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Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with multi-organ inflammation and defect, which is linked to many molecule mediators. Oxylipins as a class of lipid mediator have not been broadly investigated in SLE. Here, we applied targeted mass spectrometry analysis to screen the alteration of oxylipins in serum of 98 SLE patients and 106 healthy controls. The correlation of oxylipins to lupus nephritis (LN) and SLE disease activity, and the biomarkers for SLE classification, were analyzed. Among 128 oxylipins analyzed, 92 were absolutely quantified and 26 were significantly changed. They were mainly generated from the metabolism of several polyunsaturated fatty acids, including arachidonic acid (AA), linoleic acid (LA), docosahexanoic acid (DHA), eicosapentanoic acid (EPA) and dihomo-γ-linolenic acid (DGLA). Several oxylipins, especially those produced from AA, showed different abundance between patients with and without lupus nephritis (LN). The DGLA metabolic activity and DGLA generated PGE1, were significantly associated with SLE disease activity. Random forest-based machine learning identified a 5-oxylipin combination as potential biomarker for SLE classification with high accuracy. Seven individual oxylipin biomarkers were also identified with good performance in distinguishing SLE patients from healthy controls (individual AUC > 0.7). Interestingly, the biomarkers for differentiating SLE patients from healthy controls are distinct from the oxylipins differentially expressed in LN patients vs. non-LN patients. This study provides possibilities for the understanding of SLE characteristics and the development of new tools for SLE classification.

Additional file 1: Figure S1. scRNA-seq analysis of two human embryonic kidneys from 9 and 18 post-conception weeks. (a) Schematic of the experimental design and analysis. (b) Distribution of the number of transcripts and genes detected per spot at 9 and 18 PCW kidneys. Blue dashed lines indicate mean values, while the red dashed lines indicate the standard deviation. (c) Dimensionality reduction and clustering of the scRNA-seq data of the 9 and 18 PCW kidneys based on UMAP. (d) Quality control of the scRNA-seq data. ScRNA-seq dataset of fetal kidneys at 9 and 18 weeks post-conception are downloaded online (accession number: GSE114530). (e) Annotation of the cell types identified by spatial transcriptomics data based on the specific cell-type marker genes known from the literature. ST-seq, spatial transcriptomics sequencing; scRNA-seq, single-cell RNA sequencing; PCW, post-conception weeks; ICs, interstitial cells; PTA, pretubular aggregate; UBCD, ureteric bud/collecting duct; SSBmd/DTLH, distal tubule/loop of Henle and s-shaped body medial precursor cell; NPC, nephron progenitor cell; SSBpr, s-shaped body proximal precursor cell; ErprT, early proximal tubule. PETE, Pelvic segment transitional epithelium; Mes, mesangial cell; Pod, podocyte. Figure S2. Cell-type deconvolution of the kidney tissues from 9 and 18 weeks post-conception. (a) Spatial location and clustering of ST-seq spots. (b) The proportion of signatures of different scRNA-seq cell types in each ST spot of the 18 PCW kidney tissue. Each pie chart represents the contribution of scRNA-seq cell types to the transcriptomic signature of each ST spot. Only cell types that contribute at least 10% to the spot signature are shown. (c) Up: multimodal intersection analysis (MIA) of scRNA-seq-identified cell types and ST-defined clusters. * represents the closest relationship between scRNA-identified cell types and the ST-defined clusters (P < 0.01). Down: the spatial mapping of scRNA-seq cell subsets performed by MIA. (d) The proportion of each scRNA-seq cell subpopulation arising from each ST spot in the 9 and 18 PCW kidneys. (e) Fraction of total signature of each cell type present in the cortex, medulla, and pelvis in the 18 PCW kidney. (f) Fraction of total signature of each cell type present in the cortex, medulla, and pelvis in the adult human kidney (Ricardo et al.). Figure S3. Energy metabolism features of the renal cortex and medulla. Heatmaps displaying the expression levels of (a) OXPHOS-related genes and (b) glycolysis-related genes in scRNA-identified cell types in Hochane’s study. (c) G2/M scores and mean expression levels of proliferation markers (Z-scores) per scRNA-identified cell subpopulation. This image is from Hochane’s study. (d) The distribution feature of the dominated cell types in the cortical region of the 18 PCW kidney. (e) The distribution feature of the dominated cell types in the medullary region of the 18 PCW kidney. Figure S4. The biological function of the 16 distinct co-expressed patterns obtained from weighted gene co-expression network analysis. Left, diagrammatic drawing showing the relevance between the 16 modules and corresponding biological function. Scale bar: red represents a strong correlation, and gray indicates that the change in biological function did not reach the P-value threshold. Right, identification of biological function based on the differentially expressed genes in each module. Figure S5. Expression levels of M5 network genes for each (a) ST-seq and (b) scRNA-seq cell subpopulation. Figure S6. The expression of ribosome-related genes in NPC and PTA clusters identified in Hochane’s study. The structure chart on the top left corner shows the spatial location of the NPC and PTA clusters. Violin plots showing the expression of ribosome-related genes in NPC and PTA clusters. Figure S7. Development of the nephric duct. (a) Schematic diagram showing the outgrowth of the UB and its regulatory network. UMPA showing the expression of regulatory genes controlling UB development in the 15 spatial cell clusters. (b) Matching plots show the significant signaling transduction regulation between NPC, PTA, and UB cells (score >0.5 and a P

