dataesr/scientific-paragraphs-categorization huggingface dataset:
Results The Sec22b SNARE and Its Partner Are Overexpressed in PCs. To identify molecular actors that contribute to PC biology, we performed a mass spectrometry analysis of sorted splenic B cells and in vitro generated PCs. All the data generated are available through a user-friendly interface (https://plasmacytomics. shinyapps. io/home/). We first confirmed that the expression of well-known factors of B cell and PC differentiation behaved as one would predict. As expected, the B cell transcription factor Pax5 was significantly down-regulated in PCs compared to B cells, while the transcription factor Xbp1 was significantly up-regulated in PCs (Fig. 1A ). Molecules involved in ER to Golgi trafficking, including SNAREs [syntaxin 5 (Stx5), Sec22b, and Ykt6], the syntaxin regulator Sec1/Munc-18 protein (Scfd1/Sly1), and tethering factors (Uso1/p115), were also up-regulated at the protein level in PCs compared to B cells (Fig.
1A  and SI Appendix, Fig. S1 ). We confirmed these results by western blotting and observed that the SNAREs Stx5, Sec22b, and Ykt6 were twice more expressed in PCs compared to B cells (Fig. 1B ). Published microarray and RNAseq datasets were also included in the PlasmacytOMICs interface, and in support of our proteomic data, we also observed a significant increase in the levels of expression of the genes encoding these three SNAREs in sorted murine PCs compared to B cells (Fig. 1C ). Data mining of human RNAseq datasets also showed significantly increased expression of SEC22B and STX5A in human PCs compared to B cells (Fig. 1D ), suggesting that this is a conserved feature in PCs across species. Previous works have shown that the ER-localized SNARE Sec22b forms a complex with the Golgi-localized SNARE Stx5 to allow fusion of transport vesicles between these two organelles  (9, 14, 15).
Dramatic Loss of PCs and Circulating Antibodies in the Absence of Sec22b. Taking into consideration the effect of Stx5 on PC maintenance and antibody secretion, we next explored the implication of its partner Sec22b. We crossed a Sec22b-floxed mouse model  (10)  with the mb1-cre strain  (18)  to generate a mouse model lacking Sec22b expression specifically in the B cell lineage (Sec22b flox/flox × mb1-cre, hereafter referred to as Sec22b B-KO ) (SI Appendix, Fig. S3  A and B). Data mining of publicly available RNAseq datasets (Immgen) suggests that Sec22b is expressed at a constant low level throughout B cell differentiation (SI Appendix, Fig. S3C ), so we investigated whether Sec22b deficiency may alter this process. In the absence of Sec22b, B cell development was roughly normal with only a mild reduction of the number of BM mature B cells and splenic follicular B cells compared to WT mice, whereas BM B cell precursors, splenic immature, marginal zone, and CD93 -/CD21 -CD23 -B cells were unaffected (SI Appendix, Fig.
S4 A  and B ). Sec22b was recently proposed to contribute to transcription factor nuclear shuttling, and this might account for the important transcriptional differences observed in our datasets  (13). Gene set enrichment analyses (GSEAs) revealed that several pathways were significantly different between WT and Sec22b B-KO PCs. In line with our experimental results (Fig. 3H ), Sec22b seems essential for promoting cell cycle possibly via control of the Myc/E2F signaling axes. Pathways pertaining to "Mitochondria" were also significantly down-regulated in Sec22b B-KO compared to WT PCs, whereas "UPR," "ER-Golgi transport," and "protein secretion" pathways were up-regulated (Fig. 3H  and SI Appendix, Figs. S5  and S6 ). We thus explored more precisely how Sec22b deficiency may affect these processes.
pnas. org Sec22b Is Essential for ER Expansion and Structure in PCs. Our transcriptional data indicate that in the absence of Sec22b, all three branches of the UPR (i. e. , Ern1, Atf4/Ddit3/Perk, and Atf6) (Fig. 4  A and B) were up-regulated, including the Atf4/Chop pathway normally actively repressed in PCs to protect from ER stress-induced cell death (4). Our results suggest that in the absence of Sec22b, this protective mechanism is deficient and could lead to Chopmediated cell death and exit from the cell cycle. Exacerbated UPR may be a sign of enhanced ER stress, and we thus assessed whether ER biogenesis might be altered. Using a permeable cell tracker, we showed that the ER membrane expansion normally observed as PC differentiate was significantly reduced in the absence of Sec22b (Fig.
