Integrated proteome and malonylome analyses reveal the potential meaning of TLN1 and ACTB in end-stage renal disease.

BACKGROUND: End-stage renal disease (ESRD) is a condition that is characterized by the loss of kidney function. ESRD patients suffer from various endothelial dysfunctions, inflammation, and immune system defects. Lysine malonylation (Kmal) is a recently discovered post-translational modification (PTM). Although Kmal has the ability to regulate a wide range of biological processes in various organisms, its specific role in ESRD is limited. METHODS: In this study, the affinity enrichment and liquid chromatography-tandem mass spectrometry (LC-MS/MS) techniques have been used to create the first global proteome and malonyl proteome (malonylome) profiles of peripheral blood mononuclear cells (PBMCs) from twenty patients with ESRD and eighty-one controls. RESULTS: On analysis, 793 differentially expressed proteins (DEPs) and 12 differentially malonylated proteins (DMPs) with 16 Kmal sites were identified. The Rap1 signaling pathway and platelet activation pathway were found to be important in the development of chronic kidney disease (CKD), as were DMPs TLN1 and ACTB, as well as one malonylated site. One conserved Kmal motif was also discovered. CONCLUSIONS: These findings provided the first report on the Kmal profile in ESRD, which could be useful in understanding the potential role of lysine malonylation modification in the development of ESRD.

Analysis of proteome and post-translational modifications of 2-hydroxyisobutyrylation reveals the glycolysis pathway in oral adenoid cystic carcinoma.

PURPOSE: Oral adenoid cystic carcinoma (OACC) has high rates of both local-regional recurrence and distant metastasis. The objective of this study is to investigate the impact of Khib on OACC and its potential as a targeted therapeutic intervention. EXPERIMENTAL DESIGN: We investigated the DEPs (differentially expressed proteins) and DHMPs between OACC-T and OACC-N using LC-MS/MS-based quantitative proteomics and using several bioinformatics methods, including GO enrichment analysis, KEGG pathway analysis, subcellular localization prediction, MEA (motif enrichment analysis), and PPI (protein-protein interaction networks) to illustrate how Khib modification interfere with OACC evolution. RESULTS: Compared OACC-tumor samples (OACC-T) with the adjacent normal samples (OACC-N), there were 3243 of the DEPs and 2011 Khib sites were identified on 764 proteins (DHMPs). DEPs and DHMPs were strongly associated to glycolysis pathway. GAPDH of K254, ENO of K228, and PGK1 of K323 were modified by Khib in OACC-T. Khib may increase the catalytic efficiency to promote glycolysis pathway and favor OACC progression. CONCLUSIONS AND CLINICAL RELEVANCE: Khib may play a significant role in the mechanism of OACC progression by influencing the enzyme activity of the glycolysis pathway. These findings may provide new therapeutic options of OACC.

Corrigendum to "The novel putative methyltransferase METTL7A as one prognostic biomarker potentially associated with immune infiltration in human renal cancer".

[This corrects the article DOI: 10.1016/j.heliyon.2023.e15371.].

Dysregulation of lipid metabolism in the pseudolobule promotes region-specific autophagy in hepatitis B liver cirrhosis.

BACKGROUND: Chronic hepatitis B (CHB) infection leads to liver cirrhosis (LC), the end stage of liver fibrosis. The precise diagnosis and effective therapy for hepatitis B cirrhosis are still lacking. It is highly necessary to elucidate the metabolic alteration, especially the spatial distribution of metabolites, in LC progression. METHODS: In this study, LC-MS/MS together with an airflow-assisted ionization mass spectrometry imaging system was applied to analyze and compare the metabolites' spatial distribution in healthy control (HC) and hepatitis B LC tissue samples. The liver samples were further divided into several subregions in HC and LC groups based on the anatomical characteristics and clinical features. RESULTS: Both the LC-MS/MS and mass spectrometry imaging results indicated separated metabolite clusters between the HC and LC groups. The differential metabolites were mainly concentrated in lipid-like molecules and amino acids. The phosphatidylcholines (PCs), lysoPCs, several fatty acids, and amino acids reduced expression in the LC group with region specific. Acyl-CoA thioesterase 2 and choline/ethanolamine phosphotransferase 1, which regulate PC and fatty acid metabolism, were significantly decreased in the pseudolobule. Meanwhile, the increased expression of LC3B and p62 in the pseudolobule indicated the upregulation of autophagy. CONCLUSIONS: Hepatitis B LC induced region-specific autophagy by increasing the expression of LC3B and p62 in the pseudolobule and by dysregulation of unsaturated fatty acids, amino acids, and PC metabolism. The mass spectrometry imaging system provided additional metabolites' spatial information, which can promote biomarker screening technology and support the exploration of novel mechanisms in LC.

