dataesr/scientific-paragraphs-categorization huggingface dataset:
Tertiary objects were classified as cytosol and were created by subtracting nuclei from cells. The cropped images and all object masks were compiled using an ImageJ (RRID: SCR_003070) macro (https://github. com/Hill-Lab/Mito-MGN-Scripts). The mean intensity of FOXO1 was measured in each of these objects and exported as a. csv file. The cell, nuclei and cytosol. csv files were merged using R and the mean intensity of FOXO1 in the nucleus was divided by the mean intensity of FOXO1 in the cytosol (R script also available on https://github. com/Hill-Lab/Mito-MGN-Scripts). Statistics were calculated using ANOVA followed by Tukey post hoc analysis within the R scripts. The immunofluorescence analytical workflow is detailed in Figure  S6.
T cell culture and activation Spleens were harvested from naive mice and single cell splenocyte suspensions made using manual dissection. Erythrocytes were lysed and CD3 + T cells were negatively selected using a biotin antibody cocktail followed by incubation with magnetic streptavidin nanobeads. The magnetically sorted fraction was retained by the use of a magnetic separator and the untouched CD3 + T cells collected. Cells were maintained in RPMI 1640 medium containing 10% v/v fetal bovine serum, penicillin (100 U/mL), and streptomycin (100 mg/mL). T cells were activated using CD3/CD28 Dynabeads (Thermofisher, Waltham, MA) in the presence of 20 U/mL of recombinant human IL-2 (PeproTech, Rocky Hill, NJ). For T cell proliferation experiments, T cells were labeled with Cell Trace Violet prior to the stimulation of Mito-MGN and data was acquired using Fortessa X20 Flow Cytometer and analyzed using FlowJo 10.
ACKNOWLEDGMENTS This work is supported in part by grants from the  National Institutes of General Medicine   R01 GM067180  (RBH), the  National Cancer Institute ,  U01 CA178960  ( BK, MBD ) and  R01 CA226279  (MBD), the  MCW Cancer Center and The Advancing a Healthier Wisconsin Endowment , and continuing philanthropic support from the  Bobbie Nick Voss Charitable Foundation. LC-MS/MS and Seahorse-XFe96-based analyses were performed in the  MCW Cancer Center Redox & Bioenergetics Shared Resource. We thank  Ms. Katie Chaney  in the  MCW Bone Marrow Transplant & Cell Therapy Laboratories  for assistance with completing the IsoPlexis T cell cytokine analyses. We gratefully thank  Children's Research Institute Flow Cytometry Shared Resource. The content is solely the responsibility of the author(s) and does not necessarily represent the official views of the  NIH.
Materials and Methods: This work is based on the clinical study LIMEX (NCT03155412). Non-obese 12 non-diabetic (FDR), and 12 control men without family history of diabetes matched for age and BMI underwent OGTT. Blood samples taken before and at the end of OGTT were used for isolation of circulating CD14 + and CD14 -PBMC. In these cells, mRNA levels of 94 genes related to lipid and carbohydrate metabolism, immunity, and inflammation were assessed by qPCR. Results: Irrespectively of the group, the majority of analyzed genes had different mRNA expression in CD14 + PBMC compared to CD14 -PBMC in the basal (fasting) condition. Seven genes (IRS1, TLR2, TNFa in CD14 + PBMC; ABCA1, ACOX1, ATGL, IL6 in CD14 - PBMC) had different expression in control vs. FDR groups. OGTT regulated mRNA levels of nine genes selectively in CD14 + PBMC and of two genes (ABCA1, PFKL) selectively in CD14 -PBMC.
