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  • Proteomics and metabolomics studies in pigmented villonodular synovitis uncover the regulation of monocyte differentiation by the ADGRE5-NF-κB pathway | BMC Medicine

    Proteomics and metabolomics studies in pigmented villonodular synovitis uncover the regulation of monocyte differentiation by the ADGRE5-NF-κB pathway | BMC Medicine

    Patients and clinical samples

    This study was approved by the Biomedical Research Ethics Committee of Sichuan University (No. 125 2020-(921)). The baseline characteristics of the patients are provided in Table 1. Arthroscopic images showed red-brown synovial tissue in PVNS patients, which was markedly hyperplastic compared to the control group due to hemosiderin deposition (Fig. 1A). MRI revealed that the synovial tissue in PVNS patients displayed high signal intensity in the fat-saturated proton density sequence (Fig. 1B). The knee joints of PVNS patients exhibited severe joint swelling and cartilage erosion (Fig. 1B).

    Fig. 1

    Proteomics of differential protein expression. A Arthroscopic observation of pigmented villonodular synovitis (PVNS). B MRI Features of PVNS (the green box indicates the PVNS lesion tissue). C Principal component analysis (3D PCA) plot based on protein expression data. The first three principal components explain 38.3%, 12.4%, and 6.93% of the total variance, respectively. Samples were colored by group. D Sample correlation analysis: in the upper triangle (above the diagonal), red indicates positive correlations, blue indicates negative correlations, and the size of the circles represents the magnitude of the correlation coefficients. The numerical values in the lower triangle (below the diagonal) display the correlation coefficients, with colors differentiating between positive and negative correlations. The different colors of the sample labels indicate different groups. E Statistical plot of differential proteins. F Volcano plot of differential generation proteins in joint fluid; red represents metabolites up-regulated in group T compared to group N, blue represents down-regulated

    Table 1 Baseline characteristics

    Biological samples: We enrolled ten patients with knee PVNS who were admitted to our hospital between December 1, 2019, and May 30, 2020, and met the inclusion criteria. We retrospectively analyzed these patients’ clinical characteristics, imaging changes, and arthroscopic findings. The PVNS group specimens were obtained from patients diagnosed with knee PVNS through postoperative pathological examination, while the control group specimens were obtained from patients with meniscus or ligament injuries. Patients with infections, autoimmune diseases, and metabolic disorders were excluded. Synovial fluid from both groups was collected during arthroscopy, centrifuged, and stored in liquid nitrogen, then transported and frozen at − 80 °C for future use. General clinical and demographic data for the PVNS group (P) and the relative control group (C) are shown in Table 1. Patients with PVNS undergo arthroscopic synovectomy to remove the affected synovial tissue. For meniscus injuries, the treatment is based on the severity of the damage, with options including meniscus repair or meniscectomy. Ligament injuries are treated according to the severity, with options for ligament repair or reconstruction surgery. Among the 10 PVNS patients (mean age = 36.10 ± 11.26 years), 2 (20%) were male. The mean body mass index (BMI) (23.86 ± 2.885 vs. 22.60 ± 2.382 kg/m2, p > 0.05), C-reactive protein (CRP) (mg/L, 5.751 ± 3.947 vs. 1.915 ± 1.053; p = 0.0137), triglycerides (TG) (mmol/L, 2.216 ± 1.303 vs. 1.151 ± 0.7565; p = 0.0383), and erythrocyte sedimentation rate (ESR) (28.10 ± 18.42 vs. 8.400 ± 5.481; p = 0.0082) were higher in the PVNS group than in the control group. Serum biochemical parameters, including white blood cell count (WBC), high/low-density lipoprotein (HDL/LDL), total cholesterol (TC), absolute monocyte count (MO#), and absolute lymphocyte count (LY#), were measured using an automated serum biochemical analyser.

    Proteomics

    Sample preparation and protein extraction: synovial fluid samples (40 μL each) were mixed with 250 μL phosphate-buffered saline (PBS) containing a protease inhibitor (Roche, 4,693,132,001) to prevent protein degradation. Proteins were denatured by adding 250 μL of ST buffer (2% SDS, 100 mmol/L Tris–HCl, pH 7.6), followed by centrifugation at 8000 × g for 1 min. The supernatant was collected, boiled for 5 min, and sonicated to ensure complete protein solubilization. After a second centrifugation (8000 × g, 15 min), the supernatant was collected, and protein concentration was determined using a bicinchoninic acid (BCA) assay.

    Protein digestion and peptide preparation: proteins were reduced with 10 mmol/L dithiothreitol (DTT) at 56 °C for 1 h and alkylated with 20 mmol/L iodoacetamide (IAA) in the dark for 30 min at room temperature. The solution was buffer-exchanged into 50 mmol/L ammonium bicarbonate using a FASP column (PALL, OD010C34) and digested overnight with trypsin at 37 °C (enzyme-to-protein ratio, 1:50). The resulting peptides were quantified using a fluorometric peptide assay (Thermo Fisher Scientific, 23,275).

    High-pH Reverse Phase Fractionation: Equal amounts of peptides from all enzymatically digested samples were pooled and fractionated using an Agilent 1100 HPLC system with a mobile phase at pH 10. Separation was performed using an Agilent Zorbax Extend-C18 column (2.1 × 150 mm, 5 μm) with UV detection at 210 nm and 280 nm. The mobile phases consisted of the following: mobile phase A: ACN-H₂O (2:98, v/v); mobile phase B: ACN-H₂O (90:10, v/v); both mobile phases were adjusted to pH 10 with ammonium hydroxide.

    The gradient elution program was as follows: 0–10 min: 2% B (isocratic); 10–10.01 min: 2–5% B; 10.01–37 min: 5–20% B (linear gradient); 37–48 min: 20–40% B (linear gradient); 48–48.01 min: 40–90% B; 48.01–58 min: 90% B (isocratic); 58–58.01 min: 90–2% B; 58.01–63 min: 2% B (re-equilibration). Fractions were collected at 1-min intervals starting from the 10th minute into 10 consecutive tubes (1 → 10 in a cyclic manner), vacuum freeze-dried, and stored at − 80 °C until mass spectrometry analysis.

    Liquid chromatography-tandem mass spectrometry (LC–MS/MS): prior to LC–MS/MS analysis, fractionated peptides were reconstituted and spiked with iRT peptides (1:10 ratio) as internal standards. Peptides were analyzed using a high-resolution mass spectrometer (Q Exactive HF-X, Thermo Fisher Scientific) with the following parameters: scan range: 350–1250 m/z; isolation window: 26 m/z; fragmentation: higher-energy collisional dissociation (HCD); resolution: 60,000 (MS1), 15,000 (MS2).

    Data processing and bioinformatics analysis: raw spectra were matched against a UniProt human protein database using Spectronaut Pulsar software (Biognosys). Data-independent acquisition (DIA) processing included retention time alignment, peak extraction, and library-based quantification. Missing values were imputed using the k-nearest neighbors (KNN) algorithm, and proteins missing in > 50% of samples or with a coefficient of variation (CV) > 30% were excluded.

    Differential protein expression was assessed using one-way ANOVA (p < 0.05). Multivariate analyses, including principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), were performed to evaluate sample clustering and discriminatory features. Functional enrichment analysis was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Protein–protein interaction networks were constructed using STRING (v11.5) and visualized in Cytoscape (v3.9.1).

