dataesr/scientific-paragraphs-categorization huggingface dataset:
Included datasets The systematic search in the GEO database, which identified records published until July 6th, 2022, yielded a total of 88 datasets (see Supplementary Fig. 1  for the flow chart). After applying inclusion criteria, the number of datasets was filtered to a total of 12 eligible datasets. Eventually, 9 GEO datasets were included in the present study (see Supplementary Fig. 1  and Supplementary Methods for details on the selection procedure and exclusions). The included GEO datasets consist of 764 samples obtained from 266 individuals with SZ and 237 HCs. They provided data from 8 different brain regions relevant to SZ (cerebellum, associative striatum, hippocampus, anterior prefrontal cortex (BA10), dorsolateral prefrontal cortex (DLPFC, BA46), superior temporal cortex (BA22), and broad sections of the frontal cortex and parietal cortex) and 3 peripheral tissues (whole blood, peripheral blood mononuclear cells (PBMCs), and skin fibroblasts) (see Tables  1 2 for sample sizes, demographic and quality characteristics of included datasets).
Transcriptional alterations of microglia genes in brain tissues First, we report altered transcriptional patterns of microglia genes in the cerebellum, associative striatum, hippocampus, and parietal cortex of individuals with SZ compared with HCs. Notably, in these 4 latter brain regions, all the identified transcriptional alterations were underexpressions. This is consistent with a recent metaanalysis reporting underexpression of microglia genes in the brain of individuals with SZ  [15]. While meta-analytic results from Snijders et al. [15]  were obtained by pooling expression data collected from 4 different brain regions (frontal, temporal, cingulate cortices and hippocampus), we analyzed separately postmortem datasets from different brain regions, allowing for more precise localization of effects.
Thus, variations in gene expression are expected between those two compartments since they exhibit distinct specific expression profiles  [75]. Furthermore, PBMCs and whole blood samples used in the present study were not derived from the same subjects. Therefore, the discrepant results may be attributed to differences in their collection, storage, and extraction methods, as well as in heterogeneity of demographic characteristics of the included datasets  [76]. In individuals with SZ, while all the candidate genes differentially expressed at the brain level were also differentially expressed in the whole blood, the peripheral transcriptional alterations only partially reflected the cerebral ones. Indeed, AIF1, IRF8 and OLR1 showed opposite directions of change between the two levels. Furthermore, TLR2 was overexpressed in the whole blood contrasting with the absence of change at the brain level.
Limitations Interpretation of the present results might be affected by limiting factors. First, and peripheral tissues were obtained from a total of 9 different and modest-sized samples of patients with SZ and healthy controls. These modest sample sizes inherently limit statistical power, leading to an increased risk of generating spurious findings. Although measures were taken to mitigate this limitation, such as focusing on a specific list of candidate genes and pre-registering the analysis plan, the constraint imposed by the small sample sizes remains a crucial consideration. Additionally, the unavailability of independent validation datasets within the GEO database prevented us from replicating our results. Future studies with larger sample sizes are thus warranted to corroborate and expand the current findings, thereby providing more robust insights into the gene expression patterns of microglia associated with SZ.
Second, it should be highlighted that SZ is a highly heterogeneous disorder. Notably, recent research suggests that heightened inflammation may be characteristic of a specific subgroup within the SZ population  [88, [91] [92] [93]. Interestingly, a recent study investigating the gene expression of four microglia markers found that patients with SZ who demonstrated a high inflammatory profile showed a downregulation of CD68, in comparison to both patients with low inflammation and healthy controls  [91]. However, due to the unavailability of clinical information and biomarker data within the GEO database, we were unable to perform an inflammatory profiling analysis to characterize these subgroups in our study. Therefore, future investigations should address the heterogeneity of SZ by exploring potential differential gene expression in these distinct subgroups.
Third, due the limited availability of demographic information provided with GEO datasets, we could not investigate the influence of some potential confounding variables. For instance, we could not explore the effect of smoking or of suicide as a cause of death although such factors have been shown to influence gene expression  [94, 95]  and enhance proinflammatory processes  [96, 97]. Similarly, the effect of antipsychotic treatment could not be evaluated, while it has been suggested that it may inhibit microglia activation  [98]. Fourth, all the included datasets were acquired through microarray technology. Our results are thus subject to the methodological limitations that lie within the use of microarrays: (i) background hybridization limit gene expression measurement, especially for low abundance transcripts, (ii) probe performance may have led to specificity issues (through cross-hybridization and non-specific hybridization), (iii) only genes for which probes are designed can be explored.