Subjects Characteristics This work is based on the clinical study LIMEX (NCT03155412) including 51 healthy non-obese men. The two groups of men -1) non-diabetic first-degree relatives of T2DM patients (FDR); 2) control group -subjects without any family history of diabetes (CON) -were matched for age and BMI. Family history of diabetes was considered as follows: two first-degree relatives (parents, siblings) or one firstdegree and one or more second-degree relatives (grandparents, uncle, aunt) that were diagnosed with T2DM. Exclusion criteria for both groups were: body weight change more than 3 kg within 3 months preceding the study, smoking, any medication, hypertension, hyperlipidaemia, and drug or alcohol abuse. Subjects were examined at 8. 00 h in the fasting state. Body weight, waist, and hip circumferences were measured.
Statistical Analysis Data are presented as means ± SEM (anthropometric and biochemical variables) or median with 25 th and 75 th percentile. Statistical analysis was performed using GraphPad Prism 9. 0 for Windows (LaJolla, USA). Differences in the baseline values of the measured anthropometric, and biochemical variables were evaluated by Multiple Unpaired t test with multiple comparisons by False Discovery Rate (method by Benjamini, Krieger, and Yekutieli) set to 5%. Differences in mRNA levels (2 -DCt ) and responses to OGTT (mRNA fold changes) between groups (FDR vs. controls) were evaluated by Multiple Mann-Whitney tests, with multiple comparisons by False Discovery Rate (method by Benjamini, Krieger, and Yekutieli) set to 5%. Evolution of log2 transformed glucose and insulin levels during OGTT was evaluated by Two Way ANOVA with Sidak multiple comparison test.
Importantly, we demonstrated that the detected alterations in basal gene expression and its OGTT-induced regulation were different in respect to the type of immune cell population, i. e. CD14 + and CD14 -PBMC. Among the genes that could be considered as markers of one of the PBMC fractions (i. e. , they were expressed only in one fraction or had more than 20 times higher/lower level of expression in CD14 + vs. CD14 -PBMC), we identified genes already recognized for its predominant expression in CD14 + PBMC (CCL2, TLR2/4) or CD14 -PBMC (CCL5, PRF1) (18) but also genes that were not previously recognized as markers of circulating CD14 + PBMC, namely two members of EGR family of transcription factors (EGR1, EGR2) and CD36, a transporter of fatty acids. These findings are generally in accordance with the recently published dataset by Monaco et al.
ACKNOWLEDGMENTS This work was supported by the  Institut Pasteur , the  Institut National de la Santé et de la Recherche Médicale  (INSERM) and by the  European Research Council (ERC) - Seventh Frame-work Program  ( ERC-2013-CoG 616050 ) and a  Contrat Local d'Interface  to PB of the  Assistance Publique des Hôpitaux de Paris (AP-HP ).
Each barcoded library was mixed in equal amounts and diluted to 100 pM. The libraries were run on chip 540 on the Ion S5 sequencer (Life Technologies). Data were analyzed using the CSReport software  (21). This algorithm first aligns sequences with Sµ (set for identities higher than 90% and longer than 40 nucleotides) and explores identities to downstream S regions when the alignment with Sµ stops (again tracking identities higher than 90% and longer than 40 bp). The annotated IgH locus switch sequences considered for aligning mouse and human sequences are provided in Supplementary Table  S1. Quality controls for the whole CSRseq procedure process were done by processing non-lymphoid DNA samples (embryonic stem cell DNA for mouse assays and DNA from the human carcinoma cell line Hep2 for human assays).
The structures of CSR DNA junctions in lymphoid tissues from AID-deficient mice and patients were compared with regards to repair and the relative occurrence of either flush junctions or junctions revealing short insertions or microhomologies between both DNA ends. In both mice (Figure  3B , left) and humans (Figure  3B , right), junctions characterized in AID-deficient conditions revealed a lower occurrence of flush junctions which directly corresponded to the ligation of blunt ends and an increased occurrence of microhomology between both ends. Sequences from all reads, including junctions and their detailed analysis using CSreport, have been deposited on GEO (GSE183034); examples of junctions obtained in human patients and controls are provided in Supplementary Table  S2. The examination of junction sequences also determined the distance between the positions of DNA breaks and sites corresponding to classical AID target sites.