    Metabolomics

    Fifty microliters of synovial fluid from each sample were mixed with 250 μL of isotope-labeled methanol, vortexed, and incubated at − 20 °C for 20 min. After ultrasonic extraction on ice for 15 min, samples were centrifuged at 13,300 rpm (4 °C, 15 min). The supernatant (200 μL) was collected, vacuum-dried at 30 °C for 2 h, and reconstituted in 0.1 mL of HILIC reconstitution solution. Following sonication, vortexing, and centrifugation, 80 μL of the supernatant was transferred to an LC–MS vial for analysis using an ACQUITY UPLC system coupled to an AB TripleTOF 5600 high-resolution mass spectrometer. For quality control (QC), 20 μL of supernatant from each sample was pooled, aliquoted, and analyzed alongside experimental samples.

    Raw data were processed using Progenesis QI (Waters) for peak alignment, annotation, and filtering (retention score ≥ 35, intensity > 1000). After median normalization and missing value imputation (KNN algorithm), PCA assessed data quality. Differential metabolites were identified by t-test (p < 0.05) and OPLS-DA (variable importance in projection (VIP) ≥ 1). Pathway analysis was performed via MetaboAnalyst 4.0 using the KEGG database.

    Weighted gene co-expression network analysis (WGCNA)

    A total of 376 proteins and 20 samples were analyzed. Proteins with low expression variability (standard deviation ≤ 0.5) were filtered out, leaving 208 proteins and 20 samples for further analysis. A weighted co-expression network model was constructed using a power value of 22, and the remaining 208 proteins were divided into three modules. Data analysis and visualization were performed using the WGCNA package in R, and data visualization was performed using R and Python. The Pearson correlation algorithm calculates the correlation coefficient and p-value between module characteristic proteins and traits. Modules with an absolute correlation coefficient ≥ 0.3 and a p-value < 0.05 were considered significant. For each considerable module, the correlation between module gene expression and trait gene significance (GS) was calculated, and the correlation between module gene expression and Eigengene was analyzed to construct a module-trait correlation analysis.

    Integrated omics analysis

    Orthogonal partial least squares (O2PLS) analysis was employed to integrate the proteomics data and metabolomics data. O2PLS is a multivariate data integration technique that decomposes the variation in two datasets into joint, dataset-specific, and noise components, enabling the identification of correlations between the datasets while accounting for their intrinsic structure [61].

    Prior to analysis, the data were centered around zero and scaled. To determine the optimal number of components, cross-validation was performed using the “crossval_o2m_adjR2()” function, which adjusts for cross-validation in O2PLS analysis. This process yielded values for “n” (number of joint components), “nx” (number of transcriptome-specific components), and “ny” (number of metabolite-specific components). In this study, “n = 3,” “nx = 0,” and “ny = 1” were selected to best fit the omics data to the O2PLS model.

    In the O2PLS model, the joint components capture the covariance between the proteins and metabolite data, while the dataset-specific components capture the variation unique to each dataset. The loading values for each variable (gene or metabolite) on the joint components indicate their relative importance in determining the joint variation. Variables with high loading values on the same joint component are strongly correlated. Therefore, by examining the variables with high loading values on the joint components, it is possible to identify groups of proteins and metabolites that are related, potentially reflecting underlying biological processes or pathways.

    Malondialdehyde (MDA) assay

    Synovial tissue and synovial fluid samples were retrieved from − 80 °C storage. Synovial tissue was homogenized in ice-cold lysis buffer (50 mM Tris–HCl, pH 7.4, 150 mM NaCl, 1% Triton X-100, 1 mM EDTA, and protease inhibitors). After homogenization or lysis, samples were centrifuged at 12,000 × g for 10 min at 4 °C, and the supernatant was collected for analysis. Protein concentration was determined using a BCA protein assay kit (Beyotime, P0009). Reaction setup: blank: 0.1 mL of homogenization buffer, lysis buffer, or PBS. Standard curve: 0.1 mL of serial dilutions of MDA standard (provided in the kit). Samples: 0.1 mL of tissue or synovial fluid supernatant. To each tube, 0.2 mL of MDA detection working solution (Beyotime, S0131S) was added. Samples were mixed thoroughly and heated at 100 °C for 15 min using one of the following methods: Post-incubation processing: samples were cooled to room temperature and centrifuged at 1000 × g for 10 min. Two hundred microliters of supernatant was transferred to a 96-well plate, and absorbance was measured at 532 nm using a microplate reader. A reference wavelength of 450 nm was used for dual-wavelength correction. Quantification: For synovial fluid, MDA concentration (μM) was calculated directly from the standard curve. For tissue samples, MDA content was normalized to total protein (μmol/mg protein). Each sample was analyzed in triplicate (n = 3).

    Western blot

    Western blotting was performed as described previously [25]. Briefly, synovial samples were lysed and centrifuged at 12,000 g for 25 min at four °C, and the supernatant was collected as the tissue protein solution. Protein concentration was measured using the BCA kit. The primary antibodies used were TNFSF11 (1:1000, ABcloal, A2550); CTSK (1:500, ABcloal, A5871); ADGRE5 (1:2000, ABcloal, A22218); NF-κB (1:1000, ABcloal, A2547); and β-Tubulin (1:1000, ABcloal, AC008). Immunoblots were visualized using BeyoECL Plus (Beyotime, Beijing, China), and protein bands were photographed and stored using the Tanon 2500R gel imaging system (Tanon, Shanghai, China). Band intensity was quantified using ImageJ 1.39 V software (n = 3).

    Statistical analysis

    All quantitative data are presented as mean ± standard error of the mean (S.E.M.). Statistical analyses were conducted using GraphPad Prism 5.0 (GraphPad Software, San Diego, USA). For comparisons between two groups, a two-tailed unpaired Student’s t-test was employed. For multiple group comparisons, one-way analysis of variance followed by Tukey’s post hoc test was used to adjust for multiple comparisons. A p-value < 0.05 was considered statistically significant.

    For proteomic and metabolomic differential expression analyses, all statistical computations were performed in R (version 4.41). Differential expression was conducted using the limma package (or other applicable packages), and raw p-values were adjusted for multiple hypothesis testing using the Benjamini–Hochberg method to control the false discovery rate (FDR). Features with FDR-adjusted p < 0.05 and absolute fold change ≥ 2 were considered significantly altered.

    Multivariate statistical analyses, including PCA and O2PLS, were performed using the mixOmics R package. In the O2PLS modeling, variables with VIP scores > 1.0 were considered key contributors to group separation. All data visualization and preprocessing steps were conducted in R using standard packages such as ggplot2 and pheatmap.

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  • Practice of data sharing plans in clinical trial registrations and concordance between registered and published data sharing plans: a cross-sectional study | BMC Medicine

    Practice of data sharing plans in clinical trial registrations and concordance between registered and published data sharing plans: a cross-sectional study | BMC Medicine

    We reported this study according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline [14]. This study was registered on the Open Science Framework (https://osf.io/k6etb).

