Phosphorylation is a key post-translational modification involved in many cellular processes. Embryonic stem cells (ESCs), characterized by their self-renewal capacity and pluripotent differentiation potential, are widely used in studies of developmental biology and regenerative medicine. However, existing phosphoproteomic data for ESCs remain limited in throughput, restricting our understanding of phosphorylation-mediated regulatory mechanisms. In this study, we performed high-throughput phosphoproteomic profiling and identified 3711 phosphoproteins and 11,410 phosphosites. Integrated analyses showed that nearly half of the interacting proteins of the core pluripotency factors OCT4, SOX2, and NANOG are phosphorylated. Moreover, we found that phosphorylation is more prevalent in scaffold proteins involved in phase separation compared to clients and regulators, and is highly enriched in specific membraneless organelles, such as those in stress granules and Cajal bodies. Together, these findings provide a valuable resource for phosphoproteomics and offer important insights into the role of phosphorylation in pluripotency and phase separation.

Background:We performed proteome-wide Mendelian randomization (MR) and colocalization analyses to explore the causal relationships between proteins and schizophrenia (SCZ). Methods:In the primary analysis, genetic instruments of 4,907 plasma protein from 35,559 Icelanders served as the exposure, summary statistics for SCZ (35,476 cases, 46,839 controls) Working Group of the Psychiatric Genomics Consortium (PGC) served as the outcome. The initial findings underwent sensitivity analyses and were externally validated using cis-pQTLs from the Fenland study (4,979 proteins, 10,708 participants) and UK Biobank Pharma Proteomics Project (UKB-PPP, 2923 proteins, 33,043 participants), and brain cis-eQTLs from Genotype-Tissue Expression (GTEx). Bayesian colocalization assessed shared causal variants. Protein-protein interactions with antipsychotic drug targets were explored. Results:In the primary analysis, genetically predicted levels of seven plasma proteins were significantly associated with SCZ risk: ADAM22 (OR = 0.85, P = 8.97×10-7), LIMA1 (OR = 0.43, P = 1.28×10-5), CTSS (OR = 1.27, P = 9.18×10-7), FOXO3 (OR = 2.80, P = 6.05×10-6), IRF3 (OR = 2.09, P = 1.15×10-6), KLC1 (OR = 2.17, P = 3.38×10-¹¹), and MMP16 (OR = 2.00, P = 1.60×10-5). External validation partially confirmed these associations: LIMA1, CTSS, FOXO3, KLC1, and MMP16 replicated in the Fenland study; ADAM22 and CTSS replicated in UKB-PPP; MMP16 and CTSS expression in specific brain tissues replicated using GTEx brain eQTLs. Colocalization strongly supported shared causal variants for FOXO3 (PPH4 = 0.966), IRF3 (PPH4 = 0.932), and LIMA1 (PPH4 = 0.985) with SCZ. CTSS, FOXO3, IRF3, and MMP16 showed interactions with known antipsychotic drug targets. Conclusion:This large-scale MR study provides robust evidence supporting causal roles for specific plasma proteins in SCZ pathogenesis, highlighting promising candidates for mechanistic studies and therapeutic development.

Background and aimsRadical cystectomy (RC) remains the standard treatment for localized and regionally muscle-invasive bladder cancer (MIBC). However, only half of patients with MIBC survive more than 5 years after RC. We explored the factors associated with overall survival (OS) and constructed a prognostic nomogram for predicting 1-, 3-, and 5-year OS after RC.MethodsThe data were sourced from the Surveillance, Epidemiology, and End Results (SEER) database and Peking University First Hospital (PKUFH). Univariate and multivariate Cox regression analyses were performed using the minimum value of the Akaike information criterion to select independent prognostic factors that significantly contributed to patient survival. A prognostic nomogram was designed to predict 1-, 3-, and 5-year OS.ResultsAmong the 16,949 patients with MIBC undergoing surgery, 31.15% survived for more than 5 years. The nomogram we created demonstrated satisfactory discriminative ability to predict the survival of MIBC patients with RC, with area under curve (AUC) of 0.939, 0.880 and 0.852 for 1-, 3- and 5-year OS in the testing set. Moreover, the nomogram still exhibited good performance in an externally independent dataset (1-year: AUC=0.970; 3-year: AUC=0.847; 5-year: AUC=0.790). Furthermore, decision curve analyses showed a modest net benefit for the use of the MIBC nomogram in the current cohort compared to the use of American Joint Committee on Cancer staging alone.ConclusionsA prognostic nomogram was developed and validated to help clinicians evaluate the prognosis of postoperative MIBC patients. The future integration of additional data will likely improve model performance and accuracy for personalized prognostics.