Mass Spectrometry (MS) data were analyzed using MaxQuant (44) version 1. 5. 8. 3. Data were searched against mouse UniProt sequence databases. Full technical details can be found in SI Appendix. Cell Preparation and Flow Cytometry. Spleen and BM cells were isolated as previously described  (45, 46). Briefly, single-cell suspensions were stained with appropriate antibodies (SI Appendix, Table  S3 ) in Phosphate Buffered Saline (PBS) supplemented with 2% Bovine Serum Albumin (BSA) and 2 mM ethylene-diamine-tetraacetic acid (EDTA) for cell surface staining. Staining with ER tracker (Invitrogen), MitoTracker Green (Invitrogen), and MitoTracker Orange CMTMRos (Invitrogen) were performed as recommended by the supplier. Intracellular staining was performed using the FoxP3/transcription factor staining buffer set (eBioscience) according to the provider recommendation.
Flow cytometry analyses were performed on a BD LSR Fortessa cytometer, and cell-sorting experiments for RNAseq and Biomark analysis were performed using a BD FACS AriaIII cell sorter. Data were analyzed with the FlowJo software (TreeStar). In Vitro Cell Differentiation and Infection of Primary Cells. A total of 1 × 10 6 splenocytes were stimulated with 5 µg/mL of LPS (InvivoGen) for 2 or 4 d in complete culture medium composed of Roswell Park Memory Institute medium (RPMI) supplemented with 10% fetal calf serum (Sigma), 0. 05 mM 2-mercaptoethanol, 100 U/mL penicillin-streptomycin, 1 mM sodium pyruvate, and 0. 1 mM nonessential amino acids (Gibco). Where indicated, cells were treated with 20 μM M1 and 10 μM Mdivi (Sigma) for 2 d from day 2 to day 4. Stx5a-specific shRNAs were designed thanks to the RNAi consortium (https:// portals.
Libraries were prepared with the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina protocol according to supplier recommendations. Briefly, the key stages of this protocol were successively the purification of PolyA-containing mRNA molecules using poly-T oligo-attached magnetic beads from 100 ng total RNA (with the Magnetic mRNA Isolation Kit from NEB), a fragmentation using divalent cations under elevated temperature to obtain approximately 300-bp pieces, double-strand cDNA synthesis, and finally Illumina adapters ligation and cDNA library amplification by PCR for sequencing. Sequencing was then carried out on Paired End 100-b reads of Illumina NovaSeq. Image analysis and base calling were performed using Illumina Real Time Analysis (3. 4. 4) with default parameters. For analysis, Spatio temporal array (STAR) was used to obtain the number of reads associated with each gene in the Gencode vM24 annotation (restricted to protein-coding genes, antisense, and lincRNAs).
Raw counts for each sample were imported into R statistical software. Extracted count matrix was normalized for library size and coding length of genes to compute Fragments Per Kilobase of transcript per Million mapped read (FPKM) expression levels. The Bioconductor edgeR package was used to import raw counts into R statistical software and compute normalized log2 counts per millions of mapped reads using the weighted trimmed mean of M-values as the normalization procedure. The normalized expression matrix from the 500 most variant genes (based on standard deviation) was used to classify the samples according to their gene expression patterns using principal component analysis (PCA), hierarchical clustering, and consensus clustering. PCA was performed by the FactoMineR::PCA function with "ncp = 10, scale.
unit = FALSE" parameters. Hierarchical clustering was performed by the stats::hclust function (with euclidean distance and ward. D method). Differential expression analysis was performed using the Bioconductor limma package and the voom transformation. To improve the statistical power of the analysis, only genes expressed in at least one sample (FPKM ≥ 0. 1) were considered. A q value threshold of ≤0. 05 and a minimum fold change of 1. 2 were used to define differentially expressed genes. A gene list from the differential analysis was ordered by decreasing log2 fold change. GSEA was performed by the clusterProfiler::GSEA function using the fgsea algorithm. Gene sets from MSigDB v7. 2 database were selected among the C2_curated and Hallmark classes, keeping only gene sets defined by 10 to 500 genes.