    Search strategy and eligibility criteria

    We first identified the top 10 medical general journals that frequently published clinical trials and were ranked by journal impact factor in the category of “Medicine, General & Internal” based on Journal Citation Reports (as of June 2023). After excluding those journals that primarily focused on basic science or published less than 10 clinical trials annually, a total of 6 journals were chosen, including The Lancet, The New England Journal of Medicine (New Engl J Med), Journal of the American Medical Association (JAMA), British Medical Journal (BMJ), JAMA Internal Medicine, and Annals of Internal Medicine (Ann Intern Med). Subsequently, we searched MEDLINE (via PubMed) to systematically retrieve clinical trials published in these journals (Additional file 1: Table S1 shows the search strategy used). Given that ICMJE required a data sharing plan in trial registration from Jan 2019 onwards, we only included trials that started participant enrollment on or after Jan 1, 2019, and trial publications published between Jan 1, 2021, and Dec 31, 2023, to allow sufficient time and samples of trials for evaluation. We included clinical trial publications with primary results; methods papers, publications of secondary results, relevant reviews, commentaries, perspectives, or editorials were excluded. The detailed selection process is presented in Additional file 1: Fig. S1.

    If the same trial was registered on different platforms, we only extracted and analyzed information from ClinicalTrials.gov. Some publications may pool different trials for analysis, thereby having ≥ two registration identifiers (IDs). We treated such publications as different trials by their corresponding registration IDs for data extraction and analysis; i.e., each registration ID represented an individual trial. Some publications with updated data may share the same registration ID with prior publications; in this case, the most recent publication was kept for analysis to avoid double counting.

    Study outcomes

    The outcomes were the inclusion of a plan to share data in the trial registration and the concordance between registered and published plans to share data.

    Trials that clearly responded with a “Yes” to “Plan to share” in registration were considered as planning to share data, while those reporting with a “No” were considered as not planning to share data. In this study, the data that were planned to share included study protocols, statistical analysis plans, analytic codes, and IPD, where trials reporting a plan to share any of these data were considered to “plan to share data” in registration. We searched trial registration platforms based on registration IDs to determine the stated plan to share data. If a trial had multiple registration records, we used the latest registration record before the trial publication was published. On the registration platform, all information on the data sharing plan description was extracted, including plans to share IPD and supporting information (study protocols, statistical analysis plans, and analytic codes). If the question “Plan to share IPD” was left blank or answered “Undecided,” responses were pooled as “undecided/missing”.

    We further explored the data sharing concordance between registered and published plans to share. From data sharing statements in trial publications, trials that clearly stated a willingness to share data were defined as published plans to share data. We also treated trials as having published plans to share data if a link to a data repository was provided, even if the shared data were accessible only after the user registered and signed a data use agreement. Trials that were unwilling to share data or did not report/obtain data sharing statements were considered not to have a published plan to share. Subsequently, data sharing concordance was assessed: (1) plan to share data in registration and publication (both Yes in registration and publication, i.e., “Yes/Yes”) and (2) no plan to share data in registration and publication (both No in registration and publication, i.e., “No/No”). Discordance between registered and published plans to share data included (1) plan to share data in registration but no plan to share data in the publication (Yes in registration but No in publication, i.e., “Yes/No”) and (2) no plan to share in registration but a plan to share in publication (No in registration but Yes in publication, i.e., “No/Yes”). Seven trial publications pooled two trials/registration IDs in which case the registration IDs with a later study start date were used to assess data sharing concordance.

    We also assessed the details of the data sharing plans, which are elaborated in registration platforms and statements in trial publications. Therefore, the specific information extracted included the following: (1) data sharing content (analytic code, statistical analysis plan or study protocol, IPD), (2) data access time after publication or trial completion (< 12 months, ≥ 12 months, unclear), and (3) data access method (public, private, unclear). If trial authors clearly stated that the shared data would be publicly available, we considered the trials to have a public data access method. If the shared data were only available from trial authors, funders, or trial review committees after review, trials were grouped to have a private data access method. Trials were categorized as having an unclear data access method if no relevant details were provided regarding how the trial authors would share data.

    Data extraction

    Data extraction and coding were completed independently by four study authors in pairs (J. Z. and X. B., Y.L., and G. L.). Any disagreement was resolved by discussion between the study authors and, if no consensus could be reached, resolved through consultation with the senior author (D. M.).

    Data on trial characteristics from registration platforms were extracted, including whether the trial was multicenter, country of origin, design information (with or without control group, parallel or crossover, with or without randomization), trial phase (1–2, or 3–4), planned sample size, intervention type (drug or other, where “drug” included both drug alone or drug in combination with non-drug), whether the trial was COVID-19-related, and funding source (industry or other, where “industry” included industry alone or the combination of industry and non-industry funder) [15]. For those that did not report a trial phase, they were classified as phase 3–4 if they planned to enroll ≥ 400 participants and grouped as phase 1–2 if the planned sample size was < 400 [10, 16].

    We predefined data extraction from trial publications, where the extracted data included the year of publication, whether trial publication mentioned authors’ conflict of interest (yes or no), and the risk of bias (ROB). If the trial publication mentioned authors’ conflict of interest, we further categorized the conflict of interest as either financial, non-financial, or both [17]. We did not aim to assess the ROB for each outcome of the included trials; therefore, the ROB 1.0 tool was used to evaluate the overall ROB for individual trials [18]. A trial was grouped as having high ROB if at least one domain (random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and others) was rated as high ROB. Trials were defined to have low ROB if all domains were rated as low ROB, while they were considered to have unclear ROB if there was ≥ one domain rating as unclear ROB [19].

    Statistical analysis

    We described continuous trial characteristics with medians and lower and upper quartiles (Q1, Q3) and categorical variables using counts and percentages. We used the McNemar’s test to evaluate whether the concordance between registered and published plans to share data was significant [20]. We plotted the proportions of trials with plans to share data in registration and proportions of data sharing concordance from 2021 to 2023 by country of trial origin and journal.

    We assessed the associations between trial characteristics and registered plans to share data and between trial characteristics and data sharing concordance. Trials with undecided/missing plans to share data were treated as “No” plan to share data in our association analysis. The univariate logistic regression analysis was used to explore trial characteristics in relation to registered plans to share data, taking those trials without registered plans to share data as reference. For the association between trial characteristics and data sharing concordance (Yes/Yes, and No/No), trials with the two types of discordance (Yes/No, and No/Yes) were combined as the reference group, given the concern over small sample sizes for each type of discordance. This approach to combine these two types of discordance was also used by some previous methodological studies [7, 21].

    We performed univariable logistic regression analysis for each trial characteristic in relation to registered plans to share data, including the year of publication, whether being multicenter, funding source, planned sample size, whether being a COVID-19 trial, intervention type, country of origin, phase of clinical trial, and whether a parallel design. Similarly, we conducted univariable analysis to investigate whether the trial characteristics (including the year of publication, whether being multicenter, planned sample size, whether being a COVID-19 trial, intervention type of drug, country of origin, trial phase, whether a parallel design, funding source, authors’ conflict of interest, and ROB) were associated with data sharing concordance between registered and published plans to share data. Odds ratios (ORs) with 95% confidence intervals (CIs) were used for the relationship between trial characteristics and registered plans to share data and between trial characteristics and data sharing concordance. An OR > 1.0 presented that the trial characteristic was associated with increased odds of registered plans to share data in registration or data sharing concordance.

    Regarding the associations between trial characteristics and registered plans to share data, we performed a prespecified sensitivity analysis by removing trials with undecided/missing plans to share data from the association analysis. We performed another post hoc sensitivity analysis by excluding non-randomized trials from the association analysis.

    We redescribed the counts and percentages of data sharing concordance between registered and published plans by removing trials with undecided/missing plans to share data and by treating trials with undecided/missing plans as having registered plans to share data. Moreover, for trial characteristics in relation to data sharing concordance, we conducted two post hoc sensitivity analyses by replacing the seven registration IDs that had a later study start date with those having an earlier study start date and by excluding non-randomized trials. We performed a third post hoc sensitivity analysis by using the two types of discordant groups as a separate control group for the association analysis (i.e., Yes/Yes and No/No vs Yes/No, Yes/Yes and No/No vs No/Yes).

