dataesr/scientific-paragraphs-categorization huggingface dataset:
Model validation Adopting definitions by Porgo et al. [126] , model validation (i. e. 'how well a model performs and how applicable the results are to a particular situation') was performed for around two-thirds (23/37) of models. We do not consider model calibration here (see §3. 5). There was considerable variation in the methods by which model performance was assessed. This extended from the types of network properties considered, the methods of validation used, and the rigour to which this was carried out. In terms of the types of validation used, 17 models were internally validated, while nine were externally validated. Approaches for external validation included splitting the data into training and validation sets (e. g. [68] ), or through comparison with different datasets  [15, 56, 65, 67] , such as for different time points  [74, 85, 87].
With increasing recognition of the need for evidence-based approaches to livestock production and health, particularly in the context of multitudinous high-profile, and often economically devastating, livestock and zoonotic disease outbreaks in recent decades, it seems reasonable to assume that efforts towards livestock network data collection will continue to gain ground. The types of modelling approaches reviewed here are well positioned to derive key insights from this data. Furthermore, such models can be used to inform the design of future empirical studies and livestock tracking systems, in order to optimize their efficiency and utility in generating data needed for effective disease surveillance and control  [26, 28]. Data accessibility. The data extracted for this review and the R code used to generate the figures in this review are available from https:// github.
Gene expression analysis For gene expression time series, 20+ plants were harvested at each timepoint (7, 15, 21 days old plants). Plant material was snap frozen with liquid nitrogen, ground and RNA was extracted using the Phenol:Chloroform:Isoamyl alcohol (25:24:1) protocol  (Yang, Howard et al. 2014). RNA was purified with the TurboDNase (Ambion) kit to remove DNA contamination and reverse transcribed into cDNA using SuperScript IV Reverse transcriptase (Invitrogen) and RT primers for genes of interest. Gene expression was measured by qPCR, data was normalized to PP2A and UBC. Primer sequences are summarized in Supplementary Table  1.
We used a truncated normal distribution with the mean and standard deviation matching the measured values for epidermis cells. A minimum value was set such that the cell cycle could not be shorter than 13 hours (the lowest value observed for epidermis or cortex cells in  (Rahni and Birnbaum 2019) ). Custom code is available from: https://github. com/ReaAntKour/fca_alleles_root_model.
Acknowledgements We thank all members of the  Dean, Rosa and Howard groups  for excellent discussions. Special thanks to  Dr. Silvia Costa  for imaging advice and  Dr. Cecilia Lövkvist  for project coordination. This work was supported by  BBSRC   Institute Strategic Programme GEN  ( BB/P013511/1 ),  BBSRC  grant  BB/P020380/1  to C. D. and M. H. , European Union's  Horizon 2020 research and innovation programme  ( Marie Skłodowska-Curie  grant No. 813282 ) to Sv. R. and M. H. and  Vetenskapsrådet  grant  2018-04101  to A. M. and S. R.
1C, D ). In line with these data, lysosomal GCase localization was decreased in mutant GCase cells (Supplementary Fig. 1A ). Next, we performed FLAG immunoprecipitation followed by isobaric labeling (tandem mass tag, TMT) and MS analysis. T-Rex HEK cells expressing the empty vector were used as a control. GCase interactors were defined as proteins that were quantified by ≥2 unique peptides and enriched 1. 5-fold over the control (Supplementary Dataset 1). First, we investigated the interactome of WT-GCase. The compiled list contained known GCase interactors, such as LIMP2, calnexin, calreticulin, and DNAJB11. Analysis of the 100 top hits of the WT-GCase interactome showed that proteins from different compartments, including the ER, Golgi, lysosomes, and vesicles, interact with GCase (Supplementary Fig.
