dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, further studies are needed to definitively prove a potential metabolic benefit from the reduction of BCAA. Our data have clear clinical relevance as we have identified a gene at the intersection of several pathways involved in the benign steatosis to deleterious steatohepatitis transition. In particular, lower hepatic ITCH expression in human subjects is associated with two factorsdthat is, apoptosis and regulation of cellular response to stressdbelieved to be involved in the NAFLD to NASH transition  [53]. In human pathology, mutations in the ITCH gene (located on chromosome 20q11. 22) can lead to a deficiency of the encoded protein and increased T cell activity with manifestation of a complex, systemic autoimmune disease, including hepatic failure  [54, 55]. Our study did not include subjects with autoimmune disease, but analysis of Gene Ontology of ITCH-associated pathways highlighted cytokine signaling, neutrophil functions, and T cell activation, suggesting that ITCH is also a regulator of immune and inflammatory pathways in hepatic metabolic disorders.
In conclusion, our human and mouse data suggest that increased circulating BCAA levels, a hallmark of metabolic and cardiovascular diseases, may be partly mediated by loss of ITCH regulation of BCAA degradation enzymes in the liver cells. Beyond NAFLD, given the prevalence of BCAA dysregulation in obesity, this work highlights ITCH as an actionable candidate to normalize BCAA metabolism in obesity and more generally in cardiometabolic disorders.
GRANT SUPPORT This work was in part supported by  EU -FP7 FLORINASH (Health-F2-2009-241 , 913 ),  Fondazione Roma   NCD 2015 grant ,  PRIN 2015MPESJS ,  PRIN 2017FM74HK ,  ARS01_00876 BIO-D  and  IMI2   SOPHIA  (to M. F. ), by the  NIHR Imperial Biomedical Research Centre  and by grants from the  French National Research Agency  ( ANR-10-LABX-46  [European Genomics Institute for Diabetes]), from the  National Center for Precision Diabetic Medicine [PreciDIAB] , which is jointly supported by the  French National Agency for Research  ( ANR-18-IBHU-0001 ), by the  European Union (FEDER) , from the  Hauts-de-France Regional Council  and from the  European Metropolis of Lille  (MEL) and from  Isite ULNE , also jointly funded by  ANR  ( ANR-16-IDEX-0004-ULNE ) the  Hauts-de-France Regional Council  and by the  European Metropolis of Lille  (MEL), from  Fondo Europeo de Desarrollo Regional (FEDER)  through the  Programa Interreg V-A España-Francia-Andorra  (POCTEFA 2014e2020), from  FIS  project from the  Instituto de Salud Carlos   III PI18/01022 , from  Fondo Ordinario Enti  ( FOE D.
M 865/2019 ), agreement  Italian National Research Council e EBRI  (2019e2021) (I. A. ). This work used the computing resources of the UK MEDical BIOinformatics partnershipdaggregation, integration and visualisationdand analysis of large, complex data (UK MED-BIO), which was supported by the  Medical Research Council  (grant number  MR/L01632X/1 ). L. H. was an  MRC  Intermediate Research Fellow in Data Science, funded by the  UK MEDical BIOinformatics partnership (UK Med-Bio , grant number  MR/L01632X/1 ); M. V. is funded by  European Regional Development Fund-Project MAGNET  (No. CZ. 02. 1. 01/0. 0/0. 0/15_003/0000492 ).
Our data captured differences in how the second wave of the pandemic (September 2020 onwards) was handled compared to the first wave (March 2020-June 2020). During the second wave of the pandemic, IRs reported reduction in operating lists and segregation of working teams less frequently despite the fact that COVID-19 patient care was considered more or equally intense. Likewise, less reduction in Violin plots with type of effect on x-axis and services on y-axis emergency work, outpatient clinics and day-case units were reported during the second wave of the pandemic. Interventional radiology appeared to be well-suited to adapt to times of limited resources regarding hospital beds, anaesthesiologists and staff. Minimally invasive procedures such as radioembolisation and ablation can be optimal treatments for local tumour control while requiring shorter hospital stays  [34] [35] [36].
