dataesr/scientific-paragraphs-categorization huggingface dataset:
8. 1. Candidate variants were annotated with the same ANNOVAR and BEDtools (version 2. 27. 1) scripts also provided on GitHub (https://github. com/shishenyxx/MCD_mosaic) and exact binomial confidence intervals were calculated for the same variants in the target sample as well as normal controls. Variants detected from AmpliSeq and/or WES are considered to be positively validated in a given tissue by TASeq if 1) the 95% lower binomial confidence interval is higher than 0. 5% ; 2) the 95% higher binomial confidence interval is lower than 40%; 3) the 95% lower binomial confidence interval of the negative control is below 0. 5%.
Data Fig. 1 Workflow of genetic discovery and bioinformatic pipeline to detect sSNVs in the MCD cohort. (a) Workflow chart describing the flow of cases for each phase of genetic discovery. QNS: quantity not sufficient. 2 cases labeled by a star are sequenced in phase 1 but not phase 2. (b) The pipeline for paired samples. Notably, the dashed square indicates that the sharing variants between MuTect2 paired mode and Strelka2 were used for the downstream analysis. BSMN common pipeline and DeepMosaic were used only for WES datasets. The DeepMosaic input variants were generated by MuTect2 single mode. (c) The pipeline for unpaired samples. The pipeline is similar except that MuTect2 single mode without Strelka2 is used. PM: paired mode, SM: single mode. Extended Data Fig. 2 The locations of the selected MCD variants.
01; ns, non-significant. Extended Data Fig. 7 Cell-type identification by DEGs and WGCNA in the MCD snRNAseq dataset. (a) DEG analysis using FindAllMarker function in Seurat v4 package. The top 10 genes for each cluster were presented. Some notable marker genes are presented on the left side. Color: scaled gene expression level. (b) Description of WGCNA. The most variable 3000 genes were used for generating six Extended Data Fig. 10 Functional implication of MCD genes in MCD snRNAseq dataset. (a) The 75 MCD genes overlap with DEGs of MCDs in contrast to controls. p-values derived from permutation tests (10,000 times) show a chance to show this overlap in a random sampling of DEGs from 19909 protein-coding genes used in these DEGs. Red or blue coloring of gene names indicates upregulated or downregulated DEGs in MCDs compared to CTRLs, respectively.
Acknowledgments AmpliSeq, TASeq, and snRNAseq were supported by  NIH   P30CA023100  and  S10OD026929  at the  UCSD IGM Genomics Center. Rady Children's Institute for Genomic Medicine, Broad Institute  ( U54HG003067 ,  UM1HG008900 ), the  Yale Center for Mendelian Disorders  ( U54HG006504 ), and the  New York Genome Center  provided whole-exome sequencing. UCSD Microscopy core ( NINDS P30NS047101 ) provided imaging support. UCSD Tissue Technology Shared Resources Team (National Cancer Institute Cancer Center Support Grant ,  P30CA23100 ) supported paraffin sectioning and H&E staining. This study is supported by the 2021  NARSAD Young Investigator Grant  from  Brain & Behavior Research Foundation  ( 30598  to CC),  NIH  ( NIMH U01MH108898  and  R01MH124890  to JGG and GWM, and  NIA R21AG070462 ,  NINDS R01NS083823  to JGG), the  Regione Toscana  under the Call for Health 2018 ( DECODE-EE  to RG) and  Fondazione Cassa di Risparmio di Firenze  (to RG).
Data availability WES and AmpliSeq data are deployed on NIMH Data Archive under study number 1484 "Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development" and SRA under accession number PRJNA821916: "Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development". The BSMN neurotypical brain data are available at NIMH Data Archive (NDA study 644, 792 and 919, https://nda. nih. gov/study. html?tab=result&id=644, https://nda. nih. gov/study. html?tab=result&id=792 and https://nda. nih. gov/study. html?tab=result&id=919) and SRA: PRJNA736951. The raw and processed snRNAseq dataset was deposited in Gene Expression Omnibus (GEO) under accession number GSE218022. gnomAD: https://gnomad. broadinstitute. org/ COSMIC: https://cancer.
