Ion entropy and accurate entropy-based FDR estimation in metabolomics.

Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target-decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application.

Bile metabolic fingerprints distinguish biliary tract cancer from benign biliary diseases.

BACKGROUND AND AIMS: Biliary tract cancers are aggressive gastrointestinal malignancies characterized by a dismal 5-year overall survival rate <20%. Current diagnostic modalities suffer from limitations regarding sensitivity and specificity. This study aimed to develop a bile metabolite-based platform for precise discrimination between malignant and benign biliary diseases. APPROACH AND RESULTS: Samples were collected from 336 patients with biliary tract cancer or benign biliary diseases across 3 independent cohorts. Untargeted metabolic fingerprinting was performed on 300 bile samples using novel nanoparticle-enhanced laser desorption/ionization mass spectrometry. Subsequently, a diagnostic assay was developed based on the exploratory cohort using a selected bile metabolic biomarker panel, with performance evaluated in the validation cohort. Further external validation of disease-specific metabolites from bile samples was conducted in a prospective cohort (n = 36) using quantitative analysis. As a result, we established a novel bile-based assay, BileMet, for the rapid and precise detection of malignancies in the biliary tract system with an AUC of 0.891. We identified 6-metabolite biomarker candidates and discovered the critical role of the chenodeoxycholic acid glycine conjugate as a protective metabolite associated with biliary tract cancer. CONCLUSIONS: Our findings confirmed the improved diagnostic capabilities of BileMet assay in a clinical setting. If applied, the BileMet assay enables intraoperative testing and fast medical decision-making for cases with suspected malignancy where brush cytology detection fails to support malignancy, ultimately reducing the economic burden by over 90%.

Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints.

High-performance metabolic analysis is emerging in the diagnosis and prognosis of breast cancer (BrCa). Still, advanced tools are in demand to deliver the application potentials of metabolic analysis. Here, we used fast nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to record serum metabolic fingerprints (SMFs) of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection without treatment. Subsequently, machine learning of SMFs generated by NPELDI-MS functioned as an efficient readout to distinguish BrCa from non-BrCa with an area under the curve of 0.948. Furthermore, a metabolic prognosis scoring system was constructed using SMFs with effective prediction performance toward BrCa (P < 0.005). Finally, we identified a biomarker panel of seven metabolites that were differentially enriched in BrCa serum and their related pathways. Together, our findings provide an efficient serum metabolic tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

MRMPro: a web-based tool to improve the speed of manual calibration for multiple reaction monitoring data analysis by mass spectrometry.

BACKGROUND: As a gold-standard quantitative technique based on mass spectrometry, multiple reaction monitoring (MRM) has been widely used in proteomics and metabolomics. In the analysis of MRM data, as no peak picking algorithm can achieve perfect accuracy, manual inspection is necessary to correct the errors. In large cohort analysis scenarios, the time required for manual inspection is often considerable. Apart from the commercial software that comes with mass spectrometers, the open-source and free software Skyline is the most popular software for quantitative omics. However, this software is not optimized for manual inspection of hundreds of samples, the interactive experience also needs to be improved. RESULTS: Here we introduce MRMPro, a web-based MRM data analysis platform for efficient manual inspection. MRMPro supports data analysis of MRM and schedule MRM data acquired by mass spectrometers of mainstream vendors. With the goal of improving the speed of manual inspection, we implemented a collaborative review system based on cloud architecture, allowing multiple users to review through browsers. To reduce bandwidth usage and improve data retrieval speed, we proposed a MRM data compression algorithm, which reduced data volume by more than 60% and 80% respectively compared to vendor and mzML format. To improve the efficiency of manual inspection, we proposed a retention time drift estimation algorithm based on similarity of chromatograms. The estimated retention time drifts were then used for peak alignment and automatic EIC grouping. Compared with Skyline, MRMPro has higher quantification accuracy and better manual inspection support. CONCLUSIONS: In this study, we proposed MRMPro to improve the usability of manual calibration for MRM data analysis. MRMPro is free for non-commercial use. Researchers can access MRMPro through http://mrmpro.csibio.com/ . All major mass spectrometry formats (wiff, raw, mzML, etc.) can be analyzed on the platform. The final identification results can be exported to a common.xlsx format for subsequent analysis.

How Much Storage Precision Can Be Lost: Guidance for Near-Lossless Compression of Untargeted Metabolomics Mass Spectrometry Data.