The droplet array in the 2D chamber was imaged using a Nikon Ti-2 Eclipse inverted microscope with a motorized stage and excitation light source (Lumencor Spectra X). Fluorescence was captured using a 10× objective and a Cy5 filter, and images were recorded by a digital Complementary Metal Oxide Semiconductor camera (ORCA-flash 4. 0, Hamamatsu). For each time point, an array of 10 × 10 images was acquired, and a total of six acquisitions were recorded through 37. 5 min. The images were analyzed with a custom Matlab script as described  (1). In brief, the ratio of fluorescent signal between the beadline and the background was estimated for every droplet at every time point. The fluorescence ratio was then used to estimate the concentration of IgM in the droplets by using a calibration curve, which was generated by measuring the fluorescent ratio from different concentrations of purified monoclonal IgM antibody.
Statistical Analysis. The P-values were determined as indicated in the figure legends using Prism GraphPad software with the two-tailed unpaired Mann-Whitney nonparametric test for the WT vs. Sec22b B-KO comparison (*P < 0. 05; **P < 0. 01; ***P < 0. 001; ****P < 0. 0001, ns, nonsignificant P-value) or with the two-way ANOVA with Sidak correction for multiple comparisons (££P < 0. 01, £££P < 0. 001). Data, Materials, and Software Availability. RNAseq data have been deposited in GEO (GSE188452)  (49).
We thank Dr. N. Setterblad, C. Doliger, and S. Duchez (Plateforme technologique IRSL, Paris, France), Dr. V. Parietti (Mouse facility IRSL, Paris, France), S. Guibert (IntegraGen), M. Khamyath, and V. Gourhand for their technical assistance. We are grateful to Pr. A. Toubert for his comments on this manuscript. The study was supported by the Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LabEx LERMIT) (M. E. and K. B. ); an ANR JCJC grant (ANR-19-CE15-0019-01), an ANR @RAction.
pnas. org grant ( ANR-14-ACHN-0008 ), a "Fondation ARC pour la recherche sur le cancer" grant ( P JA20181208173 ), and a grant from  IdEx Université Paris-Cité  ( ANR-18-IDEX-0001 ) to M. E. ; and an  ANR   PRC grant  ( ANR-17-CE14-0019 ) and an  INCa grant  ( PRT-K 2017 ) to K. B. P. B. acknowledges funding from the  French National Research Agency  grant  ANR-18-CE15-0001  project  Autoimmuni-B, by the Institut Carnot Pasteur Microbes et Santé  grant  ANR-11-CARN-0017-01 , the  Institut Pasteur , and the  Institut National de la Santé et de la Recherche Médicale  (INSERM). M. A. L. is supported by  Biotechnology and Biological Sciences Research Council  ( BBS/E/B/000C0427 ,  BBS/E/B/000C0428 , and the  Campus Capability Core Grant  to the  Babraham Institute). A. A. P. and M. O. C. were supported by a grant from the  Biotechnology and Biological Sciences Research Council  ( BB/L022389/1 ).
ACKNOWLEDGMENTS. We thank Dr. N. Setterblad, C. Doliger, and S. Duchez (Plateforme technologique IRSL, Paris, France), Dr. V. Parietti (Mouse facility IRSL, Paris, France), S. Guibert (IntegraGen), M. Khamyath, and V. Gourhand for their technical assistance. We are grateful to Pr. A. Toubert for his comments on this manuscript. The study was supported by the Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LabEx LERMIT) (M. E. and K. B. ); an ANR JCJC grant (ANR-19-CE15-0019-01), an ANR @RAction.
IMPORTANCE Plant-based diets have gained popularity for both environmental and health reasons, but a comprehensive assessment of their quality in relation to risk of mortality and major chronic diseases is lacking. OBJECTIVE To examine whether healthful vs unhealthful plant-based dietary patterns are associated with mortality and major chronic diseases among UK adults. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study used data from adults in the UK Biobank, a large-scale population-based study. Participants were recruited between 2006 and 2010 and followed up using record linkage data until 2021; follow-up for different outcomes ranged between 10. 6 and 12. 2 years. Data analysis was conducted from November 2021 to October 2022. EXPOSURES Adherence to a healthful vs unhealthful plant-based diet index (hPDI vs uPDI) derived from 24-hour dietary assessments.
