dataesr/scientific-paragraphs-categorization huggingface dataset:
acknowledgements The authors would like to acknowledge  Rolf Sommer Kaas  and  Thomas Nordahl Petersen  for assisting in creating the necessary in-house data management infrastructure to enable compliance with FAIR and  Baptiste Avot  with assistance in data upload. A contribution also goes to  Frederik Duus Møller  for creating summary scripts for KmerFinder and ResFinder, enabling easy extraction of summary output from these analyses as well as to  Victor Hyltoft  for maintaining the TWIW web app. The authors would also like to acknowledge  Ana Rita Bastos Rebelo  for guidance and advice in relation to the assessment of the genomes generated and how to validate these. The authors would also like to thank  Martin Koliba  for technical support and expertise related to the development of the data-sharing infrastructure and partner database and to  Christina Aaby Svendsen ,  Jacob Dyring Jensen ,  Michella Oppenheuser ,  Birthe S.
Data records The dataset consists of: 1. Raw sequence reads available at ENA: Accession ERP141886  10  2. One MySQL database (available as MySQL data dump) for download at DTU Data, https://doi. org/ 10. 11583/DTU. 21758456 (URL: https://doi. org/10. 11583/DTU. 21758456. v2) 9 3. One web application for browsing the data and selected findings, available at TWIW web app (URL: http:// twiw. genomicepidemiology. org) The Technical University of Denmark has acted as data brokers to the partners. Data brokering is the act of submitting data on behalf of another institute. This was done to ascertain that the partners would be properly referenced when the data is reused for other purposes in the future. The MySQL database contains metadata and summary output data as well as information regarding the generation of the analysis output.
Présentation de la méthode Le site Web PeerWise (https://peerwise. cs. auckland. ac. nz) a été utilisé pour cette activité. Il permet de créer une base de données en ligne de questions à choix multiples développées par les élèves. Il a ainsi été demandé aux élèves de première année de l'ENSIC de développer des questions portant sur les cours de GRC et GRH, et visant des niveaux de compétence élevés, c'est-à-dire ne portant pas sur de simples définitions ou descriptions de concepts vus en classe, mais plutôt sur la compréhension et l'application des notions vues en cours ou TD à des exemples originaux. Le fait de développer des questions originales pousse les élèves à réfléchir aux concepts qu'ils visent à évaluer, aux idées fausses que leurs camarades de classe peuvent avoir sur la question.
Longitudinal weighted gene coexpression network analysis (WGCNA) of AD brains reveals progressive alteration of transcriptomic pathways To investigate the pathological mechanisms progressively altered during the progression of Alzheimer's disease, we performed WGCNA on temporal lobe samples from healthy and AD human brains. WGCNA uses a distance matrix based on gene coregulation to identify networks of coexpressed genes called modules, which can later be mapped to Gene Ontology (GO) categories to infer their biological functions. Modules are built on all available data with an unbiased approach, and GO annotation is performed a posteriori on the whole module. In this way, the analysis is not limited to genes with known GO annotation: genes not included in the GO database can still be assigned to the module based on coregulation, and their function may be inferred from the GO of the module.
The coregulation networks also enable groups of concerted genes that include more than one GO category to be addressed. In addition, the changes in intramodular connectivity, both in the number of genes included in a module (i. e. , the nodes) and in the strength of their connections (i. e. , the edges), are highly informative for predicting the regulation point at which a pathway is altered. For these analyses, we employed a published dataset, GSE84422 (GPL97 platform), containing data for AD brain samples structured into 3 groups: possible AD (posAD), probable AD (proAD) and definitive AD (defAD), representing early, intermediate and late phases, respectively  (Wang et al. , 2016). Healthy samples were analysed to build the reference, and 25 consensus modules were identified in control subjects.
To follow the evolution of coregulated genes during the progression of the disease, the genes in proAD and defAD were assigned to these consensus modules  (Fig 1A). Then, the conservation of reference modules in the proAD and defAD phases was measured via assessment of module preservation. To identify subsets of genes with the same trend, genes were clustered by their expression values at each stage with the K-means algorithm and Euclidean distance to preserve information on both the sign and magnitude of the log2 (fold change). By network analysis, we observed 2 modules that are not conserved in AD brains compared to healthy brains: the Pink and the Darkgrey modules  (Fig 1B). In GO analysis of these modules, DNA binding and transcription regulation  (Fig 1C)  were featured among the functions listed in Table  EV1  and Table  EV2 , suggesting that pathways associated with chromatin and gene expression might be altered during the progression of the disease.