31 Extended 31 Data Fig. 1 Workflow of genetic discovery and bioinformatic pipeline to detect sSNVs in the MCD cohort. (a) Workflow chart describing the flow of cases for each phase of genetic discovery. QNS: quantity not sufficient. 2 cases labeled by a star are sequenced in phase 1 but not phase 2. (b) The pipeline for paired samples. Notably, the dashed square indicates that the sharing variants between MuTect2 paired mode and Strelka2 were used for the downstream analysis. BSMN common pipeline and DeepMosaic were used only for WES datasets. The DeepMosaic input variants were generated by MuTect2 single mode. (c) The pipeline for unpaired samples. The pipeline is similar except that MuTect2 single mode without Strelka2 is used. PM: paired mode, SM: single mode. Extended Data Fig. 2 The locations of the selected MCD variants.
Treatment recommendations from the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis have highlighted interleukin (IL)-23 inhibitors (IL-23i), along with TNFi, IL-17i, and IL-12/IL-23i, as appropriate for use in both biologic-naïve and biologic-experienced patients with peripheral joint symptoms  [4]. Guselkumab is a high-affinity, fully human, monoclonal antibody that targets the IL-23p19 subunit  [13, 14]. In the phase 3 DISCOVER-1 (ClinicalTrials. gov Identifier: NCT03162796) and DISCOVER-2 (ClinicalTrials. gov Identifier: NCT03158285) trials, guselkumab demonstrated significant efficacy in participants with active PsA  [13, 14]. Both studies enrolled patients with PsA who were biologic-naïve, and DISCOVER-1 also included patients (31% of randomized participants) who had previously received one or two TNFi.
Moreover, in the phase 3b COSMOS trial (ClinicalTrials. gov Identifier: NCT03796858), guselkumab was shown to be efficacious in reducing disease signs and symptoms in participants with an inadequate response (IR), characterized as lack of efficacy or intolerance, to TNFi (TNFi-IR)  [15]. Previous analyses have shown that C-reactive protein (CRP) and type 17 effector cytokine serum levels are elevated in patients with PsA and can be reduced by inhibiting the IL-23p19 subunit or the IL-12/23p40 subunit  [16, 17]. The objectives of this analysis were to evaluate baseline serum levels of proinflammatory biomarkers in participants with TNFi-IR PsA in the COSMOS trial in comparison with healthy controls and the relationship of these biomarker levels with baseline disease activity. Analyses also aimed to evaluate changes in biomarker levels with guselkumab versus placebo and to assess the association of clinical response with biomarker levels over time.
Biomarker sample collection In COSMOS, blood samples for biomarker analyses were collected from all participants at Weeks 0, 4, 16, 24, and 48 into standard serum separation tubes. After 30 min, serum was separated via centrifugation at room temperature (15-20 min at 1500 × g) and then subsequently aliquoted and stored at -20 °C. Biomarker data were retrospectively generated for a subgroup of participants (biomarker cohort). The total sample size was chosen to align with prior biomarker analyses from the DIS-COVER-1 and -2 trials. The primary criteria for selection into the biomarker cohort were based on the availability of participant samples, treatment group, and clinical response defined by multiple endpoints. To avoid inadvertently skewing selection of samples for any known demographic or clinical characteristic, a representative subgroup of samples was selected to reflect the overall trial population based on availability of biomarker samples at all three time points of interest, treatment group, clinical response (ACR20), prior TNFi treatment, and demographics.
Analysis of baseline serum biomarker levels and correlation with disease activity Differences in baseline serum cytokine levels between participants with PsA and healthy controls were assessed using log2-transformed data with a general linear model. Serum protein expression levels were log2-transformed to normalize the data distribution. Differences of ≥ 1. 4fold with p < 0. 05 were considered significant. Correlations between baseline serum biomarker levels and baseline disease activity (i. e. , DAPSA scores, PASDAS, and PASI scores) were assessed using Spearman linear regression, with a Spearman correlation (rho) > 0. 25 and p < 0. 05 considered significant.
Analysis of treatment effect on biomarker levels For the evaluation of treatment effects (pharmacodynamic responses), changes in biomarker levels were compared between the active treatment and placebo groups. A contrast dataset for within-participant changes in biomarkers was generated from log2-transformed data, with the difference between the time point and baseline calculated for each participant and time point (Week 4, 16, 24, and 48). Differences were considered significant if they were ≥ 1. 4-fold with a p < 0. 05.
Analysis of association between biomarker levels and clinical response Differences in baseline biomarker levels by clinical response at Week 24 (i. e. , response versus nonresponse for ACR20/50, IGA 0/1, and PASI75) were evaluated using general linear model analyses and log2-transformed biomarker levels. Clinical response categorical variable was set as the primary fixed factor. In determining response, participants with missing efficacy data were considered nonresponders (nonresponder imputation). To evaluate the significance of changes in biomarkers from baseline among clinical responders and nonresponders, levels were compared between the specified time point and baseline separately for each clinical response group using general linear model analyses. Visit was set as the primary fixed variable.
