Category: 3. Business

  • Claire’s files for bankruptcy as online competition bites

    Claire’s files for bankruptcy as online competition bites

    Faarea Masud

    BBC Business reporter

    AFP via Getty Images A Claire's store in Paris, with it's purple logo, and array of colourful items inside the store on racks.AFP via Getty Images

    Claire’s has hundreds of stores across Europe, including in fashion capital Paris

    Fashion accessories chain Claire’s has filed for bankruptcy in the US for the second time as it struggles with fewer people buying its products, and from online competition eating into profits.

    It confirmed it had already been in discussions “with other partners” about its future. The BBC understands there will be no immediate impact on the firm’s UK stores.

    The US-based firm said it was in discussion “with our vendors and landlords” about its North American stores, but shops there would remain open while it explored “alternatives”.

    The US-based firm first filed for bankruptcy in 2018. It currently operates 2,750 stores in 17 countries throughout North America and Europe.

    Getty Images Lots of necklaces on sale on a Claire's shopping rackGetty Images

    It has about 280 stores in the UK, down from 370 in 2018 when it filed for bankruptcy because it was unable to repay a loan.

    Claire’s is known for selling jewellery, colourful accessories such as necklaces and bracelets, and is part of millions of young people’s memories for its ear piercing services.

    The firm operates under two brand names, Claire’s and Icing, and is the latest casualty of consumers shifting away from physical stores.

    Claire’s is owned by a group of firms, including investment giant Elliott Management.

    Getty Images Claire's colourful bracelets on a typical rack in a storeGetty Images

    “This decision is difficult,” wrote Claire’s chief executive Chris Cramer, “but a necessary one”.

    He blamed “increased competition, consumer spending trends and the ongoing shift away from brick-and-mortar retail” for the declaration of bankruptcy, as well as “debt obligations” and wider economic turmoil.

    The firm’s global supply chain means it is likely to suffer as a result of tariffs imposed on goods, part of US President Trump’s escalating trade war with several countries, including China and Canada.

    The company has a $500m (£375m) loan due in December next year, said Julie Palmer, partner at Begbies Traynor, which would be “looming heavily over management’s minds”.

    Tariffs “have added to the strain”, she said.

    According to retail analyst Catherine Shuttleworth, it is “fair to assume they’re exposed to the same pressures many accessories retailers are.

    “A lot of that category is sourced from Asia, and any increase in import costs hits hard when your price points are low and margins are tight,” she added.

    Mr Cramer said the firm remained in active discussions with potential “strategic and financial partners”, which did not exclude the possibility that it was still looking for a buyer.

    ‘Cheap to super-cheap’

    The Claire’s bankruptcy was a “clear signal” that some parts of retail across the world were “failing to keep up with how people want to shop right now”, said Ms Shuttleworth.

    She said young people didn’t just browse on the High Street for cheap accessories like they used to, with online competitors such as Shein targeting young shoppers with “super cheap” accessories.

    Younger people were now “far more likely” to discover new brands through TikTok than in a shopping centre – and with footfall still well below pre-Covid levels, it had hit Claire’s very hard.

    Ms Palmer said Claire’s “reliance on physical stores – once a key strength – has become a major liability”.

    Outside of its European and American stores, Claire’s says it has a further 300 franchised stores located primarily in the Middle East and South Africa. It also sells its products in thousands of concessions stores in Europe and the US.

    Its lavender-hued storefronts attracted young people particularly in the 1990s and early 2000s, who rifled through its neon and glitter accessories racks for good-value buys. It was often a usual stop for a Saturday shopping trip by families and tweens at malls across the world.

    It also occasionally makes a foray into selling toys including slime, headphones or fluffy toys.

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  • NSD2 inhibitors rewire chromatin to treat lung and pancreatic cancers

    NSD2 inhibitors rewire chromatin to treat lung and pancreatic cancers

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  • Microglia regulate GABAergic neurogenesis in prenatal human brain through IGF1

    Microglia regulate GABAergic neurogenesis in prenatal human brain through IGF1

    Human tissue samples

    De-identified human specimens were collected from the Autopsy Service in the Department of Pathology at the University of California San Francisco (UCSF) (Supplementary Table 2) with previous patient consent in strict observance of the legal and institutional ethical regulations. Autopsy consents and all protocols for human prenatal brain tissue procurement were approved by the Human Gamete, Embryo and Stem Cell Research Committee (Institutional Review Board GESCR no. 10-02693) at UCSF. All specimens received diagnostic evaluations by a board-certified neuropathologist as control samples and were free of brain-related diseases. The diagnostic panel included assessments of neural progenitor and immune cells using IHC to ensure that all control cases were not affected by any inflammatory diseases. Tissues used for snRNA-seq were snap-frozen, either on a cold plate placed on a slab of dry ice or in isopentane on dry ice. Tissues later used for IHC were cut coronally into 1-mm tissue blocks, fixed with 4% paraformaldehyde (PFA) for 2 days, cryoprotected in a 15–30% sucrose gradient, embedded in optimal cutting temperature (OCT; SciGen; 4586) compound, sectioned at 30 μm using a Leica cryostat and mounted onto glass slides.

    Authentication of cell lines used

    All hiPSC and hESC lines used in this study were karyotyped and regularly tested for Mycoplasma. The eWT-1323-4 hiPSC line84 (female; Research Resource Identifier (RRID): CVCL_0G84) was obtained from the Conklin Laboratory (UCSF). WA09/H9 (female; RRID: CVCL_9773; National Institutes of Health (NIH) registration number: NIHhESC-10_0062) and WA01/H1 (male; RRID: CVCL_9771; NIH registration number: NIHhESC-10-0043) were obtained from the WiCell Research Institute. NKX2.1-GFP hESC line56 (female) was obtained from Murdoch Children’s Research Institute and Monash University.

    Mice

    All mice were handled in accordance with the guidelines of the Institutional Animal Care and Use Committee of UCSF. Minimal sample sizes were chosen on the basis of standards commonly used in the field and previous experience with similar experiments. All animals of the same genotype and sex were randomly selected for breeding and/or experimentation in this study. Wild-type C57/B6 mice were purchased from Taconic Biosciences and bred in the laboratory. Igf1f/f mice (strain number 012663) and Cx3cr1CreERt/+ mice (strain number 020940) were purchased from The Jackson Laboratory. For timed pregnancy, males and females were paired, and females were observed daily for the presence of a copulation plug. The noon of the day when a plug was observed was noted as embryonic day 0.5. For Igf1 cKO experiments, 100 mg kg−1 of tamoxifen in corn oil was injected intraperitoneally into pregnant dams on embryonic days 11.5 and 12.5. Igf1f/f; Cx3cr1CreERt/+ fetuses were used as Igf1 cKO mice, and their littermates Igf1+/+; Cx3cr1CreERt/+ and Igf1f/f; Cx3cr1+/+ fetuses were used as controls. During all subsequent experimental procedures, including sample collection, processing, imaging and quantification, the experimenter was blinded to the genotype, sex and age of the mice. Both males and females were included in the mouse experiments. In the EdU labelling experiment, a single dose of EdU (10 mg kg−1; provided in the Click-iT EdU Alexa Fluor 647 Imaging Kit from Invitrogen; C10340) was injected intraperitoneally into pregnant mice at embryonic day 14.5. At embryonic days 14.5 and 16.5, the pregnant dams were killed, and the fetal brains were collected, fixed in 4% PFA at 4 °C overnight, cryopreserved in 30% sucrose at 4 °C overnight, embedded in OCT and cryosectioned at 20 μm (EdU labelling and IGF1 staining experiments) or 40 μm (microglia staining experiments) using a Leica cryostat. Additionally, wild-type P5 pups were transcardially perfused with 4% PFA, and their brains were extracted and post-fixed in 4% PFA overnight, cryoprotected in 30% sucrose overnight, embedded in OCT and cryosectioned at 20 μm.

