EMBO/SourceData huggingface dataset:
Figure 6(A) Dot plot depicting expression entropy on Y-axis against average gene expression on X-axis for the thyrocyte population. Each dot depicts a gene, with red dots depicting genes that show statistically significant (p-value < 0.05) difference in entropy from expected value. Expected value is represented by black regression line. pax2a and ctsba are marked on the graph.(B) t-SNE plot of the thyrocyte cluster with expression of tg, pax2a and ctsba. The color scale represents the normalized expression counts for each gene ranging from lowest (yellow) to highest (red). (B') Histogram overlaid with density plots depicting the distribution of normalized gene expression for tg, pax2a and ctsba.
Figure 7(B) Whole mount immunofluorescence of 9.5hpf pax2amKO2 embryos stained with anti-mKO2 antibody (red) and anti-PAX2A antibody (green). Anterior is to the left, and dorsal side is to the top. Scale bar: 0.1 mm.(C) Whole mount immunofluorescence of 55 hpf pax2amKO2; Tg(tg:nls-EGFP) stained with PAX2A antibody (PAX2A-Ab) displays an overlap of mKO2 and PAX2A-Ab signal. The otic vesicle (OV), mid-hindbrain barrier (MHB), interneurons (IN) and thyroid gland (THY) is labelled. White dashed line represents the outline of the zebrafish larvae. Scale bar: 100 µm. Anterior to the right.(D - F) Confocal microscopy imaging of a sagittal section of a 55 hpf pax2amKO2 (red); Tg(tg:nls-EGFP) (green) embryos immunostained with PAX2A antibody (cyan) showing co-localization of mKO2 and pax2a in the pronephros (D), thyroid gland (E) and mid-hindbrain barrier (F). In the thyroid gland, mKO2 (red), PAX2A-Ab (cyan) and thyrocyte-specific GFP (green) show co-localization. Scale bar: 50 µm.(G , H) Snapshots from live imaging of 55 hpf pax2amKO2; Tg(tg:nls-EGFP) embryos injected with sgRNA targeting pax2a coding sequence. The anterior part of a representative control embryo (G) is shown alongside a representative crispant (H). Crispants display a strong reduction of mKO2 fluorescence, as well as an absence of GFP signal suggesting absence of thyroid (THY) tissue. White dashed line represents the outline of the zebrafish larvae. Scale bar: 100 µm. Anterior to the right.
Figure 8(A-D) Analysis of 3 mpf thyroid gland from pax2amKO2 zebrafish shows heterogeneity in pax2a reporter expression. (A) Whole mount confocal imaging of a 3 mpf pax2amKO2 thyroid labelled with mKO2 (red), E-cadherin (cyan, not shown in 'A' for clarity reasons) and DAPI (dark blue) for nuclear localisation. (B - D) Optical sections of three follicles, with mKO2-Low cells labelled with arrows. E-cadherin (B' - D') and DAPI (B'' - D'') staining shows that absence of mKO2 signal does not correspond to an absence of cells. Anterior to the bottom of the pictures.(E,F) (E) Confocal image of thyroid gland section from Tg(tg:nls-EGFP) at 4 mpf stained with PAX2A antibody and DAPI. The dotted region is displayed at high magnification in (F). Arrows marks thyrocytes displaying low PAX2A staining. Notably, PAX2A-Low thyrocytes display tg-driven EGFP expression, demonstrating their differentiated status.
Figure 9(A - C) Cells from the thyroid gland of 5 mpf Tg(tg:nls-GFP); pax2amKO2 animals were labelled with calcein (live cell marker) and analysed using FACS. (A) A FACS plot showing calcein on Y-axis and GFP on X-axis. The box encompassing the GFP+ cells represents the thyrocyte population, which was gated for further analysis. (B) Histogram showing the distribution of GFP intensity in thyrocytes. (C) Histogram showing the distribution of mKO2 intensity in thyrocytes. Thyrocytes were selected by gating for GFP+ population. Horizontal lines indicate the mKO2-Low and mKO2-High expression level, with percentage values representing proportion of thyrocytes with mKO2-Low and mKO2-High expression.(D , E) RNA-Seq. analysis of transcriptome isolated from pax2amKO2-High and pax2amKO2-Low subpopulations. (D) Volcano plot representing the differential expression of genes between the two thyrocyte subpopulations. Dots for pax2a, tg, mKO2 and EGFP are marked. Dots for genes that display significant differential expression (DESeq2, adjusted p-value < 0.05) are coloured. (E) Heatmap displaying the expression of selected genes in the two subpopulations. Biological replicates are represented by individual columns.