    Furthermore, we evaluated the differences in data sharing content, data access time after publication or trial completion, and data access method among those trials having Yes/Yes in registration and publications. The McNemar’s test was used to assess whether a significant discordance existed.

    All statistical tests were two-sided with a significance level of 0.05. Analyses were conducted in R software version 4.4.1.

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  • Fecal microbiota transplantation improves bile acid malabsorption in patients with inflammatory bowel disease: results of microbiota and metabolites from two cohort studies | BMC Medicine

    Fecal microbiota transplantation improves bile acid malabsorption in patients with inflammatory bowel disease: results of microbiota and metabolites from two cohort studies | BMC Medicine

    Incidence of BAM in patients with IBD

    This study included 37 pairs of patients with UC and 69 pairs of patients with CD before and after treatment into the final analysis, along with a healthy control group of 24 individuals without liver and gastrointestinal diseases. The serum level of C4 in patients with IBD considered to be pathologically elevated when exceeding the upper limit of healthy control. As shown in Fig. 1a, 21 patients with IBD were considered as BAM and their serum C4 levels were significantly higher than those of healthy controls and non-BAM group (P < 0.0001, respectively). A total of 20.29% (14/69) of patients with CD and 18.92% (7/37) of patients with UC were diagnosed with BAM using the upper limit of C4 in healthy controls (Fig. 1b, c). Clinical characters such as age, sex, disease activity, and age of diagnosis had no difference between BAM group and non-BAM group (Table 1).

    Fig. 1

    The serum level of C4 in patients with IBD and HC, and metabolites related to bile acid synthesis. The serum level of C4 a between patients with IBD and healthy controls. b in CD patients with or without BAM. c in UC patients with or without BAM. d in CD patients with ileal resection/non-ileal resection. e in CD patients with ileal/ileocolonic lesions. f Differential metabolites related to bile acid synthesis between BAM group and non-BAM group. CD, Crohn’s disease; UC, Ulcerative Colitis; HC, healthy control; BAM, Bile Acid Malabsorption. **** p < 0.0001

    Table 1 Characteristics of BAM and non-BAM in patients with IBD

    Additionally, among the BAM group in patients with CD, the proportion of ileal type and ileocolonic type were as high as 85.71% (12/14), and 50.00% (7/14) of the patients had a history of ileal resection (Table 1), only 12.73% (7/55) of non-BAM patients had a history of ileal resection (P = 0.005). The C4 levels in CD patients with ileal resection were higher than that in non-resection group (P < 0.005, Fig. 1d).

    IBD patients with BAM have altered serum metabolome and fecal microbiome

    A total of 2012 metabolites classified into 193 metabolic class or super classes based on HMDB taxonomy were identified using untargeted metabolomic analysis. When comparing metabolites related to BA synthesis between BAM group and non-BAM group, C4 and chenodeoxycholic acid 3-sulfate significantly increased in BAM group before FMT, while taurochenodeoxycholate-3-sulfate, glycochenodeoxycholate-3-sulfate, and taurochenodeoxycholic acid (TCDCA) showed a significant decrease trend (Fig. 1f). Concurrently, the level of sulfated BAs catalyzed by enzymes such as sulfotransferase 2A1 (SULT2A1) in the liver, also decreased significantly in BAM group (Fig. 1f).

    There were significant differences in serum metabolism between disease groups (BAM and non-BAM) and health group (Additional file 1: Fig. S1a, b) both in the negative mode and positive mode. A total of 238 serum metabolites exhibited significant differences were observed in serum metabolome between BAM and non-BAM groups (Additional file 1: Fig. S1c, d).

    PLS-DA analysis in metabolites showed significant differences between disease groups and healthy group (Additional file 1: Fig. S2a), while OPLS-DA analysis showed no difference between disease groups (Additional file 1: Fig. S2b). There were differences in 12 metabolic classes, in which quinone and hydroquinone lipids, retinoids, glycerophosphoethanolamines, oxosteroids and cholestane steroids, and glycerophosphocholines significantly increased in the BAM group (P < 0.05, Additional file 1: Fig. S2c), which belong to the “Lipids and Lipid-like Molecules” superclass. The predominant differential metabolites (55.56%, 75/135) such as glycerophosphocholines and glycerophosphoethanolamines in BAM group belonged to Lipids and Lipid-like Molecules superclass (Additional file 2: Table S1).

    The 16S rRNA sequencing data clustered and annotated 875 OTUs, the distribution of OTUs before FMT across different classifications was illustrated in Additional file 1: Fig. S3a (Venn). There were 385 OTUs shared among the BAM, non-BAM, and healthy groups, while 150, 74, and 4 OTUs were uniquely present in healthy group, non-BAM, and BAM groups, respectively. The top 15 genera were displayed in Additional file 1: Fig. S3b. Notably, the composition of Bacteroides and Parabacteroides were similar in BAM, non-BAM, and healthy groups. At the genus level of the dominant bacteria in the disease group, pathogens including Escherichia and Enterococcus showed the highest proportion in BAM group, followed by non-BAM group, while Veillonella demonstrates the opposite trend. Streptococcus and Lachnoclostridium exhibited a similar composition in BAM and non-BAM groups. Conversely, Prevotella, Faecalibacterium, Subdoligranulum, Ruminococcus, and Megamonas were relatively enriched in healthy individuals.

    At the diversity level, both BAM and non-BAM groups exhibited lower richness and Shannon diversity compared to healthy group, but there was no significant difference between BAM and non-BAM groups (Additional file 1: Fig. S3c, d). The analysis of β-diversity revealed that both BAM group and non-BAM group exhibited significant separation from healthy group (Additional file 1: Fig. S3e).

    Results from the LEfSe analysis at the genus level (Additional file 1: Fig. S3f) revealed that the characteristic microbiota of healthy group interestingly resembled those with high abundance in the healthy condition. Microbiota of non-BAM group were characterized by genera such as Gemella, Parasutterella, Morganella, Tyzzerella, Peptostreptococcus, and Bifidobacterium. Elevated levels of Escherichia, Enterococcus, Fusobacterium, Akkermansia, and Erysipelatoclostridium were related to the occurrence of BAM.

    To further clarify the microbial differences associated with BAM in patients with IBD, we compared and identified 18 differential microbiota at the OTU level between disease groups (Additional file 1: Fig. S4). Of these, 15 OTUs were dominant in BAM group, only 3 OTUs exhibited high relative abundance in non-BAM group, such as Bacteroides coprocola DSM 17136 (OTU48), Alloprevolella sp. (OTU60), and Barnesiella sp. (OTU117).

    Simultaneously, we further conducted Mantel analysis correlating the differential microbiota with predicted KEGG pathways. As shown in Fig. 2a in BAM group, a high correlation was observed among Selenomonas sp. oral taxon 136 (OTU480), Lachnoanaerobaculum sp. (OTU349), and Selenomonas artemidis (OTU434). Stomatobaculum sp. (OTU715) was highly correlated with Eubacterium brachy ATCC 33089 (OTU592) and showed moderate positive correlation with KEGG pathways of amino acid metabolism (r = 0.465), digestive system (r = 0.309), and metabolism of cofactors and vitamins (r = 0.415). OTU592 also demonstrated a positive correlation with the digestive system pathway (r = 0.367). Stomatobaculum sp. (OTU631) exhibited a positive correlation with cellular processes and signaling (r = 0.317).