ACKNOWLEDGMENTS We thank  Claire Carrion  and  Sandrine Lecardeur  for technical help and the staff from the  Limoges University core animal  facility for animal care. We thank  Dr. Yannic Danger  and  Franck Veŕite , at the  French Blood Center , for providing mouse monoclonal IgM. SLN and SK are  Centre National de la Recherche Scientifique  staff investigators.
In acute myeloid leukemia (AML), a low level of reactive oxygen species (ROS) is associated with leukemic stem cell (LSC) quiescence, whereas a high level promotes blast proliferation. ROS homeostasis relies on a tightly-regulated balance between the antioxidant and oxidant systems. Among the oxidants, NADPH oxidases (NOX) generate ROS as a physiological function. Although it has been reported in AML initiation and development, the contribution of NOX to the ROS production in AML remains to be clarified. The aim of this study was to investigate the NOX expression and function in AML, and to examine the role of NOX in blast proliferation and differentiation. First, we interrogated the NOX expression in primary cells from public datasets, and investigated their association with prognostic markers.
Next, we explored the NOX expression and activity in AML cell lines, and studied the impact of NOX knockdown on cell proliferation and differentiation. We found that NOX2 is ubiquitously expressed in AML blasts, and particularly in cells from the myelomonocytic (M4) and monocytic (M5) stages; however, it is less expressed in LSCs and in relapsed AML. This is consistent with an increased expression throughout normal hematopoietic differentiation, and is reflected in AML cell lines. Nevertheless, no endogenous NOX activity could be detected in the absence of PMA stimulation. Furthermore, CYBB knockdown, although hampering induced NOX2 activity, did not affect the proliferation and differentiation of THP-1 and HL-60 cells. In summary, our data suggest that NOX2 is a marker of AML blast differentiation, while AML cell lines lack any NOX2 endogenous activity.
Likewise, NOX were shown to participate in AML transformation  [24] , proliferation  [6, 25] , migration  [25] , differentiation blockage  [26, 27] , and in the self-renewal of leukemic stem cells  [27]. However, the characterization of the NOX family in myeloid leukemia remains limited, and studies had mainly focused on NOX2 and NOX4 complexes  [6, 8, 24, 25, 28]. Besides this, the studies mostly relied on gene expression and NOX inhibition, by chemicals or knockdown, and the enzymatic activity of NOX has not been accurately investigated. Therefore, the contribution of NOX to ROS production in AML needs further investigation. In this work, we performed an extensive transcriptional analysis of all of the NOX subunits in AML, using public transcriptomic datasets and a large panel of 24 AML cell lines.
Analysis of the Public Datasets Transcriptomics: raw CEL files from the AML microarray datasets GSE6891 and GSE10358 (Affymetrix GeneChip Human Genome U133 Plus 2. 0) were downloaded from the GEO Omnibus (https://www. ncbi. nlm. nih. gov/geo/ (last accessed on 18 March 2021)). The datasets were subjected to background-correction and Robust Multichip Average (RMA) normalization using BioConductor's Affy package  [29]  in the R environment  [30]. Preprocessed microarray datasets of normal hematopoiesis (GSE24759)  [31] , paired diagnosis and relapse AML samples (GSE65625)  [32] , and functionally annotated leukemic stem cell enriched (LSC + ) and depleted (LSC -) AML populations (GSE76009)  [33]  were also downloaded from the GEO Omnibus. For each gene, the probe set with the highest average expression across the samples was used for the analysis.