Computational predictions of interactions between Salmonella effectors and core autophagy genes Protein-protein interactions (PPIs) were inferred by domain-domain interaction (DDI) prediction using the MicrobioLink pipeline  (Andrighetti et al. , 2020). Briefly, DDIs with high confidence values (interactions predicted by at least two different in silico methods or using multiple sources) were collected from the DOMINE resource  (Yellaboina et al. , 2011). We assumed that Salmonella and human proteins are connected if the interacting domains were represented in the database. The interaction prediction was merged with already existing predictions  (Krishnadev and Srinivasan, 2011; Kshirsagar et al. , 2012)  and experimentally validated transcription factorgene interactions related to autophagy.
Scripts for processing the interaction predictions, databases and other tables were written in R (See Table  S1  for a list of host transcription factors and bacterial effectors found to interact). For the network analysis the Cytoscape network visualization program was used  (Shannon et al. , 2003). Autophagy core genes and their direct transcriptional interactions were downloaded from the following databases: Autophagy Regulatory Network (ARN, http://autophagyregulation. org/,  (Türei et al. , 2015) , HTRIdb (http://www. lbbc. ibb. unesp. br/htri,  (Bovolenta et al. , 2012)  and TRRUST (https://www. grnpedia. org/trrust/,  (Han et al. , 2015). All scripts are available at https://github. com/ korcsmarosgroup/HMIpipeline.
Image analysis The analysis of SQSTM1/p62 and LC3 staining was done using semi-automated macros within FiJi (Image J2) software. To avoid unconscious-bias, imaged areas were chosen randomly based solely on the DAPI staining. DAPI staining was used for the identification of nuclei and individual cells were identified by extension of the nuclei mask. Segmented cells touching the edge of the images or artefacts (small objects) were eliminated. Individual regions of interest (ROIs) were saved for each image and subsequently used for quantification of SQSTM1/ p62 and LC3 staining. Intensity and puncta information for individual cells in each fluorescent channel was exported into Excel spreadsheet and used for statistical analysis.
and our prediction. This layer of the final network contains 71 connections from Kshirsagar, 0 connections from Krishnadev and 4 connections from our prediction  (Krishnadev and Srinivasan, 2011; Kshirsagar et al. , 2012). On the human side there were 154 transcription factor-autophagy core interactions from ARN, 105 from HTRI and TRRUST database, with altogether 33 transcription factors, 35 core autophagy genes and 159 regulatory interactions between them. Our network analysis highlights that several Salmonella effectors can impact several transcription factors. For example, effectors such as SseI, SseL, SlrP and SspH2 target 9, 10, 16 and 33 transcription factors, respectively (Figure  1A ). This reflects the multiple routes Salmonella has evolved to subvert host intracellular clearance mechanisms, here by affecting all stages of the autophagy process (from induction, through to the autophagosome formation to the fusion with lysosomes; Figure  1 ).
, 2019). Here we asked whether S. Typhimurium virulence effector proteins can influence this clearing process by acting on the regulatory level above the core autophagy genes, i. e. on the transcription factors that normally regulate them. Using protein-protein interaction predictions and transcription factorgene interaction databases we built a network that links several S. Typhimurium virulence effectors to key transcription factors and predicted that S. Typhimurium can modulate the regulation of autophagy core gene expression. We showed that six SPI-1 effectors could interact with overall 33 transcription factors that normally regulate the expression of core autophagy genes (Figure  1A ). As expected, SPI-2 SseL and SspH2 effectors showed a greater number of interactions with transcription factors, with 10 and all 33 regulatory targets, respectively.