    Human pluripotent stem-cell-derived organoids

    The hPSC-derived organoids were generated largely following a previously established protocol37,38. In brief, 1323-4 hiPSCs or WA01/H1 and WA09/H9 hESCs were expanded in StemFlex Basal Medium (Gibco; A3349401). After reaching 80% coverage, hPSCs cultured on Matrigel were dissociated into clumps using ReLeSR (STEMCELL Technologies; 100-0483) and equally distributed into a V-bottom 96-well ultra-low-attachment PrimeSurface plate (S-BIO; MS-9096VZ). The rho kinase inhibitor Y-27632 (10 μM) was added during the first 24 h of neural induction to promote survival. Between days 0 and 5, organoids were cultured in neural induction medium (Dulbecco’s modified Eagle medium/F-12, 20% knockout serum, 1% non-essential amino acids, 0.5% GlutaMAX, 0.1 mM β-mercaptoethanol and 1% penicillin–streptomycin) supplemented with the SMAD inhibitors SB431542 (10 μM) and dorsomorphin (5 μM). Between days 6 and 24, organoids were cultured in neural differentiation medium (Neurobasal-A medium, 2% B27 supplement, 1% GlutaMAX and 1% penicillin–streptomycin) supplemented with human recombinant EGF (20 ng ml−1) and human recombinant FGF2 (20 ng ml−1). Between days 25 and 43, organoids were maintained in neural differentiation medium supplemented with human recombinant brain-derived neurotrophic factor (20 ng ml−1) and human recombinant neurotrophin 3 (20 ng ml−1). For MGEOs, the media were also supplemented with 5 μM wnt inhibitor IWP-2 on days 4–23, 100 nM smoothened agonist on days 12–23, 100 nM retinoic acid on days 12–15 and 100 nM allopregnanolone on days 16–23 for ventral forebrain patterning. Cortical organoids were not supplemented with IWP-2, smoothened agonist, retinoic acid and allopregnanolone. Each organoid was then moved to six-well plates for long-term culture after week 5. All media and supplements used for organoid cultures were the same as those in a previously published protocol37,38.

    Induced microglia

    Induced microglial cells were generated from WA01/H1 or WA09/H9 hESC cells using STEMdiff kits, according to the manufacturer’s protocols. In brief, hESCs were differentiated into CD43-expressing haematopoietic progenitor cells for 12 days using a STEMdiff Hematopoietic Kit (STEMCELL; 05310). Haematopoietic progenitor cells were differentiated for 24 days using the STEMdiff Microglia Differentiation Kit (STEMCELL; 100-0019) and matured for an extra 4 days using the STEMdiff Microglia Maturation Kit (STEMCELL; 100-0020) before being added to the organoid cultures for co-culture.

    iMG–organoid engraftment and co-culture

    Mature iMG were immediately added to 4-week-old MGE organoids in 96-well ultra-low attachment PrimeSurface plates at 80–100 × 103 microglia per organoid. Trophic factors (100 ng ml−1 of IL-34 (PeproTech; 200-34), 25 ng ml−1 of CSF1 (PeproTech; 300-25) and 50 ng ml−1 TGFβ1 (PeproTech; 100-21)) were added to the culture medium to support microglial survival. One wpt, co-cultured organoid–microglia (neuroimmune organoids) were transferred to a six-well plate and placed on an orbital shaker. The co-cultures were then maintained following the usual organoid maintenance protocol, with the addition of trophic factors.

    Pharmacological manipulation of organoids

    Six-week-old organoids were treated with PBS, 100 ng ml−1 of recombinant human IGF1 (Abcam; ab269169), 1 μg ml−1 of IGF1-neutralizing antibodies (Abcam; ab9572), 1 μM GSK4529 (GSK1904529A; Selleckchem; S1093) or 1 μΜ picropodophyllin (Selleckchem; S7668) for 48 h. Then 10 μΜ BrdU (Abcam; ab142567) was added during the last 4 h to label proliferating cells. Organoids were collected immediately after the treatment for IHC analysis. For the DAPT treatment experiment, PBS or 10 μM DAPT (Abcam; ab120633) was applied to MGEOs transplanted with iMG from 10 to 14 dpt. Organoids were then collected at 14 dpt for IHC analysis.

    Immunohistochemistry

    We followed the IHC protocol, as previously reported14,32. Human tissue samples were fixed and cryosectioned, as described above. Mouse samples were prepared, as described above in ‘Mice’. Organoids were fixed in 4% PFA for 30–45 min at room temperature and cryopreserved in 30% sucrose in PBS overnight. The organoids were then embedded in OCT and cryosectioned at 14 μm using a Leica cryostat.

    The mounted human slides were defrosted overnight at 4 °C and then dried at 37 °C for 3 h. The mounted organoids and mouse slides were dried directly at 37 °C for 30 min. Antigen retrieval was performed for 5–12 min at 95–99 °C using antigen retrieval buffer (BD Pharmingen; 550524). After antigen retrieval, tissue slices were washed with 1× PBS plus 0.1% or 0.3% Triton X-100 and then blocked in blocking buffer (5–10% serum, 1% bovine serum albumin (BSA) and 0.1% Triton X-100 in PBS, or 1% BSA in 0.3% Triton X-100 in PBS) for 1–1.5 h at room temperature before proceeding to incubation with primary antibodies (Supplementary Table 3) overnight at 4 °C. After washing, sections were incubated with species-specific secondary antibodies conjugated to Alexa Fluor dyes (1:500; Invitrogen) for 1.5–2 h at room temperature. For human and embryonic mouse slides, TrueBlack Lipofuscin Autofluorescence Quencher (1:20 in 70% alcohol; Biotium; 23007) was applied for 3–5 min to block autofluorescence. For EdU staining, the EdU working solution was applied to embryonic mouse brain slices after secondary antibody application following the manufacturer’s instructions. Nuclei were counterstained with DAPI (1:1,000 from 1 mg ml−1 of stock; Invitrogen; 2031179) for 5 min. Images were captured using a Leica STELLARIS 8 confocal microscope. For organoid experiments, three slices of each organoid were imaged, quantified using ImageJ (1.54) and averaged for the final statistical analysis.

    Three-dimensional reconstruction and image analysis

    Three-dimensional reconstructions were generated using the Imaris software (Oxford Instruments). For distance analysis, microglia were reconstructed using surface modules, whereas Ki-67+ or DAPI+ cells were reconstructed with spot modules. The distance from the centre of each cell (spot) to the nearest microglia (surface) was determined using Imaris. The distance distributions of the Ki-67+ and Ki-67− cells to the nearest microglia were calculated accordingly.

    Single-nucleus preparation

    Single-nucleus suspensions were prepared from postmortem human samples. About 50 mg of sectioned freshly frozen human brain tissue was homogenized in lysis buffer (0.32 M sucrose, 5 mM CaCl2, 3 mM MgAc2, 0.1 mM EDTA, 10 mM Tris-HCl, 1 mM dithiothreitol and 0.1% Triton X-100 in diethyl pyrocarbonate-treated water) plus 0.4 U μl−1 of RNase inhibitor (Takara; catalogue no. 2313A) on ice. Then, the homogenate was loaded into a 30-ml-thick polycarbonate ultracentrifuge tube (Beckman Coulter; catalogue number 355631), and 9 ml of sucrose cushion solution (1.8 M sucrose, 3 mM MgAc2, 1 mM dithiothreitol and 10 mM Tris-HCl in diethyl pyrocarbonate-treated water) was added to the bottom of the tube. The tubes with tissue homogenate and sucrose cushions were then ultracentrifuged at 107,000g for 2.5 h at 4 °C. The pellet was recovered in 250-μl ice-cold PBS for 20 min, resuspended in nuclei sorting buffer (PBS, 1% BSA, 0.5 mM EDTA and 0.1 U μl−1 of RNase inhibitor) and filtered through a 40-μm cell strainer to obtain single-nucleus suspensions for FACS/fluorescence-activated nucleus sorting.