To accomplish this, we overexpressed Tau 0N4R, one of the most abundant Tau isoforms in the adult human brain, in differentiated neuroblastoma cells, a cellular model previously used to study molecular mechanisms in tauopathies  (Bell and Zempel, 2022; Siano et al. , 2023). The expression efficiency was confirmed by immunoblot analysis (Figure  2A ), and global transcript expression was quantified by RNA sequencing (RNA-seq) (dataset reference number GSE239956). Principal component analysis (PCA) was applied to visualize the data and confirmed that the increased expression of Tau is the main factor contributing to variation in the global transcriptome profile (Fig  2B ). RNA-seq data analysis revealed 3235 differentially expressed genes (DEGs) between Tauoverexpressing and control cells  (Fig 2C).
Tau-dependent alteration of global gene expression in vitro resembles early AD conditions To assess the importance of using a neuronal cell line to investigate Tau roles, we compared our SH-SY5Y data to the transcriptome of HEK cells with inducible Tau overexpression obtained by  Montalbano et al. (Montalbano et al. , 2021). Regardless of whether they observed Tau-dependent changes in gene levels  (Montalbano et al. , 2021) , gene expression was not correlated between the two sets (R = 0. 16 and p = 0. 1) (Fig EV3 ), thus showing that the cell model of choice has a significant impact. We compared our RNA-seq data with DEGs in various AD stages in humans from GSE84422 to ascertain whether Tau-dependent transcriptomic alteration replicates the dysregulation of genes in particular AD phases. The proAD condition significantly overlapped our dataset  (Fig 3A).
We found a positive correlation between our dataset and proAD (R=0. 58 and p < 2. 2e-16; Fig  3B ) by plotting the log fold changes (log2FCs) of our DEGs and posAD, proAD, and defAD. Approximately 75% of common genes displayed the same direction of regulation. According to this analysis, the Taudependent gene alterations in the proAD-mimic cellular model represented a developing intermediate AD condition in the temporal region. In addition, there was a negative correlation for posAD and no correlation for defAD (posAD: R= -0. 3 and p < 0. 00022; defAD: R=-0. 086 and p value = 0. 43; Fig  3C-D ), with approximately 60-65% common genes in the second and fourth quadrants. Upon focusing on the amplitude and direction of the log2FCs of common DEGs between our dataset and proAD, we observed that while most genes showed the same direction, the amplitude was higher in the proAD-mimic model  (Fig 3E) , suggesting an exacerbation of the pathological phenotype in the cellular system.
Siano et al. , 2020; Siano et al. , 2019b). By using unbiased WGCNA of microarray data from human temporal lobes at different stages (posAD, proAD and defAD), we observed a progressive loss of coregulation of genes associated with AD pathways such as synaptic transmission and neuronal function, as already observed  (Hu et al. , 2020; Miller et al. , 2008; Sun et al. , 2022). Of note, we also identified pathways related to chromatin and transcription, suggesting that these mechanisms can undergo functional changes during the course of the disease. Transcription-associated modules have not been described in previous coregulation analyses employing WGCNA on AD samples. Moreover, in longitudinal studies, either the number of samples has been low, thus limiting the depth of the analysis, or the tissues considered have not been brain-derived.
In any case, the previous studies enabled us to identify not only relevant pathways associated with AD pathology but also gene candidates that can drive the pathology or be employed as biomarkers  (Hu et al. , 2020; Miller et al. , 2008; Soleimani Zakeri et al. , 2020; Sun et al. , 2022; Upadhyaya et al. , 2020; Zhang and Kiryu, 2023). Here, the dataset employed provided transcriptomic data of early, intermediate and late AD stages identified via integration of several pathological parameters. Moreover, the higher number of subjects per group compared with those in other WGCNAs increased the sensitivity of the coregulation network, thus revealing pathways not previously described, such as those related to chromatin and transcription. Given our previous observations on the subcellular imbalance of Tau in early stages and its involvement in gene expression modulation, we focused on the association between Tau and global transcriptional changes.
