dataesr/scientific-paragraphs-categorization huggingface dataset:
We also uploaded expression data from cultivated fibroblasts of healthy human donors ranging from 1 to 94 years of age (48) (GEO accession: GSE113957). Since decline in mitochondrial quality and activity are well-known contributors to age-related conditions and diseases  (72) , this dataset should help uncover the contribution of altered mito-gene expression dynamics to the ageing process. We made available several datasets from mouse knockout studies: we uploaded differential transcriptomic and proteomic data of five different mouse conditional heart knock-out strains of genes involved in mitochondrial replication, transcription and translation (40) (Lrpprc, Mterf4, Tfam, Polrmt, Twnk (Twinkle), (GEO accession: GSE96518)). These data are especially helpful in unraveling the transcriptional and post-transcriptional effects on mito-genes upon disruption of gene expression at different levels in mitochondria.
Furthermore, we added data from a mouse model of a known mitochondria-associated condition, the Barth syndrome. Barth syndrome patients develop severe cardiomyopathy  (73). This syndrome is caused by mutations in or loss of the TAZ gene coding for the protein Tafazzin which is involved in cardiolipin biosynthesis  (74). Failure of enzyme activity of Tafazzin leads to altered mitochondrial membrane composition, structure and metabolism  (74, 75). We provide differential expression data of Taz knock-out mouse embryonic fibroblasts (MEFs) compared to wildtype in normoxic and hypoxic conditions generated by Chowdhury et al. (46)  (GSE accession: GSE119775); these data should help reveal the effect of Tafazzin loss of function during hypoxia. To extend mitoXplorer to other model organisms, we added data from D.
melanogaster, namely expression data from 185 wild-derived, inbred strains (males and females) from the Drosophila Genetics Reference Panel (DGRP2)  (49) : this set of lines stems from an out-crossed population in Raleigh, North Carolina. These wild-derived fly strains display a substantial quantitative genetic variation in gene expression. The availability of these data on mitoXplorer allows a focused analysis of mito-genes to elucidate whether mitochondrial expression dynamics is equally impacted in these strains. Moreover, we have uploaded data from a recently published systematic study of flight muscle development in D. melanogaster  (44)  (GEO accession: GSE107247). This enables the analysis of mitochondrial expression dynamics during the development and differentiation of a tissue that is highly dependent on an efficient mitochondrial metabolism and especially ATP production for proper functioning.
Regarding budding yeast, we imported data from a timecourse expression profiling experiment of meiosis of a synchronized cell culture (Hanlon SE, Lieb JD (unpublished), GEO accession: GSE75257), allowing users to mine the expression dynamics of mito-genes over 12h of sporulation. This project is the only microarray-based dataset we have uploaded on mitoXplorer. Finally, we uploaded data from Protein Phosphates 2A (PP2A) yeast deletion strains; these strains show a diminished response of nuclear gene expression associated with mtDNA damage compared to wild-type (47) (GEO accession: GSE52242). This dataset should help shed light on the role of the conserved protein phosphatase PP2A in protecting cells from mtDNA damage. To verify mitoXplorer predictions experimentally, we use data from human trisomy 21: we provide data from human trisomy 21 patients (GEO accession numbers: GSE55426; GSE79842;  (42, 43) ), from trisomy 21 studies in mouse (GSE5542  (42) , GSE79842 (43)), as well as differential datasets generated in the course of this study from human trisomic cell lines (11 datasets) which have been partially published elsewhere (51,52) (GEO accessions: GSE39768; GSE47830; GSE102855).
User-provided expression and/or mutation data Researchers can upload and explore their own data in mitoXplorer, given that they originate from one of the species contained in the mitoXplorer platform. Data must be pre-analyzed. Differential expression data must contain the dataset ID (describing the experimental condition), the gene name and the log2FC. Optional values include the P-value, as well as the averaged read counts (or intensities) of the replicates of the compared conditions. Mutation data must contain the dataset ID, gene name, the chromosome, the position, as well as reference and alternative allele. Optional values include the effect, as well as the consequence of the mutation. Users have the option to either generate their own data according to the format described on our website.