Several lossy compressors have achieved superior compression rates for mass spectrometry (MS) data at the cost of storage precision. Currently, the impacts of precision losses on MS data processing have not been thoroughly evaluated, which is critical for the future development of lossy compressors. We first evaluated different storage precision (32 bit and 64 bit) in lossless mzML files. We then applied 10 truncation transformations to generate precision-lossy files: five relative errors for intensities and five absolute errors for m/z values. MZmine3 and XCMS were used for feature detection and GNPS for compound annotation. Lastly, we compared Precision, Recall, F1 - score, and file sizes between lossy files and lossless files under different conditions. Overall, we revealed that the discrepancy between 32 and 64 bit precision was under 1%. We proposed an absolute m/z error of 10-4 and a relative intensity error of 2 × 10-2, adhering to a 5% error threshold (F1 - scores above 95%). For a stricter 1% error threshold (F1 - scores above 99%), an absolute m/z error of 2 × 10-5 and a relative intensity error of 2 × 10-3 were advised. This guidance aims to help researchers improve lossy compression algorithms and minimize the negative effects of precision losses on downstream data processing.

Deciphering the molecular and functional basis of TMexCD1: the plasmid-encoded efflux pump of resistance-nodulation-division superfamily.

Horizontal gene transfer has been demonstrated to be an important driver for the emergency of multidrug-resistant pathogens. Recently, a transferable gene cluster tmexCD1-toprJ1 of the resistance-nodulation-division (RND) superfamily was identified in the plasmids of animal-derived Klebsiella pneumoniae strains, with a higher efflux capacity for various drugs than the Escherichia coli AcrAB-TolC homolog system. In this study, we focused on the differences in the inner membrane pump of these two systems and identified some key residues that contribute to the robust efflux activity of the TMexCD1 system. With the aid of homologous modeling and molecular docking, eight residues from the proximal binding pocket (PBP) and nine from the distal binding pocket (DBP) were selected and subjected to site-directed mutagenesis. Several of them, such as S134, I139, D181, and A290, were shown to be important for substrate binding in the DBP region, and all residues in PBP and DBP showed certain substrate preferences. Apart from the conservative switch loop (L613-623TMexD1) previously identified in the E. coli AcrB (EcAcrB), a relatively unconservative loop (L665-675TMexD1) at the bottom of PBP was proposed as a critical element for the robust activity of TMexD1, due to variations at sites E669, G670, N673, and S674 compared to EcAcrAB, and the significantly altered efflux activity due to their mutations. The conservation and flexibility of these key factors can contribute to the evolution of the RND efflux pumps and thus serve as potential targets for developing inhibitors to block the widespread of the TMexCD1 system.

3D-MSNet: a point cloud-based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data.

MOTIVATION: Liquid chromatography coupled with high-resolution mass spectrometry is widely used in composition profiling in untargeted metabolomics research. While retaining complete sample information, mass spectrometry (MS) data naturally have the characteristics of high dimensionality, high complexity, and huge data volume. In mainstream quantification methods, none of the existing methods can perform direct 3D analysis on lossless profile MS signals. All software simplify calculations by dimensionality reduction or lossy grid transformation, ignoring the full 3D signal distribution of MS data and resulting in inaccurate feature detection and quantification. RESULTS: On the basis that the neural network is effective for high-dimensional data analysis and can discover implicit features from large amounts of complex data, in this work, we propose 3D-MSNet, a novel deep learning-based model for untargeted feature extraction. 3D-MSNet performs direct feature detection on 3D MS point clouds as an instance segmentation task. After training on a self-annotated 3D feature dataset, we compared our model with nine popular software (MS-DIAL, MZmine 2, XCMS Online, MarkerView, Compound Discoverer, MaxQuant, Dinosaur, DeepIso, PointIso) on two metabolomics and one proteomics public benchmark datasets. Our 3D-MSNet model outperformed other software with significant improvement in feature detection and quantification accuracy on all evaluation datasets. Furthermore, 3D-MSNet has high feature extraction robustness and can be widely applied to profile MS data acquired with various high-resolution mass spectrometers with various resolutions. AVAILABILITY AND IMPLEMENTATION: 3D-MSNet is an open-source model and is freely available at https://github.com/CSi-Studio/3D-MSNet under a permissive license. Benchmark datasets, training dataset, evaluation methods, and results are available at https://doi.org/10.5281/zenodo.6582912.