    Fig. 2
    figure 2

    Mantel analysis correlating the differential microbiota with predicted KEGG pathways and random forest classifier. a Mantel analysis in BAM group. b Mantel analysis in non-BAM group. c A random forest classifier constructed with 18 differential OTUs to predict the occurrence of BAM in patients with IBD. Adjusted P values were shown through false discovery rate control. BAM, Bile Acid Malabsorption

    In the non-BAM group (shown in Fig. 2b), only Bacteroides sp. (OTU315) was not enriched. Similarly, OTU480, OTU349, and OTU434 continued to exhibit high correlations. Moreover, OTU631 had a high correlation with OTU349, OTU480, and Leptotrichia sp. oral clone FP036 (OTU447). Out of 20 annotated KEGG pathway categories, 12 pathways showed some correlations with the changes in OTUs.

    Interaction and prediction between differential microbiota and metabolites

    As illustrated in Additional file 1: Fig. S5a and b, there is a close correlation between differential microbiota and metabolites in non-BAM group. Conversely, the connections between differential taxa in BAM group are not so close, with only a significant negative correlation between OTU631 and 7alpha-hydroxy-3-oxochol-4-en-24-oic acid. However, the relationship between different microbiota in non-BAM group was relatively close, with more positive correlations between the differential metabolites and microbiota. For example, OTU592 is associated with glycochenodeoxycholate-3-sulfate and taurochenodeoxycholate-3-sulfate, OTU60 is linked with glycochenodeoxycholate-3-sulfate. Gitogenin is related to Actinomycetaceae sp. (OTU531) and Megasphaera micronuciformis (OTU607) and positively related to C4, while Scardovia wiggsiae F0424 (OTU391) is directly associated with C4.

    Furthermore, we constructed a random forest model based on 18 OTUs to predict the occurrence of BAM in patients with IBD. As shown in Fig. 2c, the AUC reached 0.92 on the test set, with an accuracy of 86.7% and an F1-score of 0.909. After selecting the top 4 OTUs (OTU60, OTU226: Eubacterium nodatum group, OTU592, and OTU715) based on importance ranking for another round of random forest model construction, the results indicated a slight decrease in AUC on the test set to 0.83, but the F1-score and accuracy remained unchanged.

    BAM and IBD improved after FMT

    Of the BAM group, 1-month post-FMT, 61.90% (13/21) and 52.38% (11/21) achieved clinical response and clinical remission, respectively, while 65.88% (56/85) and 43.53% (37/85) achieved clinical response and clinical remission in non-BAM group (Fig. 3a). 66.67% (14/21) and 52.38% (11/21) achieved clinical response and clinical remission in BAM group, respectively, while 49.41% (42/85) and 40.00% (34/85) achieved clinical response and clinical remission in non-BAM group 3-month post-FMT (Fig. 3a). Whether the patients with IBD were diagnosed as BAM or non-BAM had no effect on the clinical efficacy 1 month and 3 months post-FMT, either in clinical response (P = 0.80, 1 month post-FMT; P = 0.22, 3 months post-FMT) or in clinical remission (P = 0.48, 1 month post-FMT; P = 0.33, 3 months post-FMT). Compared with non-BAM group, BAM group had a higher proportion of improvement in abdominal pain and diarrhea after FMT. As illustrated in Fig. 3b, the number of patients in BAM group with abdominal pain decreased from 85.71% (18/21) to 42.86% (9/21) 1 month post-FMT and to 38.10% (8/21) 3 months post-FMT. While in non-BAM group, 52.94% (45/85) and 57.65% (49/85) of patients still had abdominal pain, 1 month and 3 months post FMT, respectively. In terms of diarrhea (Fig. 3c), 42.86% (9/21) and 54.12% (46/85) of patients (BAM vs. non-BAM) complained of diarrhea 1 month post FMT, while at 3 months post FMT, there were 42.86% (9/21) and 52.94% (45/85) of patients (BAM vs. non-BAM) still had diarrhea. The detailed changes of abdominal pain grades and diarrhea times are shown in Fig. 3f–i.

    Fig. 3
    figure 3

    Patients’ clinical outcomes in BAM and non-BAM group. a Proportion of clinical response and remission. b Rate of patients with abdominal pain in BAM group before and after FMT. c Rate of patients with diarrhea in BAM group before and after FMT. d Proportion of clinical response and remission in CD patients with ileal resection/non-ileal resection. e Proportion of clinical response and remission in CD patients with ileal/ileocolonic lesions. f, g Sankey diagram of grade changes in patients with abdominal pain in BAM group and non-BAM group before and after FMT. h, i Sankey diagram of times changes in patients with diarrhea in BAM group and non-BAM group before and after FMT. BAM, Bile Acid Malabsorption

    The C4 levels in CD patients with ileal resection decreased significantly after FMT, while the decrease of C4 in non-resection group showed no significant difference (P = 0.12, Fig. 1d). Patients with ileal/ileocolonic lesions got more obvious decrease of C4 than colonic lesions group (P = 0.01 vs. P = 0.53, Fig. 1e). CD patients with ileal/ileocolonic lesions or ileal resection were attended to achieve higher and more stable rate of clinical response and remission than colonic CD or non-resection (Fig. 3d, e).

    In BAM group, the symptoms of diarrhea and abdominal pain improved when the level of serum C4 decreased significantly after FMT (P < 0.001, Fig. 4a). There was no significant change in the level of BAs except glycochenodeoxycholate-3-sulfate (Fig. 4a). In non-BAM group, the decrease trend of C4 had no significant difference and even exhibited an increase trend in some patients. However, bile acid metabolism changed significantly in non-BAM group, the levels of CA, CDCA, DCA, glycolithocholic acid, glycoursodeoxycholic acid, and chenodeoxycholic acid 3-sulfate increased significantly after FMT (Fig. 4b).

    Fig. 4
    figure 4

    The serum level of C4 and metabolites changes related to bile acid synthesis in BAM and non-BAM group. a C4 level in BAM after FMT and Volcano plot of bile acids in BAM patients before and after FMT. b C4 level in non-BAM after FMT and Volcano plot of bile acids in non-BAM patients before and after FMT. c Volcano plot of other differential metabolic classes in BAM before and after FMT. d Volcano plot of other differential metabolic classes in non-BAM before and after FMT. BAM, Bile Acid Malabsorption. *** p < 0.001

    Significant improvement in lipid metabolism can be observed at both metabolic classes and single metabolite levels in BAM group (Additional file 2: Table S2). The levels of Lactones, Fatty alcohol esters, Fatty acid esters, Monoradylglycerols, Cholestane steroids, Triradylcglycerols, Lineolic acids and derivatives, Vitamin D and derivatives, Glycerophosphoethanolamines, Diradylglycerols, and Retinoids significantly decreased after FMT (Fig. 4c), which belonged to Lipids and Lipid-like Molecules superclass. A similar distribution can also be observed in metabolites which significantly decreased after FMT in BAM group, 42.75% (115/269) of which belonged to Lipids and Lipid-like Molecules superclass (Additional file 1: Table S2).