Additional file 1: Figure S1. Quality control of kidney and serum proteomics. Figure S2. Quality control of kidney and serum metabolomics. Figure S3. The identification of differential expressed metabolites between db/db and Empa group.

Objective: To explore the effect of countermeasures for hemodialysis patients with fever during the incubation period of novel coronavirus pneumonia (NCP) in a non-designated hospital. Methods: For 22 hemodialysis patients with fever in our hospital, we developed a specialist program based on the latest NCP treatment guidelines for nephrology and hemodialysis. We trained medical staff accurately and rapidly online. According to the specialist program, we implemented meticulous treatment for patients including dialysis, psychological and environmental care. We provided protection of medical staff in wards and dialysis rooms. Medical waste was deposited properly. Moreover, we implemented quality improvement in a timely manner according to the situation. Results: The 22 hemodialysis patients with fever in our hospital were not infected with novel coronavirus 2019. The epidemiological screening rate was 100%, and the percentage of family care giving was reduced by 29.8%. All medical stuff were uninfected. The patient’s satisfaction rate in the observation group is higher than that in the control group (96% vs. 74%, p = 0.048). Conclusion: The countermeasures for hemodialysis patients with fever developed by our specialty were effective. We can satisfy requirements for the NCP prevention for hemodialysis patients with fever in non-designated hospitals through implementing these countermeasures.

Introduction: Arteriovenous fistula (AVF) operation is recognized a gold standard of hemodialysis for end-stage renal disease. Because various complications may occur during perioperative period, medical care plays an important role in improving the possibility of AVF operation. The objective of the study was to explore the effect of the mode of multiple disciplinary team (MDT) medical care on the patients with end-stage renal disease treated by arteriovenous fistula (AVF) operation. Method: 98 patients with end-stage renal disease under the AVF operation were divided into observation group and control group. The control group was performed with regular medical care and for the observation group was treated under the mode of MDT medical care. Complications after operation, hospitalized time and medical satisfaction were comprehensively compared between the both groups. Results: The overall complications including swelling, bleeding, infection and thrombosis in observation group accounted for 5%, however, 19% of patients suffered different complications in the control group. The total complications significantly decreased in the observation group compared with control group (P < 0.001). The hospitalization time was shortened to 9.8 ± 5.6 days in the observation group, and the average hospitalization in control group was 17.8 ± 8.9 days. The medical satisfaction was increased by 20.83 percent in the observation group compared with the control group (P < 0.001). 75% patients in control group were satisfied with medical care, and 98.53% patients were satisfied for observation group. Conclusion: The mode of MDT medical care was beneficial for patients with end-stage renal disease after AVF operation, which could significantly reduce complications, hospitalized time and improve patients’ degree of satisfaction. Thus, it should be recommended in the clinical work.