    Single-cell preparation

    Single-cell suspensions of 1323-4 hiPSC-derived organoids were prepared using neural tissue dissociation kits (P) (Miltenyi Biotec; 130-092-628) following the manufacturer’s instructions. In brief, 12–16 organoids per experimental condition were processed through a gentle two-step enzymatic dissociation procedure, as instructed. Five mg ml−1 of Actinomycin D (Sigma-Aldrich; A1410), 10 mg ml−1 of anisomycin (Sigma-Aldrich; A9789) and 10 mM triptolide (Sigma-Aldrich; T3652) were added before tissue digestion to inhibit the cellular transcriptome. Following digestion, organoids were mechanically triturated using fire-polished glass pipettes, filtered through a 40-μm cell strainer test tube (Corning; 352235), pelleted at 300g for 5 min and washed twice with Dulbecco’s phosphate-buffered saline (DPBS) before proceeding to 10× genomics scRNA library preparation. For samples that needed FACS, the single-cell pellet was resuspended in cell sorting buffer (DPBS, 1% BSA and 0.1 U μl−1 of RNase inhibitor).

    FACS and fluorescence-activated nucleus sorting

    Single-nucleus suspensions from fresh-frozen human samples were stained with antibodies of PU.1 (Cell Signaling Technology; 81886S; 1:100) and OLIG2 (Abcam; ab225100; 1:2,500) overnight at 4 °C. PU.1 and OLIG2 antibodies were conjugated with fluorescence upon purchase. DAPI (1:1,000) was added for 5 min on the second day. Single-cell suspensions from organoids were stained with DAPI (1:1,000) for 5 min in cell sorting buffer (DPBS; 1% BSA and 0.1 U μl−1 of RNase inhibitor). The single-nucleus/cell suspension was then centrifuged at 300g for 5 min, resuspended in nucleus/cell sorting buffer and filtered through a 40-μm cell strainer for final analysis and sorting using a FACSAria II Cell Sorter (BD Biosciences). Target cells were collected in nucleus/cell sorting buffer for future sequencing library preparation.

    Single-cell and single-nucleus RNA library preparation

    Nuclei and cells were counted using a haemocytometer and resuspended to a final concentration of 300–1,000 cells/nuclei per microlitre in PBS. Single-nucleus/cell RNA-seq libraries were prepared using 10× Genomics Chromium Next GEM Single Cell v.3.1 kit according to the manufacturer’s instructions, targeting for 5,000 nuclei/cells per sample. Single-cell/nucleus libraries were then sequenced on the NovaSeq 6000 machine, with a sequencing depth of 50,000 reads per cell.

    Single-cell and single-nucleus RNA-seq data analysis

    Sequencing results were then aligned to the GRCh38 genome (gex-GRCh38-2020-A) using Cell Ranger v.6.1.2 (10× Genomics). Then ‘–include-introns’ was used to include premature messenger RNA in single-nucleus samples. Gene counts then underwent a doublet removal step using DoubletFinder v.2.0.3 (https://www.cell.com/cell-systems/fulltext/S2405-4712(19)30073-0).

    The output (count matrix) was used as the main input file for all downstream analyses using Seurat v.5.1.0. For human snRNA-seq, nuclei with UMIs of less than 1,000, gene features of less than 1,000 or more than 100,000 or percentage of mitochondrial genes of more than 3% were filtered out. For organoid scRNA-seq, cells with UMIs of less than 800 or more than 50,000, gene features of less than 500 or more than 10,000 or percentage of mitochondrial genes less than 2% or more than 25% were filtered out. For FACS-isolated iMG scRNA-seq, cells with UMIs of less than 1,000 or more than 80,000, gene features of less than 1,000 or more than 20,000 or mitochondrial genes less than 20% were filtered out. MALAT1, mitochondrial genes (MT-), ribosomal protein-encoding genes (RPS- and RPL-) and haemoglobin genes (HB-) were excluded from further analysis. Standard data normalization, variable feature identification, linear transformations, dimensional reduction, UMAP embedding and unsupervised clustering were conducted using the standard Seurat pipeline35. Cell-type cluster identification was performed on the basis of the expression of known marker genes, as shown in Extended Data Figs. 2, 7 and 10. For scRNA-seq of GFP-labelled iMG, iMG were purified in silico using canonical microglia/macrophage markers, including AIF1, CX3CR1, C3, PTPRC, ITGAM and CD68.

    We analysed cell–cell interaction using CellChat v.2 (ref. 36). For development-based analysis, independent CellChat files were generated from ‘embryonic’ and ‘perinatal’ Seurat objects, and a comparison analysis was conducted between them. A heat map was created using GraphPad Prism 9 according to the interacting probability of significant ligand–receptor interactions involved in microglial regulation of interneurons (CIN).

    DEG analysis was conducted on the basis of the Seurat-default non-parametric Wilcoxon rank-sum test. Pathways with enriched DEGs were generated using Enrichr (https://maayanlab.cloud/Enrichr/#) on the basis of the Reactome Pathway Database, Kyoto Encyclopedia of Genes and Genomes, GEO and Gene Ontology database. The full names of the pathways shown in Fig. 4 are as follows: IGF1R 46: IGF1R drug inhibition 46 (kinase perturbations from GEO down; GSE14024); IGF1R 52: IGF1R knockdown 52 (kinase perturbations from GEO down; GSE16684); mitotic sister: mitotic sister chromatid segregation (GO:0000070); aerobic electron: aerobic electron transport chain; respiratory: respiratory electron transport, ATP synthesis by chemiosmotic coupling, heat production by uncoupling proteins (R-HSA-163200).

    Principal component analysis

    The published sequencing datasets for comparison were collected from eleven previous papers39,41,42,43,44,45,46,47,48,49,50. The specific papers and corresponding NIH GEO datasets used were as follows: GSE89189 (ref. 39); (GSE123021, GSE123022, GSE123024 and GSE123025) (ref. 41); GSE121654 (ref. 42); GSE141862 (ref. 43); (GSE133345 and GSE137010) (ref. 44); GSE180945 (ref. 45); GSE178317 (ref. 46); (GSE139549 and GSE139550) (ref. 47); GSE85839 (ref. 48); GSE97744 (ref. 49); GSE99074 (ref. 50). Each dataset was collected, filtered and grouped by appropriate characteristics, including species, real/derived, bulk/single cell, age and protocol details. To facilitate comparison, the groups within each set were pooled into single representations.

    Once the data were collected and preprocessed, the pooled samples were processed using Scanpy v.1.10.3 (https://github.com/scverse/scanpy). Specifically, the cells were normalized by total counts over all genes using scanpy.pp.normalize_total. They were then logarithmized using scanpy.pp.log1p. For use in downstream PCA, highly variable genes were calculated using scanpy.pp.highly_variable_genes. The number of top genes was configured to 2,000. In the final steps, PCA was performed using scanpy.tl.pca (default of 50 components), and a scatter plot using the coordinates of PCA 1 and 2 was plotted for each cell representation using scanpy.pl.pca.

    CRISPR–Cas9 gene editing

    A WA09/H9 stem cell line with an IGF1 loss-of-function mutation (IGF1 knockout) was generated using CRISPR–Cas9-based non-homology end joining, largely following the protocol of the Alt-R CRISPR–Cas9 System from Integrated DNA Technologies (IDT). The guide RNA (5′TCGTGGATGAGTGCTGCTTC3′) was selected from the predesigned Alt-R CRISPR–Cas9 guide RNA (IDT). Equal amounts of CRISPR RNA and ATTO 550 labelled tracrRNA (IDT; 1075927) were mixed to a final concentration of 100 μΜ, heated to 95 °C for 5 min and then cooled to room temperature for annealing followed by the formation of the ribonucleoprotein complex with Alt-R S.p. HiFi Cas9 Nuclease V3 (IDT; 1081061) at room temperature for 20 min. The ribonucleoprotein complex was delivered to single-stem-cell suspensions using the Neon Electroporation System (1,400 V; 20 ms; one pulse) according to the manufacturer’s instructions. After electroporation, ATTO 550+ cells were selected by FACS after 3 days of culture and sparsely seeded to form a single-cell colony. A loss-of-function mutation cell line was selected by Sanger sequencing with out-of-frame mutations at the target site, followed by exclusion of any mutations at the top 5 potential off-target sites. Further Sanger sequencing confirmation, reverse transcription–quantitative polymerase chain reaction and IHC were performed to confirm IGF1 knockout.