1B ). Interestingly, 19 out of the top 100 binding partners of WT-GCase were proteins associated with mitochondria (Supplementary Fig. 1B  and Supplementary Dataset 1). The enrichment analysis of the biological processes (BP) revealed several mitochondrial pathways converging at energy metabolism (Fig. 1E ). Interestingly, the analysis of the only interactome study of endogenous WT-GCase, which was performed using SILAC proteomics in HeLa cells  13  , revealed that 8% of endogenous GCase-interacting proteins were mitochondrial proteins (Supplementary Fig. 1C ). Using GCase-FLAG immunoprecipitation followed by western blotting, we validated the interaction with cytosolic chaperones involved in protein folding prior to mitochondrial import (HSC70) as well as with mitochondrial outer membrane (TOM70), inner membrane (TIM23 and ATP5B), and mitochondrial matrix (HSP60 and LONP1) proteins (Fig.
1F  and Supplementary Fig. 1D ). Furthermore, we confirmed the specific interactions of WT-GCase with selected proteins from the cytosol, ER, and mitochondria (Supplementary Fig. 1E ). Interactome and whole proteome analysis reveals the enrichment of mitochondrial proteins in L444P-and E326K-GCase mutants Next, we compared the TMT-based quantitative interactomes of WT-, E326K-, and L444P-GCase in T-Rex HEK cells. Interestingly, among the dysregulated interactors of mutant GCase, we identified an enriched interaction with proteins involved in mitochondrial protein quality control, including LONP1, as well as a decreased interaction with the mitochondrial CI assembly factor TIMMDC1 (Fig. 1G  and Supplementary Dataset 1). In addition, we performed a proteome-wide analysis of TMT-labeled whole-cell lysates from WT-, E326K-, and L444P-GCase T-Rex HEK cells.
The analysis of the differentially expressed proteins (DEPs) revealed an increased abundance of mitochondrial proteins in WT-, E326K-, and L444P-GCase T-Rex HEK cells compared to the control (15, 25, and 20%, respectively) (Supplementary Fig. 2A, B  and Supplementary Dataset 2). These included representative CI subunits from the matrix (NDUFA7, NDUFA12, NDUFS4, NDUFS5, and NDUFA10) and membrane arms (NDUFC2 and NDUFB8). In agreement, gene ontology (GO) analysis of DEPs revealed dysregulation of proteins linked to mitochondrial energy metabolism, especially to CI and the mitochondrial respirasome, in the E326K-and L444P-GCase proteomes (Supplementary Fig. 2C ).
GCase localizes to mitochondria The TMT-based interactome dataset supports the hypothesis that both WT and mutant GCase can be imported into mitochondria. To confirm the mitochondrial localization of GCase, we performed subcellular fractionation of WT-, E326K-, and L444P-GCase T-Rex HEK cells, and we subjected isolated mitochondria to digitonin permeabilization and proteinase K digestion followed by western blotting (Fig. 2A  and Supplementary Fig. 3A ). Possible contamination of the mitochondrial fraction with lysosomal proteins was prevented by digitonin lysis and proteinase K digestion. The lysosomal proteins hexosaminidase B (HexB) and LAMP1 were successfully removed by this treatment, whereas GCase and the mitochondrial matrix protein LONP1 were still present in this fraction (Fig. 2A , and Supplementary Fig.
GCase is imported into mitochondria by the mitochondrial import machinery Most mitochondrial proteins are imported by the mitochondrial protein import machinery (TOM, TIM23, mitochondrial HSP70). Upon translocation through the TOM channel, proteins are transferred to the presequence translocase of the inner membrane (TIM23 complex)  17. Based on the interactome analysis, GCase interacts with several components of the mitochondrial import machinery as well as mitochondrial chaperones and proteases (Fig. 3A  and Supplementary Dataset 1). Using a CoIP assay in T-Rex HEK cells, we confirmed that both WT and mutant GCase interact with HSC70, a cytosolic chaperone protein belonging to the HSP70 family involved in mitochondrial import  18  (Figs. 1F, 3B ). Furthermore, CoIP experiments confirmed the interaction of WT and mutant GCase with TOM70 and TIM23 (Figs.