Conclusions Overall, these data suggest that guselkumab reduces the levels of key effector inflammatory cytokines, including those associated with the IL-23/IL-17 pathway, at early time points in participants with TNFi-IR PsA. Reductions were generally sustained through Week 48, with levels of IL-17F, IL-22, CRP, and SAA approximating those observed in healthy controls. Higher baseline levels of certain cytokines and acute phase reactants were observed in ACR20 (IL-22 and IFNγ) and IGA 0/1 (SAA, IFNγ, and IL-17A) responders than in nonresponders at Week 24. The clinical utility and treatment implications of these findings require further investigation. GS has received speaker fees from AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Gilead, Janssen LCC has received consultant fees from AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, MoonLake, Novartis, Pfizer, and UCB; grant/research support from AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB; and speaker fees from AbbVie, Amgen, Biogen, Celgene, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Medac, Novartis, Pfizer, and UCB.
Phylogenomic analysis reveals an ancestral core of the Mla system The possibility of a minimal, transenvelope MlaEFD system in V. parvula prompted us to explore its potential existence in other bacterial lineages. We therefore carried out an exhaustive search for homologues of the six components (MlaABCDEF) in a local databank containing 1083 genomes, representing all current bacterial phyla. To identify MlaA, MlaC, MlaD, and MlaE homologues, we used the Pfam domains PF04333, PF05494, PF02470, and PF02405, respectively. As MlaB and MlaF belong to the generic STAS-domain and ATPase families, respectively, we used MacSyFinder2 32 to identify them when found in synteny with at least one of the other Mla components in the genome. The distribution of all six components was then mapped onto a reference phylogeny of Bacteria  16  (Fig.
5 , Supplementary Fig. 10 , Supplementary Dataset 1). In agreement with the fundamental role of the Mla system in maintaining OM lipid asymmetry, mla homologues are largely found in diderm bacteria (Fig. 5 ). Some diderm phyla in the Terrabacteria lack any identifiable homologues, which we attribute to two main reasons: either because they also lack the ability to synthesise LPS, like Borrelia, Treponema, Thermotogae and Deinococci  17  , which would render the Mla system with its current known function in E. coli useless; or possibly due to their atypical envelope architectures: the Thermotogae and Candidate phylum Bipolaricaulota have an OM that is detached at the cell poles  33  ; members of the phyla Dictyoglomi and Deinococcus-Thermus both form rotund bodies where cell aggregates share a single OM  33, 34  ; and cryo-electron tomography of a member of Atribacteria (Atribacter laminatus) has recently revealed an atypical cell envelope architecture with an intracytoplasmic lipidic bilayer surrounding the nucleoid  35.
Within the three diderm lineages belonging to the Firmicutes  18  , Mla components are found in the Negativicutes and a few Limnochordia, whilst they are completely absent from the Halanaerobiales (Supplementary Fig. 11 , Supplementary Dataset 1). More striking is the overall distribution of MlaDEF vs MlaABC; whilst MlaDEF are widely distributed across almost all diderm phyla, with the exception of those mentioned above, the MlaABC components have a much narrower distribution, present mostly within Proteobacteria, and totally absent in diderm Terrabacteria. Notably, the absence of lipoprotein component MlaA corresponds well to the absence of the Lol system within Terrabacteria (Fig. 5 , Supplementary Fig. 11 , Supplementary Dataset 1). Interestingly, while we found MlaDE proteins in Corynebacteriales (consistent with their function as cholesterol transporters through the mycobacterial outer membrane, as recently shown  31  ), we also identified multiple copies of MlaDE homologues in monoderm members of Actinobacteria -mainly the orders Solirubrobacterales, Thermophilales, Pseudonocardiales -and whose function is unknown.
5 , Supplementary Fig. 12 ). The β-barrel is completely absent in MlaD sequences from members of the PVC (Planctomycetes, Verrucomicrobia, Chlamydia) and FCB (Fibrobacteres, Chlorobi, Bacteroidetes) groups (Fig. 5 , Supplementary Figs. 11, 12 , Supplementary Dataset 1). Interestingly, many of the long MlaD sequences with no identifiable complete βbarrel domain contain a predicted disordered C-terminal region with some β-structure, often short β-hairpins, which may represent remnants or precursors of a β-barrel (Supplementary Fig. 14 ). For two of these barrel-less sequences, homohexameric predictions generated by AlphaFold revealed a tiny 6-stranded C-terminal barrel, formed by each monomer contributing a single β-strand (Supplementary Fig. 15 ). A similar conformation was recently structurally characterised in M.