    Time-lapse imaging of microglia–MGE progenitor interactions

    To visualize the interactions between engrafted microglia and MGE progenitors in MGEOs, we used MGE organoids generated from NKX2.1-GFP cells and iMG derived from tdTomato-labelled WA09/H9 cells using lentiviral transduction (SignaGen Laboratories; SL100289). Live imaging was performed 2–3 weeks after iMG transplantation. For imaging, engrafted organoids were transferred to a flat glass-bottom six-well plate, with one organoid per well with 500 μl of culture medium. Time-lapse imaging was conducted using a Leica SP8 confocal microscope at 37 °C and 5% CO2. Z-stacks were captured every 5 min over a 12-h period and processed using maximum intensity projections to visualize dynamic cellular interactions.

    Data analysis, statistics and presentation

    For all quantifications, images were acquired and quantified blindly to genotype or treatment. Statistical analyses were performed using GraphPad Prism (v.10.1.0), as shown in each figure legend.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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  • Whole-genome sequencing of 490,640 UK Biobank participants

    Whole-genome sequencing of 490,640 UK Biobank participants

    We integrated deep phenotyping data27 available for most UKB participants and performed genetic association analysis across selected disease outcomes captured with electronic health records and molecular and physical measurement phenotypes, many of which are well-established disease biomarkers. Association testing was performed for all observed genetic variants and using several genetic models; we included single-variant tests, multi-ancestry meta-analysis, rare-variant collapsing analysis and SV analysis (Supplementary Methods).

    Genome-wide association analysis

    Genome-wide association analysis for individual SNPs and small indels was performed using the GraphTyper dataset in each ancestry cohort for 764 ICD-10 codes (n cases >200) and 71 selected quantitative phenotypes (n > 1,000; Supplementary Table 7). For the NFE cohort, we estimated the gain in discovery and improvement of fine mapping in association signals observed with the WGS call set versus variants observed in the imputed array genetic dataset1 using equivalent analysis results with the same cohort and phenotyping strategy. We observed that whereas the increase in discovery was modest for common variant associations (Supplementary Fig. 3), the ability to fine map association signals was improved, and this was not due only to the loss of power in association tests attributable to imputation accuracy in the array dataset. We identified 33,123 associations (P value < 5 × 10−8) across 763 binary and 71 quantitative genome-wide association study (GWAS) datasets (Supplementary Methods). Of these, 3,991 (12.05%) are new to the WGS data when compared to those identified using only array imputed variants. As expected, most associated variants novel to WGS are rare variants, including 86% of associations with minor allele frequency (MAF) <0.0001, whereas only 2% of associations with MAF > 0.1 are novel to WGS (Supplementary Fig. 3). Among the 29,357 associations identified using array imputed variants, 2,984 had a different, more significant, lead variant in the WGS variants, resulting in improved fine mapping of the association signals observed (Supplementary Table 8). For example, a common variant association uncovered by WGS that was previously missed by the imputed array data is near genes MRC1 and TMEM236 in chromosome 10, where we identified an association between rs371858405 (NFE MAF = 0.24) and reduced hypothyroidism risk (odds ratio (OR) = 0.94, P value = 2.6 × 10−11). In the imputed data, the region within the WGS lead variant has sparse SNP coverage when compared to adjacent regions (Supplementary Fig. 4b), probably a result of a patch to the hg19 reference genome (chr10_gl383543_fix) that occurred after the UKB genotyping array was designed. A second example illustrating a new biological findings with rare genetic variation is the observation of a rare frameshift variant (MAF = 5.1 × 10−5) in FOXE3 chr. 1: 47417015:GC:G (rs1176723126) found to be significantly associated with the first occurrence phenotype ‘other cataract’ (ICD-10 code H26; P value = 6.2 × 10−9; Supplementary Fig. 4b). The link between FOXE3 and cataract, and other ocular diseases, was reported in previous familial studies and human and mouse disease models28, but the association was not observed in the UKB imputed array or meta-analyses that included the UKB imputed array29.

    Multi-ancestry meta-GWAS

    To examine multi-ancestry genetics of tested health-related phenotypes, we performed trans-ancestry meta-analysis of the GraphTyper GWAS data across 5 ancestries for 68 quantitative traits with ≥1,000 measurements in at least 2 ancestries and 228 ICD-10 disease outcomes with ≥200 cases in at least 2 ancestries. We identified 28,674 genome-wide significant (GWS; P value < 5.0 × 10−8) associations in the meta-analysis (Supplementary Methods, Supplementary Fig. 5 and Supplementary Table 9); of these, 1,934 associations were observed only in the meta-analysis, 26,478 were also observed in the NFE analysis, 82 were observed only in 1 of the non-NFE cohort analyses, and the remaining 180 associations were observed in more than 1 ancestry cohort (Fig. 2 and Supplementary Table 10). Among the 28,674 identified associations, 4,760 (16.6%) were not previously reported in the GWAS Catalog or OpenTargets30 (Supplementary Methods, Supplementary Fig. 3b and Supplementary Table 9).

    Fig. 2: UpSet plot of GWS associations across ancestries.

    Ancestry labels are sorted by number of GWS associations in each set: meta-analysis (Meta), NFE, SAS, AFR, ASJ and EAS.

    Of the meta-analysis significant associations, 126 were more significant in non-NFE ancestries (lead variant with the smallest P value) despite the much smaller sample size compared to NFE (Supplementary Fig. 6a): 83 with strongest signals in AFR, 37 in SAS, 5 in EAS and 1 in ASJ. Almost all 126 significant sentinel variants had MAF <0.5% in NFE; the median MAF enrichment compared with NFE is highest in AFR (MAFAFR/MAFNFE) = 828.49, followed by EAS and SAS with a relatively wide range of enrichment (Supplementary Fig. 6b). For example, we observed ancestry-specific associations in the HBB locus (Extended Data Fig. 3). The lead variant, rs334 (chr. 11:5227002:T:A), a missense variant in the HBB gene, is the primary cause of sickle cell disease, resulting in abnormal haemoglobin. Despite causing sickle cell disease, rs334-A is specifically common in AFR, driven by its protective effect against malaria and selective advantage in AFR31. One HBB splice site variant rs33915217 (chr. 11:5226925:C:G) is associated with β-thalassaemia and anaemia with elevated frequency specifically in SAS, potentially shaped by genetic drift, founder effect or unknown selective advantage32. Another HBB nonsense variant, rs11549407 (chr. 11:5226774:G:A), is associated with thalassaemia and anaemia detectable only in NFE given the large size (P value < 5.6 × 10−62, β = 6.9). rs11549407-A introduces a premature stop codon, leading to an unstable haemoglobin molecule, but it has not been shown to confer protection against malaria or other pathogens. Under the same selection pressure of malaria, a G6PD missense variant rs1050828 (chr. X:154536002:C:T), which causes the G6PD deficiency and haemolytic anaemia but provides protection against severe malaria, reaches high frequency in AFR (14.7%) but remains rare in NFE (0.005%). It is an AFR-specific GWS signal linked to increased reticulocyte and bilirubin levels, indicating compensatory release triggered by haemolysis.