1F, 3C, D). Interestingly, L444P-GCase showed a significantly higher interaction with TOM70 (Fig. 3C ). The GCase interactome revealed an increased interaction of L444P-GCase with the mitochondrial protease LONP1 compared to WT-and E326K-GCase (Fig. 1G  and Supplementary Dataset 1). CoIP experiments performed in T-Rex HEK cells confirmed the increased interaction of L444P-GCase with LONP1 compared to WT-and E326K-GCase (Fig. 3E ). We also detected a significantly increased interaction between L444P-GCase and the mitochondrial chaperone HSP60 (Fig. 3F  and Supplementary Fig. 3C ). Furthermore, immunostaining for the mitochondrial protease LONP1 followed by ExM confirmed the interaction between GCase and LONP1 and revealed increased colocalization between GCase and LONP1 in L444P-GCase cells compared to WT-GCase and E326K-GCase cells (Supplementary Fig.
10A-C ). In subsequent experiments, we pooled five organoids/line per experiment. Upon quality control (Supplementary Fig. 10D ), normalization and integration were performed to reduce the batch effect and compare the genotypes (Supplementary Fig. 10E ). We identified 21 cell clusters present in all lines in both mutant and gene-corrected iPSC-derived organoids (Supplementary Dataset 3). After cluster annotation, subclusters were merged into five main clusters (Fig. 6A ), namely, progenitor cells such as radial glia-like cells (MSX1, PAX3, SOX2, and RFX4) as well as neural progenitor cells (NEUROG1 and NEUROD4), neuronal (DCX and NEFM), astrocyte (TNC and AQP1), and oligodendrocyte (SOX10 and APOD) clusters (Fig. 6A-C ). Residual floor-plate progenitor markers were found within the radial glia cluster (Supplementary Fig.
Confirming neuronal maturation, cells belonging to State 2 expressed high levels of CALB1, SYP, SNCA, GAD1, and TH (Supplementary Fig. 11C, D ). To assess the similarity between midbrain organoid generation protocols, we employed a recently published scRNA-seq dataset  23  , and we found a high similarity between floor-plate markers along the radial glia clusters as well as a high similarity of neuronal markers and DA markers (Supplementary Fig. 11E, F ). Interestingly, while the reference dataset showed an abundance of radial glial cells, our dataset displayed enrichment in neuronal clusters at 42 DIV (Supplementary Fig. 11G ). To dissect the impact of GBA1 mutations on specific neuronal populations in our dataset, we subclustered neuronal populations in eight neuronal clusters, including two distinct DA neuronal clusters, two GABAergic (GAD1, GAD2, and SLC32A1) and two glutamatergic (SLC17A6 and GRIN2B) neuronal clusters and an undefined neuronal cluster (DCX, Fig.
12C ). Differential gene expression and pathway analysis of neuronal clusters in scRNA-seq data revealed enrichment of metabolic pathways across isogenic pairs, with a stronger impact on mitochondrial ATP-related pathways in the severe mutant recombinant L444P GBA1 organoids (Fig. 6E  and Supplementary Fig. 12D ). In this respect, pathway analysis revealed a selective dysregulation of mitochondrial energy metabolism in DA neuron clusters from GBA1 mutant organoids compared to isogenic controls (Fig. 6E  and Supplementary Fig. 12D ), suggesting that severe GBA1 mutations drive selective mitochondrial disturbances in DA neurons. Such selective impairment of metabolism in DA neurons was not evident in E326K and L444P mutant organoids. However, more than 50% of the neuronal mitochondrial DEGs in all isogenic couples belonged to respiratory chain complexes (Supplementary Dataset 4).