CRISPR/Cas9-Mediated Gene Correction in Osteopetrosis Patient-Derived iPSCs.

BACKGROUND: Osteopetrosis represents a rare genetic disease with a wide range of clinical and genetic heterogeneity, which results from osteoclast failure. Although up to 10 genes have been identified to be related with osteopetrosis, the pathogenesis of osteopetrosis remains foggy. Disease-specific induced pluripotent stem cells (iPSCs) and gene-corrected disease specific iPSCs provide a platform to generate attractive in vitro disease cell models and isogenic control cellular models respectively. The purpose of this study is to rescue the disease causative mutation in osteopetrosis specific induced pluripotent stem cells and provide isogenic control cellular models. METHODS: Based on our previously established osteopetrosis-specific iPSCs (ADO2-iPSCs), we repaired the point mutation R286W of the CLCN7 gene in ADO2-iPSCs by the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) mediated homologous recombination. RESULTS: The obtained gene corrected ADO2-iPSCs (GC-ADO2-iPSCs) were characterized in terms of hESC-like morphology, a normal karyotype, expression of pluripotency markers, homozygous repaired sequence of CLCN7 gene, and the ability to differentiate into cells of three germ layers. CONCLUSIONS: We successfully corrected the point mutation R286W of the CLCN7 gene in ADO2-iPSCs. This isogenic iPSC line is an ideal control cell model for deciphering the pathogenesis of osteopetrosis in future studies.

Urinary microbiota and serum metabolite analysis in patients with diabetic kidney disease.

Background: Diabetic kidney disease (DKD) is a common and potentially fatal consequence of diabetes. Chronic renal failure or end-stage renal disease may result over time. Numerous studies have demonstrated the function of the microbiota in health and disease. The use of advanced urine culture techniques revealed the presence of resident microbiota in the urinary tract, undermining the idea of urine sterility. Studies have demonstrated that the urine microbiota is related with urological illnesses; nevertheless, the fundamental mechanisms by which the urinary microbiota influences the incidence and progression of DKD remain unclear. The purpose of this research was to describe key characteristics of the patients with DKD urinary microbiota in order to facilitate the development of diagnostic and therapeutic for DKD. Methods: We evaluated the structure and composition of the microbiota extracted from urine samples taken from DKD patients (n = 19) and matched healthy controls (n = 15) using 16S rRNA gene sequencing. Meanwhile, serum metabolite profiles were compared using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Associations between clinical characteristics, urine microbiota, and serum metabolites were also examined. Finally, the interaction between urine microbiota and serum metabolites was clarified based on differential metabolite abundance analysis. Results: The findings indicated that the DKD had a distinct urinary microbiota from the healthy controls (HC). Taxonomic investigations indicated that the DKD microbiome had less alpha diversity than a control group. Proteobacteria and Acidobacteria phyla increased in the DKD, while Firmicutes and Bacteroidetes decreased significantly (P < 0.05). Acidobacteria was the most prevalent microbiota in the DKD, as determined by the Linear discriminant analysis Effect Size (LEfSe) plot. Changes in the urinary microbiota of DKD also had an effect on the makeup of metabolites. Short-chain fatty acids (SCFAs) and protein-bound uremic toxins (PBUTs) were shown to be specific. Then we discovered that arginine and proline metabolism was the primary mechanism involved in the regulation of diabetic kidney disease. Conclusions: This study placed the urinary microbiota and serum metabolite of DKD patients into a functional framework and identified the most abundant microbiota in DKD (Proteobacteria and Acidobacteria). Arginine metabolites may have a major effect on DKD patients, which correlated with the progression of DKD.