    In non-BAM group, with a significant improvement in lipid metabolism 1 week post-FMT (Additional file 2: Table S3). The levels of Lactones, Fatty alcohol esters, Fatty acid esters, Monoradylglycerols, Vitamin D and derivatives, Triradylcglycerols, Endocannabinoids, Triterpenoids, Monoterpenoids, and Tetraterpenoids significantly decreased after FMT, which belonged to Lipids and Lipid-like Molecules superclass (Fig. 4d).

    The 16S rRNA sequencing data clustered and annotated 934 OTUs. The top 10 genera were displayed in Fig. 5a. The dominant genera before and after treatment are similar with each other. Notably, the pathogenic genus Fusobacterium, ranked 7th, is absent in healthy group. Surprisingly, among the genera with low abundance in healthy group, such as Escherichia and Veillonella, there was a decreasing trend after FMT treatment in both BAM and non-BAM group. Similarly, among the genera with high abundance in healthy group, such as Faecalibacterium, Prevotella, and Subdoligranulum, there was an increasing trend after FMT treatment.

    Fig. 5
    figure 5

    Microbial signatures associated with short-term responsiveness to FMT in BAM, non-BAM and HC group. a Relative abundance of the top 10 genera after FMT. b, c Comparison of α-diversity (richness and Shannon index) before and after FMT. d Comparison of β-diversity (PCoA of Bray–Curtis distance) before and after FMT. e Heat map of differential microbiota between response and non-response group (here the response status included clinical response and clinical remission) in BAM and non-BAM group. Adjusted P values were shown through false discovery rate control. HC, healthy control; BAM, Bile Acid Malabsorption; BAM_R, patients with BAM and have response after FMT; BAM_NR, patients with BAM and have no response after FMT; nonBAM_R, patients have no BAM and have response after FMT; nonBAM_NR, patients have no BAM and have no response after FMT; NA, no significance. * p < 0.05; ** p < 0.01; *** p < 0.001

    At the diversity level, the richness and Shannon diversity of both BAM non-BAM group after FMT were still lower than those of healthy group but had a trend towards healthy group. However, there is no significant difference between BAM and non-BAM group (Fig. 5b, c). PCoA analysis revealed that the distribution of samples after FMT in BAM and non-BAM groups tended to converge towards healthy group (Fig. 5d).

    The abundance of differential microbiota between BAM and non-BAM groups after FMT were shown in Fig. 5e. In BAM group, most genera exhibited significantly decreased abundance after FMT, such as OTU480, OTU226, Veillonella sp. (OTU347), OTU349, Shuttleworthia satelles DSM 14600 (OTU473), and OTU531, etc. Except for a fraction of genera such as OTU48 (P = 0.003) and OTU117 (P = 0.008) were significantly increased, and OTU447, OTU60, and OTU592 showed an increasing trend. Notably, after FMT treatment, both the response group (here the response status included clinical response and clinical remission) and non-response group experienced a significant increase in OTU48 and OTU117, demonstrating a transition from absence to presence. Additionally, compared to the non-response group, the response group exhibited relatively lower growth of OTU48 but higher growth of OTU117. Furthermore, OTU60 and OTU715 started to show enrichment in response group compared to their baseline levels before FMT, while OTU715 still displayed a decreasing trend in non-response group. Similarly, OTU447 and OTU391 exhibited an opposite trend of enrichment between response group and non-response group.

    Within non-BAM group, the differential genera displayed trends similar to which observed in the BAM group, except for OTU48 and OTU592. The abovementioned genera OTU48 and OTU592 exhibited a declining trend after FMT treatment in non-BAM group, with OTU48 notably showing an opposing trend of increase in non-response group (P = 0.012). While the majority of microbiota exhibited no statistically differences, other OTUs that showed opposing trends between response group and non-response group within non-BAM group were Actinomyces dentalis (OTU474), OTU480 (up in non-response group, P = 0.022), OTU226, OTU715, OTU60, OTU607, OTU434, and OTU447. The differential microbiota may be focal points of our future research.

    FMT can improve PBA supplementation-exacerbated colitis in acute mice models

    In an acute enteritis mice model (Fig. 6a), disease activity index (DAI) scores progressively increased after PBA (CA and CDCA) supplementation (Fig. 6f). From day 4, DAI scores in CA + DSS group were stably and significantly higher than that in single DSS group, and at day 5, DAI scores in CDCA + DSS group began to have the same rising trend. At the endpoint, the differences of DAI between groups were most pronounced, with CA + DSS group showing a significant increase over the DSS group (P < 0.001), and CA group showing a significant increase over control group (P < 0.001) (Fig. 6f). Even after DSS withdrawn, the exacerbated colitis phenotype continued. The severity of colitis was inversely correlated with colon length and positively correlated with spleen index. Mice in CA + DSS group had significantly shorter colons than those in the DSS group (Fig. 6c, g, P = 0.007), and their spleen index were significantly higher than DSS group (Fig. 6d, h). At the endpoint, representative images of the colon from different groups showed smooth mucosa in control group while CA and CDCA groups exhibited unclear vasculature with granular and reddened surfaces in mucosa. The DSS group showed rough mucosa with small erosions and bleeding, and CA + DSS and CDCA + DSS groups displayed extensive ulceration and obvious spontaneous bleeding (Fig. 6e). Histopathological scores (including severity of inflammation, extent of injury, and crypt damage) in CA + DSS and CDCA + DSS group were significantly higher than DSS group (Fig. 6b, i). We measured inflammatory cytokine expression in the serum to explore whether CA and CDCA supplementation exacerbated colitis by modulating cytokine levels. As shown in Additional file 1: Fig. S6a and b, compared to the DSS group, CA and CDCA significantly increased the expression of pro-inflammatory cytokines such as TNF-α and IL-1β in single-DSS mice. However, the anti-inflammatory cytokine IL-10 was significantly decreased in CA + DSS and CDCA + DSS groups (Additional file 1: Fig. S6c).

    Fig. 6
    figure 6

    Primary BA administration in acute modeling mice. a Flow chart. b Representative H&E images, c Representative colon images, d Representative spleen images, e Representative endoscopic images of the colon, f DAI score, g Colon length, h Spleen index, i Pathological score of acute modeling mice. ns p > 0.05; * p < 0.05; ** p < 0.01; *** p < 0.001

    Treatment with FMT from healthy donors ameliorated the exacerbated colitis phenotype induced by CA and CDCA (Fig. S7a). This was evidenced by reduced DAI scores: which progressively decreased in the treatment experiment and were significantly lower than untreated CA/CDCA + DSS group (Fig. S7f). The colon length, spleen index, and histopathological scores in FMT-treated CA/CDCA + DSS group were also significantly different from untreated CA/CDCA + DSS group (Fig. S7b–d, g–i). At the endpoint, representative images of the colon showed healed smooth mucosa in FMT-treated CA/CDCA + DSS group (Fig. S7e). Intestinal barrier integrity indicated that FMT treatment significantly increased mRNA levels of tight junction proteins in the colonic tissue compared to untreated CA/CDCA + DSS group, including Zonula Occludens-1 (ZO-1) and Occludin (Additional file 1: Fig. S8b, c, P < 0.001), as well as the mucus layer protein Mucin 2 (Muc2) (Additional file 1: Fig. S8a, P < 0.001). Additionally, FMT reduced the mRNA expression of pro-inflammatory cytokines such as TNF-α and IL-1β in the intestinal mucosa compared to untreated CA/CDCA + DSS group (Additional file 1: Fig. S6h, i, P < 0.001), while significantly increasing IL-10 levels (Additional file 1: Fig. S6g, P < 0.001). These changes of cytokine levels in serum were consistent with those observed in the colon (Additionalfile 1: Fig. S6d, e, f). Interestingly, FMT treatment significantly reduced C4 levels in CA + DSS/CDCA + DSS groups (Additional file 1: Fig. S8d, P < 0.001), accompanied by a decrease in cytochrome P450 7A1 (CYP7A1), cytochrome P450 8B1 (CYP8B1), and an accumulation of cytochrome P450 27A1 (CYP27A1), both of which are involved in the synthesis and metabolism of C4 and BAs (Additional file 1: Fig. S8e-g).