The AML dataset for the Cancer Genome Atlas TCGA was acquired using cBioPortal's cgdsr package in R environment (https://www. cbioportal. org/) (last accessed on 18 March 2021). Proteomics: the MaxQuant output files for the PXD019785 dataset were downloaded from the PRIDE database  [34, 35]. The data processing and normalization were performed using the DEP package in the R environment  [36]. Boxplots and principal component analysis (PCA) plots were created in the R environment using the ggplot2 package  [37]. Hierarchical clustering and correlation plots were generated in R using the ComplexHeatmap  [38]  and corrplot  [39]  packages. For the cell lines and the GSE24759 dataset, the heatmaps were created using the Clustvis web tool  [40].
Survival Analysis Overall survival (OS) was defined as the time from the AML diagnosis until death, and the patients who were alive at last follow-up were censored. Event-free survival (EFS) was defined as the time from the diagnosis until an event occurred (induction failure, relapse, or death), and the patients with no events at their last follow-up were censored. Cox proportional hazard (CPH) regression was used to perform the univariate and multivariate analyses  [41]. The violation of the proportional hazards assumption was examined using Schoenfeld residuals  [42]. For the TCGA dataset, age violated the proportional hazard's assumption, and was therefore used as the stratifier, as took place in other research  [33]. Wald's test was used to evaluate the significance of the individual regression coefficients.
1-hNox1 (Addgene, Watertown, MA, USA) plasmid #58344), pcDNA3. 1-hNoxo1 (Addgene plasmid #58530) and pcDNA3. 1-hNox3 (Addgene plasmid #58341) were gifts from Botond Banfi and Karl-Heinz Krause. Universal ProbeLibrary probes were purchased from Roche (Meylan, France). The sequences of primers and probes are documented in Table  S2. The RT-qPCR reactions were performed on 20 ng of cDNA using a LightCycler ® 480 Probes Master (Roche), as described previously  [45]. The samples were subjected to an initial denaturation step (5 min, 95 • C), followed by 45 PCR cycles (10 s, 95 • C, then 30 s, 60 • C) and a final cooling step (40 • C, 30 s). Triplicates of each sample were analyzed using the cycle threshold (Ct) values determined by the LightCycler ® 480 software. The geometric Ct mean of human ACTB, YWHAZ and RPL13A genes was used as the endogenous control, in order to calculate the expression of the target genes: ∆CT = "Ct gene" -"Ct reference geomean".
Enzymatic Activity Assay The NOX activity was measured as previously described, with some modifications  [14]. The cells were washed in PBS, and were seeded in 96-well plates at 2 × 10 5 cells per well in an isotonic glucose solution (300 mM) containing luminol (10 µM) (Sigma-Aldrich, Lyon, France) and horseradish peroxidase (10 U/well) (Sigma-Aldrich). Phorbol 12-myristate 13-acetate (PMA; Sigma-Aldrich) was added at a final concentration of 100 nM prior to the measurement. The luminescence was measured every minute for 2 h at 25 • C using a ClarioStar microplate reader (BMG Labtech, Champigny-sur-Marne, France). The area under the curve was calculated via ClarioStar Data Analysis software from the average of replicates of blank-corrected data. Diphenyleneiodonium chloride (DPI; Sigma-Aldrich) and VAS3947 (Merck Millipore), both at 20 µM, were used to block the NOX activity.
Cell Differentiation The differentiation of THP-1 cells was induced by PMA (100 nM), while that of the HL-60 cells was induced by a mix of all-trans retinoic acid (ATRA, 100 nM) and DMSO (1. 25%), as previously described  [47]. In brief, 2 × 10 5 cells were cultured in 3 mL media in 6-well plates, either in the presence or absence of the differentiation agents, and were incubated at 37 • C for 96 h. The cell differentiation was checked by flow cytometry using anti-CD11b-PE antibody (Becton Dickinson, Le Pont de Claix, France). A Phycoerythrin (PE)-coupled mouse IgG1κ was used as the isotype control (Becton Dickinson). The data acquisition was performed on a C6 Accuri ® flow cytometer, and the analysis was performed using FlowJo ® software (Becton Dickinson).
