dataesr/scientific-paragraphs-categorization huggingface dataset:
Meta-Analysis of Gene Expression Networks Using WGCNA We conducted WGCNA meta-analysis  (Miller et al. , 2010)  and utilized other functions of the WGCNA  (Langfelder et al. , 2008 ) R package (version 1. 66) to compare coexpressed gene networks between (1) human aging and human neurodegenerative disease and (2) mouse aging. For both, genes were hierarchically clustered and groups of co-expressed genes (modules) were generated using the dynamic tree cutting algorithm with the following major parameters: soft power = 12, TOMType = "signed, " deepSplit = 2, pamStage = TRUE, cutHeight = 0. 99 and minClusterSize = 30-3 * deepSplit. Each module was assigned a unique color label. Hub genes were identified for each module using gene-module association K ME values. Statistical significance was determined by regressing the traits onto the eigengene of the module, also called trait analysis  (Plaisier et al.
Identification of Cell Type Signatures in Gene Modules To identify cell type signatures enriched in the aging and neurodegenerative disease preserved modules in mouse and human, we utilized the inbuilt hypergeometric test function "userListEnrichment" from the WGCNA 1. 66 R package. We found overlapping genes between WGCNA modules and the set of cell type signature genes by pairwise comparison, and determined the significance of the overlaps with p-values adjusted with Bonferroni's method for multiple testing. Cell type signatures of microglial hub genes were confirmed using the RNA-Seq Data Navigator tool, which is freely available online as part of the Allen Cell Types Database (© 2015 Allen Institute for Brain Science) 1. This was done by inputting hub genes in the "Gene Selection" box and building a "Group Plot" using default parameters.
Cross-Species Comparison of Networks We then assessed which modules, if any, were preserved between the human and mouse networks using module preservation Z statistics and p-values as described in the WGCNA package documentation  (Langfelder et al. , 2011). Similarly, we used this hypergeometric test described in the previous section to identify modules in the human and mouse networks with a significant number of overlapping genes. Together, these strategies allowed us to identify mouse and human modules that represent equivalent cell types of biological processes.
RESULTS The present article comprises results from the Systems Genetics of Neurodegeneration Summer School held in 2017 and combines analyses from multiple published high-throughput transcriptome datasets (Table  1 ) that were re-analyzed according to the experimental diagram shown in Figure  1. We first combined two large microarray expression datasets for three neurodegenerative disorders: AD and HD with their respective controls  (Narayanan et al. , 2014) , and PD with its  respective controls  (Corradini et al. , 2014)  (Figure  1 ). These gene expression data were either taken from human postmortem prefrontal cortex (AD + HD), or human post-mortem substantia nigra (PD). We obtained one large dataset of n = 637 patients and controls with balanced gender distribution and an age range of 18-106 years (Figure  2A ).
This approach increased power, but had the disadvantage to mask any potential tissue-specific networks. Hierarchical clustering of expression data using WGCNA partitioned this large human data set into 12 modules (Figure  2D ). These modules, which are designated in different colors, can be thought of as functionally different compartments of the human brain transcriptome, forming groups of highly interconnected transcripts that may shape a pathway that is relevant to aging and/or neurodegenerative disease  (Fuller et al. , 2007; Langfelder et al. , 2008; Struebing et al. , 2016 Struebing et al. , , 2018)). No module had any relationship to the confounding variables study, gender or tissue, validating our batch adjustment strategy (Figure  2D ). In order to gain more insight on the biological function of these modules, we overlapped genes within each of the previously found 12 co-expression modules with a signature gene list that was specific for several cortical cell types.
We ensured to only include data that was taken from the same mouse strain (C57BL/6J). After re-mapping of raw reads  (Langfelder et al. , 2011) , batch effects were successfully removed using the same strategy as previously demonstrated (Figures  3A, B ). WGCNA partitioned the combined dataset into 18 modules, including some that depended on mouse age (Figure  3C ). Just as with the human data, we also performed cell type-specific enrichment for the mouse aging modules. Among a few significant enrichments, we again found a strong overrepresentation of microglial genes, this time in the mouse "yellow" module (p = 2. 28e-37; Supplementary Table  S2 ). This module also had the strongest positive correlation to mouse age (r 2 = 0. 63, p = 5e-9; Figure  3C ). Other associations included oligodendrocyte-or neuron-enriched modules, however, their relations were not as prominent as the microglia module (Supplementary Table  S2 ).