Gastrin-releasing peptide precursor (ProGRP) is a bioactive precursor of GRP and might play an important role as an emerging tumor marker in early cancer diagnosis. It might also be abnormal in the nonmalignant disease and renal function abnormalities. The present study was undertaken to investigate the changes of ProGRP levels in patients with kidney injuries, especially with chronic kidney disease (CKD), determine the upper reference intervals and clinical diagnostic value of ProGRP in CKD, and thus help oncologists in interpreting ProGRP levels and making clinical judgments of malignances. 676 individuals were enrolled in this cross-sectional study and divided into five groups: healthy control (n=194), CKD (n=272), nephrotic syndrome (NS) (n=137), antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) (n=41), and urinary tract infection (UTI) (n=32). A total of 27 features including age, gender, and 25 laboratory markers were analyzed. Machine learning algorithms were built for the diagnostic models of CKD. Statistical analysis was performed by R software. It was shown that serum ProGRP level in CKD was significantly higher than that in healthy controls, UTI and NS (P < 0.01). The upper reference limit of ProGRP was 188.42 pg/ml for CKD, 245.40 pg/ml for CKD IV-V, and 97.25 pg/ml for NS. Compared with the healthy control, the level of serum ProGRP in CKD stages II, III, IV-V was significantly increased and elevated progressively with CKD grade (P < 0.01). Random Forest (RF) model works best among 4 building machine learning algorithms. 5 vital indicators, ProGRP, estimated glomerular filtration rate (eGFR), urea, albumin (ALB), and direct bilirubin (DBIL), were selected to establish RF model for diagnosing CKD with an area under the curve (AUC) of 0.96 (95% confidence interval [CI]: 0.94-0.97) and high sensitivity (0.89) and specificity (0.92). This study demonstrates that the level of ProGRP in patients with CKD, nephrotic syndrome or AAV, was significantly higher than that in the healthy population. The machine learning model of ProGRP with DBIL, eGFR, ALB, and urea, could provide good clinical value for CKD evaluation.

To the editor: Major depressive disorder(MDD)is a heterogeneous disorder with varying symptom presentations and underlying biological mechanisms.1 The mainstream neurobiological hypotheses of depression involve monoamine neurotransmitters,hypo-thalamic-pituitary-adrenal axis,immune-inflammation and the glutamate system.

Background:: Clear cell renal carcinoma (ccRCC) is one of the most common urological tumors worldwide and metabolic reprogramming is its distinguishing feature. A systematic study on the role of the metabolism-related genes in ccRCC cancer stem cells (CSCs) is still lacking. Moreover, an effective metabolism-related prediction signature is urgently needed to assess the prognosis of ccRCC patients. Methods:: Gene expression profiles of GSE48550 and GSE84546 were analyzed for the role of metabolism-related gene in ccRCC-CSCs. The GSE22541 dataset were used to construct and validate an effective metabolism-related prediction signature to assess the prognosis of ccRCC patients. Results:: For glycolytic metabolism, we found that HKDC1, PFKM and LDHB were significantly upregulated in ccRCC-CSCs in GSE84546. For TCA cycle, ACO1, SDHA and MDH1 were significantly downregulated in ccRCC-CSCs in both GSE48550 and GSE84546. For fatty acid metabolism, CPT1A and ACACB were significantly upregulated in ccRCC-CSCs in GSE84546. It is worth noting that SCD was significantly downregulated in both GSE48550 and GSE84546. For glutamine metabolism, SLC1A5, GLS and GOT1 were significantly upregulated in GSE84546. An eight-gene CSCs metabolism-related risk signature including HKDC1, PFKM, LDHB, IDH1, OGDH, SDHA, GLS and GLUL were constructed to predict the overall survival (OS) of ccRCC patients. Patients could be separated into two groups, and the patients with lower risk scores had longer survival time. Conclusion:: Our study indicated that metabolic reprogramming, including glycolytic metabolism, TCA cycle, fatty acid metabolism and glutamine metabolism, is more obvious in CD105+ renal cells (GSE84546) than CD133+ renal cells (GSE48550). An eight-gene metabolismrelated risk signature including HKDC1, PFKM, LDHB, IDH1, OGDH, SDHA, GLS and GLUL can effectively predict OS in ccRCC.