Finally, brain expression data were obtained from postmortem cerebral samples composed of heterogeneous cell populations instead of isolating pure microglia. Despite this limitation, it is noteworthy that all the candidate genes were part of a core transcriptional signature of human microglia  [21] , as well as part of a microglia signature highly expressed in bulk brain tissues  [22]. Nonetheless it is essential to conduct further investigations using high-throughput sequencing techniques such as single-cell RNAseq technology  [99] , which can provide insights into the heterogeneity and diversity of microglia populations.
Conclusion Using 9 gene expression datasets, we report evidence of a widespread transcriptional alteration of genes from a core human microglia signature both in brain tissues and whole blood of individuals with SZ compared with HCs. In brain tissues, we expand and refine previous knowledge regarding underexpression of microglia genes in the cerebellum, associative striatum, hippocampus, and parietal cortex. Such alterations may be considered as a candidate transcriptional signature for SZ disorders. At the peripheral level, we identified differential expression of the candidate genes in the whole blood of individuals with SZ. The dual brain-whole blood transcriptional alterations of microglia/macrophage genes identified in individuals with SZ support the model of SZ as a whole-body disorder and lend weight to the use of blood samples as a potential source of biological peripheral biomarkers.
37 ± 0. 29 0. 018* -- 0. 742 10/9 13/10 1 9. 12 ± 4. 33 7. 13 ± 5. 75 0. 023* 6. 49 ± 0. 32 6. 16 ± 0. 17 0. 001* -- 0. 549 10/9 7/8 1 19. 53 ± 5. 09 18. 91 ± 6. 69 0. 763 6. 59 ± 0. 22 6. 53 ± 0. 39 0. 542 7. 85 ± 0. 62 7. 65 ± 0. 68 NS b 23/7 23/6 1 29. 97 ± 12. 71 31. 40 ± 16. 93 NS b 6. 61 ± 0. 28 6. 45 ± 0. 25 0. 01* b -- SD standard deviation, M/F male/female, RIN RNA Integrity Number, PFC prefrontal cortex, DLPFC dorsolateral prefrontal cortex, NA not available, Brain region Subjects (n) Age (mean ± SD) HC SZ Hippocampus 18 HC, 15 SZ 48. 16 ± 10. 95 45. 73 ± 8. 79 GSE53987 Associative 18 HC, 18 SZ 48. 44 ± 10. 82 45 ± 8. 75 striatum GSE53987 Anterior PFC 23 HC, 28 SZ 69. 04 ± 21. 55 73. 32 ± 15. 2 GSE17612 Cerebellum 50 HC, 44 SZ 45. 8 ± 9. 33 43. 18 ± 9. 53 GSE35974 Parietal cortex 50 HC, 51 SZ 45.
5 ± 8. 99 42. 65 ± 9. 87 GSE35977 Superior 19 HC, 23 SZ 67. 68 ± 22. 24 72. 17 ± 16. 94 temporal cortex GSE21935 DLPFC -BA46 19 HC, 15 SZ 48. 05 ± 10. 65 46 ± 8. 64 GSE53987 Frontal cortex 43. 5 ± 8. 57 42. 34 ± 8. 95 30 HC, 29 SZ GSE62191 SZ individuals with schizophrenia, HC healthy controls, significant. *p < 0. 05. a Unpaired t-test and Fisher exact tests were conducted to assess group differences for continuous and discrete variables, respectively. b As reported in ref. [101].
More recently, a number of additional putative SAATs have been uncovered, namely YapA, YcgV, YapC and Ypj, as well as the more distantly related RadD  [6]. These new and putative SAATs await further functional characterisation. Ag43 is considered to be the major phase-variable outer membrane protein of E. coli and the most well investigated SAAT  [17, 18]. Besides the shared and common structural features of ATs secreted by the T5aSS, Ag43 exhibits some additional conserved domains (Figure  1 ). The SP is processed between amino acid position 51 and 52 and the passenger is cleaved off from the AC1 and translocator regions between amino acid 551 and 552. The 3D structures are given as surface (upper part) and ribbon diagrams (lower part), which were generated from UCSF Chimera  [19]. The passenger and AC1 correspond to the resolved structure of Ag43 C3 from UPEC CFT073 (PDB: 4KH3, 7KOB)  [20, 21] , whereas the translocator was modelled with Phyre2  [22]  with the translocator from AIDA-I (PDB: 4MEE)  [23]  used as a template.