Acknowledgments We are truly grateful to  Jost Enninga  ( Department of Cell Biology & Infection, Pasteur Institute, Paris, France ) for his instrumental advice and discussion. The work of AD, RI, TK and IH were supported by the  UKRI BBSRC   Gut Microbes and Health Institute Strategic Programme   BB/R012490/1  and its constituent projects  BBS/E/F/000PR10353  and  BBS/E/F/ 000PR10355  as well as a  BBSRC   Core Strategic Programme Grant for Genomes to Food Security  ( BB/CSP1720/1 ) and its constituent work packages,  BBS/E/T/000PR9819  and  BBS/E/T/ 000PR9817. LG was supported by a  BBSRC -Norwich Research Park Biosciences Doctoral Training Partnership grant  ( BB/ M011216/1 ). RK was supported by research grants  BB/ N007964/1  and  BB/M025489/1 , and by the  BBSRC   Institute Strategic Programme Microbes in the Food Chain   BB/R012504/ 1  and its constituent projects  BBS/E/F/000PR10348  and  BBS/E/ F/000PR10349.
Two runs of RNA sequencing were performed for each library on an Illumina NextSeq 500 platform using single-end 75 bp. The RNA-seq analysis was performed with Sequana 0. 8. 5  91  using an RNA-seq pipeline 0. 9. 13 (https:// github. com/sequana/sequana_rnaseq) built on top of Snakemake 5. 8. 1  92. Reads were trimmed from adapters using Cutadapt 2. 10 93 then mapped to the mouse reference genome GRCm38 using STAR 2. 7. 3a  94. FeatureCounts 2. 0. 0 was used to produce the count matrix, assigning reads to features using annotation from Ensembl GRCm38_92 with strand-specificity information  95. Quality control statistics were summarized using MultiQC 1. 8  96. Statistical analysis on the count matrix was performed to identify differentially expressed genes (DEGs), comparing WT and cMKO. Clustering of transcriptomic profiles were assessed using a Principal Component Analysis (PCA).
Differential expression testing was conducted using DESeq2 library 1. 24. 0 scripts based on SARTools 1. 7. 0  97  indicating the significance (Benjamini-Hochberg adjusted p-values, false discovery rate FDR < 0. 05) and the effect size (fold-change) for each comparison. For RT-qPCR, 1 µg of total RNA was converted into cDNA using the iScript Reverse Transcription Supermix (Bio-Rad). RT-qPCR was performed using the CFX384 Touch Real-Time PCR Detection System (Bio-Rad) and SYBR Green Master Mix (Bio-Rad) using the primers listed in Supplementary Data 1. Gapdh was amplified as internal standard. Data were analyzed according to the 2-ΔΔCT method  98.
Peptides were eluted using a gradient of solvent B (ACN, 0. 1% FA) from 3 to 7% in 8 min, 7 to 23% in 95 min, 23 to 45% in 45 min (total length of the chromatographic run was 170 min including high ACN level step and column regeneration). Mass spectra were acquired in data-dependent acquisition mode with the XCalibur 2. 2 software (Thermo Fisher Scientific, Bremen) with automatic switching between MS and MS/MS scans using a top 12 method. MS spectra were acquired at a resolution of 35000 (at m/z 400) with a target value of 3 × 10 6 ions. The scan range was limited from 300 to 1700 m/z. Peptide fragmentation was performed using higher-energy collision dissociation (HCD) with the energy set at 27 NCE. Intensity threshold for ions selection was set at 1 × 10 6 ions with charge exclusion of z = 1 and z > 7.
The MS/MS spectra were acquired at a resolution of 17,500 (at m/z 400). Isolation window was set at 1. 6 Th. Dynamic exclusion was employed within 45 s. Data were searched using MaxQuant (version 1. 5. 3. 8) using the Andromeda search engine 101 against a reference proteome of Mus musculus (53449 entries, downloaded from Uniprot the 24th of July 2018). The following search parameters were applied: carbamidomethylation of cysteines was set as a fixed modification, oxidation of methionine and protein N-terminal acetylation were set as variable modifications. The mass tolerances in MS and MS/MS were set to 5 ppm and 20 ppm respectively. Maximum peptide charge was set to 7 and 5 amino acids were required as minimum peptide length. A false discovery rate of 1% was set up for both protein and peptide levels.