Screening biomarkers for Sjogren's Syndrome by computer analysis and evaluating the expression correlations with the levels of immune cells.

Background: Sjögren's syndrome (SS) is a systemic autoimmune disease that affects about 0.04-0.1% of the general population. SS diagnosis depends on symptoms, clinical signs, autoimmune serology, and even invasive histopathological examination. This study explored biomarkers for SS diagnosis. Methods: We downloaded three datasets of SS patients' and healthy pepole's whole blood (GSE51092, GSE66795, and GSE140161) from the Gene Expression Omnibus (GEO) database. We used machine learning algorithm to mine possible diagnostic biomarkers for SS patients. Additionally, we assessed the biomarkers' diagnostic value using the receiver operating characteristic (ROC) curve. Moreover, we confirmed the expression of the biomarkers through the reverse transcription quantitative polymerase chain reaction (RT-qPCR) using our own Chinese cohort. Eventually, the proportions of 22 immune cells in SS patients were calculated by CIBERSORT, and connections between the expression of the biomarkers and immune cell ratios were studied. Results: We obtained 43 DEGs that were mainly involved in immune-related pathways. Next, 11 candidate biomarkers were selected and validated by the validation cohort data set. Besides, the area under curves (AUC) of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF in the discovery and validation datasets were 0.903 and 0.877, respectively. Subsequently, eight genes, including HES4, IFI27, LY6E, OTOF, STAT1, TTC21A, XAF1, and ZCCHC2, were selected as prospective biomarkers and verified by RT-qPCR. Finally, we revealed the most relevant immune cells with the expression of HES4, IFI27, LY6E, OTOF, TTC21A, XAF1, and ZCCHC2. Conclusion: In this paper, we identified seven key biomarkers that have potential value for diagnosing Chinese SS patients.

The novel putative methyltransferase METTL7A as one prognostic biomarker potentially associated with immune infiltration in human renal cancer.

Among urological cancers, renal cancer has the highest fatality rate. In a previous pan-cancer study of the METTL family, we observed a stronger association between the METTL family members and the risk of renal cancer compared to other cancers. Among these members, METTL7A, a potential methyltransferase, was identified as a protective factor, although its role and mechanism in renal cancer remain unclear. In this study, we utilized public databases to examine the expression of METTL7A in renal cancer tissues and normal tissues and found that METTL7A expression was much lower in renal cancer tissues. We also noticed a link between low METTL7A expression and poor prognosis for patients. According to the results of our functional enrichment analysis, METTL7A may have a role in immunological functions in renal cancer. METTL7A expression was strongly linked with the degrees of immune cell infiltration and expression of numerous immunological components. METTL7A had significantly different effects on the survival times of renal cancer patients with high or low immune infiltration. Our findings suggest that METTL7A may be used as both a prognostic biomarker and an immunological target for kidney cancer. In conclusion, our study sheds light on the importance of METTL7A in renal cancer and emphasizes the potential of targeting METTL7A as a novel therapeutic strategy for kidney cancer.

Gene regulatory network study of rheumatoid arthritis in single-cell chromatin landscapes of peripheral blood mononuclear cells.