We recommend to follow the bestpractice-guidelines available for analyzing transcriptomic or proteomic data prior to uploading data to mitoXplorer (see for instance  (76, 77)  for differential expression analysis or (78) for variant calling). Alternatively, users may use the RNA-seq pipeline for differential expression analysis and mutation calling that we provide at https://gitlab. com/ habermannlab/mitox rnaseq pipeline/. The entire list of genes from a study should be uploaded to the platform for several reasons: first, a restriction to only differentially expressed or mutated genes will suppress links between proteins in the interactome; second, an integration of user data with publicly provided data is difficult with incomplete datasets; third, mitoXplorer will automatically select the mito-genes from the user data.
Analysis and visualization tools in mitoXplorer The mitoXplorer web-platform provides a set of powerful, easy-to-read and highly interactive visualization tools to analyze and visualize public, as well as user-provided data by VDM (Figure  1 ): an Interactome View to analyze the overall expression and mutation dynamics of all mito-processes of a single dataset containing differentially expressed genes between two conditions and potential mutations in mitogenes; the Comparative Plot, consisting of an interactive scatterplot, as well as an interactive heatmap for comparing up to six datasets; the Hierarchical Clustering, as well as the Principal Component Analysis for comparative analysis of many datasets.
INTERACTOME VIEW The Interactome View can be used to get an at-a-glance view of the overall expression dynamics of all mito-processes of a single dataset of differentially expressed mito-genes and potential mutations (Figure  2A ). It allows users to identify the most prominently changed mito-processes or -genes in a dataset. The genes are grouped according to mito-processes and displayed in the process they are assigned to. The Inter-actome View is highly dynamic and can be adjusted by users to their needs. When the Interactome View is launched, each mitoprocess is primarily shown as a grey circle with elements colored in grey, blue and/or red, indicating up-or downregulated genes within the process, respectively (Figure  2A ). Thus, mito-processes with the most up-or down-regulated genes can be quickly identified.
When clicking on a process name, its circle opens up to display all its member genes as bubbles. The size of the bubble relates to the strength of the differential regulation while the color indicates up-(blue) or down-(red) regulation of the gene (Figure  2B ). If information about mutations is included in the dataset, this is indicated by a thicker, black border of the gene bubble. Hovering over a gene will display the gene name, its function, its mito-process, the log2FC and the P-value of the differential expression analysis, as well as potential mutations in the information panel (Figure  2C ). If a gene physically interacts with other mito-genes, hovering over it or over the process circle will in addition display these connections (Figure  2C ). Thus, the user is immediately informed about the location and connectivity of the protein of interest within the mitochondrial interactome.
Users can also search for specific genes using the 'FIND A GENE' box at the top of the page. The Interactome View can be launched by clicking on the 'eye' symbol next to dataset names from the ANALYSIS page of mitoXplorer, after having chosen the organism, the project and the dataset. Alternatively, users can access single datasets from the DATABASE page of the platform, by clicking on the eye symbol of a listed dataset after having chosen a species, as well as a project. A new page will be opened for the Interactome View, which allows opening and comparing multiple datasets at the same time. This is especially useful for comparing the overall expression change of mito-processes of multiple datasets.
COMPARATIVE PLOT The Comparative Plot visualization combines several interactive graphs to analyze one mito-process, allowing the comparison of up to six datasets. It includes a scatterplot with a dynamic y-axis, as well as an interactive heatmap at the bottom of the page. The mito-process to be visualized can be selected in the process panel (Figure  3A ). Red and blue coloring of the dots and the heatmap indicates the directionality of differential expression (blue: upregulated; red: down-regulated); bright blue, larger gene bubbles in the scatterplot indicate mutations, if available from the dataset. This Comparative Plot offers an overview of the expression dynamics of all members of one mito-process for up to six individual datasets and thus can be helpful in identifying co-regulated genes e.