Quantification of the LONP1/GCase interaction in L444P/L444P iPSC-derived NPCs and isogenic controls is shown on the right. Mean ± SEM; unpaired two-tailed t-test, *P = 0,048, n = 3 independent experiments. H ExM images showing LONP1 (green) and GCase (red) staining in L444P/L444P neurons and corresponding isogenic gene-corrected controls. Scale bar, 100 μm. Colocalization analysis of LONP1/GCase was performed in the neuronal soma and axons (H). Mean ± SEM; unpaired two-tailed t-test; ****P < 0. 0001, *P = 0. 0275. Five images were taken from n = 3 independent experiments. Source data are provided as a Source Data file. work has shown that intracellular seeding events occur preferentially on membrane surfaces, especially at mitochondrial membranes  34. As mitochondrial proteostasis can influence the homeostasis of cytosolic aggregation-prone proteins  27  , one hypothesis could be that LONP1 regulates A-SYN pathology at mitochondrial membranes.
Flp-In™ 293 T-Rex cells (Thermo Fisher Scientific, # R78007; RRID:CVCL_U427) were grown in media composed of Dulbecco's modified Eagle's medium (DMEM, Sigma-Aldrich), 10% fetal bovine serum (Gibco), 1% GlutaMax (Gibco) supplemented with 100 μg/ ml Zeocin (InvivoGen) and 15 μg/ml blasticidin (InvivoGen). Inducible Flp-In T-Rex 293 cells were generated according to the manufacturer's protocol (Thermo Fisher Scientific). The selection was performed with DMEM supplemented with 15 μg/ml blasticidin and 100 μg/ml hygromycin B Gold (InvivoGen) 48 h after transfection and continued until the expression of the gene of interest was induced by treating the cells with 50 ng/ml doxycycline hyclate (Sigma-Aldrich) for 48 h. A list of primer sequences is provided in Supplementary Table  2.
MS data analysis IsobarQuant (https://github. com/protcode/isob/archive/1. 0. 0. zip) and Mascot v2. 2. 07 (http://www. matrixscience. com/server. html; RRID: SCR_014322) were used for data analysis. The UniProt Homo sapiens database (UP000005640, https://www. uniprot. org/proteomes/UP00 0005640) was used as the reference proteome. The search criteria were determined as follows: carbamidomethyl (C) and TMT11 (K) (fixed modification), acetyl (N-term), oxidation (M), and TMT11 (Nterm) (variable modifications). For the full scan (MS1), a mass error tolerance of 10 ppm was set, and a spectrum of 0. 02 Da was used for MS/MS (MS2). Two skipped cleavages by trypsin were the maximum permitted. For protein identification, the following criteria had to be fulfilled: (a) at least two distinctive peptides were recognized per protein and (b) the peptide length was required to be at least seven amino acids long.
05). Human Mitochondrial proteins were identified according to the Human Mitocarta 3. 0 database (https://www. broadinstitute. org/files/shared/metabolism/mitocarta/ human. mitocarta3. 0. html; RRID:SCR_004869). The subcellular localization of the top 100 candidate proteins was assigned according to the GeneCards database version 5. 6 (https://www. genecards. org; A Schematic representation of the experimental protocol for midbrain organoid generation. B Representative brightfield images of midbrain organoids generated from L444P/L444P-1 GBA1 iPSCs and corresponding isogenic controls. Scale bar, 200 µm. C Immunostaining for β-TUBIII (green), SOX2 (red), and OTX2 (red) in 18day-old midbrain organoids. Cell nuclei were counterstained with DAPI (blue). Scale bar, 100 µm. D Immunostaining for MAP2 (green or teal), TH (red), and FOXA2 (green) in midbrain organoids at the indicated timepoints.
A CellBrite™ Steady 488 Membrane Staining Kit (Biotium) was used to visualize cell membranes according to the manufacturer's instructions. Images were acquired using a Leica TCS SP8 confocal microscope (Leica, Germany) with a 63 × /1. 4 numerical aperture oil-immersion objective. Z-stacks were acquired for calculation of the PFF particle area and fluorescence intensity. For each condition, 5-8 images were acquired from at least four independent experiments. Data were obtained from n > 20 cells per experiment per condition. For the quantification of colocalization and image processing, images were analyzed using the "Analyze particles" and "EzColocalization" plugins in Fiji-ImageJ.