OBJECTIVES: Assays for transposase-accessible chromatin with single-cell sequencing (scATAC-seq) contribute to the progress in epigenetic studies. The purpose of our project was to discover the transcription factors (TFs) that were involved in the pathogenesis of rheumatoid arthritis (RA) at a single-cell resolution using epigenetic technology. METHODS: Peripheral blood mononuclear cells of seven RA patients and seven natural controls were extracted nuclei suspensions for library construction. Subsequently, scATAC-seq was performed to generate a high-resolution map of active regulatory DNA for bioinformatics analysis. RESULTS: We obtained 22 accessible chromatin patterns. Then, 10 key TFs were involved in RA pathogenesis by regulating the activity of mitogen-activated protein kinase. Consequently, two genes (PTPRC and SPAG9) regulated by 10 key TFs were found, which may be associated with RA disease pathogenesis, and these TFs were obviously enriched in RA patients (P < .05, fold change value > 1.2). With further quantitative polymerase chain reaction validation on PTPRC and SPAG9 in monocytes, we found differential expression of these two genes, which were regulated by eight TFs [ZNF384, HNF1B, DMRTA2, MEF2A, NFE2L1, CREB3L4 (var. 2), FOSL2::JUNB (var. 2), and MEF2B], showing highly accessible binding sites in RA patients. CONCLUSIONS: These findings demonstrate the value of using scATAC-seq to reveal transcriptional regulatory variation in RA-derived peripheral blood mononuclear cells, providing insights into therapy from an epigenetic perspective.

Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus.

Introduction: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease for which there is no cure. Effective diagnosis and precise assessment of disease exacerbation remains a major challenge. Methods: We performed peripheral blood mononuclear cell (PBMC) proteomics of a discovery cohort, including patients with active SLE and inactive SLE, patients with rheumatoid arthritis (RA), and healthy controls (HC). Then, we performed a machine learning pipeline to identify biomarker combinations. The biomarker combinations were further validated using enzyme-linked immunosorbent assays (ELISAs) in another cohort. Single-cell RNA sequencing (scRNA-seq) data from active SLE, inactive SLE, and HC PBMC samples further elucidated the potential immune cellular sources of each of these PBMC biomarkers. Results: Screening of the PBMC proteome identified 1023, 168, and 124 proteins that were significantly different between SLE vs. HC, SLE vs. RA, and active SLE vs. inactive SLE, respectively. The machine learning pipeline identified two biomarker combinations that accurately distinguished patients with SLE from controls and discriminated between active and inactive SLE. The validated results of ELISAs for two biomarker combinations were in line with the discovery cohort results. Among them, the six-protein combination (IFIT3, MX1, TOMM40, STAT1, STAT2, and OAS3) exhibited good performance for SLE disease diagnosis, with AUC of 0.723 and 0.815 for distinguishing SLE from HC and RA, respectively. Nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1, and STAT2) showed a robust performance in assessing disease exacerbation (AUC=0.990). Further, the potential immune cellular sources of nine PBMC biomarkers, which had the consistent changes with the proteomics data, were elucidated by PBMC scRNAseq. Discussion: Unbiased proteomic quantification and experimental validation of PBMC samples from two cohorts of patients with SLE were identified as biomarker combinations for diagnosis and activity monitoring. Furthermore, the immune cell subtype origin of the biomarkers in the transcript expression level was determined using PBMC scRNAseq. These findings present valuable PBMC biomarkers associated with SLE and may reveal potential therapeutic targets.

Microbiome and metabolome analysis to clarify the interaction between the urine microbiota and serum metabolites in Chinese patients with immunoglobulin A nephropathy.

Background: The bacterial and metabolic networks in immunoglobin A nephropathy (IgAN), the most common type of primary chronic glomerulonephritis worldwide, have not been extensively studied. To help develop better methods for the diagnosis, treatment, and prognosis of IgAN, we characterized the alterations of the urinary microbiome and serum metabolome in patients with IgAN. Methods: We analyzed serum and urine samples from Chinese patients with IgAN and healthy controls (HCs) using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and 16S ribosomal RNA gene sequencing. Results: Patients with IgAN had a higher relative abundance of Actinomyces and a lower relative abundance of Lactobacillus. The elements of metabolism have been affected, including free amino acids, polyunsaturated fatty acids, and oligopeptides. We also identified the 9 metabolites that might be the core metabolites, including guanidinoacetic acid, apo-[3-methylcrotonoyl-CoA:carbon-dioxide ligase (ADP-forming)], and diethanolamine, which linked the metabolic networks between the urinary tract (UT) and blood. Other core metabolites, such as homocitrulline, apo-[3-methylcrotonoyl-CoA:carbon-dioxide ligase (ADP-forming)], butyrylcarnitine, formiminoglutamic acid (FIGLU), diethanolamine, and prolylhydroxyproline, were positively correlated with urinary mili-total protein (MTP). Conversely, Lactobacillus was negatively correlated with MTP. Conclusions: We verified the connection between the disruption of the microbiota and serum metabolites, along with the clinical parameters, in patients with IgAN, which may help provide a tool for IgAN interventions.