By directly interrogating their expression changes during the aging process, we found significant associations of these hub genes with human aging in the "black" module (Figure  5B  and Tables  2, 3 ). All of these genes were strongly enriched in microglia, as evidenced by comparison with our cell type-specific gene list (Supplementary Tables  S1, S2 ). Cross-referencing them with RNA-seq data from the Allen Cell Types Database (Figure  5C ) further confirmed the cellular identity of these modules. These results suggest that even though gene networks enriched in microglial signatures that potentially direct human and mouse aging show significant overlap and are preserved, they are controlled by different regulatory hubs in the two species. In conclusion, our analysis identifies co-expressed networks involved in human aging and neurodegeneration that are well preserved in mouse aging and associated with similar molecular pathways.
A large part of this conserved regulatory pathway is mediated by microglia. Ultimately, our analyses suggest that the mouse is a valid model system to study changes in microglia during human aging and neurodegeneration. FIGURE 5 | Microglia-specific hub genes in mouse and human and enrichment with the Allen Brain Cell Types Database. (A) The human "black" module and the mouse "yellow" module shared 17 genes, out of which five were hub genes in human and two were hub genes in mouse. All 17 shared genes were microglia signature genes as confirmed with the published cell type signature genes. (B) These hub genes correlated positively with age in both mouse and human, while mean expression is higher in human neurodegeneration (ND) than in controls (CON). Each gray dot represents one sample and gene, whereas the blue (ND) and the red (CON) dots indicate means and the colored lines represent a linear fit with a gray-shaded 95% confidence interval.
(C) The hub genes were enriched in human (left) and mouse (right) microglia. Gene symbols are given on the y-axis, while different cell types as defined in the Allen Cell Types Database are displayed on the x-axis. Dot size and color correspond to the fraction of cells in a cluster expressing a given gene and the median gene expression, respectively. CPM, counts per million; MTG, middle temporal gyrus; VISp, primary visual cortex; ALM, anterior lateral motor cortex; Inh, inhibitory neurons; Exc, excitatory neurons; oligodendro, oligodendrocytes, LM, leptomeningeal; IT, intratelencephalic; PT, pyramidal tract; NP, near-projecting; CT, corticothalamic. Microglia are highlighted with a red box.
Combining transcriptomic data from different sources and different neurodegenerative diseases enabled us to investigate a large dataset that covered different diseases and a representative age range from 18 to 106 years. Nevertheless, using datasets from different sources often introduces unwanted batch effects. To overcome this hurdle, we leveraged a unique combination of batch-correcting statistical methods (SVA and linear regression models) to segregate the effects of age and neurodegeneration in their respective molecular networks. We first identified three gene co-expression networks that were significantly correlated to aging and neurodegenerative diseases. Each of these was enriched in genes specific for a distinct cell type, namely neurons (human "turquoise" module), endothelial cells (human "blue" module), and microglia (human "black" module).
Recent findings support the role of these genes in aging or neurodegeneration: For example, C1qa as part of the complement system mediates early synapse loss in AD, while TYROBP expression is increased in AD and simultaneously increases the expression of AD-related genes such as Cd33 in an AD mouse model, consistent with its role being a hub gene  (Zhang et al. , 2013; Herms and Dorostkar, 2016; Haure-Mirande et al. , 2018). Furthermore, TREM2 mutations are well known to increase AD risk in humans, and increased Trem2 expression in microglia has been found to correlate with amyloid deposition  (Brendel et al. , 2017). The intersection of hub genes with the Allen Brain Cell Types Database showed that they were indeed strongly expressed in microglia. Consistent with their role as being major gene network drivers, their expression pattern significantly correlated with aging (mouse and human) and neurodegeneration (human).
ACKNOWLEDGMENTS We would like to acknowledge all organizers of the 2017  Systems Genetics of Neurodegeneration summer school , namely  Rupert Overall ,  Robert W. Williams ,  Gerd Kempermann , and  Rudi Balling , furthermore the nuns of the Abtei Frauenchiemsee, headed by  Sister Scholastica , for hosting us. We would like to thank  Moeen Riaz ,  Victoria Perreau , and  Karl Broman  for their input in developing the idea for this project. Cloud computing processing time was obtained free of cost with special permission from  Arizona State University, Tempe, AZ, United States. We also thank the  Allen Institute Founders ,  Paul G. Allen , and  Jody Allen , for their vision, encouragement and support.