    Loss-of-function variants in WGS

    Naturally occurring human genetic variation known to result in disruption of protein-coding genes provides an in vivo model of human gene inactivation. Individuals with loss-of-function (LoF) variants, particularly those with homozygous genotypes, can therefore be considered a form of human ‘knockouts’. Studying human knockouts affords an opportunity to predict phenotypic consequences of pharmacological inhibition. Besides putative LoF (pLoF) variants that can be predicted on the basis of variant annotation, ClinVar23 also reported pathogenic or likely pathogenetic (P or LP, respectively) variants with clinical pathogenicity. Among the 490,000 UKB WGS participants (GraphTyper dataset), we found that there are 10,071 autosomal genes with at least 100 heterozygous carriers and 1,202 autosomal genes with at least 3 homozygous carriers. Among the 81 genes recommended by the American College of Medical Genetics and Genomics (ACMG)33 for clinical diagnostic reporting, we found 7,313 pLoF, P or LP variants carried by 51,107 individuals. Furthermore, there are 81 homozygous carriers of pLoF, P or LP variants found in 14 ACMG genes, of which 56 participants carry mutations in DNA repair pathway genes such as MUTYH, PMS2 and MSH6 (Supplementary Table 11). Among them, a subset are clinically actionable genotypes with a confirmed functional impact in the corresponding inheritance mode. Further validation, and confirmation with ACMG diagnostic criteria, is needed to determine which variants are clinically actionable.

    Comparing the UKB WGS dataset versus the WES dataset, among the same set of 450,000 participants, about 16,000 autosomal genes harbouring pLoF, P or LP variants in ≥1 carriers in both WGS and WES. However, WGS enabled us to find more carriers of high-impact variants (for example, the median difference in the number of carriers is 44 more in the WGS dataset compared to the WES dataset for the gene sets with >100 carriers; Fig. 3). Partially attributable to quality control criteria (Supplementary Methods), this is also expected given the more even and deeper coverage in WGS.

    Fig. 3: Observed number of genes in carriers of heterozygous pLoF, P or LP variants in WGS and WES.
    figure 3

    The number of autosomal genes (y axis) with at least 1, 25, 50 and 100 heterozygous carriers among the number of individuals (x axis) to the total number of 452,728 participants with both WES and WGS data.

    Rare-coding-variant association studies with WES and WGS

    Gene-level collapsing analysis, in which aggregation of rare variants is tested for association with disease, has emerged as a powerful method for identifying gene–phenotype associations with high allelic heterogeneity21,34. So far, most collapsing analyses have used WES data35. We reasoned that the greater coverage of WGS compared to WES could increase power to detect gene–phenotype associations. We performed two collapsing analysis-based phenome-wide association studies (PheWAS) on an identical sample of 460,552 individuals using both WES- and WGS-based protein-coding regions (Supplementary Methods). All results for rare-variant collapsing analyses use the single-sample DRAGEN variant calls. In total, we tested for the association between 18,930 genes and 751 phenotypes (687 binary ‘first occurrence’ phenotypes and 64 quantitative traits that met our inclusion criteria; Supplementary Methods and Supplementary Table 12) using 10 non-synonymous and 1 synonymous control collapsing analysis models (Supplementary Table 13 and Supplementary Methods). We meta-analysed the separate ancestry strata and set the significance threshold at P value ≤ 1 × 10−8, which was previously empirically validated21.

    In total, we identified 1,359 significant gene–phenotype associations, of which 87.4% (1,188) were significant in both the WES and WGS PheWASs (184 binary and 1,004 quantitative associations), 7.7% (105) were significant only in the WGS PheWAS (23 binary and 82 quantitative associations), and 4.9% (66) were significant only in the WES PheWAS (15 binary and 51 quantitative associations; Supplementary Table 14). There was high correlation between −log10[P values] derived from WES and WGS (Spearman’s rank correlation coefficient = 0.95, P < 2.2 × 10−16; Supplementary Fig. 7). Across both binary and quantitative traits, there were 29 genes with significant associations unique to WGS and 20 genes with significant associations unique to WES (Supplementary Fig. 8). Three genes uniquely associated with either technology are in the major histocompatibility complex region: VWA7 (WES) and HLA-C and C2 (WGS). Fewer than 3.3% of gene–phenotype pairs had an absolute difference in −10 × log10[P values] of greater than 5 units and fewer than 0.56% had greater than 10 units (Supplementary Fig. 9). Across all 14,130,325 gene–phenotype associations (significant and non-significant), there were 54,818 with greater than a 10-unit difference that achieved a lower P value in the WGS results, compared to 23,687 that achieved a lower P value in the WES results (Extended Data Fig. 4).

    We identified 95 significant gene–phenotype associations with 15 genes recurrently mutated in clonal haematopoiesis and myeloid cancers as described previously36, which are potentially driven by somatic qualifying variants. Of these, 70 were detected by both technologies, 11 were unique to WGS and 14 were unique to WES. Associations unique to WGS included protein-truncating variants in TET2 and other disorders of white blood cells (WGS P value = 3.62 × 10−13, OR = 8.08, 95% confidence interval (CI) = 5.02–12.40; WES P value = 4.23 × 10−7, OR = 6.18, 95% CI = 3.26–10.70). We also found an association between protein-truncating and predicted damaging missense variants in SRSF2 and reticulocyte percentage (WGS P value = 1.92 × 10−6, β = 0.30, 95% CI = 0.17–0.42; WES P value = 3.7 × 10−18, β = 0.60, 95% CI = 0.47–0.74) significant only in the WES results (Supplementary Table 14).

    Overall, although association results between the WES and WGS DRAGEN datasets are highly correlated, there are genes for which coverage is improved in WGS, resulting in modestly improved association statistics. One example is PKHD1, for which associations with three quantitative phenotypes were more significant in WGS than WES: γ-glutamyl transferase (WES P value = 4.63 × 10−18, β = 0.19, 95% CI = 0.15–0.24; WGS P value = 1.24 × 10−19, β = 0.20, 95% CI = 0.16–0.24), creatinine (WES P value = 3.85 × 10−10, β = −0.04, 95% CI = −0.06 to −0.03; WGS P value = 2.14 × 10−12, β = −0.05, 95% CI = −0.06 to −0.03) and cystatin C, which achieves significance only in the WGS data (WES P value = 3.02 × 10−8, β = −0.05, 95% CI = −0.07 to −0.03; WGS P value = 3.04 × 10−9, β = −0.04, 95% CI = −0.06 to −0.03; Supplementary Table 14). The number of samples with ≥10× coverage of PKHD1 is lower in WES than WGS at specific protein-coding sites (Supplementary Fig. 10), demonstrating the value of WGS to ascertain variants and associations in regions not well captured by WES.

    We calculated coverage statistics in the WES and WGS datasets for each protein-coding gene (Supplementary Table 15). There are only 638 genes in the WGS for which <95% of the protein-coding sequence had on average at least 10× coverage across the cohort, compared to around twice as many (1,299) in the WES dataset21. This improved coverage of some genes in the WGS data compared to WES demonstrates the value of WGS for improved discovery potential in some protein-coding regions.

    Rare-variant PheWAS of UTRs

    To understand the contributions of rare UTR variants to phenotypes, we used the UKB single-sample DRAGEN WGS data to compile about 13.4 million rare (MAF < 0.1%) variants from both 5′ and 3′ UTRs of protein-coding genes across the 5 defined ancestries. We performed two multi-ancestry collapsing PheWASs: UTR alone and UTR plus protein coding.

    We tested the aggregate effect of UTR-alone qualifying variants on binary and quantitative phenotypes for 5′ UTRs alone, 3′ UTRs alone and 5′ and 3′ UTRs combined (Supplementary Table 12). Each was run using six collapsing analysis models to capture a range of MAF and CADD37,38,39 thresholds. Any UTR sites that overlapped a protein-coding site were omitted. Using a previously described n-of-1 permutation approach21, we confirmed that P value ≤ 1 × 10−8 is an appropriate significance threshold (Supplementary Methods). We observed 63 significant associations (1 binary trait and 62 quantitative traits) comprising 32 unique genes and 37 unique phenotypes (Fig. 4 and Supplementary Table 16). Many of these gene–phenotype associations have previously been identified with rare protein-coding variants or have GWAS support38,39. For example, 32 of 63 (51%) signals were also significant in the WGS protein-coding collapsing PheWAS already described, and 52 of 63 (83%) had a common variant within 500 kilobases (kb) significantly associated with the same phenotype in the UKB WGS Consortium GWAS already described (Supplementary Methods and Supplementary Table 16). The observed associations are likely to include some UTR variants that are causally linked to the phenotype, and some that are in partial linkage disequilibrium with nearby common variant associations.