12, 13 However, strict PBDs may be associated with insufficient vitamin B 12 and calcium intakes, the latter of which may increase the risk of osteoporosis and fracture. 14 Moreover, despite increasing numbers of individuals consuming a vegan or vegetarian diet in many countries, less strict flexitarian PBDs that contain lower amounts of animal products may be adhered to more easily by many people compared with a vegan or vegetarian diet. 2  Our objective was to examine the potential health benefits and risks associated with PBDs using data from the UK Biobank, a large-scale population-based study of UK adults, with a focus on diet quality. Specifically, we evaluated whether adherence to a healthful vs unhealthful type of PBD was associated with total and cause-specific mortality and with major chronic diseases (eg, CVD, cancer, and fracture, the latter particularly because higher fracture risk may be an adverse effect of PBDs).
Study Population The UK Biobank is a large-scale, population-based, prospective study consisting of more than 500 000 participants aged between 40 and 69 years at the time of recruitment between 2006 and 2010. 15 Participants attended 1 of 22 assessment centers located across England, Scotland, and Wales, where they completed a comprehensive baseline assessment. Further details of the study protocol have been described elsewhere. 16 The National Health Service (NHS) North West Multicentre Research Ethics Committee approved the UK Biobank study. At recruitment, all participants provided written informed consent. For this cohort study, participants who withdrew consent during follow-up, had missing data on diet or key covariates, had completed fewer than two 24-hour dietary assessments, or had implausible energy intakes (>17 573 or <3347 kJ for men and >14 644 or <2092 kJ for women  17  ) were excluded from analyses.
Covariate Assessment and Case Ascertainment Baseline examinations of the UK Biobank cohort between 2006 and 2010 included questionnaire assessments of sociodemographic, dietary, and lifestyle factors. Anthropometric measurements and biological samples were collected from all study participants by trained staff. 16 Further information on the covariates used in this study can be found in eMethods 2 in Supplement 1. The case ascertainment in the UK Biobank cohort is also described in eMethods 3 in Supplement 1. In brief, data on mortality were available from the 2021 NHS death registries. 16.
Subgroup analyses were carried out to assess potential heterogeneity in associations between PDIs and end points across strata of key confounders. These included sex, smoking status (ever or never), body mass index (BMI; <25 or Ն25 [calculated as weight in kilograms divided by height in meters squared]), and education level (low, medium, or high; eMethods 2 in Supplement 1). Moreover, heterogeneity by subgroups of polygenic risk score tertiles (low, medium, or high genetic risk) was assessed for outcomes for which data on polygenic risk scores were available from the UK Biobank (CVD or breast, bowel, or prostate cancer). 21 The likelihood ratio test was used to test for interactions between PDIs and the covariates listed earlier in relation to mortality and disease end points, comparing the fits of Cox proportional hazards regression models with and without the respective interaction terms.
We further carried out sensitivity analyses restricting the study sample JAMA Network Open | Nutrition, Obesity, and Exercise to the majority of participants of White European ancestry, as both food preferences and disease risks may in part differ across races and ethnicities. 22, 23 Self-reported data on race and ethnicity for these analyses were obtained from the UK Biobank and categorized as Asian, Black, White, multiple races or ethnicities, and other race or ethnicity. To test the reproducibility of the PDIs over time, we calculated intraclass coefficients (ICCs) between hPDI and uPDI values based on average food intakes at the second and third (collected in.
For the CVD and fracture end points, hospital admission data were additionally available from the Hospital Episode Statistics for England (until September 2021), from Scottish Morbidity Records (until the end of July 2021), and from the Patient Episode Database for Wales (until March 2016). Cancer diagnosis data were provided through record linkage to National Cancer Registries in England, Wales (follow-up data available from the NHS Information Centre until February 2020), and Scotland (follow-up data available from the NHS Central Register of Scotland until January 2021).
It was therefore noted that the fjords of the Arctic are lacking in FAIR (Findability, Accessibility, Interoperability, and Reuse) data. To address this issue a single dataset has been created from publicly available, predominantly in situ data from 7 study sites in Svalbard and Greenland. After finding and accessing the data from a number of online platforms, they were amalgamated into a single project-wide standard, ensuring their interoperability. The dataset was then uploaded to PANGAEA so that it itself can be findable and reusable into the future. The focus of the data collection was driven by the key drivers of change in Arctic fjords identified in a companion review paper. To demonstrate the usability of this dataset an analysis of the relationship between the different drivers was performed.