However, the low correlation between our dataset and the dataset from  Montalbano et al. indicates  that the outcome of research on the role of Tau in gene expression significantly relies on the cellular model employed. A transcriptome shift is generally associated with functional alterations leading to pathological conditions. The GO analysis identified terms related to synaptic functions as Tau-dependent pathways, as expected (G. Siano et al. , 2020; Siano et al. , 2019b). Additionally, we found terms that are relevant for neuronal physiology and pathology, such as protein metabolism, which is well known to have a key role in tauopathy progression, and less canonical pathways, such as those related to RNA metabolism or extracellular matrix remodelling. These proteins are known to be altered in tauopathies but are still not completely characterized  (Chauderlier et al.
, 2018; Hernández-Ortega et al. , 2016; Ma et al. , 2020; Violet et al. , 2014). To functionally validate our transcriptomic analysis, we measured the phosphorylation of eIF2α in our cellular model and observed a significant increase in eIF2α-P after Tau overexpression. Phosphorylation of eIF2α is a well-known marker of response to ER stress and translational mechanisms in tauopathies  (Clemens, 2001; Gerakis and Hetz, 2018; Radford et al. , 2015; Shacham et al. , 2021). Our findings indicate that these pathways, which are essential for neuronal functionality, are transcriptionally altered by Tau. The data collected with the cellular model, here defined as proAD-mimic, showed that changes in Tau cellular availability affected the global transcriptome. We performed a meta-analysis to compare the transcriptome of this model with the datasets from temporal regions at three AD stages: posAD, proAD and defAD.
This worsening might be weakened in the brain by the cellular heterogeneity of the tissue, which could dampen the global impact of neuronal transcriptomic changes. The evidence regarding the specificity of the proAD-mimic model in resembling proAD tissue was further strengthened by the comparison with posAD and defAD. The posAD and defAD transcriptomes were negatively correlated and not correlated with our dataset, respectively. The negative correlation with posAD might suggest early transcriptomic compensation to prevent Taudependent alteration of gene expression and to maintain neuronal homeostasis, since at this early stage, Tau undergoes initial destabilization and oligomerization. In contrast, the loss of correlation with the terminal stages could be the consequence of excessive transcriptomic and functional damage in proAD leading to global gene dysregulation.
This evidence closely relates to our previous study on the human prefrontal cortex at different Braak stages. We observed significant deregulation of synaptic genes mainly at Braak stages 3 and 4 (corresponding to an early or intermediate AD stage), and we hypothesized a causal link with destabilized and oligomerized Tau protein (G. Siano et al. , 2020). Our meta-analysis further supports this thesis, confirming that in early and intermediate pathological stages, there is a Tau-dependent transcriptomic alteration impinging on the synaptic pathway. The meta-analysis revealed that Tau has a significant role in the transcriptomic modifications observed in proAD and indicated the Tau-dependent pathways altered in proAD and how they are regulated in the previous and following phases. The GO analysis of common genes between our dataset and proAD brains identified several terms related to transcription and chromatin remodelling, according to the WGCNA data.
WGCNA Public transcriptomic datasets were sourced from the Gene Expression Omnibus (GEO) platform hosted on NCBI (https://www. ncbi. nlm. nih. gov/geo/). The GSE84422 dataset  (Wang et al. , 2016) , which includes data from 1053 postmortem brain samples across 19 brain regions from a total of 125 patients dying at different AD stages, with 50-60 subjects per brain region, was used. The patients were clustered by AD severity into 4 groups, the normal (n=110), posAD (n=110), proAD (n=83) and defAD (n=163) groups, according to their clinical dementia rating (CDR), Braak NFT score, sum of NFT density across different brain regions, average neuritic plaque density and average Consortium to Establish a Registry for Alzheimer's Disease (CERAD) score. We considered for our analyses the CDR since it integrates several parameters to define the AD condition.
Gene expression profiling was performed on the Affymetrix Human Genome U133B Array (GPL97)  (Wang et al. , 2016). Overrepresentation analysis (ORA) provided a knowledgebased classification of genes and list of associated pathways. For further insight into the biological functions of the sets of DEGs, without any a priori assumption on the involvement of genes in specific pathways, WGCNA was performed on a larger patient set from the GSE84422 dataset. All the samples from the temporal cortex of GSE84422 were grouped and divided into control, proAD and defAD groups. Because posAD modules were conserved when compared with the modules healthy control brains, the posAD group was excluded from the analysis. We used the WGCNA package and customized the WGCNA tutorial available at https://horvath.