    Fig. 4: UTR-based collapsing analysis.
    figure 4

    Miami plot of UTR-based rare-variant PheWAS associations for 687 binary (top) and 64 quantitative (bottom) phenotypes across all 6 collapsing models. Significant 5′, 3′ and 5′ and 3′ combined associations are represented in different colours. The top significant binary associations and the significant quantitative associations with P value < 1 × 10−30 are labelled. P values are unadjusted and are from Fisher’s exact two-sided tests (for binary traits) and linear regression (for quantitative traits).

    We next explored the combined effect of rare UTR variants and protein-truncating variants using two different models. We observed 27 and 157 significant associations for binary and quantitative phenotypes, respectively (Supplementary Table 16). Ten associations that achieved significance in this UTR plus protein-coding PheWAS were not significant in the protein-coding-alone collapsing PheWAS, suggesting that those associations were augmented by incorporating UTRs (Supplementary Table 16). Furthermore, 27 suggestive (1 × 10−8 < P < 1 × 10−6) associations in the UTR plus protein-coding PheWASs did not reach this threshold in the protein-coding-alone collapsing PheWAS (Supplementary Table 16). For instance, NWD1 is suggestively associated with kidney calculus (P value = 7.53 × 10−7, OR = 1.63) in the UTR plus protein-coding PheWAS, but not in the protein-coding-alone or the UTR-alone collapsing PheWASs. This is mostly driven by rare 3′ UTR variants (Supplementary Table 17), although the qualifying variants are distributed throughout the gene. No significant common variant associations were observed between NWD1 (±500 kb) and kidney calculus in the UKB WGS Consortium GWAS; however, a common synonymous variant, rs773852, is associated with kidney calculus in a Chinese Han population40 Our study demonstrates the potential of WGS in identifying non-protein-coding variant to phenotype associations.

    Phenotypic effects of SVs

    Associations identified in the previous UKB 150,119 release22 from the WGS consortium were mostly replicated. The new UKB release allows the identification of rarer SVs and assesses their impact on phenotypes. We present exemplary associations, anchoring on genes and variants that have a well-established association with phenotype.

    Genes are typically affected by several SVs. Previously22, we highlighted an association of non-HDL cholesterol with a 14,154-bp deletion overlapping PCSK9, a gene encoding a proprotein convertase involved in the degradation of LDL receptors in the liver. In the current release, 13 SVs overlapping coding exons in PCSK9 are found, carried by 163 individuals, bringing the total number of PCSK9 pLoF carriers to 1,124 The previously reported SV is the most common of the 13 variants, seen in 111 individuals. The carriers had (1.22 s.d.) lower levels of non-HDL cholesterol, with carriers of other PCSK9 deletions collectively averaging 0.51 s.d. lower levels.

    A 5,200-bp deletion on chr. 12: 56,451,164–56,456,364, is carried by 15 NFE individuals and it strongly associates with cataracts (OR = 25.3, P value = 6.3 × 10−7, MAF = 0.0015%). It deletes all 4 coding exons of MIP while preserving its 5′ UTR region and not affecting other genes. MIP encodes the major intrinsic protein of the lens fibre and rare deleterious missense, and LoF variants are linked to autosomal dominant cataract41,42.

    The ACMG43 recommends reporting actionable genotypes in genes linked with diseases that are highly penetrant with established interventions. We previously reported22 that 4.1% of UKB individuals carry an actionable SNP or indel genotype. An additional 0.60% of individuals carry SVs predicted to cause LoF in autosomal dominant LoF, P or LP genes. If confirmed44, this increases the number of individuals with an actional genotype by 14.8%.

    ClinVar45, a database of clinically significant variants, contains 2,256,088 records at present, but only 4,062 are SVs. Of these, 458 SVs presented here matched 486 (12.0%) in ClinVar. As expected, benign or likely benign variants have a higher frequency than P or LP variants (Supplementary Table 18). The large cohort and rich medical history allows us to assess the likely clinical impact of these variants and potentially refine the ClinVar classification.

    Most ClinVar-annotated pathogenic SVs are very rare (MAF < 0.01%; Supplementary Table 18). One example is a 52-bp deletion on chr. 19: 12,943,750–12,943,802 in the first exon of CALR resulting in a stop gain. This recurrent somatic mutation46,47,48 is listed as pathogenic for primary myelofibrosis and thrombocythaemia is carried by 47 NFE individuals and 1 AFR individual. It strongly associates with measures of platelet distribution; most strongly with platelet width, effect 2.02 s.d. (95% CI = 1.72–2.34, P value = 3.1 × 10−38). It is present in the SNP and indel call set, but is not found in the WES data, despite being exonic.

    Although most ClinVar variants are very rare in the UKB some have a higher frequency in the sub-cohorts. One example is a 2,502-bp deletion on chr. 2: 151,645,755–151,648,057 deleting exon 55 of NEB, linked with nemaline myopathy and traced to a single founder mutation49; it is carried by 33 individuals in the cohort, 17 of whom belong to the ASJ cohort. Another example is a 613-bp deletion on chr. 11 : 5,225,255–5,225,868 removing the first 3 exons of HBB seen in 19 individuals all belonging to the SAS cohort. The deletion has been annotated in ClinVar to be clinically significant for β-thalassaemia, and we find it to be associated with a 1.96 s.d. (95% CI = 1.49–2.43, P value = 5.4 × 10−16) decrease in haemoglobin concentration.

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  • FDA Approves Tocilizumab Biosimilar for CAR T-Cell Therapy–Induced CRS

    FDA Approves Tocilizumab Biosimilar for CAR T-Cell Therapy–Induced CRS

    The FDA has approved the intravenous (IV) formulation of tocilizumab-anoh (Avtozma), a biosimilar referencing IV tocilizumab (Actemra) for the treatment of adult and pediatric patients at least 2 years of age with CAR T-cell therapy–induced severe or life-threatening cytokine release syndrome (CRS).1

    Previously, in January 2025, the FDA approved the IV infusion of tocilizumab-anoh for the treatment of patients with inflammatory diseases, including rheumatoid arthritis, giant cell arteritis, polyarticular juvenile idiopathic arthritis, systemic juvenile idiopathic arthritis, and COVID-19. The indications for the IV formulation of tocilizumab-anoh now align with all FDA-approved indications for IV tocilizumab.

    “We are proud that IV [tocilizumab-anoh] has now achieved full indication alignment with the reference IV [tocilizumab]. This milestone marks an important step forward in our mission to deliver a safe and effective therapy for CRS,” Thomas Nusbickel, chief commercial officer at Celltrion USA, stated in a news release. “This FDA approval expands access to high-quality biologics and supports beneficial patient outcomes across multiple therapeutic areas.”

    Tocilizumab-anoh contains the active ingredient tocilizumab and is a recombinant interleukin-6 receptor–directed human monoclonal antibody. The recommended IV dosage for patients with CRS is 60-minute infusions at 12 mg/kg in patients weighing less than 30 kg and 8 mg/kg in those weighing at least 30 kg.2 In the CRS population, tocilizumab-anoh can be given alone or in combination with corticosteroids. In patients with CRS—as well as those with rheumatoid arthritis and COVID-19—tocilizumab-anoh doses exceeding 800 mg per infusion are not recommended. Notably, if clinical improvement in the signs and symptoms of CRS is not observed after the first dose of tocilizumab-anoh, a maximum of 3 additional doses of the agent may be given. Consecutive doses should be administered at least 8 hours apart from each other.