Cell Proliferation Cell counting: transduced cells were seeded in 3 mL media at a density of 2 × 10 5 cells/well in 6-well plates, and were incubated at 37 • C for 72 h. The cell numbers were counted on day 3 using a trypan blue exclusion assay. Resazurin assay: transduced cells were seeded in 200 µL media at a density of 5 × 10 4 cells/well in 96 well-plates, and were incubated at 37 • C for 72 h. The cell proliferation was followed at days 0 and 3 using a resazurin reduction assay. At both timepoints, resazurin (0. 1 mg/mL) (Sigma-Aldrich) was added at a volume of 20 µL per well, and the plates were incubated for 4 h at 37 • C. The fluorescence (λ ex = 529. 5-19 nm, λ em = 582-36 nm) was measured using a ClarioStar microplate reader. The blank corrected signals were calculated using ClarioStar Data Analysis software, and normalization was performed with respect to day 0 for each condition.
Statistical Analyses For the cell lines, the results are expressed as mean ± SEM of three independent experiments. R software was used to construct the figures and perform the statistical analyses. In order to characterize the expression profile of NOX-coding genes in AML cells (Figure  1a ), we examined three public expression datasets of primary AML: GSE6891 (microarray, N = 443)  [48] , GSE10358 (microarray, N = 223) [49], and TCGA (RNA-seq, N = 173)  [50]. The data showed that genes coding for the subunits of NOX2 complex (CYBB, NCF1, NCF2, NCF4 and CYBA) were highly expressed in most AML samples, whereas genes coding for NOX1, NOX3-5, DUOX1-2, NOXA1 and NOXO1 were either weakly expressed or absent (Table  S3 ). Besides this, three genes (CYBB, NCF1 and NCF2) coding for NOX2 complex subunits were constantly deregulated across the three datasets (Figure  1b -d and Table  S4 ).
NOX-Coding Genes Are Not Prognostic in AML Because gene expression varies between subtypes, we aimed to examine whether NOXcoding genes may have a prognostic value in AML. Thus, we performed Cox regression analysis across the three AML datasets. The results showed that none of the 13 NOX-coding genes were consistently prognostic in all of the datasets, either in univariate analysis (Table  S5 ) or after adjustment for age and cytogenetic abnormalities (Table  S6 ). However, NCF4 expression correlated with a bad prognosis for EFS in the GSE10358 and TCGA datasets (Tables  S5  and S6 ). We also examined the correlation between NOX expression and somatic mutations within the cytogenetically-normal AML subset, but found no correlation with NPM1/FLT3ITD status (Table  S7 ). We also found that none of the genes was consistently prognostic within the cytogenetically-normal AML, subset either in the univariate analysis or after adjustment for age and NPM1/FLT3ITD status (Tables  S8  and S9 ).
Subunits of the NOX2 Complex Are Highly Expressed among M4 and M5 AML Primary Blasts Before the European LeukemiaNet classification, AML patients were historically stratified into eight French-American-British (FAB) subtypes (M0-M7), according to the differentiation orientation and degree of maturation of the blasts  [51, 52]. We noticed that the t(8;21) group, in which NOX2-coding genes are down-regulated, is correlated with the M2 FAB subtype (71. 4-85. 7%), whereas the inv  (16)  group, in which NOX2-coding genes are up-regulated, is correlated with the more mature M4 subtype (70-93. 8%) in all three datasets (Table  S10 ). This respective correlation between the two Core Binding Factor (CBF) translocations and M2/M4 subtypes has been previously reported  [53]. We thus speculated that the NOX expression profile in AML could be associated with the commitment of AML blasts and their stage of blockage.
Therefore, we investigated the expression profile of all NOX-coding genes according to the FAB subtypes. The expression of genes coding for the NOX2 complex was the highest in the more mature myelomonocytic (M4) and monocytic (M5) subtypes, as shown by hierarchical clustering and principal component analysis (Figure  2a-c ). Genes coding for other complexes are virtually undetectable, as indicated in Table  S3  (Figure  2a-c ). Furthermore, the expression of NOX2 complex genes showed positive inter-correlation in all of the datasets (Figure  2d-f ).