, 2011; Bonnet-Lebrun et al. , 2022). The rate of loss for the Arctic cryosphere driven by the changing climate is alarmingly rapid  (Schlegel et al. , in review) , making it critical that Arctic fjord systems be as actively monitored as possible. Even though this monitoring in the extreme north is both challenging and costly, there has been a concerted international effort to maintain and increase it. The majority of the resulting in situ data collection is however done via large cruise ships, and the sampling of data throughout all but the mouths of fjords tends to be limited due to their depth. The sampling of data within fjords is therefore carried out in a more ad hoc manner, with many smaller teams and experiments creating disparate datasets that suffer from issues of FAIR (Findability, Accessibility, Interoperability, and Reuse) data, and some fjords are sampled much more heavily than others  (Bischof et al.
, 2019a). This is a known issue and there has already been work done to create unified datasets for specific aspects of Arctic fjords (e. g. physical oceanography via UNIS CTD database;  Skogseth et al. , 2019;  https://data. npolar. no/dataset/39d9f0f9-af12-420c-a879-10990df2e22d). There is not yet however a unified dataset that provides data for investigating the range of possible relationships throughout the entire socio-ecological fjord system. The dataset detailed in this report aims to address this shortcoming. The combination of the many different socio-ecological datasets is not as simple as identifying the sources of in situ data and putting them together into a folder. With the exception of a network of meteorological stations operated by the Norwegian Meteorological Institute (frost.
There are now many well funded international projects and research institutions that are working to close knowledge gaps in Arctic systems, but they tend to continue to produce these smaller datasets. A primary consideration therefore during the creation of an Arctic fjord dataset designed to allow for the investigation of the full socio-ecological system within fjords is how to combine many spatially and temporally disjointed datasets when they may not have the same units of measurement or have otherwise not been sampled with comparable methodologies. To begin to address this issue a panel of experts in a range of natural and social science fields identified the most relevant aspects of Arctic fjord socio-ecological systems  (Schlegel et al. , in review). The structure of these systems were organised by: category -> driver -> variable.
For example, the proportion of sea-ice cover within a fjord is a variable of the driver sea-ice, which is in the category of the cryosphere. This structure was used to guide the collection of data and to organise how the many small yet very important datasets in the Arctic were amalgamated. This structuring of the available data also allowed for better management and conversion of the different units and methodologies into a project-wide standard. In the following text we first explain why certain study sites were focussed on when collecting the datasets that contained the variables of interest. We then document the methods by which these datasets were accessed, assembled, and quality controlled to create a final product  (Schlegel & Gattuso, 2022). A basic summary is then presented, based on the different categories of the data (e.
Different again from the Svalbard fjords are those found in Greenland. With the east coast (e. g. Young Sound) currently less influenced by warming coastal waters than the fjord systems on the west coast (e. g. Qeqertarsuup Tunua and Nuup Kangerlua). Using the names of these seven sites (accounting for various different spellings) as well as their geographical coordinates, the databases detailed below were queried to create individual data collections per site. The main city/research station was also used in queries for data at Kongsfjorden (Ny-Alesund/Ny Alesund/Ny-Ålesund), Isfjorden (Longyearbyen), Young Sound (Zackenberg), Qeqertarsuup Tunua (Qeqertarsuup), and Nuup Kangerlua (Nuuk). The search parameters were not case sensitive. (Huang et al. , 2021). The location of the study sites are denoted with coloured points and are shown with colour-coordinated inset windows.
Categories -> drivers -> variables Due to the diverse range of avenues of inquiry one must consider when amalgamating data across the scope of a socioecological system, it was necessary to establish a consistent terminology. Each individual variable of measurement of the 4 95 natural and social world (e. g. the presence of ice, tourist arrivals, or nitrate concentration) was characterised into one of 14 drivers (sensu  Möller et al. , 2022) , with each of these grouped into one of five categories (Table  1 ). The list of 14 drivers was not initially evident, nor was there a consensus on them from the start. At the outset of the project a long list of relevant variables was agreed upon and links to the necessary datasets were provided when possible. When no link was provided, a series of data sources (see Section 2.