    Patients are required to undergo baseline complete blood count and liver function tests prior to treatment with tocilizumab-anoh. According to the tocilizumab-anoh prescribing information, patients with severe or life-threatening CRS often present with cytopenias or elevated alanine aminotransferase/aspartate aminotransferase levels due to lymphodepleting chemotherapy or the CRS itself. Therefore, the decision to treat patients with tocilizumab-anoh should weigh the potential benefits of CRS management with the risks associated with short-term tocilizumab-anoh treatment.

    A retrospective pooled analysis of data from several clinical trials evaluated 45 patients with severe or life-threatening CAR T-cell therapy–induced CRS who received IV tocilizumab at the recommended dose with or without high-dose corticosteroids. The median time from the start of CRS to the first dose of tocilizumab was 4 days (range, 0-18), and patients received a median of 1 (range, 1-4) dose of tocilizumab.

    Tocilizumab response was defined as CRS resolution (lack of fever and vasopressor independence for at least 24 hours) within 14 days of the first dose of tocilizumab if the patient required no more than 2 doses of tocilizumab and received no drugs other than tocilizumab and corticosteroids for the management of CRS. Overall, 69% of patients (95% CI, 53%-82%) responded to tocilizumab. Investigators did not observe adverse effects related to tocilizumab.

    Among the patients studied in the analysis were 25 children between 2 and 12 years of age and 17 adolescents between 12 and 18 years of age. Notably, investigators saw no safety or efficacy differences with tocilizumab use between the pediatric and adult patient populations. However, the clinical studies included in the analysis did not enroll sufficient numbers of patients at least 65 years of age to demonstrate whether this age group responds differently from younger patients.

    The IV formulation of tocilizumab-anoh is expected to be available in the US beginning on August 31, 2025.1

    References

    1. FDA approves expanded indication for Avtozma (tocilizumab-anoh) intravenous (IV) formulation in cytokine release syndrome (CRS). News release. Celltrion, Inc. August 6, 2025. Accessed August 6, 2025. https://www.prnewswire.com/news-releases/fda-approves-expanded-indication-for-avtozma-tocilizumab-anoh-intravenous-iv-formulation-in-cytokine-release-syndrome-crs-302522996.html
    2. Avtozma. Prescribing information. Celltrion; 2025. Accessed August 6, 2025. https://www.celltrion.com/ko-kr/comm/surl/107

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  • Let It Flow: OWS Shanghai Site Soars with FLIGHT DECK

    Let It Flow: OWS Shanghai Site Soars with FLIGHT DECK

    Imagine the sight of massive aircraft engines, weighing more than three tons each, gliding smoothly down a production line in a bustling workshop.

    At GE Aerospace’s On Wing Support (OWS) quick-turn shop in Lingang, Pudong, Shanghai, this remarkable and bustling scene comes to life every day. Engines move continuously through the facility, from arrival to maintenance and finally departure, being repaired one after another and returned to customers.

    In traditional engine maintenance, engines remain stationary at fixed workstations, much like cars being serviced at a car dealership. But at the OWS shop, a moving repair line has brought about big gains in efficiency. This innovation is the result of GE Aerospace engineers’ efforts to improve maintenance efficiency by activating FLIGHT DECK, the company’s proprietary lean operating model.

     

    Challenges with the Traditional Model

    Established in July 2023, the Shanghai OWS facility is the newest of GE Aerospace’s seven global quick-turn sites and the first of its kind in China’s civil aviation industry. It delivers rapid maintenance services for CFM LEAP-1A/1B* and CFM56-5B/7B* engines to customers across China and Asia, handling module repair and replacement for critical components like fans, compressors, combustion chambers, and high- and low-pressure turbines.

     

    The Shanghai OWS facility maintains multiple engine types for customers all across China and Asia, handling module repair and replacement for critical components.

     

    Under traditional maintenance methods, each engine entering the shop is placed at a fixed workstation for repair. The process generally involves three steps: disassembly, module repair and replacement, and reassembly. It typically takes 95 days to complete the entire process and deliver the engine back to the customer. However, supply chain disruptions significantly impacted the shop’s maintenance rhythm, extending total turnaround time (TAT) by 47%. The main challenge lies in the “module repair and replacement” phase, in which many core components need to be sent overseas for repair before being shipped back to the Shanghai facility for installation and reassembly. When overseas repair times are prolonged, engines are forced to remain at their fixed workstations, waiting for replacement parts. This not only ties up maintenance tools and keeps engineers on standby but also prevents new engines from entering the shop.

    “It’s like sending a ‘sick’ engine to a hospital and placing it in a bed,” explains Wang Tao, the Shanghai OWS site leader. “The doctors use tools and the appropriate medicine to treat the patient. But when the medicine doesn’t arrive, the engine becomes like a patient waiting for medication, stuck in bed indefinitely. Doctors and tools are also tied up at the bedside. With limited beds available, the result is that uncured patients can’t leave and new ones can’t be treated.”

    “In the first quarter of 2025, we had a backlog of LEAP engines awaiting repair, with others parked for service,” Wang says. “In the end, we weren’t able to meet our projected target.” Faced with the growing backlog of repair orders and anxious airline customers, Wang and his team began to rethink their approach.

     

    Making “Patients” Move: Implementing a Flow Line Approach

    How could the Shanghai OWS team prevent the module repair phase from stalling disassembly and reassembly? How could they streamline repairs and increase throughput?

    Wang recalls a daring idea from Maintenance, Repair, and Overhaul Vice President Farah Borges: “Why not bring an assembly-line-style flow line into our shop?” With strong support from Global OWS Leader Alexander Henderson, the Shanghai team pioneered a shift from fixed workstations to a “flow line.”

    Now engines move through distinct stages — after disassembly, they advance to module repair and, once completed, they “flow” to reassembly. To tackle the module repair challenges, Wang’s team introduced the lean concept of heijunka — a “buffer storage” solution that optimizes floor space with additional engine storage stands to hold disassembled engines while they await parts.

     

    Heijunka stations
    The OWS team applied “heijunka,” a buffer storage solution that optimizes floor space with additional engine storage stands to hold disassembled engines while they await parts.

     

    “We added 10 sets of storage tooling for the core module and LPT module,” Wang explains. This allows the team to disassemble up to 10 engines and store them until overseas-repaired parts arrive, at which point they move quickly to module repair.

    “It’s like having 10 patients in a holding area waiting for medication,” Wang says. “When it arrives, they’re treated and moved forward together, freeing up beds for new arrivals. This approach is allowing us to meet the needs of our customers.” 

    These stands were designed and built locally in China, matching the quality of imported units while cutting delivery time by 75% and costs by 67% — further accelerating TAT and reducing costs.

    The new process also transformed the workforce, improving the way the team works. Technicians who used to oversee the entire repair process are now more focused on specific phases. The team has also been able to free up other resources across the site to enhance airline customer support and field service. “As efficiency continues to improve, we’ll bring in new talents to the team to build a more structured hierarchy,” Wang adds.

     

    Continuous Improvement Never Stops

    After FLIGHT DECK was activated and the fundamentals were applied, the results came rushing in. Almost immediately, TAT quickly increased nearly 40%, and deliveries soared from one engine in the first quarter to nine in the second quarter. The Shanghai team was energized and, with their eyes on the future, they aimed to further reduce TAT in 2026.

    Yet Wang’s team isn’t stopping there. While the three main phases — disassembly, module repair and replacement, and reassembly — now flow smoothly, each requires finer segmentation into sub-zones to optimize efficiency and minimize backtracking. The team is actively refining these details to continuously improve the process. For example, the on-site daily visual management boards will also be adjusted in conjunction with the flow to facilitate more efficient and real-time management of daily production.

    Since the launch of FLIGHT DECK in 2024, OWS sites like the one in Shanghai have made meaningful progress, but this is only the beginning. FLIGHT DECK is about how — through the behaviors and fundamentals — GE Aerospace works to deliver for its customers. Behaviors create and reinforce the lean mindset, and the fundamentals provide the path to ensure safety, quality, delivery, and cost (SQDC), in that order.