Data were obtained from more than 20 cells per experiment per condition. Scale bar, 10 µm. B iPSC-derived neurons of the indicated genotypes were incubated with or without CDDO-Me treatment, and CI activity was measured in mitochondrial extracts. Mean ± SEM; two-way ANOVA with Bonferroni post hoc test; left panel, ***p = 0. 0004, *p = 0. 019, 0. 021, in sequence; right panel, *p = 0. 0175, 0. 0148, in sequence; n = 5 independent experiments. C Graphical representation of GCase import and function in mitochondria and the proposed role of LONP1 in WT and mutant GCase models. Source data are provided as a Source Data file. Images were created with BioRender. com. respectively, 6691 cells and 6223 cells for the E326K/WT-1 mutant and gene-corrected lines, respectively, and 6750 cells and 9886 cells for the L444P/L444P-1 mutant and gene-corrected lines, respectively.
For clustering analysis, the dataset was SC transformed and normalized, and variation due to the cell cycle and mitochondrial expression was regressed out. For analysis at DIV 42, data from different 10x runs were integrated into Seurat using individual 10X runs as grouping variables (IntegrateData function). Principle components (PCs) were determined, and using the first 40 PCs, cells were clustered using a K-nearest neighbor (KNN) graph with a clustering resolution of 0. 6, resulting in 21 clusters. Cell clusters were visualized using UMAP. In the UMAP projection, no clusters were found that could indicate cell doublets (doubletFinder_v3 function). Conserved markers were determined across all samples for each cluster (FindConservedMarkers function) using default settings (min. pct = 0. 25, logfc.
threshold = 0. 25) and used for assigning cell-type to cell clusters. Clusters were manually assigned using La Manno et al as a reference  22. For differentially expressed (DE) analysis, nonintegrated samples were analyzed using FindMarkers and MAST statistical tests (p adj >0,05, logFC >2). DE genes were analyzed using Panther pathway analysis software 14. 0, and Biological Processes as a GO aspect (http://www. pantherdb. org; RRID:SCR_004869). Pseudotime analysis was performed in the L444P/L444P-2 gene-corrected line using Monocle 3 (version 1. 0. 0; https://cole-trapnell-lab. github. io/ monocle3/; RRID:SCR_018685). Single cells isolated from five pooled organoids per time point were used for the analysis (DIV 42: 9692 cells, DIV 65: 8998 cells, DIV 100: 7422 cells). After data normalization and integration to reduce the batch effect, the Monocle 3 object was generated using the as.
cell_data_set function, and cells were clustered using the cluster_cells function with a resolution of 1e-3. Clusters were manually assigned based on ref. 22. When ordering the cells along the trajectory using the "order_cells" function, SOX3-expressing cells were specified as the starting state. Nebulosa's kernel function was used to show the density estimation for cell-type specific markers in the UMAP plot. Scripts and R session info are available at https://doi. org/10. 5281/ zenodo. 6475864 58. The corresponding annotation file was downloaded from https://raw. githubusercontent. com/hbctraining/scRNAseq/master/data/annotation. csv.
A-SYN PFFs The production and purification of recombinant human A-SYN was conducted according to ref. 61. A-SYN cDNAs were cloned into pET-21d(+) DNA (Novagen, Merck Millipore, #69743), and the plasmids were expressed in BL21DE3 E. coli (Novagen, Merck Millipore, #69450; RRID:CVCL_M639). Cultures of 750 ml were grown to mid-log phase, and isopropyl-1-thio-3-D-galactopyranoside was added to 0. 4 mM. After 2 h, the cells were pelleted, washed in PBS, and resuspended in 50 ml of 20 mM HEPES, 100 mM KCl, pH 7. 2. The resuspended bacteria were heated to 90 °C for 5 min. Aggregated protein was removed by centrifugation (40 min at 40,000 × g at 4 °C). Contaminating nucleic acids and proteins were removed by ion exchange chromatography on Q-Sepharose Hi-Trap columns equilibrated with Solution A (50 mM Tris pH 7.