The schematic below the tree (right hand side) highlights the three major domains which can be found in MlaD sequences: the transmembrane domain (TMD) and MCE domain, the elongated α-helical region, and the C-terminal βbarrel. The distribution of these 3 'types' of MlaD are represented on the tree, corresponding to which clades contain these MlaD structures. Elongated versions of MlaD are widely distributed across diderm phyla. (Yellow = Terrabacteria; Blue = Gracilicutes). Tree generated using custom-made scripts and iTOL  65. For detailed species names, see Supplementary Fig. 11. Regardless of the role of the β-barrel domain, or whether the extended MlaD in V. parvula is capable of transporting GPLs via a winding fairground slide or a direct tunnel, the implications are the same: this mechanism is one of higher throughput than a chaperone system.
Lipid identification Nuclear magnetic resonance (NMR). After labile proton to deuteron exchange with CD 3 OD, phospholipids were dried under vacuum and dissolved in a mixture of CDCl 3 /CD 3 OD (2:1, v/v). The samples were then introduced into a 3 mm NMR tube. Solution NMR recordings were conducted at 10 or 20 °C on Bruker AVANCE NEO 400 and 900 spectrometers equipped with a 5 mm TBI and 5 mm cryo-TCI probe, respectively. Standard experiments were run: 1D-1 H, 2D-1 H-COSY, 2D-1 H-TOCSY, 2D-1 H-ROESY, 2D-1 H-  13  C-HSQC-DEPT, 2D-1 H-13 C-HSQC-TOCSY. After acquisition and phase correction, chemical shifts calibration on CHD 2 OD signals were performed for 1 H (3. 31 ppm) and 13 C (49. 29 ppm). The data were analysed using the TopSpin software (Bruker).
OMV quantification Overnight cultures were adjusted for OD 600 and diluted 1/25 and 1/50 in filtered PBS solution. The size distribution and particle concentration within the samples were analysed by nano-flow cytometry (nFCM) (NanoFCM, Inc. , Xiamen, China). First, the instrument was calibrated for particle concentration using 200 nm PE and AF488 fluorophoreconjugated polystyrene beads, and for size distribution using Silica Nanosphere Cocktail (NanoFCM, Inc. , S16M-Exo and S17M-MV). The flow rate and side scattering intensity were then calculated based on the calibration curve, and used to infer the size and concentration of the large and small events present in each sample using the NanoFCM software (NanoFCM Profession V2. 0). All samples were diluted to ensure the particle count fell within the optimal range of 2000-12,000/ min, and all particles that passed by the detector during time intervals of 60 s were recorded for each test.
Structural modelling For the prediction of protein domains, SMART (Simple Modular Architecture Research Tool)  54  and I-TASSER  55  were used. AlphaFold2 29 was used to generate structural predictions of the Mla proteins in V. parvula. To specifically analyse the presence of β-barrels within our target sequences, the BOCTOPUS2 database was used  36. As another transmembrane site-determining web-based tool, TMHMM 2. 0 (Transmembrane Hidden Markov Model) was also used to analyse the transmembrane sites of proteins  56. All structure predictions with AlphaFold2 were performed through the ColabFold 57 (Colabfold v1. 5. 2) implementation. Output models of monomeric full-length MlaD (5 models) were ranked according to the pLDDT score. MlaEFD 1-130 predictions (5 models) were obtained without structural templates using AlphaFold2-multimer v2.
3. 1 and sorted by multimer score 30. To impose the 2:2:6 stoichiometry of the complex, 2 copies of the MlaE and MlaF sequences were concatenated together with 6 copies of the MlaD 1-130 sequence. The AlphaFold model of MlaEFD 1-130 can be found in the Supplementary Dataset 2. Model of the hexameric full-length MlaD complex was obtained by stitching together predictions for the MlaD 1-130 , MlaD 36-263 and MlaD 239-419 domains , via structural superimposition of the overlapping regions. Predictions for the MlaD 36-263 and MlaD 239-419 domains were first obtained using the AlphaFold2-multimer v2. 3 parameters with templates found in the PDB70 database  58  and ranked by multimer score  30. The AlphaFold model of hexameric MlaD can be found in the Supplementary Dataset 3. Full-length monomeric models of long MlaD sequences were obtained with AlphaFold2 as described above.