Background and Objective Chronic kidney disease (CKD) is a major public health issue, and accurate prediction of the progression of kidney failure is critical for clinical decision-making and helps improve patient outcomes. As such, we aimed to develop and externally validate a machine-learned model to predict the progression of CKD using common laboratory variables, demographic characteristics, and an electronic health records database. Methods We developed a predictive model using longitudinal clinical data from a single center for Chinese CKD patients. The cohort included 987 patients who were followed up for more than 24 months. Fifty-three laboratory features were considered for inclusion in the model. The primary outcome in our study was an estimated glomerular filtration rate ≤15 mL/min/1.73 m2 or kidney failure. Machine learning algorithms were applied to the modeling dataset (n = 296), and an external dataset (n = 71) was used for model validation. We assessed model discrimination via area under the curve (AUC) values, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Results Over a median follow-up period of 3.75 years, 148 patients experienced kidney failure. The optimal model was based on stacking different classifier algorithms with six laboratory features, including 24-h urine protein, potassium, glucose, urea, prealbumin and total protein. The model had considerable predictive power, with AUC values of 0.896 and 0.771 in the validation and external datasets, respectively. This model also accurately predicted the progression of renal function in patients over different follow-up periods after their initial assessment. Conclusions A prediction model that leverages routinely collected laboratory features in the Chinese population can accurately identify patients with CKD at high risk of progressing to kidney failure. An online version of the model can be easily and quickly applied in clinical management and treatment.

OBJECTIVES:Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Its diagnosis poses significant challenges especially at early stages and in atypical cases. The aim of this study was to develop a machine learning model based on common laboratory tests that can aid SLE diagnosis.METHODS:A standard protocol was developed to collect data of SLE and control immune diseases. A 10-fold cross-validation was performed in the modeling dataset (n=862), and an external dataset (n=198) was used for model validation. Machine learning algorithms were applied to construct a diagnostic model. Performance was evaluated based on area under the curve (AUC) values, F1-score, negative predictive value, positive predictive value, accuracy, sensitivity, and specificity.RESULTS:The optimal model was based on a random forest algorithm with 10 clinical features. Thrombin time, prothrombin activity, and uric acid contributed most to the diagnostic model. The SLE diagnostic model showed sufficient predictive accuracy, with AUC values of 0.8286 in the validation dataset.CONCLUSIONS:Our diagnostic model based on 10 common laboratory tests identified the patients with SLE with high accuracy. An online version of the model can potentially be applied in clinical settings for the differential diagnosis of SLE.

ObjectivesHereditary elliptocytosis is a group of erythroid hereditary diseases characterized by elliptically shaped erythrocytes in peripheral blood. It is mainly inherited through autosomal dominant inheritance. This study aimed to conduct a genetic etiology analysis in a case with a clinical diagnosis of hereditary elliptocytosis and an unexpectedly low HbA1c.MethodsWhole-exome sequencing was performed to find the possible pathogenic mutations. At the same time, bioinformatics software was used to predict the mutation function. Sanger sequencing was performed to verify the suspected pathogenic mutations.ResultsWhole-exome sequencing results showed that the proband with mild anemia had a heterozygous c.2303G>A (p.G768D) missense mutation in the 13th exon of the SPTB gene. The Sanger sequencing confirmed this heterozygous mutation. This mutation was extremely rare in the population, and multiple software’s predictions were harmful. Conservative analysis revealed that this site was highly conserved in various species.ConclusionThe c.2303G>A mutation of the SPTB gene is the suspected cause of hereditary elliptocytosis in the patient. Our data show that microscopic examination of red blood cells on blood smears is an important means of diagnosing hereditary elliptocytosis. Whole-exome sequencing is an effective tool to determine the genetic etiology of erythrocyte membrane diseases, which can promote accurate diagnosis and genetic counseling.