NFE2L3 as a Potential Functional Gene Regulating Immune Microenvironment in Human Kidney Cancer.

With the increasing incidence and mortality of renal cancer, it is pressing to find new biomarkers and drug targets for diagnosis and treatment. However, as one negative upstream regulator of p53, the prognostic and immunological role of NFE2L3 in renal cancer is still barely known. We investigated the expression, prognostic value, and relevant pathways of NFE2L3 using the datasets from public databases, including The Cancer Genome Atlas Program (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), and UALCAN. Furthermore, we analyzed the relationship between NFE2L3 expression and the immune microenvironment using distinct methods. We found that NFE2L3 was higher expressed in kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) tissues than adjacent normal tissues. Additionally, we identified NFE2L3 as one survival-related factor for KIRC and KIRP. The enrichment analyses revealed that NFE2L3 was associated with a variety of immune-relevant pathways in KIRC and related to the infiltration ratios of 17 types of immune cells in KIRC patients. Ultimately, we demonstrated nine significantly enriched mutations, such as TP53 and MET, in NFE2L3-expression-changing groups. The elevated expression of NFE2L3 in renal cancerous tissues versus normal tissues is associated with poor outcomes in patients. Besides, NFE2L3 has a role in the regulation of the immune microenvironment in renal cancer patients. The findings of our study provide a potential prognostic biomarker and a new drug target for renal cancer.

Multiplexed analysis of gene expression and chromatin accessibility of human umbilical cord blood using scRNA-Seq and scATAC-Seq.

Nowadays, umbilical cord blood (UCB) cells has been increasingly replacing fetal and adult-derived cells in adoptive cell therapy. However, gene expression and chromatin accessibility in umbilical cord blood cells has not yet been fully elucidated. In this study, we used an integration of scRNA-seq with the scATAC-seq technology to perform unbiased analysis of UCBCs over developmental time from 31 gestational week (GW) to 37 GW in humans. We identified several distinct cell types (erythroid cell, T cell, B cell, erythroid precursor cells, NK cell, and endothelial progenitor cell) and subpopulations (6 different clusters of erythroid cells) in UCB cells. In addition, we also identified a series of differentially expressed genes and chromatin accessibility in each cell type between different gestational weeks. Interestingly, the gene expression pattern of umbilical cord blood cells from normal fetuses of similar gestational weeks were more consistent. In conclusion, our analysis presents a better understanding of the chromatin landscape and regulatory networks in UCB cells.

Spatial metabolomics on liver cirrhosis to hepatocellular carcinoma progression.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the deadliest cancers and is mainly developed from chronic liver diseases such as hepatitis-B infection-associated liver cirrhosis (LC). The progression from LC to HCC makes the detection of diagnostic biomarkers to be challenging. Hence, there have been constant efforts to improve on identifying the critical and predictive changes accompanying the disease progression. METHODS: In this study, we looked to using the mass spectrometry mediated spatial metabolomics technique to simultaneous examine hundreds of metabolites in an untargeted fashion. Additionally, metabolic profiles were compared between six subregions within the HCC tissue to collect spatial information. RESULTS: Through those metabolites, altered metabolic pathways in LC and HCC were identified. Specifically, the amino acid metabolisms and the glycerophospholipid metabolisms experienced the most changes. Many of the altered metabolites and metabolic pathways were able to be connected through the urea cycle. CONCLUSIONS: The identification of the key metabolites and pathways can expand our knowledge on HCC metabolic reprogramming and help us exam potential biomarkers for earlier detection of the malignant disease progression.