    PBA supplementation-exacerbated colitis in chronic mice models

    We also explored the significant role of primary BAs (PBAs, CA, and CDCA) in inducing chronic intestinal injury in mice model (Fig. S9a). After 7 days’ antibiotics supplementation and PBA supplementation for 1 month, DAI evaluation for mild to moderate intestinal injury was observed and more obvious than non-antibiotics group (Fig. S9g). Histopathological scores and representative images showed local accumulation of inflammatory cells and some crypt damage in antibiotics + PBA supplementation (Fig. S9b, e). The colon length and spleen index in antibiotics + PBA supplementation group were also significantly different from antibiotics + ctrl group (Fig. S9c, d, f).

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  • Volkswagen Promises More ‘Likable’ EVs Coming Soon

    Volkswagen Promises More ‘Likable’ EVs Coming Soon

    • Volkswagen’s design boss says that it’s time to focus on what VW is good at in order to drive sales.
    • This means revamping its approach to building and marketing EVs.
    • VW will focus on making more “likeable” cars instead of cars that feel like refrigerators and spaceships.

    Volkswagen’s past may be checkered in controversy, but its core mission has always been about creating smart, accessible vehicles that the public wants to drive. Its true first generation of consumer EVs? Not that. Not by a long shot. VW admits this, openly, and promises that it’s working to create more “likeable” electric cars to get back to its roots.

    In an interview with Auto Motor und Sport, VW’s design boss, Andreas Mindt, said that the Germans are moving past the early adopters phase for EVs and have officially “reached the mainstream.” This means no more creating EVs that resemble some sort of exotic refrigerator; instead, building cars that simply look like cars.

    Photo by: Volkswagen

    Likeable is a really generic term. What does that mean exactly? For some, likable might mean reliability. For others, it’s pleasing aesthetic visuals (Mindt has previously said this is why VW modeled the ID.Every1 around the three-fifths and golden ratio design principles). It’s an overly broad use of the term; however, the Germans are clearly looking to tackle every side of the definition in its next generation of EVs.

    Mindt explains that VW has learned from its mistakes. These aren’t just visual irks or marketing mistakes, either. Some are fundamental design flaws that inherently made certain designs worse than others in the name of aesthetics. One clear example of that is sacrificing front-end space just because the car no longer needed a hood since it doesn’t have a combustion engine up front.

    “An electric car doesn’t have a combustion engine, but a small electric motor, so I don’t need a hood,” said Mindt in the interview. “So I make the hoods very short and I make a sloping, long windshield.”

    Mindt says that this resulted in a problem when the car was exposed to direct sunlight for prolonged periods. The interior would quickly heat up, resulting in vehicles sapping valuable electrons from the car’s high-voltage battery to cool the car. Not exactly ideal, and a learning moment for VW.



     Volkswagen ID.Every1

    Photo by: Volkswagen

    However, it’s not just physical design that Volkswagen is focusing on. The automaker is rebuilding its brand to get back to a point where consumers can identify with the brand. Remember the Un-Pimp Your Ride marketing campaign from nearly two decades ago? That’s the VW that was “likeable” and didn’t feel like some corporate post-dieselgate suit trying to sell you an appliance.

    “Above all, we must derive the character from our identity, from the VW identity,” said Mindt. “The Porsche is the fastest of all. The Lamborghini is the most aggressive of all. The Cupra is the coolest of all. And what are we? We have the opportunity to be the most likable.”

    Part of this strategy will be to abandon the ID designation for its vehicles. Volkswagen will push forward with “real” car names again, beginning with the ID.Every1—which, no, is not its final name. This proper branding will make its cars feel more approachable. Couple that with a design that feels fun and bubbly rather than some sort of spaceship, and you’ve got what Mindt hopes is a recipe for success.

    It might not feel like it, but this is a really big deal to VW’s success. The automaker has been struggling with sales and relevancy (especially in the U.S.) for years. Approachability has historically been a successful way for VW to sell its cars to consumers, too. For example, the Beetle didn’t conquer the world by being the fastest or most useful vehicle—it was just likable. It almost got this right with the ID. Buzz, too, but just couldn’t pull it off from a value perspective.

    Thankfully, the automaker and its executives have finally realised what it could take to bring the brand back into the good graces of the world. The more important question is whether or not they can pull it off.

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  • Full-service publisher Curveball Games breaks cover

    Full-service publisher Curveball Games breaks cover

    Welsh distributor Curveball Leisure has launched a new full-service publishing division called Curveball Games that will offer financing options and production support to independent and double-A developers.

    The nascent division will be helmed by former PQube senior global commercial manager Ed Gregory, who suggested the company will work to nurture the “green shoots of recovery” that have sprouted amid a turbulent period in the game industry. 

    “This moment calls for a fresh approach and that’s where Curveball Games comes in. Passion drives this industry, but studios need more than passion to thrive. Our role as a publisher is to champion developers, nurture creativity, and provide the support they truly need,” said Gregory. 

    “With our team’s experience and the backing of the wider Curveball group, we feel we’re in a strong position to help surface the next wave of standout talent.”

    Curveball has previously worked on titles such as Unholy, Choo-Choo Charles, and Jetrunner. The company’s publishing division will provide everything from funding and marketing assistance to QA, localization, PR, platform support, and options for physical releases. Notably, it claims developers can tailor a publishing deal to suit their exact needs. 

    “Need the whole toolkit? Great. Just need help in a few key areas? Fine by us! Our support can be wholly shaped around what your project needs most,” reads an explainer on the Curveball website. 

    Related:Nintendo unveils new-look division dedicated to supporting its movie business

    Curveball CEO Stuart Harries explained the company has been exploring the world of publishing for the past year and ultimately chose to launch its own dedicated division to demonstrate both its confidence in the game industry and its commitment to developers.

    “We’re here to support, not just today, but for the long term,” added Harries. “We’re not chasing overnight success. This is a sustained investment and and I have full confidence that Ed and his team will build something truly meaningful over the years to come.” 

    Curveball Games has already established partnerships with Wales Interactive, Top Hat Studios, Duality Games, Reignite Group, Riddlebit Software, and others on several upcoming physical and digital releases.


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  • AmfAR to Make London Debut During Frieze Week Honoring Tracey Emin

    AmfAR to Make London Debut During Frieze Week Honoring Tracey Emin

    LONDON — The Foundation for AIDs Research, amfAR, will make its London debut on Oct. 17, coinciding with the art fair Frieze and Frieze Masters, which will run from Oct. 15 to 19.

    The event will take place at the newly opened Chancery Rosewood Hotel, where the British artist Tracey Emin will be honored with the amfAR Award of Inspiration for her artistic accomplishments and efforts supporting AIDS- and HIV-related causes.

    The award has previously been picked up by Richard Gere, Ava DuVernay, Cher, Miley Cyrus and Charlize Theron.