g. in time-course data, patients or multiple mutant datasets. Hovering over a gene bubble, or over a tile in the heatmap will again display the respective associated information of the gene in the information panel (gene name, function, mito-process, log 2 FC, P-value, potential mutations) (Figure  3B ). The heatmap can be sorted according to the dataset, as well as the differential expression values within one   dataset (Figure  3C ). The Comparative Plot is especially useful for performing a detailed, comparative, mito-process based analysis of differential expression dynamics between different datasets. We applied this analysis method to visualize differential expression data from a time-series study of flight muscle development during pupal stages in Drosophila (44) (Figure  3 ). While enrichment analysis has revealed a general positive enrichment of processes like Tricarboxylic acid cycle (TCA Cycle) in the course of flight muscle development, mitoXplorer identifies 12 genes of TCA cycle that are coregulated.
THE HEATMAP: HIERARCHICAL CLUSTERING Hierarchical Clustering visualization allows the analysis of up to 100 datasets, analyzing one process at a time. This creates a heatmap with mito-genes, as well as -datasets, which are clustered according to the log 2 FC using hierarchical clustering (Figure  4 a ). The results are displayed as a clustered heatmap, with a dendrogram indicating the distance between datasets or between genes. Hovering over a gene will display its associated information, as well as dataset information in the information panel (Figure  4B ). The user can furthermore zoom into parts of the heatmap to get a more detailed view of the data. The heatmap is particularly useful for discovering groups of similarly regulated mito-genes or datasets within one mitoprocess. We applied this visualization tool to display transcriptome and proteome data from a recent, systematic study of mouse conditional knock-out strains for five genes involved in mitochondrial replication (Twinkle (Twnk)), mtDNA maintenance (Tfam), mito-transcription (Polrmt), mito-mRNA maturation (Lrpprc) and mito-translation (mTerf4)  (40).
Interestingly, the expression dynamics of the mitochondrial transcriptomes and proteomes in heart tissue did not cluster together for the mutants, suggesting strong posttranscriptional effects or protein stability changes of mitoproteins upon the loss of any of these genes. In accordance with this, the expression of some mito-genes in the process Pyruvate metabolism that is shown here differs on transcriptome and proteome level. This demonstrates the usefulness of hierarchical clustering and the heatmap display in identifying the correlation or divergence between genes as well as datasets.
PRINCIPAL COMPONENT ANALYSIS A larger number of datasets can be compared using Principal Component Analysis (PCA), either for an individual mito-process, or considering all mito-genes together (Figure  5A ). In PCA, the expression value (e. g. log 2 FC) of each gene is considered as one dimension, and each dataset represents one data point. In the resulting 3D PCA plot, the three axes represent the first three principal components and each bubble represents one dataset. The PCA is again interactive. Mito-processes can be selected via a drop-down menu on the top of the page. The plot can be turned and moved in 3D and has a zooming function. Hovering over a bubble will give the information associated with an individual dataset in the information panel, including the values of the first three principal components (Figure  5B ).
The information differs for each project chosen. Individual datasets can be selected and colored via the dataset panel next to the plot (Figure  5C ). For instance with data from TCGA the filter and coloring can be used to highlight or to limit the plot to data from different tumors, different tumor stages or according to any other additional information provided. The PCA is especially useful for analyzing a large number of datasets and displaying specific trends in sub-groups. We used the PCA plot to visualize data from the TCGA for four cancer types stored in mitoXplorer in Figure  5A , whereby the colors of the bubbles represent the different tumor types. The plot clearly highlights the variance of the different tumor types. In particular, kidney and liver cancer are highly distinct with respect to the first three components of all mito-genes (Figure  5A ).
GROUPS function In order to allow a more detailed, gene-centered analysis of correlated datasets, we added the possibility to select and group datasets in the Heatmap and the PCA views. Groups of datasets can be compared against each other with the Comparative Plot, whereby the log 2 FC is averaged over the data within a group. This functionality is useful, for instance when different groups of donors with similar expression patterns should be compared to each other. We demonstrate the usability of the GROUPS function in Supplementary Figure  S2 , where we analyzed the averaged expression patterns of ageing human fibroblasts from healthy donors from 1 to 94 years of age  (48). A first analysis using the PCA plot revealed that individuals which are older than 80 years were separated from the rest of the donors (Supplementary Figure  S2A ).