The passenger and AC1 correspond to the resolved structure of Ag43 C3 from UPEC CFT073 (PDB: 4KH3, 7KOB)  [20, 21] , whereas the translocator was modelled with Phyre2  [22]  with the translocator from AIDA-I (PDB: 4MEE)  [23]  used as a template. The SP harbours a highly conserved extended region (ESPR) that regulates the rate of export through the Sec translocase at the IM. This allows the formation of a competent passenger-translocator complex in the OM, which is required for efficient passenger transport to the bacterial cell surface  [24, 25]. A structurally conserved autochaperone domain of type 1 (AC1) is present between the passenger and the translocator. It is considered to be essential for the folding of β-helical passengers  [26, 27]. Following Ag43 production as a preproprotein, the SP is cleaved off after export across the IM by a type 1 signal peptidase and the passenger is cleaved between the AC1 and translocator after secretion across the OM by an unidentified protease (Figure  1 ).
Phylogenetic Analyses of Ag43 Passenger Domains Following our data mining approach to identify Ag43 homologues in Bacteria, 9988 Ag43 sequences were recovered, including 7336 full-length sequences corresponding to 1549 unique sequences for the passenger domain; this substantially expanded the previous dataset of 106 Ag43 sequences  [13, 18, 29]. After clustering with a threshold set at 98% identity, a dataset of 247 sequences representative of the diversity of Ag43 was generated for further phylogenetic studies (supplementary Table  S1 ). To confirm they belong to the Ag43 family, and not to other SAATs, we performed a phylogenetic network analysis, which demonstrated that all of the newly discovered Ag43 clustered separately from TibA and AIDA-I (Figure  2A ). Focusing on Ag43, the phylogenetic network analysis first suggested an ambiguity in the relatedness patterns among the Ag43 homologues as illustrated by the diamond shape of the network, most certainly related to domain shuffling (Figure  2B ).
However, bootstrapping analysis with a stringent confidence threshold (90%) revealed two major clusters corresponding to a dichotomous grouping of Ag43 as previously reported  [18] , dividing the predicted proteins into the two subfamilies SF-I and SF-II. Subsequently, split decomposition analyses were applied to the functional passenger domain of Ag43 only (Figure  3 ). A diamond shape network was obtained in the first instance, but bootstrapping analysis resolved the splits and revealed the existence of six classes of Ag43 passengers. Previous network analysis on Ag43 (using a much smaller dataset) evidenced the existence of four main passenger classes supported by robust splits but with a strong ambiguity in their relatedness patterns and no specific divergence between C1 and C4  [29]. However, our present analysis (increasing the dataset from 106 to 247 non-redundant sequences) alleviated the uncertainties between C3 and C2, C1 and C4, as well as C2 and C4, since it clearly indicated direct links only between: (i) C4 and the newly identified C5, (ii) C2 and the newly identified C6, (iii) C1 and C3, as well as (iv) C6 and C1.
coli than in commensal strains, especially members of the SF-II  [31]. However, observations of agn43 in non-pathogenic strains do not support this hypothesis and may simply reflect the bias toward sequencing more pathogenic (compared to non-pathogenic) E. coli strains  [18]. While a correlational analysis examining the potential relationship between pathogenic E. coli and the presence or absence of particular variants of agn43 would have been of relevance, it is hindered by the lack of metadata regarding the pathotype, serotype/serogroup, and/or origin of the isolates for a large number of sequenced E. coli genomes we recovered. Additional work for comparison is hampered by the lack of robust and systematic data and information in the literature or databases despite relevant initiatives such as PATRIC (Pathosystems Resource Integration Center)  [41].
Data Mining for Identification of Ag43 Protein Sequences Genomic data were retrieved from the NCBI assembly database  [50]. Ag43 homologues were recovered using a personal database of 106 protein sequences corresponding to the passenger domains and their assignment to the four classes of Ag43 recently uncovered  [29]. BLASTX analysis (E-value threshold 1 × 10 -100 and low-complexity filter disabled) was carried out with BLAST+ command-line tools version 2. 7. 1  [51]. All HSP (high scoring pairs) alignments with more than 90% similarity and encompassing more than 400 amino acids were considered for the following studies. In the first instance, genome sequences have been downloaded using Escherichia coli as keywords. According to the degree to which they have been assembled, such sequences corresponded to genomes, chromosomes, contigs, or scaffolds.