    Guided by the FLIGHT DECK behaviors, individuals like Wang seek out opportunities to improve how teams work and deliver for customers. Whether in Pudong, Shanghai or Cincinnati, in offices, workshops, or at airline field sites, they work to better serve customers and improve SQDC day in and day out.

    The Shanghai flow line model has been so successful that it will soon be adopted by the Cincinnati OWS facility. And Wang will soon visit the OWS site in Seoul to discuss new plant standards there.

    “Under the guidance of FLIGHT DECK, we are undergoing profound changes in both mindset and behavior,” Wang says. “This transformation not only allows engines to ‘flow’ but also energizes our team’s thinking, making operations more efficient. We are committed to continuous improvement through FLIGHT DECK, creating greater value for customers, lifting people up, and bringing them home safely.” 

     

    *The LEAP engine and the CFM-56 engine are manufactured by CFM International, a 50-50 joint company between GE Aerospace and Safran Aircraft Engines.

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  • Commerce Media Has Entered a New Era and IAB’s Connected Commerce Summit Is Where It’s Being Defined – Interactive Advertising Bureau

    1. Commerce Media Has Entered a New Era and IAB’s Connected Commerce Summit Is Where It’s Being Defined  Interactive Advertising Bureau
    2. Can retailers compete with Amazon in the ad business?  Marketing Tech News
    3. Retail Media’s Measurement Problem  Retail TouchPoints
    4. Commerce Media Has Entered a New Era and IAB’s Connected Commerce Summit Is Where It’s Being Defined  Yahoo Finance
    5. Is it time for retail media networks to think beyond the ads?  MarTech

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  • FDA Accepts NDA for New Formulation of PSMA PET Imaging Agent Piflufolastat F 18 in Prostate Cancer

    FDA Accepts NDA for New Formulation of PSMA PET Imaging Agent Piflufolastat F 18 in Prostate Cancer

    Prostate cancer | Image Credit:

    Sebastian Kaulitzki © stock.adobe.com

    The FDA has accepted the new drug application (NDA) seeking the approval of a new formulation of the prostate-specific membrane antigen (PSMA) PET imaging agent piflufolastat F 18 for use in patients with prostate cancer.1
    The FDA has set a Prescription Drug User Fee Act (PDUFA) target action date of March 6, 2026.

    Piflufolastat F 18 is an injectable radioactive diagnostic agent indicated for PET of PSMA in patients with prostate cancer with suspected metastasis who are candidates for initial definitive therapy. The agent is also indicated for patients with suspected disease recurrence based on elevated serum prostate-specific antigen (PSA) level.

    The new formulation of piflufolastat F 18 is expected to optimize the manufacturing process and increase batch size by approximately 50%, according to Lantheus, the developer of the agent. This formulation will also increase the radioactive concentration of the agent and potentially expand access for patients.

    “We are pleased the FDA accepted Aphelion’s NDA for the new piflufolastat F 18 formulation, which we expect will improve patient access due to a significant increase in the number of doses per batch,” Brian Markison, chief executive officer of Lantheus, stated in a news release. “This formulation is a natural next step in our commitment to advancing PSMA imaging. There is a growing burden of prostate cancer in the United States and a clear need for accurate and early detection. Building on [piflufolastat F 18 (Pylarify; formerly 18F-DCFPyL-PET/CT)]’s proven performance and accuracy, Lantheus is well positioned for continued leadership in prostate cancer imaging.”

    Findings from the phase 3 CONDOR study (NCT03739684) revealed that piflufolastat F 18 (Pylarify) imaging generated correct localization rates (CLRs) ranging from 84.8% to 87.0% (lower bound of 95% CI, 77.8%-80.4%) across 3 central 18F-DCFPyL-PET/CT readers in patients with biochemically recurrent prostate cancer (n = 208).2 Most patients (63.9%) experienced a change in intended management following piflufolastat F 18 imaging. The disease detection rates ranged between 59% and 66%.

    CONDOR was a prospective, multicenter, open-label, single-arm study that enrolled patients with suspected recurrent or metastatic prostate cancer. In order to be eligible, patients needed to have rising PSA levels of at least 2 ng/mL following prostatectomy or a PSA level at least 2 ng/mL above nadir after radiation therapy, be at least 18 years old, and have negative/equivocal findings for prostate cancer on standard-of-care (SOC) imaging performed 60 days prior to imaging with piflufolastat F 18.

    The CONDOR protocol specified that patients receive intravenous piflufolastat F 18 at 9 mCi (333 MBq) 1 to 2 hours prior to PET/CT. Patients voided prior to imaging, and the images were taken from mid-thigh through the skull vertex. All scans were submitted to the central imaging core laboratory for assessment, and patients with positive scans by local interpretation were scheduled for follow-up to verify the suspected lesions based on a composite standard of truth.

    The primary end point was CLR, which was defined as a positive predictive value with an additional requirement of anatomic lesion colocalization between piflufolastat F 18 and a compositive SOC. Secondary end points included changes in intended management and safety.

    “We have reached a key milestone and delivered on our commitment to advance prostate cancer imaging through sustainable innovation,” Paul Blanchfield, president of Lantheus, added in the news release.1 “By enhancing the efficiency of production, we expect to improve patient access, streamline operations, and support the broader health care system’s ability to deliver timely diagnostic imaging.”

    References

    1. Lantheus announces FDA acceptance of NDA for new formulation for market-leading PSMA PET imaging agent. News release. Lantheus. August 6, 2025. Accessed August 6, 2025. https://lantheusholdings.gcs-web.com/news-releases/news-release-details/lantheus-announces-fda-acceptance-nda-new-formulation-market
    2. Morris MJ, Rowe SP, Gorin MA, et al. Diagnostic performance of 18F-DCFPyL-PET/CT in men with biochemically recurrent prostate cancer: results from the CONDOR phase III, multicenter study. Clin Cancer Res. 2021;27(13):3674-3682. doi:10.1158/1078-0432.CCR-20-4573

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  • Govt Confirms Firewall Installation Amid Internet Slowdown Concerns

    Govt Confirms Firewall Installation Amid Internet Slowdown Concerns

    The government has now admitted that a firewall is being installed to manage and monitor internet traffic across the country. This comes after earlier denials from authorities, who previously dismissed the firewall as a cause of Pakistan’s slow internet. However, it now seems likely that the firewall could be a major factor behind the reduced internet speed observed since last year.

    Member of the National Assembly Muhammad Jawed Hanif Khan raised a question in parliament regarding the installation of a firewall to monitor internet activity.

    In a written response, Minister for Information Technology and Telecommunication Shaza Fatima Khawaja acknowledged the development. She stated that in the era of the fourth industrial revolution, automation, communication, and the internet are central to a society’s economy.

    She added that cybersecurity is essential to protect citizens, institutions, and entities. Therefore, it is crucial to manage internet traffic while blocking access to websites containing objectionable or offensive content, following Pakistani laws.

    The minister further stated that the government is committed to safeguarding Pakistan’s cyberspace. She emphasized that the steps being taken are consistent with actions by past governments and are aligned with legal obligations under Article 19 of the Constitution.

    Pakistan quietly implemented a national geo-fencing firewall at upstream internet gateways, allowing real-time tracking and blocking of content deemed as propaganda. Industry insiders confirmed this move is more advanced than previous systems and has caused noticeable internet slowdowns, especially on social platforms like WhatsApp and Facebook.

    Despite government claims of a “web management upgrade,” experts say it’s a full-fledged firewall with surveillance capability. Major ISPs weren’t officially informed, while call centers and users face disruptions.

    The IT minister blamed VPN usage for slow speeds, saying it disrupts traffic routing through Content Delivery Networks (CDNs), causing upstream congestion. However, reports by Bytes for All and internet speed data challenge this claim, suggesting the government rerouted traffic via proxies and deep-packet inspection. Pakistan’s poor fiber infrastructure and low teledensity further worsen the issue. While officials defend the firewall as a security measure, critics argue it compromises privacy without improving real cyber defense.

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