Integrated computer analysis and a self-built Chinese cohort study identified GSTM2 as one survival-relevant gene in human colon cancer potentially regulating immune microenvironment.

According to a recent report by GLOBOCAN, colorectal cancer is the third most common and second most deadly cancer in 2020. In our previous proteomic study, we found that the expression of GSTM2 in colon tissues was significantly lower than that in para-cancer tissues, and its lower expression was associated with reduced overall survival rate of patients, suggesting that this gene might play a role in the occurrence of colon cancer. As a member of the detoxifying enzyme family, GSTM2 is likely to play an important role in the initiation of tumors. Whereas, the functions of GSTM2 in colon cancer are barely known. In this study, using the RNA-Seq datasets of colon cancer patients from public database (ntumor = 457, nnormal = 41), we confirmed the reduced expression of GSTM2 and its prognostic value in colon cancer. Furthermore, we used our own Chinese cohort (ntumor = 100, nnormal = 72) verified the lower GSTM2 expression in colon cancer, and also its effects on patient prognosis. Subsequently, we uncovered two potential reasons for the lower expression of GSTM2 in colon cancer tissues, including the deep deletion of GSTM2 on genome, and the up-regulation of RAD21 or SP1. Moreover, we disclosed that GSTM2 might be involved in several immune-related pathways in colon cancer, such as chemokine signaling and leukocyte transendothelial migration. Finally, we revealed that the GSTM2 expression was closely related to the immune-related scores of colon cancer and the infiltration ratios of various immune cells, suggesting that GSTM2 might regulate the development of colon cancer by modulating immune microenvironment. In conclusion, we uncovered the prognostic value of GSTM2 based on the public data and our own data, revealed its potential regulatory role in tumor immune microenvironment, and disclosed the probable reasons for its lower expression in colon cancer. The findings of our study provide a potential prognostic biomarker and drug target for clinical diagnosis and treatment of colon cancer.

Comprehensive characterization of ubiquitinome of human colorectal cancer and identification of potential survival-related ubiquitination.

BACKGROUND: According to the Global Cancer Statistics in 2020, the incidence and mortality of colorectal cancer (CRC) rank third and second among all tumors. The disturbance of ubiquitination plays an important role in the initiation and development of CRC, but the ubiquitinome of CRC cells and the survival-relevant ubiquitination are poorly understood. METHODS: The ubiquitinome of CRC patients (n = 6) was characterized using our own data sets of proteomic and ubiquitin-proteomic examinations. Then, the probable survival-relevant ubiquitination was searched based on the analyses of data sets from public databases. RESULTS: For the ubiquitinomic examination, we identified 1690 quantifiable sites and 870 quantifiable proteins. We found that the highly-ubiquitinated proteins (n ≥ 10) were specifically involved in the biological processes such as G-protein coupling, glycoprotein coupling, and antigen presentation. Also, we depicted five motif sequences frequently recognized by ubiquitin. Subsequently, we revealed that the ubiquitination content of 1172 proteins were up-regulated and 1700 proteins were down-regulated in CRC cells versus normal adjacent cells. We demonstrated that the differentially ubiquitinated proteins were relevant to the pathways including metabolism, immune regulation, and telomere maintenance. Then, integrated with the proteomic datasets from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) (n = 98), we revealed that the increased ubiquitination of FOCAD at Lys583 and Lys587 was potentially associated with patient survival. Finally, we depicted the mutation map of FOCAD and elucidated its potential functions on RNA localization and translation in CRC. CONCLUSIONS: The findings of this study described the ubiquitinome of CRC cells and identified abnormal ubiquitination(s) potentially affecting the patient survival, thereby offering new probable opportunities for clinical treatment.

A comprehensive investigation discovered the novel methyltransferase METTL24 as one presumably prognostic gene for kidney renal clear cell carcinoma potentially modulating tumor immune microenvironment.