Secondary structure class of all positions in the best model (highest plDDT) for each sequence were obtained using DSSP  59  and PROSS  60. To automatically assess the presence of residues in β-strand conformation after the MCE domain, the start and end positions of the MCE domain in each sequence was obtained using HMMSEARCH 61 (HMMSEARCHv3. 3. 1) with the "MlaD protein" pfam profile (PF02470).
Phylogenetic analysis We assembled a databank of 1,083 genomes representing all bacterial phyla present at the National Center for Biotechnology (NCBI April 2020)  16. We selected three species per order for each phylum. The number of genomes per phylum therefore reflects their taxonomical diversity (Supplementary Dataset 1). We chose preferably genomes from reference species and the most complete assemblies. We then queried this databank for the presence of MlaA, MlaB, MlaC, MlaD, MlaE, MlaF, and TamB, using the pfam domains PF04333, PF05494, PF02470, PF02405 and PF04357 respectively. The alignments of protein families NF033618 (MlaB) and PRK11831 (MlaF) were downloaded from NCBI (Conserved Domain Database) and used to build HMM profiles using HMMBUILD from the HMMERv3. 3. 2 package  61. MlaA, MlaC, MlaD, MlaE and TamB homologs were searched using HMMSEARCH from the HMMERv3.
3. 2 package. As MlaB and MlaF belong to large protein families, we used MacSyFinder2 32 to identify their occurrences when they are in genomic synteny with at least one of the other components (mlaACDE) of the Mla system, with no more than five genes separating them. This also allowed the identification of the occurrences of mlaACDE when they were in a genomic cluster. All the retrieved hits were curated using functional annotations, domains organisation, alignments, and phylogeny. We identified 1119 MCEdomain-containing sequences (Supplementary Dataset 1). Among these, homologues of PqiB and LetB (containing more than one MCE domain) were identified only within Proteobacteria and closely related phyla. (Supplementary Fig. 13 ). For each protein, curated sequences were aligned with MAFFT v7.
407  62  with the L-INS-I option and trimmed using BMGE-1. 12  63  with the BLOSUM30 substitution matrix. Maximum likelihood trees were generated for each protein using IQ-TREE. 2. 0. 6  64  with the best evolutionary model assessed with ModelFinder 65 according to the Bayesian Information Criterion, and ultrafast bootstrap supports computed on 1000 replicates of the original dataset. Finally, the presence or absence of each protein was mapped onto a reference tree of Bacteria taken from  16. All tree figures were generated using custom-made scripts and iTOL 66 (iTOLv6. 8. 1).
Statistics and reproducibility Data are presented as the mean ± standard deviation (SD) and with individual data points from at least three independent biological experiments. Statistical analyses were performed using Prism 9. 5. 0 (GraphPad Software Inc. ). The distribution of data was analysed by Shapiro-Wilk test and their variance using Bartlett test. Since some of the data generated did not follow a normal distribution or had two high variance we performed nonparametric two-tailed Mann-Whitney test. P values < 0. 05 were defined as the level of statistical significance. Infectious Diseases" (grant n°ANR-10-LABX-62-IBEID) and by the Fondation pour la Recherche Médicale (grant DEQ20180339185) to JMG, and by the IR INFRANALYTICS FR2054 to YR and YG. KG was supported by a PhD fellowship of the Pasteur Paris University program (PPU).
Acknowledgements We gratefully thank  Noa Guzzi , and members of the  UGB  and EBMC units, for their assistance in generating the transposon mutant libraries used in this study. We also thank  Robert Smith  for his help in scaling up large anaerobic cultures for V. parvula. We thank  Anna Sartori-Rupp ,  Stéphane Tachon  and  Jerzy Witwinowski  for help with cryo-EM image analysis. We thank  Pierre-Henri Commere  for help with the use of the NanoFCM machine. We also thank  Philippe Delepelaire  for generously providing us with anti-SecA and anti-TolC serum, and  Jean-Michel Betton  for help in developing the membrane fractionation protocol. This work was supported by funding from the  French National Research Agency (ANR)  ( Fir-OM ANR-16-CE12-0010 ) and ( OM-LipAsy-CE44-008 ) to CB and SG, by the  French government 's  Investissement d'Avenir Program, Laboratoire d'Excellence "Integrative Biology of Emerging.