Data availability A reporting summary for this article is available as a Supplementary Information file. The main data supporting the findings of this study are available within the article and its Supplementary files. The mass spectrometry proteomics data have been deposited in Proteo-meXchange via the PRIDE partner repository under the accession code PXD032155. For scRNA-seq data, GEO accession number GSE198033. Protocols have been deposited at https://doi. org/10. 17504/protocols. io. x54v9d44mg3e/v1. Scripts, packages, and R session info used in this study are available at https://doi. org/10. 5281/ zenodo. 6475864. Publicly available databases were used in this manuscript, including the UniProt Homo sapiens database (UP000005640), Human Mitocarta 3. 0 database (https://www. broadinstitute.
Besides playing a central role in neuroinflammation, microglia regulate synaptic development and is involved in plasticity. Converging lines of evidence suggest that these different processes play a critical role in schizophrenia. Furthermore, previous studies reported altered transcription of microglia genes in schizophrenia, while microglia itself seems to be involved in the etiopathology of the disease. However, the regional specificity of these brain transcriptional abnormalities remains unclear. Moreover, it is unknown whether brain and peripheral expression of microglia genes are related. Thus, we investigated the expression of a pre-registered list of 10 genes from a core signature of human microglia both at brain and peripheral levels. We included 9 independent Gene Expression Omnibus datasets (764 samples obtained from 266 individuals with schizophrenia and 237 healthy controls) from 8 different brain regions and 3 peripheral tissues.
Noteworthily, in SZ, blood-based microarray meta-  [19]  and mega-  [20]  analyses revealed differentially expressed genes involved in immunologic functions, including microgliarelated genes. Using Gene Expression Omnibus (GEO) datasets, in this study we aimed to explore the expression of microglia genes at the central and peripheral levels in individuals with SZ compared with HCs. Notably, the present study takes into account the various limitations of current literature regarding microglia gene expression studies in SZ. Especially, we systematically explored in 8 different brain regions and 3 peripheral tissues a set of 10 genes selected through a comprehensive approach and belonging to a core transcriptional signature of human microglia  [21, 22]. There was no overlap between the data used in the present study and that from Snijders et al.
Search and inclusion criteria of dataset Datasets were searched through GEO database, a public repository which archives and freely distributes genomic data from postmortem brain or peripheral tissues samples obtained from HCs and patients suffering from various disorders  [23]. With the aim to identify altered expression of microglia genes both at the central and peripheral level in individuals with SZ, we explored the GEO database for datasets providing gene expression results from individuals with SZ and HCs. We included datasets compliant with data extraction through shinyGEO  [24] , an application implemented using R [https://www. r-project. org/], shiny [http://shiny. rstudio. com/] and GEOquery package  [25]. Using GEO database, we therefore searched for studies on SZ, using homo sapiens as the primary organism, and filtering by gene series and microarray technology.
Statistical analyses JASP software (Version 0. 11. 1) was used to perform statistical analyses on gene expression data downloaded with shinyGEO. Gene expression values are already Log2-transformed to stabilize the variance and descriptive statistics of Log2 values in brain and peripheral tissues are provided in Supplementary Table  1. The fold change expression (FC) was computed as the difference between the average expression of HCs and the average expression of individuals with SZ. Normal distribution and homogeneity of variances were tested using Shapiro-Wilk and Levene's tests, respectively. For differential expression, exact p-values were calculated using unpaired two-tailed t tests. In case of violation of the equal variance assumption, exact p-values were calculated using Welch tests.