Figure 1Plotting of the position of the 5'end of Alu RNA fragments across the Alu metagene to depict potential processing areas of Alu RNAs in all post-mortem hippocampal tissues (PM hippo) from patients from the CBB. Each row in the heatmap depicts the distribution of counts of the 5'ends of reads mapped across the Alu metagene for each patient. The x-axis represents a metagene combining all unique Alu RNA sequences (ALUome) aligned at the start site of their consensus sequence with numbers representing the distance from the Transcriptional Start Site (TSS) area. Heatmap density corresponds to normalized counts of the 5'end of the reads with red corresponding to higher density of these 5'ends at a specific position. XL1, XR1 and XR2 denote the Alu processing areas defined by the high-density areas in the heatmap at specific positions of the Alu metagene, with the middle letter corresponding to the arm of the Alu RNA (see Figure 1C, L=Left, R=Right), in which the area is located.
Figure 2Normalized counts of processed fragments mapping to the right arm of Alu RNAs that are differentially processed (rows) between AD and no AD for each CBB patient (columns) (Dataset EV1). Red corresponds to higher normalized counts of processed Alu RNA fragments.Boxplots depict differences in hippocampi of AD and no AD patients regarding SINE Alu RNA processing ratio (left panel) (a p value of 0.05 was considered as threshold for statistical significance with p=0.01 , n=24, unpaired non-directional t-test) and in total Alu RNA levels (right panel) (ns=not significant). In the boxplots, the central band (the line that divides the box into 2 parts) represents the median of the data, the ends of the box shows the upper (Q3) and lower (Q1) quartiles, the extreme line shows Q3+1.5xIQR to Q1-1.5xIQR (the highest and lowest value excluding potential outliers), while dots beyond the extreme line show potential outliers.
Figure 3Plotting of the position of the 5'end of Alu RNA fragments across the Alu metagene to depict potential processing areas of Alu RNAs in all post-mortem cortex tissues from patients from the MAP cohort. As in Figure 1B each row represents the distribution of normalized counts of the 5'ends of reads mapped across the Alu metagene for each patient. As in Figure 1B x-axis numbers represent the distance from the Transcriptional Start Site (TSS) area and heatmap density corresponds to normalized counts of the 5'end of the reads with red for higher density of these 5'ends at a specific position. XR1 as in Figure 1B.
Figure 4As in Figure 2B, normalized counts of processed fragments mapping to the right arm of Alu RNAs that are differentially processed (rows) between AD and no AD for each MAP patient (columns) (Dataset EV2). Red corresponds to higher normalized counts of processed Alu RNA fragments.Boxplots depict differences in cortex of AD and no AD patients regarding SINE Alu RNA processing ratio (left panel) (a p value of 0.05 was considered as threshold for statistical significance with p<0.001, n=118, unpaired non-directional t-test) and in full length Alu RNAs (p=0.005). Boxplot representation as in subfigure 2D.Boxplots depict full length Alu RNA levels in cortex of AD and no AD patients separated into three age groups. No significant difference observed between the different age groups of either AD or no AD patients or for the comparisons between no AD and AD of each age group (unpaired non-directional t-test, with p<0.05 considered the threshold for statistical significance, n for no AD=19 (below 85), 19 (between 85 to 90), 13 (above 90); n for AD=8 (below 85), 21 (between 85 to 90), 38 (above 90)). Boxplot representation as in subfigure 2D.Boxplots depict differences in SINE Alu RNA processing ratio in cortex of AD and no AD patients separated into three age groups. No significant difference observed between the different age groups of either AD or no AD patients. A p value 0.05 was considered as threshold for statistical significance for the comparisons between no AD and AD of each age group (unpaired non-directional t-test, with p=0.003 for the comparison below 85, and p < 0.001 for the other two comparisons and n numbers as in (E)). Boxplot representation as in subfigure 2D.
Figure 5Boxplots depict differences in SINE Alu RNA processing ratio in all MAP patients with regard to final clinical diagnosis (p < 0.01 for the comparison between NCI and MCI, between NCI and AD, and between MCI and AD, unpaired, non directional t-test). NCI=no cognitive impairment (n=71), MCI=mild cognitive impairment (n=67), AD=Alzheimer's disease (n=87). Boxplot representation as in subfigure 2D. Boxplots depict differences in SINE Alu RNA processing ratio in all MAP patients (n=241) with regard to Braak stage classified into three categories (p = 0.004 for the comparison between 0-2 and 3-4, and p < 0.001 for the comparisons between 3-4 and 5-6 and between 0-2 and 5-6, unpaired, non directional t-test, n=39[0-2],n=148[3-4] and n=54[5-6]). Boxplot representation as in subfigure 2D.Scatterplot depicting the positive correlation between TP53 expression values and Alu RNA processing ratio in AD patients (r=0.65, p < 0.001). Statistical test is based on Pearson's product moment correlation coefficient and follows a t distribution using the cor.test function in R package stats. Scatterplot depicting no correlation between TP53 expression values and full length Alu RNA levels in AD patients (r=0.06, no correlation, ns=non-significant). Statistical test as in subfigure 5C.