005 and Group, BEHEEC-FMUSP p < 0. 01, respectively) in Note: In our present study, order to identify differentially we only used the substantia expressed transcripts nigra tissue data Transcriptome Isolated and purified RNA isolated from purified Illumina HiSeq 2000 GSE52564 GSM1269903 to Zhang et al. , 2014 database of eight cell neurons, astrocytes, cell samples using a highly (Mus musculus) GSM1269916 types of mouse oligodendrocyte precursor sensitive algorithm to (GPL13112) cerebral cortex cells, newly formed detect alternative splicing in oligodendrocytes, each gene were used to myelinating identify cell type enriched oligodendrocytes, genes microglia, endothelial cells, and pericytes from mouse cerebral cortex (different mouse lines) Transcriptome data of Mouse hippocampus of 3, polyA-enriched RNA Illumina HiSeq 2000 GSE61915 SRR1593496 to Stilling et al.
, 2014 young and old mouse 24, and 29 months old extracted from mouse (Mus musculus) SRR1593512 hippocampus C57BL/6J mice hippocampus in three (GPL13112) different age groups [3M vs. 24M (n = 5-6, single-end sequencing) and 3M vs. 29M (n = 3, paired-end sequencing)] Transcriptome data of Mouse hippocampus of 2, mRNA expression profile of Illumina HiSeq 2000 GSE73503 SRR2531532 to Aaronson and knock-in mouse 6, and 10 months old male and female (n = 4 (Mus musculus) SRR2531555 Rosinski, 2015 models of Huntington's knock-in mice with CAG each) mice of three different (GPL13112) disease lengths of 20, 80, 92, 111, age points and seven 140, 175 along with different conditions (CAG littermate control wild-type lengths = normal, 20, 80, animals Note: In our 92, 111, 140, 175) present study, we only used WT data Transcriptome data of Mouse hippocampus of 1, Hippocampal mRNA from Illumina HiSeq 2500 GSE83931 SRR3734796 to Bundy et al.
Peptides (500 ng) were loaded at 1% B at a flow rate of 5 µL/min for 3 min onto the precolumn (BEH C18, 180 µm × 20 mm, 5 µm; Waters, Milford, MA, USA), and then separated using a 60 min gradient from 5-35% B on a 25 cm column (BEH C18, 25 cm × 75 µm, 1. 7 µm; Waters, Milford, MA, USA). Each of the three depletion replicates were analyzed three times (injection triplicates). 2. 6. 6. Mass Spectrometry Data Processing MS raw data processing was performed in MaxQuant (v. 1. 5. 3. 30; Max Plank Institute of Biochemistry, Martinsried, Germany) with default parameters if not specified otherwise. Using the Andromeda search engine  [17] , peak lists were searched against human protein sequences (Taxonomy ID 9606; 20194 sequences) downloaded from SwissProt [18] using the MSDA software suite  [19] , to which sequences of common contaminants (247 entries) were added automatically by MaxQuant (v.
1. 5. 3. 30; Max Plank Institute of Biochemistry, Martinsried, Germany). Identifications were filtered to obtain false discovery rates (FDR) of below 1% for both peptide spectrum matches (PSM; minimum length of seven amino acids) and proteins, using a target-decoy strategy. The "match between runs" option was enabled, in order to transfer identifications from one run to another, using a match time window of 0. 5 min after retention time alignment, based on an alignment time window of 10 min, keeping unmodified counterpart peptides for quantification. For the extracellular proteome ("Secretome"), only proteins that had been identified in at least one of the three injection replicates in all three depletion replicates were kept for further analyses. The mass spectrometry proteomics data from HepaRG cells were deposited to the ProteomeXchange Consortium via the PRIDE  [20]  partner repository, with the dataset identifier PXD010304.