Bladder cancer is a common kind of urinary system cancer, in which bladder urothelial carcinoma (BLCA) comprises approximately 90% of all bladder cancer types. In our previous study, we discovered KLHDC7B in urine exosomal messenger RNA (mRNA) as a prospective molecular marker for bladder cancer detection. To systematically study the role and mechanism of KLHDC7B in BLCA, we focused on the most common type of BLCA in this study. First, we used RNA sequencing to discover that KLHDC7B was considerably increased in BLCA patients' urine exosomes compared to healthy controls. Then, we validated this result in an independent cohort and identified it as an effective tool for diagnosing and distinguishing high-grade and low-grade BLCA. Finally, we studied the role and mechanism of KLHDC7B in BLCA at the cellular level, providing a functional basis for its expression as a novel laboratory diagnostic biomarker for BLCA exosomal mRNA, which has important theoretical and clinical significance.

In this research, we described a very rare case of thrombotic microangiopathy induced by remethylation disorders. A 16-year-old boy presented to the emergency department with 5 months of weakness and fatigue. He was diagnosed with thrombotic microangiopathy based on clinical manifestation and laboratory information, which showed microangiopathic hemolytic anemia, renal impairment, and thrombocytopenia. After a complex diagnostic workup, the metabolite screening parameters and sequencing results guided us toward the diagnosis of remethylation disorders. The patient was diagnosed with thrombotic microangiopathy induced by remethylation disorders (cblC).

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with variable clinical course and laboratory tests. The definition and diagnosis of SLE are still a difficult problem in clinic. Machine learning methods are flexible prediction algorithms with potential advantages. However, very few artificial intelligence-based approaches have been developed to diagnose SLE thus far. The aims of this study were to develop a machine learning approach by Extreme Gradient Boosting (XGBoost) based on the big data to identify SLE in hospitalized patient and to determine whether such model performs better than traditional prediction models.A standard protocol was developed to collect data from laboratory information systems (LIS) and electronic medical record (EMR) in Peaking University First Hospital between June 2008 and March 2019. All the patients with ≥ 1SLE ICD-10 codes were primarily included in this study. A XGBoost algorithm was used to select the important features and construct a diagnostic model. The receiver operating characteristic (ROC) curve, Kolmogorov-Smirnov (KS) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to measure the performance of the model.A total of 2065 SLE patients were included in this study and 28 laboratory tests were selected by the XGBoost algorithm for modeling. According to the variable ranking, prothrombin time, lactate dehydrogenase and immunoglobulin G contribute most to the diagnostic model. The area under curve (AUC) and KS value of the XGBoost model were 0.881 and 0.609. The accuracy, sensitivity, specificity, PPV and NPV of the XGBoost model were 96.4%, 35.3%, 98.7%, 49.7% and 97.7%. Additionally, the diagnostic efficiency of the model based on the 28 features selected by the XGBoost algorithm is better than that based on the 2012 Systemic Lupus International Collaborating Clinics (SLICC) classification criteria. The model performance of XGBoost was better than random forest and logistic regression.Our diagnostic model showed a higher accuracy to identify the SLE patients combined with laboratory tests and EMR. This machine learning model has a potential application in differential diagnosis and pathogenesis investigation of SLE.

Background The recent discovery of miRNAs and lncRNAs in urine exosomes has emerged as promising diagnostic biomarkers for bladder cancer (BCa). However, mRNAs as the direct products of transcription has not been well evaluated in exosomes as biomarkers for BCa diagnosis. The purpose of this study was to identify tumor progression-related mRNAs and lncRNAs in urine exosomes that could be used for detection of BCa. Methods RNA-sequencing was performed to identify tumor progression-related biomarkers in three matched superficial tumor and deep infiltrating tumor regions of muscle-invasive bladder cancer (MIBC) specimens, differently expressed mRNAs and lncRNAs were validated in TCGA dataset (n = 391) in the discovery stage. Then candidate RNAs were chosen for evaluation in urine exosomes of a training cohort (10 BCa and 10 healthy controls) and a validation cohort (80 BCa and 80 healthy controls) using RT-qPCR. The diagnostic potential of the candidates were evaluated by receiver operating characteristic (ROC) curves. Results RNA sequencing revealed 8 mRNAs and 32 lncRNAs that were significantly upregulated in deep infiltrating tumor region. After validation in TCGA database, 10 markedly dysregulated RNAs were selected for further investigation in urine exosomes, of which five (mRNAs: KLHDC7B, CASP14, and PRSS1; lncRNAs: MIR205HG and GAS5) were verified to be significantly dysregulated. The combination of the five RNAs had the highest AUC to disguising the BCa (0.924, 95% CI, 0.875–0.974) or early stage BCa patients (0.910, 95% CI, 0.850 to 0.971) from HCs. The expression levels of these five RNAs were correlated with tumor stage, grade, and hematuria degrees. Conclusions These findings highlight the potential of urine exosomal mRNAs and lncRNAs profiling in the early diagnosis and provide new insights into the molecular mechanisms involved in BCa.