Figure 6Association between SINE Alu RNA ratio (upper panel) and gene expression levels (lower panel) of genes up-regulated in AD (Dataset EV3, |log2FoldChange|> 0.5). Every column in both panels corresponds to the same MAP patient of either the no AD or AD group. Patients in each group are sorted from left to right in an ascending order with regard to Alu RNA processing ratio. Every row in the heatmap corresponds to one gene, with color density representing normalized FPKM values from RNA-seq for this gene for each patient (columns) (red represents high and blue low expression levels). Association between full length Alu RNA levels (upper panel) and gene expression levels (lower panel) of genes down-regulated in AD (Dataset EV4, |log2FoldChange|> 0.5). Every column in both panels corresponds to the same MAP patient of either the no AD or AD group. Patients in each group are sorted from left to right in an ascending order with regard to full length Alu RNA levels. Every row in the heatmap corresponds to one gene, with color density representing normalized FPKM values from RNA-seq for this gene for each patient (columns) (red represents high and blue low expression levels).Scatterplot depicting the positive correlation between average gene expression values of upregulated genes in Figure 6B and Alu RNA processing ratio in AD patients (r = 0.88, p<0.001). Statistical test is based on Pearson's product moment correlation coefficient and follows a t distribution using the cor.test function in R package stats. Scatterplot depicting the negative correlation between average gene expression values of down-regulated genes in Figure 6C and full length Alu RNA levels in AD patients (r = -0.42, p<0.001). Statistical test as in subfigure 6D.
Figure 7Correlation between gene expression and Alu RNA processing ratio for AD upregulated genes (Dataset EV3) (upper panel) and a random set of non-differentially expressed genes (Dataset EV5) (lower panel). For every gene a correlation coefficient was calculated (Pearson correlation) together with the respective p-value. Statistical test is based on Pearson's product moment correlation coefficient and follows a t distribution using the cor.test function in R package stats. Based on the r and p value genes were classified into four categories: i)NS, non-significant for p value > 0.05, ii) no correlation or weak correlation (r≤0.05), iii) significant (p < 0.05) and strong correlation (0.5Expression levels of full length Alu RNA (RT-qPCR) in the Alu RNA KD experiment. Statistical significance (p value threshold 0.05) for the comparison between anti-Alu LNA treated samples (anti-Alu) and samples treated with a scramble control LNA (ctrl) with p=0.049 and n=3, unpaired non-directional t-test. Error bars represent standard deviation from the mean. Expression levels of P53 (RT-qPCR) in the Alu RNA KD experiment. Statistical significance (p value threshold 0.05) for levels in anti-Alu LNA treated samples (anti-Alu) greater than samples treated with a scramble control LNA (ctrl) with p=0.046 and n=3, unpaired directional t-test. Error bars represent standard deviation from the mean.Expression levels (RT-qPCR)_of selected genes from panel A that are either strongly correlated with Alu RNA processing (left, r> 0.5) or only weakly/not correlated (right, r <0.5). Statistical significance (p value threshold 0.05) for anti-Alu greater than control (depicted as asterisk, n=3/group, unpaired directional t-test, error bars represent standard deviation from the mean). Pearson correlation co-efficient for each gene is depicted below the name of each gene.