Data are clustered according to genes, as well as datasets. Gene tiles are colored according to their log 2 fold change. At the top of the heatmap, the user can choose the mito-process to be displayed. (B) Hovering over one of the gene tiles will display information on the gene, such as the gene name, mito-process, log 2 fold change, P-value and -if available -observed mutations. The heatmap is also zoom-able by clicking on the magnification glass at the bottom of the plot, so that large datasets can be visualized and analyzed efficiently. Datasets can be selected within the heatmap by grouping. To do this, first a group name has to be defined; second, the datasets belonging to this group have to be selected by clicking on one of the gene boxes of the dataset. This process can be repeated and the resulting groups can then be analyzed using Comparative Plots (see Supplementary Figure  S2 ).
Each bubble represents one dataset, in this case, one cancer patient. At the right side at the top of the plot, the mito-process to be shown can be selected. In this case, 'All Processes' were selected, containing data from all mito-genes. At the right side next to the plot, different colors, as well as filters can be chosen. In this case, the Cancer Type was chosen for coloring, showing the four different cancer types in four different colors. (B) Hovering over a bubble will display associated information on the dataset, including the dataset name, and in case of the TCGA, information on the cancer type, the stage, the gender, the vital status, as well as skin color. In addition, the three PC components are shown. (C) Selecting color schemes on the right-hand side will change the coloring of the bubbles.
In this case, only lung cancer is shown, and coloring is done according to Stage, Gender, Vital, and Skin color. This panel can also be used for selecting specific datasets. For instance, clicking on one of the stages will only display the chosen stage and omit datasets from other stages. As in the heatmap, datasets can be selected from the PCA for grouping. To do this, first a group name has to be defined; second, the datasets belonging to this group have to be selected by clicking on one of the dataset bubbles. This process can be repeated and the resulting groups can then be analyzed using Comparative Plots (see Supplementary Figure  S2 ). and mito-processes, facilitating a detailed understanding of observed changes at a molecular level.
Data integration with publicly available trisomy 21 datasets After discovering this differential OXPHOS defect in our RPE T21 cell lines, we were interested in the overlap of the mito-transcriptome and -proteome of RPE T21 cells with data from trisomy 21 patients. We used proteomic and transcriptomic data from a monozygotic twin study discordant for chromosome 21  (41, 42). In agreement with our RPE T21 data, systematic proteome and proteostasis profiling of fibroblasts from monozygotic twins discordant for T21 revealed a significant, although milder downregulation of the mitochondrial proteome, including proteins involved in OXPHOS, which is not apparent from transcriptomic analysis of the same cells (see Supplementary Figure  S6A, B ). We next looked at proteomic data of fibroblasts from 11 unrelated individuals with trisomy 21 (41) (Supplementary Figure  S6C-E ).
Virtually all T21 patients showed reduction in at least a few mitochondrial-and nuclear-encoded subunits of the respiratory chain (Supplementary Figure  S6D, E ). However, we could not confirm the strong reduction of the MRPS21 protein in all individuals. The only measurable mitoribosome subunit that was consistently, though in some cases only mildly, down-regulated was MRPL19 (Supplementary Figure  S6C ). Taken together, though the precise molecular mechanisms remain elusive, our analysis of these datasets with mitoXplorer nevertheless suggests a post-transcriptional effect leading to reduced expression levels of proteins involved in OXPHOS in trisomy 21.