The first-in-human preclinical evaluation of the new probe [123I]I-PSMA-7 for real-time intraoperative targeted biopsy and SPECT/CT imaging in prostate cancer.

PURPOSE: PSMA/PET has been increasingly used to detect PCa, and PSMA/PET-guided biopsy has shown promising results. However, it cannot be confirmed immediately whether the tissues are the targeted area. In this study, we aimed to develop a novel probe, [123I]I-PSMA-7. First, we hope that [123I]I-PSMA-7 can provide instant confirmation for prostate biopsy. Second, we hope it will help detect PCa. METHODS: We synthesized a high-affinity probe, [123I]I-PSMA-7, and evaluated its properties. We included ten patients with suspected PCa and divided them into two groups. The injection and biopsy were approximately 24 h apart. The activity in biopsy lesions was measured as the cpm by a γ-counter. Moreover, we enrolled 3 patients to evaluate the potential of [123I]I-PSMA-7 for detecting PCa. RESULTS: Animal experiments verified the safety, targeting and effectiveness of [123I]I-PSMA-7, and the tumor-to-muscle ratio was greatest at 24 h, which confirmed the results of this study in humans. After injection of 185MBq [123I]I-PSMA-7, 18/55 cores were positive, and the cpm was significantly greater (4345 ± 3547 vs. 714 ± 547, P < 0.001), with an AUC of 0.97 and a cutoff of 1312 (sens/spec of 94.40%/91.90%). At a lower dose, 10/55 biopsy cores were cancerous, and the cpm was 2446 ± 1622 vs. 153 ± 112 (P < 0.001). The AUC was 1, with a cutoff value of 490 (sens/spec of 100%). When the radiopharmaceuticals were added to 370 MBq, we achieved better SPECT/CT imaging. CONCLUSION: With the aid of [123I]I-PSMA-7 and via cpm-based biopsy, we can reduce the number of biopsies to a minimum operation. [123I]I-PSMA-7 PSMA SPECT/CT can also provide good imaging results. TRIAL REGISTRATION: Chinese Clinical trial registry ChiCTR2300069745, Registered 24 March 2023.

Immunotherapy combined with apatinib in the treatment of advanced or metastatic gastric/gastroesophageal tumors: a systematic review and meta-analysis.

BACKGROUND: Immunotherapy or apatinib alone has been used as third-line adjuvant therapy for advanced or metastatic gastric/gastroesophageal junction (G/GEJ) tumors, but the efficacy of combining them with each other for the treatment of patients with advanced or metastatic G/GEJ is unknown; therefore, we further evaluated the efficacy and safety of immunotherapy combined with apatinib in patients with advanced or metastatic G/GEJ. METHODS: The main search was conducted on published databases: Embase, Cochrane library, PubMed.The search was conducted from the establishment of the database to December 2023.Clinical trials with patients with advanced or metastatic G/GEJ and immunotherapy combined with apatinib as the study variable were collected. Review Manager 5.4 software as well as stata 15.0 software were used for meta-analysis. RESULTS: A total of 651 patients from 19 articles were included in this meta-analysis. In the included studies, immunotherapy combined with apatinib had a complete response (CR) of 0.03 (95% CI: 0.00 -0.06), partial response (PR) of 0.34 (95% CI: 0.19-0.49), stable disease (SD) of 0.43 (95% CI: 0.32-0.55), objective response rate (ORR) was 0.36 (95% CI: 0.23-0.48), disease control rate (DCR) was 0.80 (95% CI: 0.74-0.86), and median progression-free survival (PFS) was 4.29 (95% CI: 4.05-4.52), median Overall survival (OS) was 8.79 (95% CI: 7.92-9.66), and the incidence of grade ≥ 3 TRAEs was 0.34 (95% CI: 0:19-0.49). PR, ORR, DCR, median PFS and median OS were significantly higher in the immunotherapy and apatinib combination chemotherapy group (IAC) than in the immunotherapy combination apatinib group (IA). And the difference was not significant in the incidence of SD and grade ≥ 3 TRAEs. CONCLUSION: This meta-analysis shows that immunotherapy combined with apatinib is safe and effective in the treatment of advanced or metastatic G/GEJ, where IAC can be a recommended adjuvant treatment option for patients with advanced or metastatic G/GEJ. However, more large multicenter randomized studies are urgently needed to reveal the long-term outcomes of immunotherapy combined with apatinib treatment.