All 4 fractions per sample were gathered and the iBAQ intensity was used to estimate the protein abundance within a sample  102. The match between runs features was allowed for biological replicate only. For this large-scale proteome analysis part, the mass spectrometry data have been deposited at the ProteomeXchange Consortium (http://www. proteomexchange. org) via the PRIDE partner repository  103, 104  with the dataset identifier PXD028516 (symptomatic mice) and (pre-symptomatic mice).
mtDNA content quantification Genomic DNA was extracted using the NucleoSpin Tissue (MACHEREY-NAGEL) and quantified with NanoQuant Plate (Infinite M200, TECAN). RT-qPCR was performed using the Real-Time PCR Detection System (Applied Biosystems StepOnePlus), 20 ng of total DNA, and the SYBR Green Master Mix (Bio-Rad). b-Actin was amplified as an internal, nuclear gene standard as previously described  28. PCR primer sequences are listed in Supplementary Data 4. Data were analyzed according to the 2^-ΔΔCT method  98.
The addition of oligomycin (Omy, 25 nM), carboxyatractyloside (CATR, 1 µM) or GTP (1 mM) was used to determine the non-phosphorylating respiration (state 4). Cytochrome c (2 µM)-mediated O 2 flux was used to evaluate the integrity of the OMM following the mitochondria isolation. Respiratory control ratios (RCR) of state 3: state 4 were used to evaluate the integrity of the IMM. RH-123 fluorescence quenching (Δ fluorescence) in energized mitochondria was used as a direct measurement of Δψ. Data was analyzed using Datlab v. 7.
Sections were contrasted with 4% aqueous uranylacetate and Reynold's lead citrate. Generation of ultra-large electron microscopy montages at a magnification of 14500x (pixel size = 6. 194 nm, bin 1, US4000 Ultrascan camera) were acquired using a TECNAI F20 Transmission Electron Microscope (FEI) with a field emission gun (FEG) as an electron source, operated at 200 kV. The SerialEM software  105, 106  was used for multiscale mapping as previously described  28. The 'Align Serial Sections/ Blend Montages' interface of IMOD16 was used for blending the stacks of micrographs collected to single large images. Quantification of mitochondrial cross-sectional area was determined by tracing the perimeter of individual mitochondria with the use of 3dmod software.
Acknowledgements We thank  Pierre-Henri Commere  and  Sandrine Schmutz  for flow cytometry services at the  Institut Pasteur  and  Anastasia Gazi  for electron microscopy services. We thank  Corinne Lesaffre  at the  Paris Cardiovascular Research Center  for the histology services,  Anu Susan Kurian  and  Priscilla Lopes  for technical assistance. We thank  Sylvie Fabrega  of the  Viral Vector for Gene Transfer core facility  of  Structure Fédérative de Recherche Necker, Université de Paris  for lentiviral particle synthesis and  Nils-Göran Larsson  for providing mitoYFP mice. We thank  Arnaud Mourier  for inciteful discussions on bioenergetics and  Marie Lemesle  for excellent administrative assistance. T. W. is supported by the  European Research Council (ERC) Starting  Grant No. 714472  ( Acronym "Mitomorphosis" ),  Fondation pour la Recherche Medicale  ( MND202003011475 ), and the  Agence Nationale pour la Recherche  ( ANR-20-CE14-0039-02 ).
Data availability Source data for all experiments are available alongside this manuscript. The datasets generated during the current study are available in the European Nucleotide Archive (NEO) repository and Proteomics Identification Database (PRIDE). Accession numbers and the web links are as follows. Bulk RNAseq: (ENA: Project: PRJEB47968), Cardiac proteome of mice at pre-symptomatic (PXD034477) and symptomatic (PXD028516) ages, and cardiac interactome data (PXD028529). The total datasets of fluorescence microscopy images generated and analyzed during the current study are not publicly available due to the incompatibility of exporting comprehensible file names linked to cell, treatment, and time identifiers from the Harmony 4. 9, Perkin Elmer software but are available from the corresponding author upon request.