NOX-Coding Genes are not Prognostic in AML Because gene expression varies between subtypes, we aimed to examine whether NOX-coding genes may have a prognostic value in AML. Thus, we performed Cox regression analysis across the three AML datasets. The results showed that none of the 13 NOX-coding genes were consistently prognostic in all of the datasets, either in univariate     (a-c ) Heatmap (left) and principal component analysis (PCA) plots (right) depicting the expression of NOX genes in primary AML samples from the GSE6891, GSE10358, and TCGA datasets, respectively. In the heatmap, the data are shown as log2-expression values. Both the rows and columns are clustered using Euclidean distance and average linkage. In the PCA plot, the first two principal components explaining most of the variability between the samples are shown.
Together, PC1 and PC2 explain 89% of the total variance in the NOX expressions between the samples. The samples from the M4/M5 subtypes are colored in red, while the other subtypes are in blue. The prediction ellipses are such that, with probability 0. 95, a new observation from the same group will fall inside the ellipse. (d-f) Correlations between the genes coding for the NOX2 complex in the GSE6891, GSE10358, and TCGA datasets, respectively. The pairwise correlation between the genes was estimated using Pearson's correlation coefficient. The positive coefficients (in red) indicate a positive correlation, while the negative coefficients (in blue) indicate a negative correlation. The BH-adjusted p-values are also reported, and an asterisk (*) indicates a significant correlation (p < 0. 05).
(g) The NOX proteomic profile in primary AML according to the FAB subtypes in PRIDE's PXD019785 dataset (N = 33). The pairwise comparisons between the M4-M5 and M0-M1-M2 groups were performed using Student's t-test (*: p < 0. 05). The transcriptomic findings were validated at the protein level in a proteomics dataset of 33 AML samples (PXD019785)  [35]. As expected, none of the NOX1, NOX3-5, or DUOX proteins was detected in this dataset. In contrast, all of the proteins from the NOX2 complex were detected, and the expression of CYBB, NCF1, and NCF2 proteins was increased in the M4-M5 primary AML compared to more immature FAB groups (Figure  2g ). Together, the data indicate that the expression of NOX2 complex genes in AML blasts might be dependent on the stage of differentiation blockage, and that these genes are tightly co-regulated.
The Expression of NOX2 Complex Subunits Increases throughout Normal Myeloid Differentiation The NOX2 complex was first discovered in the oxidative burst of phagocytes. We postulated that its expression would increase throughout normal myeloid differentiation. The transcriptomic exploration of a comprehensive dataset of normal hematopoiesis (GSE24759) showed that the expression of NOX2 complex subunits is weak in HSCs, but increases gradually during normal granulocytic and monocytic differentiation (Figure  3a, b ). This corroborates our hypothesis that the expression of genes coding for components of the NOX2 complex in AML blasts may depend on their differentiation status, rather than being related to AML malignancy. Antioxidants 2021, 10, x FOR PEER REVIEW 9 of 19 most of the variability between the samples are shown.
The Expression of NOX2 Complex Subunits Increases throughout Normal Myeloid Differentiation The NOX2 complex was first discovered in the oxidative burst of phagocytes. We postulated that its expression would increase throughout normal myeloid differentiation. The transcriptomic exploration of a comprehensive dataset of normal hematopoiesis (GSE24759) showed that the expression of NOX2 complex subunits is weak in HSCs, but increases gradually during normal granulocytic and monocytic differentiation (Figure  3a, b ). This corroborates our hypothesis that the expression of genes coding for components of the NOX2 complex in AML blasts may depend on their differentiation status, rather than being related to AML malignancy. (b) Boxplots showing the expression of genes coding for the subunits of the NOX2 complex in myeloid subsets.