Miniature mass spectrometer-based point-of-care assay for quantification of metformin and sitagliptin in human blood and urine.

Metformin (MET) and sitagliptin (STG) are widely used as the first-line and long-term oral hypoglycemic agents for managing type 2 diabetes mellitus (T2DM). However, the current lack of convenient and rapid measurement methods poses a challenge for individualized management. This study developed a point-of-care (POC) assay method utilizing a miniature mass spectrometer, enabling rapid and accurate quantification of MET and STG concentrations in human blood and urine. By combining the miniature mass spectrometer with paper spray ionization, this method simplifies the process into three to four steps, requires minimal amounts of bodily fluids (50 µL of blood and 2 µL of urine), and is able to obtain quantification results within approximately 2 min. Stable isotope-labeled internal standards were employed to enhance the accuracy and stability of measurement. The MS/MS responses exhibited good linear relationship with concentration, with relative standard deviations (RSDs) below 25%. It has the potential to provide immediate treatment feedback and decision support for patients and healthcare professionals in clinical practice.

Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study.

OBJECTIVE: Metabolic biomarkers are expected to decode the phenotype of gastric cancer (GC) and lead to high-performance blood tests towards GC diagnosis and prognosis. We attempted to develop diagnostic and prognostic models for GC based on plasma metabolic information. DESIGN: We conducted a large-scale, multicentre study comprising 1944 participants from 7 centres in retrospective cohort and 264 participants in prospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionisation-mass spectrometry (NPELDI-MS). Furthermore, the developed diagnostic model was validated in prospective cohort by both NPELDI-MS and ultra-performance liquid chromatography-MS (UPLC-MS). RESULTS: We demonstrated the high throughput, desirable reproducibility and limited centre-specific effects of PMFs obtained through NPELDI-MS. In retrospective cohort, we achieved diagnostic performance with areas under curves (AUCs) of 0.862-0.988 in the discovery (n=1157 from 5 centres) and independent external verification dataset (n=787 from another 2 centres), through 5 different machine learning of PMFs, including neural network, ridge regression, lasso regression, support vector machine and random forest. Further, a metabolic panel consisting of 21 metabolites was constructed and identified for GC diagnosis with AUCs of 0.921-0.971 and 0.907-0.940 in the discovery and verification dataset, respectively. In the prospective study (n=264 from lead centre), both NPELDI-MS and UPLC-MS were applied to detect and validate the metabolic panel, and the diagnostic AUCs were 0.855-0.918 and 0.856-0.916, respectively. Moreover, we constructed a prognosis scoring system for GC in retrospective cohort, which can effectively predict the survival of GC patients. CONCLUSION: We developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.

G-Aligner: a graph-based feature alignment method for untargeted LC-MS-based metabolomics.

BACKGROUND: Liquid chromatography-mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching. RESULTS: To comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC-MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034 CONCLUSIONS: In this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC-MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm.

Injectiondesign: web service of plate design with optimized stratified block randomization for modern GC/LC-MS-based sample preparation.

BACKGROUND: Plate design is a necessary and time-consuming operation for GC/LC-MS-based sample preparation. The implementation of the inter-batch balancing algorithm and the intra-batch randomization algorithm can have a significant impact on the final results. For researchers without programming skills, a stable and efficient online service for plate design is necessary. RESULTS: Here we describe InjectionDesign, a free online plate design service focused on GC/LC-MS-based multi-omics experiment design. It offers the ability to separate the position design from the sequence design, making the output more compatible with the requirements of a modern mass spectrometer-based laboratory. In addition, it has implemented an optimized block randomization algorithm, which can be better applied to sample stratification with block randomization for an unbalanced distribution. It is easy to use, with built-in support for common instrument models and quick export to a worksheet. CONCLUSIONS: InjectionDesign is an open-source project based on Java. Researchers can get the source code for the project from Github: https://github.com/CSi-Studio/InjectionDesign . A free web service is also provided: http://www.injection.design .

The pH sensor and ion binding of NhaD Na+ /H+ antiporter from IT superfamily.