2d ). Under thermoneutral conditions, energy expenditure was higher in DW-HFD rats, without any difference in locomotor activity (Fig. 2e, f ). We then conducted a cold-exposure test to assess the thermogenic response of BAT and found that DW-HFD rats preserved their whole-body and interscapular temperatures better than SW-HFD rats at 4 °C (Fig. 2g, h ). It should be noted that, an extra week of HFD feeding is present in the SW-HFD group. To investigate whether the extra week of HFD could have implications in the observed phenotype, the body weight data were also reanalysed by comparing the body weights of SW-HF and DW-HFD groups accordingly to the weeks of exposure to an HFD instead of weeks of age. It was found that DW-HFD rats still presented lower body weight gain, and lower fat mass than SW-HFD rats (Extended Data Fig.
3e ). In addition, protein levels of phosphorylated hormone-sensitive lipase (pHSL), a lipolytic marker, and the ratio of pHSL/HSL was also increased in the BAT of DW-HFD rats (Fig. 3f ). In agreement with the increased BAT thermogenesis, DW-HFD animals showed increased glucose uptake in the BAT at room temperature (Fig. 3g ) and after cold exposure (Fig. 3h ), as indicated by 18 F-fluoro-2-deoxy-d-glucose ( 18 F-FDG) uptake in BAT. The data   from positron emission tomography-computed tomography (PET/ CT) confirmed the activation of the thermogenic programme elicited by prolonged breastfeeding in animals fed an HFD. Given the activation of the thermogenic programme in the BAT of DW-HFD rats, we then assessed the potential thermogenic role of browning in WAT. The lipid content of the subcutaneous adipose tissue (SAT), as measured by the area covered by lipid droplets, was decreased in DW-HFD compared to SW-HFD rats (Extended Data Fig.
The next step was to elucidate why prolonged suckling increased plasma concentrations of FGF21. To test if the increased FGF21 levels in the offspring were a consequence of the transfer of maternal FGF21 through the milk, FGF21 levels were analysed in plasma and milk from the two different groups of mothers-those nursing the animals for the standard period and mothers of animals subjected to delayed weaning. The body weight of the two groups of mothers was the same at weaning (Extended Data Fig. 6a ), and FGF21 levels in plasma or milk were also similar (Extended Data Fig. 6b, c ). These findings indicated that the different levels observed in FGF21 were not due to an increased transference of FGF21 from prolonged suckling mothers to the pups. We measured protein levels of FGF21 in the liver (Extended Data Fig.
To test whether this dopaminergic mechanism could be involved in the FGF21-dependent metabolic effects of prolonged suckling, we measured the protein levels of FGF21 and D2R in the LHA/ZI. Indeed, DW-HFD rats showed increased levels of FGF21 in the LHA/ZI compared with SW-HFD animals, which were blunted when hepatic Fgf21 was knocked down (Fig. 4l ). Intriguingly, D2R levels, but not orexin, were also increased in the LHA/ ZI of DW-HFD compared with SW-HFD animals (Extended Data Fig. 7a ) but downregulated again when hepatic Fgf21 production was blocked (Fig. 4m ). Overall, these results support the view that FGF21 overproduction during extended suckling reaches hypothalamic dopaminergic circuits to mediate increased BAT thermogenesis. Reconfirming the relevance of hepatic Fgf21 on D2R expression in the LHA/ZI, mice lacking Fgf21 in the liver, which showed lower interscapular temperature and lower levels of UCP1 in BAT when exposed to cold (Extended Data Fig.