Bladder cancer (BC) is a heterogeneous disease that characterized by genomic instability and a high mutation rate. Heterogeneity in tumor may partially explain the diversity of responses to targeted therapies and the various clinical outcomes. A combination of cytology and cystoscopy is the standard methodology for BC diagnosis, prognosis, and disease surveillance. However, genomics analyses of single tumor-biopsy specimens may underestimate the mutational burden of heterogeneous tumors. Liquid biopsy, as a promising technology, enables analysis of tumor components in the bodily fluids, such as blood and urine, at multiple time points and provides a minimally invasive approach that can track the evolutionary dynamics and monitor tumor heterogeneity. In this review, we describe the multiple faces of BC heterogeneity at the genomic and transcriptional levels and how they affect clinical care and outcomes. We also summarize the outcomes of liquid biopsy in BC, which plays a potential role in revealing tumor heterogeneity. Finally, we discuss the challenges that must be addressed before liquid biopsy can be widely used in clinical treatment.

目的 探讨Crk1/2与CrkL缺失导致足细胞损伤过程中胞内蛋白表达变化情况.方法 利用小干扰RNA转染方法对足细胞内Crk1/2与CrkL进行单敲降及双敲降;通过非标记液相色谱串联质谱方法对Crk1/2与CrkL单敲降及双敲降的足细胞进行蛋白质组学分析;对得到的差异蛋白进行基因本体(GO)分析和京都基因和基因组百科全书(KEGG)分析;采用Western blot试验对差异蛋白进行验证.结果 Crk1/2与CrkL单敲降及双敲降的足细胞与正常足细胞比较,共发现98个差异蛋白,GO分析和KEGG分析提示多数上调蛋白富集在代谢途径,多数下调蛋白富集在信号转导途径,如抑制和激活蛋白1、磷脂酰肌醇三激酶-丝氨酸/苏氨酸激酶和环磷酸腺苷信号途径.经Western blot试验验证,细胞代谢相关蛋白超氧化物歧化酶2(SOD2)表达上调,信号转导相关蛋白L RP1表达上调、c-Jun表达下调,细胞骨架损伤相关蛋白T PM 4表达上调、Cdc42EP1表达下调.结论 Crk1/2与CrkL缺失可通过调节代谢途径及相关信号转导途径导致足细胞损伤,TPM4、SOD2、LRP1、c-Jun和Cdc42EP1参与损伤过程,具体分子机制值得进一步探讨.

Podocytes are actin-rich epithelial cells whose effacement and detachment are the main cause of glomerular disease. Crk family proteins: Crk1/2 and CrkL are reported to be important intracellular signaling proteins that are involved in many biological processes. However, the roles of them in maintaining podocyte morphology and function remain poorly understood. In this study, specific knocking down of Crk1/2 and CrkL in podocytes caused abnormal cell morphology, actin cytoskeleton rearrangement and dysfunction in cell adhesion, spreading, migration, and viability. The p130Cas, focal adhesion kinase, phosphatidylinositol 3-kinase/Akt, p38 and JNK signaling pathways involved in these alterations. Furthermore, knocking down CrkL alone conferred a more modest phenotype than did the Crk1/2 knockdown and the double knockdown. Kidney biopsy specimens from patients with focal segmental glomerulosclerosis and minimal change nephropathy showed downregulation of Crk1/2 and CrkL in glomeruli. In zebrafish embryos, Crk1/2 and CrkL knockdown compromised the morphology and caused abnormal glomerular development. Thus, our results suggest that Crk1/2 and CrkL expression are important in podocytes; loss of either will cause podocyte dysfunction, leading to foot process effacement and podocyte detachment.