The web-based mitoXplorer platform for mito-centric data exploration MitoXplorer is a practical web tool with an intuitive interface for users who wish to gain insight from -omics data in mitochondrial functions. It is the first tool that takes advantage of the breadth of -omics data available to date to explore expression variability of mito-genes and -processes. It does so by integrating a hand-curated, annotated mitochondrial interactome with -omics data available in public databases or provided by the user. MitoXplorer has been conceived and implemented as a visual data mining (VDM) platform: by iteratively interacting, visualizing and by allowing manipulation of the graphical display of data, the user can effectively discover complex data to extract knowledge and gain deeper understanding of the data.
MitoXplorer provides a set of particularly interactive and flexible visualization tools, with a finegrained, function-as well as gene-based resolution of the data. Clustering, as well as PCA-analysis help in addition to mine a larger number of -omics data effectively by grouping datasets with similar expression patterns. VDM-based knowledge discovery is offered by a large number of resources and platforms. However, to the best of our knowledge, no currently available tool allows to explore expression variation of a specific subset of genes in a large number of -omics datasets. It permits users to exploit publicly available transcriptome, proteome or mutation data to study the variation and thus, the adaptability of a defined gene set in different conditions or species. While mitoXplorer offers the exploration of mito-genes, we have designed the platform in such a way that users interested in a different gene group can download a local version of mitoXplorer and upload their own interactome, which may contain any gene group of interest.
This conclusion is further supported by the fact that we could not observe a significant difference in mitochondrial transcript levels, with some mt-mRNAs even being upregulated; thus, mtDNA -maintenance, -replication as well as mitotranscription seem to be unaffected. MitoXplorer analysis of previously published data of the mito-proteome of fibroblasts isolated from monozygotic twins discordant for T21, as well as 11 unrelated individuals with trisomy 21 (41) confirmed a similar posttranscriptional effect as we found in our T21 model cell lines. Taken together, our data uncovered a significant posttranscriptional regulation of the mitochondrial process OX-PHOS in our model system of trisomy 21 that could bring new insight into the mechanisms of mitochondrial defects in trisomy 21 patients.
mitoXplorer helped identify mitochondrial ribosomal protein S21 (MRPS21) as potentially causative for OXPHOS failure The most notable difference in RPE T21 cells compared to wild-type is the >10-fold downregulation of mitochondrial ribosomal protein S21 (MRPS21) on transcript level, as well as the downregulation of Mrps21 protein and other proteins of the small and--to a lesser extend--large mitoribosome subunits. Thus, our data suggest that the integrity of the mitoribosome is compromised, leading to its disintegration and subsequently, the downregulation of mitochondrial proteins of the respiratory chain. Mrps21 is a late-assembly component and lies at the outer rim of the body (or bottom) of the small subunit (SSU) of the mitoribosome. Nevertheless, it interacts with a number of other proteins of the SSU and also directly contacts bases of the 12S rRNA  (119, 120).
Direct or indirect regulation of mitoribosomal proteins could be another regulatory function of this transcription factor. GABPA is not affected on transcriptome level, but is down-regulated on protein-level in RPE T21 cells. ETS2 on the other hand has so far not been implicated in mitochondrial biogenesis or functional regulation. We see consistent downregulation of proteins involved in OXPHOS in other trisomy 21 proteomic datasets and OXPHOS defects have been reported in trisomy 21 before. MRPS21 seems deregulated only in a few T21 individuals. Thus, the causes of OXPHOS deficiencies seem to depend on genomic background or on the cell type studied. Trisomy 21 patients develop different degrees of severity of symptoms and it is likely that the genomic variability of chromosome 21 contributes to the varying phenotypes  (123, 124).
In future releases, we will consider adding information on mito-process enrichment of a dataset to guide users in their analysis, for example by visually highlighting enriched processes in the Interactome View. MitoXplorer has been optimized for mining expression data and currently it is not meaningful to analyze mito-gene mutations alone. We realize that this is a limitation when considering large, population-wide studies. We will therefore consider implementing a visual data mining interface that is specifically tailored for analyzing mito-gene mutations alone in future versions of the software. MitoXplorer expects users to provide data for which differential expression analysis or mutation calling has already been performed, as mitoXplorer was conceived as a visual data mining platform.