8c ). Next, to test whether FGFR1-mediated transport of FGF21 through tanycytes may be required for the central regulation of thermogenesis, we inhibited Fgfr1 expression specifically in tanycytes using AAVs. We gave an ICV injection to wild-type mice with AAV1/2 into the lateral ventricle expressing Cre recombinase under transcriptional control of the human type 2 iodothyronine deiodinase (hDio2) promoter (AAV1/2-hDio2-iCre) together with Cre-dependent AAVs expressing GFP or an shRNA against Fgfr1. The efficiency of the approach was demonstrated by assessing the colocalization between GFP and vimentin (Extended Data Fig. 10e ). One month after AAV injection, mice were exposed to cold for 6 h and BAT was collected. Mice expressing the Fgfr1 shRNA in tanycytes showed a lower temperature in the BAT after cold exposure (Fig.
LHA/ZI and MBH were excised from the whole hypothalamus cutting between the rostral and caudal limits of the median eminence parallel to the base of the hypothalamus and 1 mm to each lateral side of the median eminence for LHA/ZI and 0. 5 mm for MBH. The depth of each section isolated was around 1 mm thick in mice and 3 mm thick in rat brain  20. Histomorphology and immunohistochemistry. Tissues were fixed in 10% paraformaldehyde for inclusion in paraffin. BAT and SAT samples were cut and mounted in a section (3 μm) and stained (haematoxylin and eosin alcoholic (BioOptica) procedure)  64. Frozen sections of liver were cut (8 µm) with a cryostat and stained in filtered Oil Red O (10 min). Sections were washed in distilled water, counterstained with Mayer's haematoxylin (3 min), mounted in aqueous mounting medium (glycerine jelly) and observed and photographed with a Zeiss AXIO microscope digitizing the images with a coupled camera (AxioCam MRC) and quantified with ImageJ software (RRID: SCR_003070).
Acknowledgements This work was supported by grants from: L. M. S. :  Instituto de Salud Carlos III  and co-funded by  FEDER  ( PI18/00998 ),  FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación  (C. D. :  BFU2017-87721 ; M. L. :  RTI2018-101840-B-I00 ; R. N. :  RTI2018-099413-B-I00  and  RED2018-102379-T ),  Xunta de Galicia  (M. L. :  2016-PG068 ; R. N. :  2016-PG057  and  2020-PG015 ),  Fundación BBVA (R. N. : Fundación Atresmedia  (to M. L. and R. N. ),  Fundación La Caixa  (to M. L. and R. N. ),  Fundación Jesus Serra  and  European Foundation for the Study of Diabetes  (to R. N. ). The research leading to these results also received funding from the  European Community 's  H2020 Framework Programme  under the following grant:  ERC Synergy Grant - 2019-WATCH-810331  to M.
S. , V. P. and  R. N. Centro de Investigación Biomédica en Red (CIBER) de Fisiopatología de la Obesidad y Nutrición (CIBERobn). CIBERobn is an initiative of the  Instituto de Salud Carlos III (ISCIII) of Spain , which is supported by  FEDER  funds. This work was supported by  Inserm ,  Fondation pour la Recherche Médicale, ANR-EPITRACES  (to E. V. ). Research from the M. F. laboratory was supported by the  Spanish Agencia Estatal de Investigación , co-funded by the  FEDER   Programme of the EU  ( BFU2016-80899-P  and  PID2019-105739GB-I00  grants;  AEI/FEDER , UE). M. F. is recipient of a  Ramón y Cajal award  ( RYC-2014-16779 ). C. F. is a recipient of a  Sara Borrel award  ( ISCIII CD19/0078 ). O. A. is a recipient of the Miguel Servet grant (ISCIIII).
Ethics statement The protocol was approved by the National Ethics Board of Suriname (CMWO (Commissie voor Mensgebonden Wetenschappelijk Onderzoek), Opinion Number. The authorization of importation of human biological samples was obtained and the biological collection declared to the French Ministry of Education and Research (DC-2014-2160 and DC-2021-4649). The database was anonymized and registered according to the General Data Protection Regulation (GDPR). A written consent form was collected after information in the participant's language on the primary objective of the study related to malaria and secondary objectives related to zoonoses with medical examination (leading to referral to care in case of suspected health problem) and the use of the biological samples for further epidemiological investigations (without rendering personal results).