genetics. ucla. edu/html/CoexpressionNetwork/Rpackages and the code for building consensus networks from "Meta-analyses of data from two (or more) microarray datasets" by Jeremy Miller. The dataset was divided into subsets according to the AD stage, an appropriate soft thresholding power β (β = 9) was chosen so that the data of both subsets would fit a scale-free distribution, and the compatibility of the subsets was verified by calculating the correlation between the ranked gene expression and the correlation between the ranked connectivity. The signed adjacency matrix provided the distance measure for building modules on the control set. To detect even small gene clusters, deepSplit was set to 3, and the minimum cluster size was set to 30-3*deepSplit. Module eigengenes, gene module membership (i.
e. , the correlation of module genes with eigengenes) and module preservation in the disease phases were later assessed. Hub genes were defined as those with the highest module membership. Modules were visualized with Cytoscape. To choose the appropriate number of clusters, the modules were first inspected both visually and with assignment indicators with the R packages "ppclust", "factoextra", "cluster" and "fclust", and then the modified partition coefficient and the partition entropy were maximized. To associate each cluster with a molecular function, gProfiler software was used, including all known genes in the GO database. Top pathways with Benjamini-Hochberg FDR values < 0. 05 defined cluster identity.
RNA sequencing data analysis The reads were mapped onto the genome (Ensembl: GRCh38. 92,  (Zerbino et al. , 2018) ) with Tophat v2. 1  (Kim et al. , 2013; Langmead et al. , 2009)  using parameter --no-convert-bam -no-coverage-search -x 1 -g 1: on average, 80% of all reads were mapped univocally. The reads per gene was counted using featureCounts v1. 5. 0  (Liao et al. , 2019). Read counts were introduced in the the statistical environment R (v. 3. 4. 1) for further processing. Read count were normalized for reads per samples (RPM) and transcript length (RPKM)  (Mortazavi et al. , 2008). DEGs were defined by an FDR cut-off of < 0. 1 in the statistical tests of all three R packages, edgeR  (Robinson et al. , 2010) , DESeq  (Anders and Huber, 2010)  and baySeq  (Hardcastle and Kelly, 2010) , and by an absolute log2FC > 0.
5. The 300 most variant genes were selected for the PCA plot using the R package pcaExplorer after variance stabilization  (Marini and Binder, 2019). The EnhancedVolcano package was used for the volcano plot  (Blighe et al. , 2018). GO analysis of the DEGs was performed by ORA with the GO Biological Process annotations from the GO. db package  (Carlson, 2019). ORA was performed by a right-sided hypergeometric test, and terms with BH-adjusted p values < 0. 05 were considered significant. WebGestalt was used to summarize GO entries with the affinity propagation method  (Wang and Reddy, 2017). For KEGG pathway enrichment, pathways were retrieved using the KEGGREST package  (Tenebaum and Maintainer, 2023) , and the Wilcoxon test was performed for each pathway on the merged set of up-and downregulated DEGs.
Gene expression comparison with the human dataset For transcriptome meta-analysis, public transcriptomic datasets were sourced from the GEO platform hosted on NCBI (https://www. ncbi. nlm. nih. gov/geo/). GSE84422 was used. The patients included in the GSE84422 dataset were classified by AD stage into posAD, proAD and defAD. The R packages "GEOquery"  (Davis and Meltzer, 2007) , "Rcpp"  (Eddelbuettel and François, 2011)  and "tidyselect" were used to import datasets and create subsets by diagnosis and sample type or brain region. To compare different platforms, platform-specific IDs were converted into Entrez Gene IDs using the corresponding R annotation databases from Bioconductor, available via bioMart (version 3. 8)  (Durinck et al. , 2009). Multiple platform IDs matching the same gene were summarized in a single entry with gene expression as the mean of probe expression.
Probes mapping on the same gene with a discordant logFC were excluded from the analysis. Differential expression analysis of microarray data was performed with the R package "limma" (version 3. 8)  (Ritchie et al. , 2015). An FDR-adjusted p value < 0. 1 was set as the cut-off for differential expression. The temporal region data from the GSE84422 dataset were used. The GSE84422 dataset was compared to our SH-SY5Y data to identify the stage of the disease that was best recapitulated in the cellular model. To assess the conservation of specific pathways, genes belonging to the pathway were ranked according to their expression level in each dataset, as in Gene Set Enrichment Analysis. In this way, we could underscore the combinations of small contributions from the single genes to the whole pathway.