Sodium-proton (Na+ /H+ ) antiporters from the ion transporter (IT) superfamily play a vital role in controlling the pH and electrolyte homeostasis. However, very limited information regarding their structural functions is available to date. In this study, the structural model of the NhaD antiporter was proposed as a typical hairpin structure of IT proteins, with two symmetrically conserved scaffold domains that frame the core substrate-binding sites, and four motifs were identified. Furthermore, 25 conserved sites involving these domains were subjected to site-directed mutagenesis, and all mutations resulted in an impact on transport abilities. In particular, as candidates for Na+ -binding sites, D166 and D405 mutations at hairpin discontinuities were detrimental to transport activities and were found to induce pronounced conformational changes using fluorescence resonance energy transfer (FRET) assays. In addition, as observed in the NhaA structure, some charged residues, for example, E64, E65, R454, and R464, are predicted to be involved in the net charge switch of NhaD activation, by collectively form a "pH sensor" at the entrance of the cytoplasmic funnel. Mutations encompassing these residues were detrimental to the transport activity of NhaD or lost the capacity to respond to pH signals and trigger conformational changes for Na+ translocation.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification.

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

TAD boundary and strength prediction by integrating sequence and epigenetic profile information.

Topologically associated domains (TADs) are one of the important higher order chromatin structures with various sizes in the eukaryotic genomes. TAD boundaries, as the flanking regions between adjacent domains, can restrict the interactions of regulatory elements, including enhancers and promoters, and are generally dynamic and variable in different cells. However, the influence of sequence and epigenetic profile-based features in the identification of TAD boundaries is largely unknown. In this work, we proposed a method called pTADS (prediction of TAD boundary and strength), to predict TAD boundaries and boundary strength across multiple cell lines with DNA sequence and epigenetic profile information. The performance was assessed in seven cell lines and three TAD calling methods. The results demonstrate that the TAD boundary can be well predicted by the selected shared features across multiple cell lines. Especially, the model can be transferable to predict the TAD boundary from one cell line to other cell lines. The boundary strength can be characterized by boundary score with good performance. The predicted TAD boundary and TAD boundary strength are further confirmed by three Hi-C contact matrix-based methods across multiple cell lines. The codes and datasets are available at https://github.com/chrom3DEpi/pTADS.

YAP-TEAD mediates PPAR α-induced hepatomegaly and liver regeneration in mice.

BACKGROUND AND AIMS: Peroxisome proliferator-activated receptor α (PPARα, NR1C1) is a ligand-activated nuclear receptor involved in the regulation of lipid catabolism and energy homeostasis. PPARα activation induces hepatomegaly and plays an important role in liver regeneration, but the underlying mechanisms remain unclear. APPROACH AND RESULTS: In this study, the effect of PPARα activation on liver enlargement and regeneration was investigated in several strains of genetically modified mice. PPARα activation by the specific agonist WY-14643 significantly induced hepatomegaly and accelerated liver regeneration after 70% partial hepatectomy (PHx) in wild-type mice and Pparafl/fl mice, while these effects were abolished in hepatocyte-specific Ppara-deficient (PparaΔHep ) mice. Moreover, PPARα activation promoted hepatocyte hypertrophy around the central vein area and hepatocyte proliferation around the portal vein area. Mechanistically, PPARα activation regulated expression of yes-associated protein (YAP) and its downstream targets (connective tissue growth factor, cysteine-rich angiogenic inducer 61, and ankyrin repeat domain 1) as well as proliferation-related proteins (cyclins A1, D1, and E1). Binding of YAP with the PPARα E domain was critical for the interaction between YAP and PPARα. PPARα activation further induced nuclear translocation of YAP. Disruption of the YAP-transcriptional enhancer factor domain family member (TEAD) association significantly suppressed PPARα-induced hepatomegaly and hepatocyte enlargement and proliferation. In addition, PPARα failed to induce hepatomegaly in adeno-associated virus-Yap short hairpin RNA-treated mice and liver-specific Yap-deficient mice. Blockade of YAP signaling abolished PPARα-induced hepatocyte hypertrophy around the central vein area and hepatocyte proliferation around the portal vein area. CONCLUSIONS: This study revealed a function of PPARα in regulating liver size and liver regeneration through activation of the YAP-TEAD signaling pathway. These findings have implications for understanding the physiological functions of PPARα and suggest its potential for manipulation of liver size and liver regeneration.

MetaPro: a web-based metabolomics application for LC-MS data batch inspection and library curation.