Figure 8In vitro incubation of one of the Alu RNA consensus sequences for different incubation periods. In vitro transcribed Alu RNA (67nM) incubated at 37oC with 250nM HSF1 in the course of 6 hours with time intervals of 1 hour.Comparison among Hsf1 (~60KDa), denatured HSF1, Poly A polymerase (~56KDa) and no protein (just TAP buffer) with regard to Alu RNA processing (estimating relative full length RNA remaining) (two replicates). The full gels are available as Source Data For Figure 8. Relative full length RNA remaining was calculated using ImageJ area under the curve software over time. Error bars represent standard deviation from the mean.Comparison among Alu RNA (three replicates), and two control RNAs (two replicates) regarding the full-length RNA levels remaining after in vitro incubation for 90 minutes at 37C with HSF1. Sizes of control RNAs are control for RNA #1, 143nt and for control RNA #2, 432nt. Incubation in the absence of HSF1 but presence of the same buffer (TAP) was used as control to take into account any non HSF1 specific RNA destabilization due to non-specific degradation. Threshold for statistical significance was a p value of 0.05 with p=0.03 (unpaired, non-directional t-test) for the comparison between HSF1 and no protein incubation (n=3, for Alu RNA). The full gels are available as Source Data For Figure 8. Error bars represent standard deviation from the mean.Plotting of the position of the first base (5' end) of Alu RNA fragments across the Alu consensus sequence produced by Alu RNA that has been processed in vitro for 90 minutes at 37C in the presence of HSF1 (upper panel) and compared with one of the in vivo samples of Figure 1 (lower panel). The x axis represents an Alu RNA metagene aligned at the start site of the Alu consensus sequence and the y axis shows the 5' end count for Alu RNA fragments aligning to any position downstream of position +1. The in vivo sample x axis depicts a 11nt sift compared to the in vitro one.Scatterplot depicting the positive correlation between HSF1 mRNA expression values and Alu RNA processing ratio in MAP AD patients (r=0.72, p<0.001). Statistical test is based on Pearson's product moment correlation coefficient and follows a t distribution using the cor.test function in R package stats. Scatterplot depicting lack of correlation between HSF1 mRNA expression values and full length Alu RNA levels in MAP AD patients (ns=non-significant, with p value 0.05 as the significance threshold). Statistical test as in subfigure 8E.
Figure 1Human Gut microbial species explored for the existence of subspecies show wide phylogenetic spread according to NCBI taxonomy (A) and include Methanobrevibacter smithii, the main archaeal member of the human gut microbiome, as well as representatives of all abundant phyla. Species names are according to NCBI taxonomy, with species cluster (specI) identifiers according to Mende et al., which splits some named species into multiple specI clusters (Mende et al, 2013). Of 71 investigated species, 44 stratify into subspecies (highlighted in blue). Each species' average abundance across 2,144 human gut metagenomes is proportional to the size of the circles on the cladogram. Bars represent the number of subspecies identified in each, with '1' indicating no subdivision. The black portion of the bar corresponds to subspecies for which no representative genome sequence is available from NCBI. Geographic enrichments of subspecies are displayed as a heat map (showing only significant enrichment, FDR corrected Fisher test p-value < 0.05, per country as maximum log-odds ratio across con-specific subspecies). Subspecies with a restricted geographic range are predominantl­y found in the Chinese and Kazakh population. The 71 investigated species captured an average of 95.5% of sequencing reads that were assigned to any reference genome. The subset of 44 species with identified subspecies accounted for the majority of this abundance (B).
Figure 2Subspecies identified for M. smithii and B. vulgatus/dorei are shown in principle coordinate (PCoA) projections of the between-sample distances based on single nucleotide variations (see Methods). The first principal coordinate (PC) explains over 70% of the variation in both cases (panels A and C). Reference genomes have been projected into the same PCoA plots (marked with 'x' in A and C, see Methods). The numbers adjacent to the placed genomes correspond to those shown in parentheses next to NCBI taxonomy identifiers (leaves) on the phylogenetic trees in B and D, respectively. The sample density for each subspecies is highlighted by the histogram above and the total number of samples in which the species could be quantified is indicated in headers. Quantification of the frequency of each subspecies (bottom plot in A and C) reveals that for M. smithii only one sample has two subspecies co-occurring in one individual, while all the others have a single dominating one. In contrast, for B. vulgatus/dorei, co-occurrence is more commonly observed. Phylogenies reconstructed from the reference genomes (NCBI taxonomic identifiers, see Methods) are fully consistent with the SNP-based clustering. The representative genome for each species, relative to which genomic variants were called, is highlighted with a box.(E) In B. vulgatus/dorei, subspecies composition within each individual is generally stable over time, with a change of the dominant subspecies being rare even over a period of up to 1,000 days. An exception (highlighted by purple line) is seen for an individual, in which one dominant subspecies is replaced by another one after antibiotic treatment. The right hand panel summarizes subspecies frequency changes, underlining remarkable stability over time.
Figure 3Three subspecies of Eubacterium rectale (A) are color-coded in PCoA plots. In most individuals we observe dominance of one subspecies at a time (see frequency plots below PCoAs).four of Eubacterium eligens (B) are color-coded in PCoA plots. In most individuals we observe dominance of one subspecies at a time (see frequency plots below PCoAs).(D) Grouping E. rectale individuals from the Danish (DK) and Swedish (SE) studies, based on this deletion shows a significant (*p < 0.05, **p < 0.01, ***p < 0.001) increase in BMI and blood fasting insulin levels as well as a decrease in overall community diversity in individuals who are predominantly colonised by the flagellum carrying subspecies (see Methods). The same trend is observed for E. eligens subspecies in the same studies, though not statistically significant (n.s.).
