance explained by each component, which covered 70.0% of total variance. The different ellipsoids plotted in 3-dimentional space show clear separation between BMS (violet dots, N = 31) and RRMS (green dots, BMS = 36) patients.</p

Effect of RRN3 silencing on apoptosis in RRMS.

<p>A. Comparison of apoptotic level in PBMC sub-populations between BMS (white bars) and RRMS (black bars) patients. A significantly higher percent of apoptotic CD19+ B cells and CD14+ macrophages is demonstrated in BMS patients. B. Apoptosis level in PBMC sub-populations of RRMS patients before and after RRN3 silencing. Percent of apoptotic cells was measured by PI staining.</p

POL-1 pathway related genes: BMS vs. RRMS.

<p>POL-1 pathway related genes: BMS vs. RRMS.</p

Sucrose gradient fractions.

<p><b>(A)</b> Quantification of ribosome protected footprints (RPFs) according to size. The plot shows the estimated components underlying the observed distribution, by means of Gaussian mixture. Each component reflects a set of footprints with a typical size, originating from a single ribosome (mono-RPF, mRPF), a pair of ribosomes (di-RPF, dRPF), etc. The ratio of dRPFs to mRPFs is reported in the caption. <b>(B)</b> Same for Ingolia 2009 data, profile reproduced from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005951#pcbi.1005951.ref023" target="_blank">23</a>]. <b>(C)</b> Same for Guydosh 2014 data, profile reproduced from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005951#pcbi.1005951.ref033" target="_blank">33</a>] (Fig 1 in the original paper). <b>(D)</b> Same for Shirokikh 2017 data, profile reproduced from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005951#pcbi.1005951.ref035" target="_blank">35</a>] (Figure 5 in the original paper). See also <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005951#pcbi.1005951.s006" target="_blank">S1 Fig</a></b>.</p

Ribosome profiling.

<p><b>(A)</b> mRPFs vs. dRPFs, RPKM values for each gene. Spearman’s rho and an asymptotic p-value are reported in the caption. <b>(B)</b> <i>Top</i>: Meta-gene analysis around the beginning of the ORF for mRPFs and dRPFs, plotted for highly and lowly expressed genes. Read counts were normalized per gene according to gene average (NFC), and the average across genes per position is shown. <i>Bottom</i>: The distribution for all genes of the mean NFC across the window in the top panel. P-values according to rank-sum test compare highly and lowly expressed genes. The background bands show the resulting medians when sampling random positions across the same genes (95% of the sampled medians fall within this range). Background ratio (BR) of the observed median vs. null is reported below (** denotes empirical p-value < 0.01). <b>(C)</b> Same, around peaks detected in mRPF profiles. <b>(D)</b> Same, around peaks detected in dRPF profiles. See also <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005951#pcbi.1005951.s007" target="_blank">S2 Fig</a></b>, <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005951#pcbi.1005951.s008" target="_blank">S3 Fig</a></b>.</p

Functional enrichment.

<p><b>(A)</b> Sets of genes with extreme properties relative to randomizations of their synonymous codon order. <i>Left</i>: Intersection between the set of genes with the highest QFR compared to random, and genes with the highest/lowest translation rate compared to random. <i>Right</i>: The same for the lowest QFR compared to random. <b>(B)-(C)</b> Functional enrichment in different gene sets with extreme properties, according to their rank among genes, or according to their z-scores vs. codon randomizations. Each of the stacked bars represents the enrichment p-value of the corresponding term (longer bars for smaller p-values) in the gene set (denoted by color, see legend). The ramp score was defined by the ratio of QFR in the first 100 codons and the middle of the gene (excluding the first/last 100 codons). The scale of the 0.05-threshold is shown below the legend.</p