These DNA sequences were used to extract the complete Ag43 protein sequences without considering annotation information from genomic data. Such an approach allowed us to highlight mispredicted translation initiation sites (TISs), errors introducing frameshift or premature stop codon, and insertion of DNA sequence, including some mobile elements disrupting the CDS (coding DNA sequence). All incomplete sequences due to the events mentioned above were collectively considered as pseudogenes. In order to recover Ag43 homologues from Enterobacteriaceae other than E. coli, sequences from the genus Escherichia but not referenced as E. coli species and Enterobacteriaceae sequences, excluding genus Escherichia, have been also extracted from the NCBI assembly database, respectively. Finally, BLASTX analysis was performed against the NCBI assembly database excluding Enterobacteriaceae, in order to recover Ag43 homologues in the Kingdom Bacteria other than the ones identified here above.
The detailed bioinformatic analysis is provided as supplementary material (File S1) and Perl scripts developed for this study are available on GitHub (https://github. com/ivwawrzy/Ag_43, accessed on 5 March 2023). Excluding pseudogenes, Ag43 CDS recovered by this approach were clustered with a threshold of 98% using Cd-hit software  [52, 53] , and a reference sequence from each cluster was kept to generate a representative Ag43 dataset used for the phylogenetic studies as described below. Considering the modular architecture of Ag43, the Ag43 protein sequences were split as follows. For the identification of the SP, the sequences were submitted to SignalP v5. 0  [54]. Using the InterPro (IPR) database  [55] , the ESPR (IPR024973) was identified using InterProScan v5. 22  [56]. The passenger was identified following structural alignment as explained below and the SL, EJ, and BL subdomains were identified from three-dimensional (3D) structures using UCSF Chimera visualisation  [19]  as previously described  [21, 29].
The AC1 domains were identified based on a structural alignment using Phyre v2. 0 for automated modelling  [22]  and UCSF Chimera for 3D structure visualisation as previously described  [26]. The translocators were identified using the twin HMM (Hidden Markov Model) strategy for AT detection  [34]. From full length Ag43, the sequences were then split into modules corresponding to the ESPR, passenger, SL, EJ, BL, AC1, and translocator, also called translocation unit (TU).
Phylogenetic Network Analysis of Protein Sequences For multiple sequence alignment of the full length A43 including the (i) passenger and its subdomains, (ii) AC1, and (iii) translocator, a special mode of T-Coffee  [57] [58] [59]  was used to incorporate structural information, i. e. , Expresso  [60] , which is an extension of 3D-Coffee where structure based alignment is used as a template  [61]. Thus, the amino acid sequences were aligned using solved structures from the protein data bank (PDB) as templates  [62, 63] , namely (i) the passenger from Ag43 (PDB: 4KH3), (ii) AC1 from IcsA (PDB: 3ML3), and (iii) the translocation units comprising both the α-linker and βbarrel from AIDA-I (PDB: 4MEE)  [23] , EspP (PDB: 3SLJ)  [64] , EstA (PDB: 3KVN)  [65] , Hbp (PDB: 3AEH)  [66] , and NalP (PDB: 1UYN)  [67] , respectively.
Statistical Analyses Escherichia phylogroups were identified using in the ClermontTyping software  [42]. To test independence between phylogroups and the presence or absence of agn43, pairwise independence Fisher tests were performed using contingency tables with rcompanion R (v 4. 1. 2, R Core Team) package  [69, 70]. The same tests were performed to evaluate the independence between phylogroups and the different Ag43 classes, whenever agn43 was present in single or multicopy in genomes. PCA (principal component analyses) were performed with the ade4 R package, where the values were centred by mean, not scaled  [71]. To analyse the variance between genomic sequences, a PERMANOVA (permutational multivariate analysis of variance) was performed on the maximum likelihood distance matrix obtained from the phylogenetic tree of Ag43 passenger sequences.
The distance matrix was computed with a maximum likelihood approach and LG model with the phangorn v2. 9. 0 R package  [37]. Two sources of variations were used to fit the linear model: phylogroups and classes with interaction. The distance matrix and the PERMANOVA was performed, respectively, with the phangorn  [37]  and the adonis2 command of vegan  [36]  R packages. A Nonmetric Multidimensional Scaling (NMDS) ordination method fitted on Phylogroup and Class variables was used to visualise variability between them.