    “It is our privilege to be recognizing Dame Tracey Emin, one of Britain’s most distinguished artists, at our first event in London,” said Kevin Robert Frost, chief executive officer of amfAR.

    Tracey Emin and Bianca Jagger

    Dave Benett/Getty Images

    “Her longstanding, generous support of amfAR and other AIDS organizations, along with her advocacy on behalf of people living with HIV, is an inspiration to us all and we are so grateful that she continues to shine a light on our important work in the fight against HIV [and] AIDS.”

    The Oct. 17 event will include a seated dinner, musical performance and live auctions of contemporary art and luxury experiences.

    The U.K. has strong ties to amfAR, which was founded in 1985, with the foundation providing more than $2.5 million in funding to HIV researchers based in the U.K.

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  • The Sky Today on Monday, September 1: Observe Delta Cephei

    The Sky Today on Monday, September 1: Observe Delta Cephei

    The first-known Cepheid variable, Delta Cephei, is visible all night. Follow it for several days and you can watch it fade and brighten again.

    The house-shaped constellation Cepheus harbors one of the sky’s most famous stars: Delta (δ) Cephei. You can find it just off the house’s southeastern edge. Credit: Astronomy: Roen Kelly.

    • Delta Cephei, the archetype Cepheid variable star, is located in the constellation Cepheus, a circumpolar constellation visible in the northern sky.
    • Delta Cephei’s position can be determined relative to other stars in Cepheus, notably Zeta Cephei, lying approximately 2.5° to its northeast.
    • Delta Cephei exhibits a periodic variation in brightness, ranging between magnitudes 3.6 and 4.3 over a 5.366-day cycle.
    • Observational methods suggested include visual comparison with Zeta Cephei and astrophotography to track its brightness fluctuations over several nights.

    Cepheid variables are some of the most well-known variable stars in the sky, responsible for helping astronomers accurately measure cosmic distances and famously clueing Edwin Hubble in to the fact that the Andromeda Galaxy was far beyond the Milky Way. So, tonight let’s begin September by finding the Cepheid variable that started it all: Delta Cephei, the archetype Cepheid variable. 

    If you know how stellar names work, you’ll already know where to find this star — it’s in the constellation Cepheus. The King lies high in the north after dark at this time of year, his house-shaped outline appearing upside-down early in the evening, then slowly rotating onto its side as the hours progress. Cepheus is a circumpolar constellation, meaning it appears to circle the North Celestial Pole (and the North Star, Polaris) rather than rise in the east and set in the west. 

    Two hours after sunset, Cepheus is 50° high in the north, standing on the top of its peaked roof, marked by magnitude 3.2 Gamma Cep. The two stars marking the eaves of the house (at the top of its square shape when rightside-up) are above Gamma at this time — they are magnitude 3.2 Beta (β) and magnitude 3.5 Iota (ι) Cep. Above Beta is magnitude 2.5 Alpha (α) Cep, and above Iota is magnitude 3.4 Zeta (ζ) Cep. It is near Zeta that we’ll finally find our target, Delta Cep, which lies about 2.5° to Zeta’s northeast, or that star’s lower right early this evening. 

    Delta Cep varies in brightness between magnitudes 3.6 to 4.3 over a period of 5.366 days. So, you can use Zeta as a comparison once you find the star — is Delta close in brightness to Zeta, or is it notably fainter? Once you’ve noted the relative magnitude, make sure to come back every day for the next several nights and watch whether it brightens or fades. If you’re experienced in taking astrophotos, try taking a photo each night to chart the star’s changes; its cycle may be easier to follow on a series of photos than with your memory and your eyes. 

    Sunrise: 6:28 A.M.
    Sunset: 7:31 P.M.
    Moonrise: 3:55 P.M.
    Moonset: —
    Moon Phase: Waxing gibbous (64%)
    *Times for sunrise, sunset, moonrise, and moonset are given in local time from 40° N 90° W. The Moon’s illumination is given at 12 P.M. local time from the same location.

    For a look ahead at more upcoming sky events, check out our full Sky This Week column. 

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  • YouTube’s latest crackdown may affect your family plan

    YouTube’s latest crackdown may affect your family plan

    YouTube’s Premium Family plan offers a nifty way of extending the joy of a paid subscription to others in your family, all while not breaking the bank. The $23/mo subscription allows you to add up to five family members to share your Premium subscription (including YouTube Music) with, albeit with some restrictions.

    Since 2023, at the very least, YouTube has required all family plan members to be located in the same household. Although the stipulation has long been in place, YouTube has never really gone out of its way to enforce it. That seems to be changing now.

    This comes soon after YouTube started testing out a new two-person Premium plan.

    The streaming giant is now seemingly flagging accounts that are part of a family plan but not physically located in the same household as the family manager. A friend of mine, who’s also an Android Police reader, received one such email titled “Your YouTube Premium family membership will be paused.”

    “Your YouTube Premium family membership requires all members to be in the same household as the family manager. It appears you may not be in the same household as your family manager, and your membership will be paused in 14 days. Once your access is paused, you will remain in your family group and be able to watch YouTube with ads, but will no longer have YouTube Premium benefits,” reads the email.

    For context, YouTube conducts an “electronic check-in” every 30 days to ensure that each family member lives at the same residential address as the family manager. Previously, failing the check-in didn’t really seem to have any consequences, but that is now changing.

    The crackdown doesn’t seem widespread just yet

    Once flagged, users will retain their family member status, albeit with none of the YouTube Premium benefits. Additionally, flagged users will have the option to contact Google to “confirm eligibility and maintain access” via a support form.

    For what it’s worth, I, too, am part of a family plan where the plan manager lives in a different household, and I haven’t received an email like the one above yet (fingers crossed, even though I just self-snitched). As of right now, the crackdown doesn’t seem to have made its way out widely. There have been scattered reports of it on Reddit since a few months ago, with the only concrete one being user thisTja’s post who had their membership canceled after the 14-day warning.

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  • Xiaomi 16 Series Expected Launch Date Revealed

    Xiaomi 16 Series Expected Launch Date Revealed

    Xiaomi is preparing to make headlines again with its upcoming Xiaomi 16 series, which is expected to launch earlier than usual. Last year, the Xiaomi 15 and 15 Pro arrived in October, but reports now suggest the 16 lineup will break tradition.

    According to the fresh leak, the new models are set to debut between September 24 and 26. This timing closely follows Qualcomm’s major event on September 23, when it will unveil the Snapdragon 8 Gen Elite 2 (also called 8 Elite Gen 5).

    Moving forward, rumors indicate that Xiaomi will unveil three new devices this month: the Xiaomi 16, 16 Pro Mini, and 16 Pro Max. The Xiaomi 16 Ultra, however, is expected to arrive separately next year, continuing Xiaomi’s strategy of staggering its flagship releases.

    Major specifications

    When it comes to displays, the vanilla Xiaomi 16 and Pro Mini are tipped to feature 6.3-inch panels, while the Pro Max could boast a 6.8-inch display. Under the hood, all models are expected to carry Qualcomm’s latest Snapdragon 8-series chipset, ensuring top-tier performance.

    In terms of power, the vanilla Xiaomi 16 may sport a 6,800 mAh battery, while the Pro Mini could bring a 6,300mAh cell. Additionally, the lineup is rumored to support 100W fast charging, giving users faster and more reliable charging speeds than before.

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  • Enteroendocrine cells: the gatekeepers of microbiome-gut-brain communication

    Enteroendocrine cells: the gatekeepers of microbiome-gut-brain communication

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