Figure 1Confocal laser scanning fluorescence microscopy (CLSM) images showing DNA staining (blue in the merged images), rDNA FISH (green) and NCL immunofluorescence (red) in untreated HeLa cells or in cells treated with α‐amanitin (50 μg/ml), AMD (50 ng/ml) or the Pol III inhibitor ML‐60218 (200 μM) for 5 h.CLSM images of propidium iodide‐stained RNA after microinjection of buffer or nucleolar RNA into α‐amanitin‐treated HeLa cells. Nucleoli were visualized by immunofluorescence of nucleophosmin (NPM). The graph represents the average number of nucleolar domains (± 95% CI) based on the analysis of 90, 87, 80 and 86 cells, respectively. **P‐value 0.01, t‐test.
Figure 3CLSM images showing nucleolar co‐localization of NCL (immunofluorescence) with aluRNA (RNA FISH) but not with L1‐repeat RNA. Cells were pre‐treated in situ with RNase A or an RNase inhibitor. Nuclei were counterstained with DAPI. The signal intensity of nucleolar aluRNA was two‐fold higher compared to nucleoplasmic signal (n = 92, P‐value 0.05, t‐test).Graphs representing the average number of nucleolar bodies after microinjection of in vitro transcribed RNA into HeLa cells that were pre‐treated with α‐amanitin (50 μg/ml) for 5 h or left untreated (control) (± 95% CI. **P‐value 0.01, n = 90, 87, 86, 83 or 86 cells, respectively). Representative CLSM images of propidium iodide‐stained RNA are shown on the right side.
Figure 4CLSMimages showing NPM (immunofluorescence, red) in HeLacells transfected with control ASO, aluRNAASO, or treated with transfection reagent only (mock). Nuclei were counterstained with DAPI (blue). A fraction of 80 ± 8% of the cells treated with aluRNAASO (n = 143) showed irregular nucleoli or dispersed nucleoli (53% of the cells). Only 26 ± 9% of the cells treated with control ASO presented abnormal nucleoli, with none of them showing a dispersion of nucleolar domains (n = 123), P‐value 0.01, t‐test.CLSM images showing NCL localization (immunofluorescence), aluRNA (RNA FISH) and DNA (DAPI). Transfection of HeLa cells was done as in (A).HeLa cells were transfected as in (A), and nascent RNAs were visualized by pulse labeling with ethynyl uridine (EU) for 30 min. Nuclei were counterstained with DAPI (DNA).Levels of 47Spre‐rRNA in mock‐, control ASO‐ and aluRNAASO‐treated HeLacells. Top: Northern blot. Center: Agarose gel electrophoretic analysis of RNA. Bottom panel: Quantification from RT-qPCR levels of 47Spre‐rRNA normalized to 18SrRNA. Error bars represent the standard deviation (n = 6). **P‐value 0.01, t‐test.
Figure 5HeLa cells were transfected with an empty vector or plasmids expressing aluRNA or L1‐repeat RNA and GFP from a separate promoter. Transfected cells were identified by GFP fluorescence, and nucleoli volumes were evaluated based on the immunofluorescence signal of the nucleolar marker NPM. The graphs show that the volume of nucleoli is increased by 30% in the presence of ectopic aluRNA (± 95% CI, n = 20). **P‐value 0.01, t‐test. Scale bar, 10 μm.CLSM live‐cell imaging of RFP‐NCL in cells transfected with a plasmid expressing aluRNA and imaged for 10 h. The arrows indicate nucleolar domains displaying time‐dependent increase in the nucleolar size or fusion of two domains into one. Scale bars, 10 μm.HeLa cells were transfected as in (A) and incubated for 24 h. Nascent RNA was pulse‐labeled for 30 min, and nucleoli were visualized by immunofluorescence of NPM. Scale bars, 10 μm.Graphs represent the intensity of nucleolar fluorescence signals as percentage of the intensity of nuclear signals and reveal a 27% increase in the presence of ectopic aluRNA (± 95% CI, n = 30). **P‐value 0.01, *P‐value = 0.01, t‐test.
Figure 6CLSMimages of UBFimmunofluorescence in mouseNIH 3T3cells treated for 5 h with α‐amanitin to inhibit Pol II transcription show the dispersion of nucleoli, as previously also imaged via RNAstaining (Caudron‐Herger et al, ).CLSMimages showing the localization of NPM (immunofluorescence, red) in mouse NIH 3T3 cells treated with ASO as indicated and counterstained with DAPI (blue).