INTRODUCTION: Metabolomics analysis based on liquid chromatography-mass spectrometry (LC-MS) has been a prevalent method in the metabolic field. However, accurately quantifying all the metabolites in large metabolomics sample cohorts is challenging. The analysis efficiency is restricted by the abilities of software in many labs, and the lack of spectra for some metabolites also hinders metabolite identification. OBJECTIVES: Develop software that performs semi-targeted metabolomics analysis with an optimized workflow to improve quantification accuracy. The software also supports web-based technologies and increases laboratory analysis efficiency. A spectral curation function is provided to promote the prosperity of homemade MS/MS spectral libraries in the metabolomics community. METHODS: MetaPro is developed based on an industrial-grade web framework and a computation-oriented MS data format to improve analysis efficiency. Algorithms from mainstream metabolomics software are integrated and optimized for more accurate quantification results. A semi-targeted analysis workflow is designed based on the concept of combining artificial judgment and algorithm inference. RESULTS: MetaPro supports semi-targeted analysis workflow and functions for fast QC inspection and self-made spectral library curation with easy-to-use interfaces. With curated authentic or high-quality spectra, it can improve identification accuracy using different peak identification strategies. It demonstrates practical value in analyzing large amounts of metabolomics samples. CONCLUSION: We offer MetaPro as a web-based application characterized by fast batch QC inspection and credible spectral curation towards high-throughput metabolomics data. It aims to resolve the analysis difficulty in semi-targeted metabolomics.

Diagnostic efficiency of [68 Ga]Ga-DOTA-FAPI-04 in differentiating periprosthetic hip joint infection and aseptic failure.

PURPOSE: To assess the efficiency of [68 Ga]Ga-DOTA-FAPI-04 in diagnosing periprosthetic hip joint infection and establish a diagnostic standard of clinical significance based on uptake pattern. METHODS: [68 Ga]Ga-DOTA-FAPI-04 PET/CT was performed in patients with symptomatic hip arthroplasty from December 2019 to July 2022. The reference standard was based on the 2018 Evidence-Based and Validation Criteria. Two diagnostic criteria, SUVmax and uptake pattern, were used to diagnose PJI. Meanwhile, original data were imported into IKT-snap to draw the view of interest, A.K. was used to extract features of clinical cases, and unsupervised clustering analysis was applied according to the groups. RESULTS: A total of 103 patients were included, 28 of whom had PJI. The area under the curve of SUVmax was 0.898, which was better than that of all of the serological tests. The cutoff value of SUVmax was 7.53, and the sensitivity and specificity were 100 and 72%, respectively. The sensitivity, specificity and accuracy of the uptake pattern were 100, 93.1 and 95%, respectively. In radiomics analysis, the features of PJI were significantly different from those of aseptic failure. CONCLUSION: The efficiency of [68 Ga]Ga-DOTA-FAPI-04 PET/CT in diagnosing PJI showed promising results, and the diagnostic criteria of the uptake pattern were more clinically instructive. Radiomics also showed certain application prospects in the field of PJI. TRIAL REGISTRATION NUMBER: Trial registration: ChiCTR2000041204. Registered 24 September 2019.

Metabolic reprogramming in astrocytes results in neuronal dysfunction in intellectual disability.

Astrocyte aerobic glycolysis provides vital trophic support for central nervous system neurons. However, whether and how astrocytic metabolic dysregulation contributes to neuronal dysfunction in intellectual disability (ID) remain unclear. Here, we demonstrate a causal role for an ID-associated SNX27 mutation (R198W) in cognitive deficits involving reshaping astrocytic metabolism. We generated SNX27R196W (equivalent to human R198W) knock-in mice and found that they displayed deficits in synaptic function and learning behaviors. SNX27R196W resulted in attenuated astrocytic glucose uptake via GLUT1, leading to reduced lactate production and a switch from homeostatic to reactive astrocytes. Importantly, lactate supplementation or a ketogenic diet restored neuronal oxidative phosphorylation and reversed cognitive deficits in SNX27R196W mice. In summary, we illustrate a key role for astrocytic SNX27 in maintaining glucose supply and glycolysis and reveal that altered astrocytic metabolism disrupts the astrocyte-neuron interaction, which contributes to ID. Our work also suggests a feasible strategy for treating ID by restoring astrocytic metabolic function.