Mass Spectrometry Data Analysis Protein copy numbers and secretion rates were calculated using the "Total Protein Approach"  [14] , based on raw spectral intensities, and by scaling to the total protein content per cell of the three different cell types, as determined in the course of this study. All other data analyses and visualizations was performed in Perseus and R  [21] , using the Normalyzer package  [22] , including a global LOESS normalization of calculated protein copy numbers, to account for the increased proteome depth of the data set of Wisniewski et al. [5]. We intentionally did not use the recently described "histone ruler"  [23] , because of "atypical" DNA contents in these cell types, since PHH are partially multinuclear  [24]  and the other two cell types showed abnormal karyotypes (see the main text), potentially leading to biases in copy number estimations.
Proteins of given pathways were categorized on the basis of their presence in canonical pathways or other networks contained in the Ingenuity®Pathway Analysis database  [25]. For protein categorization, pathway maps from the Kyoto Encyclopedia of Genes and Genomes database  [26]  were also used, as well as the automatic extraction of Gene Ontology annotations using the MSDA software  [19]  and literature examination. Regarding the proteins identified in the HepaRG secretome, bona-fide secreted proteins were recognized using the SignalP 4. 1  [27]  and SecretomeP 2. 0  [28]  algorithms. Hence, secreted proteins are those containing a signal peptide for entering the classical secretory pathway, or those predicted to be secreted via alternative secretion routes. The remaining proteins were still considered to be secreted, if they were annotated to be localized to the extracellular space or to exosomes.
As reported above for HepaRG cells, a mixture of cell lysates from one six-well plate on days 6, 12, and 18 was analyzed in five replicates, two of them digested in-solution, and three digested in-gel after prefractionation by SDS-PAGE. All samples were injected twice as technical replicates, from which the mean was calculated for further data analysis. The quantitative comparison of protein abundances (copy numbers) in the three cell systems was assessed statistically in the R software environment (v. 3. 4. 0, The R Foundation for Statistical Computing, Vienna, Austria)  [21] , using 1-way analysis of variance (ANOVA) with Tukey as post hoc tests, including the adjustment of p-values according to Bonferroni-Holm. For selected pathways or signaling cascades, heatmaps showing the relative differences of protein abundances (copy numbers) in HepaRG and HepG2 cells versus PHH were built using R (v3.
For tandem MS experiments, the system was operated with automatic switching between MS and MS/MS modes. Fragmentation was performed by using argon as the collision gas. The eight most abundant precursors were selected on each MS spectrum for further isolation and fragmentation, with a preference for doubly-charged ions (absolute threshold of 5000, relative threshold 5%). Ions were excluded after the acquisition of one MS/MS spectra, and the exclusion was released after 0. 3 min. The Smart Parameters Setting option was used for the selected precursor ions. The MS/MS spectra were acquired using a mass range from 100 to 2000 m/z. The Ion Charge Control was fixed at 400,000 and two scans were averaged to obtain a MS/MS spectrum. Peak lists in Mascot generic format (. mgf) were generated using Data Analysis (version 4.
0; Bruker Daltonics, Bremen, Germany), and searched against a SwissProt-derived [18] combined target-decoy database (created on 31 July 2012, containing 20,250 target sequences, plus the same number of reversed decoy sequences) using Mascot (version 2. 4. 1; Matrix science, London, United Kingdom). The database contained sequences of human proteins including common contaminants (human keratins and porcine trypsin) and was created using an in-house database generation toolbox  [19]. During the database search, up to one missed cleavage by trypsin and two variable modifications (oxidation of methionine (+16 Da) and carbamidomethylation of cysteine (+57 Da) were considered. The search window was set to 0. 25 Da for both the precursor and fragment ions. Mascot result files (. dat) were imported into Scaffold 3 software (v3.
* Extrapolated values for albumin (ALB) after immunodepletion (see the Methods section and Table  S5 ). (D) 2D annotation enrichment from 3995 proteins identified in at least two of the three cell types (see also Figure  S2  and Table  S6 ). To better characterize the proteome of the HepaRG cells, we next compared our dataset to those produced previously for HepG2 and PHH cells  [5]. The use of similar proteomics strategies and computational methods for establishing protein copy numbers (see the Methods section), and overall quality of the proteomics data (Figure  S4A, B ) suggested that the comparison of the three cell systems could be done with minimal biases. In addition, the total calculated numbers of protein molecules per cell (Table  S2 ) were in good concordance with theoretical calculations, based on well-established cellular parameters  [35].