Mitochondria play leading roles in initiation and progression of colorectal cancer (CRC). Proteogenomic analyses of mitochondria of CRC tumor cells would likely enhance our understanding of CRC pathogenesis and reveal new independent prognostic factors and treatment targets. However, comprehensive investigations focused on mitochondria of CRC patients are lacking. Here, we investigated global profiles of structural variants, DNA methylation, chromatin accessibility, transcriptome, proteome, and phosphoproteome on human CRC. Proteomic investigations uncovered greatly diminished mitochondrial proteome size in CRC relative to that found in adjacent healthy tissues. Integrated with analysis of RNA-Seq datasets obtained from the public database containing mRNA data of 538 CRC patients, the proteomic analysis indicated that proteins encoded by 45.5% of identified prognostic CRC genes were located within mitochondria, highlighting the association between altered mitochondrial function and CRC. Subsequently, we compared structural variants, DNA methylation, and chromatin accessibility of differentially expressed genes and found that chromatin accessibility was an important factor underlying mitochondrial gene expression. Furthermore, phosphoproteomic profiling demonstrated decreased phosphorylation of most mitochondria-related kinases within CRC versus adjacent healthy tissues, while also highlighting MKK3/p38 as an essential mitochondrial regulatory pathway. Meanwhile, systems-based analyses revealed identities of key kinases, transcriptional factors, and their interconnections. This research uncovered a close relationship between mitochondrial dysfunction and poor CRC prognosis, improve our understanding of molecular mechanism underlying mitochondrial linked to human CRC, and facilitate identifies of clinically relevant CRC prognostic factors and drug targets.

Additional file 2. The cox analyses of HIGD1A, SUCLG2 and SLC25A24; The overall survival rates of HIGD1A, SUCLG2 and SLC25A24 in colon and rectum cancer.

Additional file 7: Table S5. Clinical data of participants.

This study aims to explore the effect of N-terminal pro-brain natriuretic peptide (NT-proBNP) variability (mean absolute difference of the log2 NT-proBNP level measured in hospital) on the prognosis of patients with cardiorenal syndrome (CRS) type 2. Patients with CRS type 2 were retrospectively included. The varied NT-proBNP indications were analyzed. They were NT-proBNP I(pre-treatment), NT-proBNP II(post-treatment), NT-proBNP II/I, ΔNT-proBNP, log2 (NT-proBNP) variability and mean log2 (NT-proBNP). A logistic regression model and survival curves (Kaplan–Meier analysis) were built to identify independent predictors associated with poor prognosis. The primary outcomes were major adverse renal and cardiac events. The secondary outcome was all-cause mortality. From 2012 to 2016, 136 patients were included in this study with 69 (50.7%) had high log2 (NT-proBNP) variability level. The optimal cutoff level for each NT-proBNP indication that predicts poor prognosis was calculated, and the area under curves ranged from 0.668 to 0.891 with different indications. Kaplan–Meier analysis revealed that there was significantly correlated with prevalence of primary outcomes and NT-proBNP variability. The hazard ratios (HRs) ranged from 1.67 to 6.61 with different indications. The multivariate regression analyses also identified the risk of the primary outcomes were associated with elevated NT-proBNP values, except NT-proBNP I. The odds ratio (ORs) ranged from 1.83 to 6.61 with different indications. When analyzing the relationship between NT-proBNP variability and all-cause mortality, the results were the same. NT-proBNP variability might serve as an independent predictor for poor prognosis and all-cause mortality in patients with CRS type 2.

Additional file 7: Table S5. Clinical data of participants.

Additional file 8: Table S6. The primers using for PCR amplification.

Additional file 10. Protocols for RNA extraction, library construction, sequencing and Bioinformatics analysis

Objective: In this study, we investigated the molecule mechanisms of podocyte injury and proteinuria and the protective effects of losartan. Methods: This study set up three groups: a control group; an Ang II group (Ang II 10 –6 mol/l, Sigma); and a losartan group (losartan 10 –6 mol/l, Sigma). We used RT-PCR assay to detect TRPC6 mRNA expression, and Western blot to detect TRPC6 protein expression. Results: TRPC6 overexpression was the basic change of podocyte injury and proteinuria occurrence. Losartan can treat podocyte injury and proteinuria induced by Ang II via downregulation of TRPC6 in podocytes. Conclusion: These findings maybe provide an ideal drug target for the diagnosis and treatment of acquired glomerular diseases.