The DEGs in our dataset (FDR < 0. 1) were compared to the genes in the temporal region dataset, and the correlation of the most significant hits was computed. Genes with absolute logFCs < 0. 1 were excluded from the analysis. Enrichment of GO terms in the SH-SY5Y transcriptome was assessed by ORA as described above in "RNA sequencing data analysis". The direction and magnitude of the fold changes of genes in the most differentially represented pathways were compared to those for patients in dataset GSE84422 to assess correlations and the ratios of fold change amplitude. All DEGs in the SH-SY5Y transcriptome were included in the analysis.
Gene expression comparison with the iHEKTau dataset To assess the impact of the cell line on the transcriptome after Tau overexpression, we compared our SH-SY5Y data to the transcriptome data of HEK cells with inducible Tau overexpression obtained by Montalbano et al. The raw data for the control cells and for iHEKTau cells were downloaded from NCBI under SRA reference number PRJNA744518 and processed as indicated in the original paper  (Montalbano et al. , 2021). Genes with absolute logFC values > 0. 1 and adjusted p values < 0. 1 in both datasets were used to compute the correlations between gene expression levels in Tau-overexpressing SH-SH5Y and iHEKTau cells. Tables  Table 1. Gene pathways altered by Tau. KEGG pathways of genes altered by Tau in neuroblastoma cell lines. Synaptic and neuronal pathways are shown in light blue.
Acknowledgements The authors are grateful to  M. Calvello ,  V. Liverani  and  A. Viegi  for technical support, the  Next Generation Sequencing facility  and the  Life Science Computing facility  of the  Leibniz Institute on Aging -Fritz Lipmann Institute -for RNA-seq and primary data analysis. We thank the  Roche postdoctoral fellowship  program for funding C. W. and M. T.
treated for 1 1 hour at room temperature with primary antibodies diluted in blocking solution, rinsed three times with PBS-T, and then incubated with secondary antibodies for 1 hour at room temperature. Following a PBS-T wash, the slices were incubated with PureBlu DAPI (Bio-Rad, Cat# 1351303) for 3 minutes before being mounted with ProLong Gold antifade mounting media (Thermo Fisher Scientific, Cat# P36934). Images were scanned using an Olympus SLIDEVIEW VS200 slide scanner. ImageJ software was used to analyse immunofluorescence images. Image5D followed by Z Projection was employed for z-stack images. H3K9me3 fluorescence was measured in NeuN and AT8 positive cells after background subtraction. In control BS1 and BS2 samples, AT8 signal was undetectable or very low. In AD samples from BS3 to BS6 we detected a significant and increasing AT8 signal.
| 2D spectra For assisting with metabolic identification, 2D spectra were also acquired, namely, 1 H-1 H total correlation spectroscopy (TOCSY) and 1 H-13 C heteronuclear single quantum coherence (HSQC). For 2D-TOCSY, a phase-sensitive experiment (mlevphpr) with a presaturation motif of 2 s was used. A MLEV-17 was employed as the spin-lock pulse sequence with a mixing time of 80 ms and 4 W power. The duration of the π/2 pulse was 11. 5 μs for 1 H and a free induction decay of 2048*96 points was acquired, with 32 scans, 16 dummy scans, and a recycling delay of 2 s between scans. For 2D-HSQC, the pulse program hsqcetgpsi2 was used, with a delay for insensitive nuclei enhancement by polarization transfer (INEPT) of 1. 28 ms. This pulse sequence corresponds to a phase-sensitive HSQC with shaped 180 pulses gradient-based coherence selection, where the sensitivity is improved with a double INEPT transfer.
| Metabolomics analysis Spectra were calibrated, phase-adjusted, and baseline-corrected with Topspin 4. 1. 0 (Bruker Biospin) software. Metabolic identification was possible thanks to the literature,  [15] [16] [17] 33, 34  the 2D spectra, Chenomx NMR Suite (version 8. 6, Chenomx Inc. , Edmonton, Canada), and the Human Metabolome Database (http://hmdb. ca/, versi n 5. 0). To generate datasets we used NMRProcFlow software (http://nmrprocflow. org/, version 1. 4) for the spectral binning with variable size binning (VSB). We performed two normalization methods, either to the sample weight or to the total area. According to the results, we found that the variations associated with the preparation protocol had more influence than the sample weight. Therefore, total area normalization was preferred.