Background: Recently, an increasing number of studies have uncovered the aberrant expression of methyltransferase-like family (METTL) plays an important role in tumorigenesis, such as METTL3 (an m6A writer). In our recent work, we discovered METTL24 expression was highly associated with the hazard ratio (HR) of kidney renal clear cell carcinoma (KIRC) compared to other tumors, implying a special function of METTL24 in KIRC carcinogenesis. Until now, the functions and mechanisms of METTL24 in KIRC have remained mostly unknown. Methods: The mRNA expression of METTL24 in KIRC was analyzed using the TIMER 2.0, GEPIA, and UALCAN databases. The immunohistochemical assay was performed to validate METTL24 expression in our self-built Chinese cohort (n tumor = 88, n normal = 85). The gene set enrichment analysis (GSEA) was used to investigate the biological processes in which METTL24 might be engaged. The Spearman analysis was used to evaluate the expression correlations between METTL24 and a range of immunological variables, and the effects of METTL24 on the infiltration levels of multiple immune cells were explored using TCGA data. The upstream transcription factors of METTL24 were screened through a multi-omics analysis. Results: METTL24 expression in KIRC tissues was significantly decreased compared to normal adjacent kidney tissues, which was associated with the lower survival rate of KIRC patients. METTL24 potentially participated in the immune-relevant biological processes such as cytokine binding, NF-kappa B binding, MHC protein complex, and interleukin-12 action. Besides, METTL24 expression was linked to a number of immune checkpoints, cytokines, chemokines, and chemokine receptors, and also correlated with the infiltration levels of 10 types of immune cells in KIRC. Meanwhile, METTL24 expression differently affected the overall survival rates (OS) of KIRC patients with high or low levels of immune infiltration. Finally, CTCF and EP300 were discovered to be the probable transcription factors of METTL24 in KIRC. Conclusion: This study revealed that METTL24 might serve as a prognostic marker in KIRC and as one immune-relevant target for clinical treatment.

Global Phosphoproteomics Unveils Kinase-Regulated Networks in Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disorder characterized by immune complex deposition in multiple organs. Despite the severe symptoms caused by it, the underlying mechanisms of SLE, especially phosphorylation-dependent regulatory networks remain elusive. Herein, by combining high-throughput phosphoproteomics with bioinformatics approaches, we established the global phosphoproteome landscape of the peripheral blood mononuclear cells from a large number of SLE patients, including the remission stage (SLE_S), active stage (SLE_A), rheumatoid arthritis, and healthy controls, and thus a deep mechanistic insight into SLE signaling mechanism was yielded. Phosphorylation upregulation was preferentially in patients with SLE (SLE_S and SLE_A) compared with healthy controls and rheumatoid arthritis populations, resulting in an atypical enrichment in cell adhesion and migration signatures. Several specifically upregulated phosphosites were identified, and the leukocyte transendothelial migration pathway was enriched in the SLE_A group by expression pattern clustering analysis. Phosphosites identified by 4D-label-free quantification unveiled key kinases and kinase-regulated networks in SLE, then further validated by parallel reaction monitoring. Some of these validated phosphosites including vinculin S275, vinculin S579 and transforming growth factor beta-1-induced transcript 1 S68, primarily were phosphorylation of Actin Cytoskeleton -related proteins. Some predicted kinases including MAP3K7, TBK1, IKKß, and GSK3ß, were validated by Western blot using kinases phosphorylation sites-specific antibodies. Taken together, the study has yielded fundamental insights into the phosphosites, kinases, and kinase-regulated networks in SLE. The map of the global phosphoproteomics enables further understanding of this disease and will provide great help for seeking more potential therapeutic targets for SLE.

Analyzing the gene regulatory network in hepatitis B patients by single-cell ATAC sequencing.