Acknowledgments: The authors are grateful to the bioinformatics platforms INRAE MIGALE (https: //migale. inrae. fr,accessed on 5 March 2023, and  UCA AuBi (Auvergne Bionformatique , https:// ressources. france-bioinformatique. fr/fr/plateformes/aubi, accessed on 5 March 2023) for providing computational resources. Molecular graphics and analyses were performed with  UCSF Chimera , developed by the  Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco , with support from  NIH   P41-GM103311  (https://www. rbvi. ucsf. edu/chimera, accessed on 5 March 2023).
Funding: This work was funded in part by (i)  INRAE (Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement , formerly called  INRA ,  Institut National de la Recherche Agronomique), (ii) Région Auvergne FRI-IRP  ( Fond Régional Innovation-Institut de Recherche Pharmabiotique )  CoMBa  project , (iii) ANR  ( Agence National de la Recherche )  PathoFood  project (n •  ANR-17-CE21-0002 ), and (iv)  UCA (Université Clermont Auvergne )  Emergence AGREG43  project. Valentin Ageorges  was a PhD Research Fellow granted by the  Région Auvergne-FEDER  ( Fonds Européen de Développement Régional). Cedric Bicep  was a post-doctoral Fellow granted by the  CPER-ARA (Contrats de Plan État Région, Auvergne-Rhône-Alpes ) défi AUDACE, action ICARES.
Protein electrophoresis was performed on NuPAGE gels (3-8% of acrylamide) (Invitrogen). After protein electrotransfer on nitrocellulose membrane (LI-COR Biosciences), immunoblots were saturated with blocking buffer for near-infrared fluorescent western blotting (Rockland Immunochemicals) and incubated with an anti-APC primary antibody (clone Ali 12-28; 2 mg/ml; Abcam). After membrane washing and incubation with secondary antibodies Alexa Fluor 680 (Invitrogen), near-infrared fluorescence was detected by using an Odyssey CLx scanner and the Image Studio v5. 2 software (LI-COR Biosciences).
5% BSA and centrifuged at 500 xg for 5 min at room temperature. Cells were resuspended and fixed with PBS + 0. 5% BSA + 1% PFA. Thirty-seven different cell surface molecules defining different immune cell lineages and populations were identified using monoclonal antibodies directly labelled with an array of fluorescent dyes. Antibodies and fluorochromes are listed in Supplementary Table  1. Labelled cells were analyzed the following day by multiparameter flow cytometry using a Spectral Flow Cytometer SP6800 or ID7000 (Sony). Data analyses were performed with FlowJo software. Gating strategies for the different panels are shown in Supplementary Figures  1 2 3.
RNA concentration was estimated using Qubit RNA HS Assay Kit (Life Technologies) according to the protocol provided by the manufacturer. An automated RNA integrity assessment was performed using the RNA Pico Chips Kit on the Agilent 2100 Bioanalyzer System (Agilent Technologies). The RNA integrity number (RIN) was calculated using the Agilent 2100 Bioanalyzer System software, and all samples with a RIN greater than four were processed for gene expression analysis.
Gene expression by NanoString technology Total mRNA was diluted with RNase-free water at 20 ng/ml in the 12-strip provided by NanoString. We analyzed 100 ng of total RNA from each sample using the Human Immunology kit v2 according to manufacturer's instructions. Each sample was analyzed in a separate multiplexed reaction including in each, eight negative probes and six serial concentrations of positive control probes. Nanostring nCounter Immunology V2 panel (Cat No XT-CSO-HIM2-12) analyzing 579 immune response genes was used (Supplementary Table  2 ). Negative control analysis was performed to determine the background for each sample. Data was imported into nSolver analysis software (version 4. 0) for quality checking and normalization of data. Several subjects did not respond to anti-CD3+anti-CD28 stimuli, as previously described  (21).
Confocal microscopy, image treatment and quantitative analysis Confocal images were acquired with an LSM 700 confocal microscope (Zeiss) using the Plan-Apochromat 63x/1. 40 NA objective. Optical confocal sections were acquired with ZEN software (Zeiss) by intercalating green and red laser excitation to minimize channel cross talk. All analyses were performed using Fiji software  (22). For F-actin analysis, optical sections were acquired at 1 mm intervals. Formation of the actin ring was performed on one confocal section at the cell-coverslip contact. F-actin intensity profiles were assessed along a line drawn across each cell image. Ranking of each cell in a category (presence of F-actin ring and central clearance; low F-actin ring and low clearance; intermediate) was decided after observing actin profiles on 3-4 different angles.