Structural Hotspots of Highly Differentiated HepaRG The polarized architecture  [36]  and structural dynamics that characterize a typical mature hepatocyte involve the extracellular matrix, cytoskeleton, tight junctions, and the active dynamic machinery. Based on a literature examination, 355 proteins that are representative of these key elements were selected from our HepaRG proteomics dataset in a non-exhaustive manner, and they were distributed among 10 functional groups which were further divided into protein families (see Table  S5A ). The key structural elements that characterize a hepatocyte are thus well-expressed in HepaRG cells (Figure  3A ). We found that the expression levels (median copy numbers) of most of the structural proteins were globally similar in the three cell systems (see Tables  S5B  and S7 ).
Funding: This research was funded by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) and  Cosmetics Europe , grant number  267038  within the framework of the ' NOTOX ' project of the  SEURAT-1 (Safety Evaluation Ultimately Replacing Animal Testing) initiative , and by the  French Proteomic Infrastructure  ( ProFI ;  ANR-10-INSB-08-03 ).
The experiments were carried out at an ambient temperature (T = 27°C) and pressure (P = 1015 hPa). The room humidity (w ∼ 0. 65 ± 0. 05) was measured for each evaporation test. Along with the force measurement, we record the shape of the liquid bridge using a camera placed on the side. We check the image if the two plates are horizontal by measuring the distance between the two plates edges. The images are automatically processed using image processing tools from Matlab. The shape descriptors of the liquid bridge (r(z) and _ r(z) ¼ @r=@z) are computed on each image. From those measurements, we deduce the surface S and volume V by S ¼ ð z1 0 2pr ffiffiffiffiffiffiffiffiffiffiffiffi ffi 1 þ _ r 2 p dz and V ¼ ð z1 0 pr 2 dz: (2:1) Finally, we obtain the simultaneous time evolution of the capillary force F(t), volume V(t) and surface S(t) of the capillary bridge during the whole drying process until break-up.
| Data collection Data extracted from the electronic medical records included signalment, history, physical examination findings at admission, CBC, plasma biochemistry profile, electrolytes, coagulation profile, and abdominal ultrasonography results obtained at the earliest after admission (within a maximum delay of 48 hours post-admission). The outcome defined as death within 30 days of admission (ie, short-term death) was recorded. A 30-day cutoff was selected to appreciate short-term death related to AP. Dogs dead within 30 days of admission for causes unrelated to AP or euthanized for financial reasons were right censored at the date of death. Dogs still alive 30 days of admission were right censored at the corresponding date (ie, date of admission +30 days). Dogs were excluded if outcome data were not available.
The survival time was defined as the interval from admission to either the outcome (ie, death resulting from AP within 30 days of admission) or censoring. Cause and date of death were obtained in medical records or via telephone contact with owners. The study protocol was approved by the Ethics Committee of NVSA. During the study period, informed consents for privacy and personal data use were not obtained at time of dogs' admission given that data protection regulation was not applicable. Therefore, an oral consent was obtained afterwards when telephone contact was needed.
Therefore, data were considered missing at random. The identification of the risk factors was performed on all dogs (to take into account confounders more efficiently); the analyses regarding score establishment and validation were performed only in dogs with nonmissing data of the identified risk factors. No imputation has been performed. An association was considered as significant if P-value ≤. 05. All statistical analyses were performed using a commercially available software (SAS version 9. 3; SAS Institute Inc. , Cary, North Carolina).
These severity scores fulfill the criteria of a good scoring system which should be applicable at the time of diagnosis, easy to use, and accurate. 34 CAPS and sCAPS scores allow specific evaluation of AP severity, whereas recently developed illness severity score such as APPLE score is intended for dogs admitted in an intensive care unit regardless of the underlying disease. 35, 36 ese scoring systems were developed in a large cohort of dogs diagnosed with AP. Demographic data and clinical presentations in the study population were in accordance to previously published data. 1, 10, 12, 23 In this study, short-term death rates (33% for training cohort and 35% for validation cohort) are similar to death rates previously reported for AP in dogs (27%-58%). 20, 21 In the training cohort, abdominal ultrasonography revealed findings suggestive of AP in 70% of the cases.