OBJECTIVE: This study aims to provide a new perspective of determining the pathophysiology of chronic hepatitis B (CHB) development by analyzing the gene regulatory network in CHB patients using single-cell ATAC sequencing. BACKGROUND: Hepatitis B virus (HBV)-related liver disease induces liver damage by hepatic immune and inflammatory responses. The exact mechanism is unknown. As such, there is an urgent need to address this problem and study the relationship between aberrant peripheral blood mononuclear cell (PBMC) immune response and progression of liver disease. METHOD: The sequencing of the chromatin accessibility of 8016 cells from the whole venous blood of normal control (NC) individuals and CHB patients was performed through assay for transposase-accessible chromatin in single-cell sequencing (ScATAC-seq). Unsupervised clustering and annotation analyses were performed by Signac (version 1.7.0) and Seurat clustering to identify different cell types. Then, TF motif enrichment analysis and differentially expressed peak analysis were performed to identify cell-type-specific candidate open chromatins related to CHB. RESULT: We identified 12 leukocytic clusters corresponding to five cell types. The specific cell types associated with CHB were found to be located in B-0 and T-3. We have drawn the regulatory network of the hepatitis B signal pathway composed of genes linked to the differentially expressed peaks of these two CHB disease-specific cell types. Further, we profoundly explored the potential mechanisms of B-0-associated TF motif IRF2 and T-3-associated TF motif FOXC2 in the occurrence of CHB. CONCLUSION: We have drawn a systematic and distinguishing gene regulatory network of CHB-related PBMCs. Key Points • Peripheral blood mononuclear cells were robustly clustered based on their types without using antibodies. • We draw a systematic and distinctive gene regulatory network of CHB-related PBMC through ScATAC-seq.

Screening Biomarkers for Systemic Lupus Erythematosus Based on Machine Learning and Exploring Their Expression Correlations With the Ratios of Various Immune Cells.

Background: Systemic lupus erythematosus (SLE) is an autoimmune illness caused by a malfunctioning immunomodulatory system. China has the second highest prevalence of SLE in the world, from 0.03% to 0.07%. SLE is diagnosed using a combination of immunological markers, clinical symptoms, and even invasive biopsy. As a result, genetic diagnostic biomarkers for SLE diagnosis are desperately needed. Method: From the Gene Expression Omnibus (GEO) database, we downloaded three array data sets of SLE patients' and healthy people's peripheral blood mononuclear cells (PBMC) (GSE65391, GSE121239 and GSE61635) as the discovery metadata (nSLE = 1315, nnormal = 122), and pooled four data sets (GSE4588, GSE50772, GSE99967, and GSE24706) as the validate data set (nSLE = 146, nnormal = 76). We screened the differentially expressed genes (DEGs) between the SLE and control samples, and employed the least absolute shrinkage and selection operator (LASSO) regression, and support vector machine recursive feature elimination (SVM-RFE) analyze to discover possible diagnostic biomarkers. The candidate markers' diagnostic efficacy was assessed using the receiver operating characteristic (ROC) curve. The reverse transcription quantitative polymerase chain reaction (RT-qPCR) was utilized to confirm the expression of the putative biomarkers using our own Chinese cohort (nSLE = 13, nnormal = 10). Finally, the proportion of 22 immune cells in SLE patients was determined using the CIBERSORT algorithm, and the correlations between the biomarkers' expression and immune cell ratios were also investigated. Results: We obtained a total of 284 DEGs and uncovered that they were largely involved in several immune relevant pathways, such as type І interferon signaling pathway, defense response to virus, and inflammatory response. Following that, six candidate diagnostic biomarkers for SLE were selected, namely ABCB1, EIF2AK2, HERC6, ID3, IFI27, and PLSCR1, whose expression levels were validated by the discovery and validation cohort data sets. As a signature, the area under curve (AUC) values of these six genes reached to 0.96 and 0.913, respectively, in the discovery and validation data sets. After that, we checked to see if the expression of ABCB1, IFI27, and PLSCR1 in our own Chinese cohort matched that of the discovery and validation sets. Subsequently, we revealed the potentially disturbed immune cell types in SLE patients using the CIBERSORT analysis, and uncovered the most relevant immune cells with the expression of ABCB1, IFI27, and PLSCR1. Conclusion: Our study identified ABCB1, IFI27, and PLSCR1 as potential diagnostic genes for Chinese SLE patients, and uncovered their most relevant immune cells. The findings in this paper provide possible biomarkers for diagnosing Chinese SLE patients.