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Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other ‘omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.

Following injury, cells in regenerative tissues have the ability to regrow. The mechanisms whereby regenerating cells adapt to injury-induced stress conditions and activate the regenerative program remain to be defined. Here, using the mammalian neonatal heart regeneration model, we show that Nrf1, a stress-responsive transcription factor encoded by the Nuclear Factor Erythroid 2 Like 1 (Nfe2l1 ) gene, is activated in regenerating cardiomyocytes. Genetic deletion of Nrf1 prevented regenerating cardiomyocytes from activating a transcriptional program required for heart regeneration. Conversely, Nrf1 overexpression protected the adult mouse heart from ischemia/reperfusion (I/R) injury. Nrf1 also protected human induced pluripotent stem cell-derived cardiomyocytes from doxorubicin-induced cardiotoxicity and other cardiotoxins. The protective function of Nrf1 is mediated by a dual stress response mechanism involving activation of the proteasome and redox balance. Our findings reveal that the adaptive stress response mechanism mediated by Nrf1 is required for neonatal heart regeneration and confers cardioprotection in the adult heart. Following injury, cells in regenerative tissues can regrow, but how they adapt to injury conditions to regenerate the tissue is unclear. Here the authors identify a stress-responsive and cardioprotective factor Nrf1 that is critical for neonatal heart regeneration.

To explore the multiparameter precursor characteristics of pre- and post-coal burst. Based on a coal burst of LW 1305 in the Zhaolou Coal Mine, an early warning method combining stress‒strain curve and microseismic multiparameter is proposed. The research results show that coal burst was induced by the intrinsic static high-stress concentration and the strong external impact loading generated by fracturing of the key stratum. The precursors mainly characterize the enhancement trend of the S value, the sudden and sharp increase in the A(t) value, the continuous and abnormal decrease in the b value, the increasing absolute value of Z sharply and larger than 2, the continuous and abnormal decrease in the Qt value, and the dominant frequency moving to the low-frequency band. Essentially, many micro-fissures inside the key stratum initiated, converged and connected to form macro-fractures, which was verified by the attenuation rate of the K value. Considering the time-varying effect of the overlying stratum movement, the curves of the six parameters agree well with those of stress vs. strain, which indicates that it is reasonable to take the observed zone as a whole system to investigate the variation in the multiple parameters and fracturing of the key stratum. The research results can be applied to the monitoring, early warning and control of coal burst so that effective safety measures can be taken in real time.

The novel magnetic biochar (MBC), derived from co-pyrolysis of sewage sludge and biomass loading nanosized iron oxide particles, was used as an environmentally friendly adsorbent. The loading of magnetic particles was in favor of increasing the adsorption capacity and separation from aqueous system for biochar (BC). The physical/chemical characteristics of MBC were revealed by elemental analysis, VSM, SEM-EDS, XRD, FTIR, zeta potential, and batch adsorption-desorption experiments. The nanosized γ-Fe 2 O 3 particles grown on the surface of biochar showed ferromagnetic property. For the remediation of Cu(II) contamination, MBC-5 showed remarkable adsorption capacity of 67.68 mg/g, and presented a wide pH range of 3.0–6.0. The Langmuir isothermal and pseudo-second-order model could describe adsorption process well. The adsorption mechanism of Cu(II) involved physical adsorption, ion exchange, and electrostatic surface complexation on the surface of MBCs. In the desorption experiments, MBC-5 holds the adsorption efficiency of 81.09% after fifth recycle still, which illustrated a remarkable performance of cyclic utilization by the solid waste of sewage sludge and biomass. Graphical abstract

Currently, the treatment and prevention of multiple sclerosis (MS) continue to encounter significant challenges. Mendelian randomization (MR) analysis has emerged as a crucial research method in the pursuit of new therapeutic strategies. Accordingly, we hypothesize that there exists a causal association between genetic variants of specific plasma proteins and MS through MR mechanisms, and that key therapeutic targets can be precisely identified by integrating multi-omics analytical approaches. In this study, we developed a comprehensive analytical framework aimed at identifying and validating potential therapeutic targets for MS. The framework commenced with a two-sample Mendelian randomization (MR) study utilizing two large plasma protein quantitative trait locus (pQTL) datasets. Building on this foundation, we performed Bayesian co-localization analysis of coding genes, followed by a full phenotype-wide association study (PheWAS) on the co-positive genes identified through both analytical methods. This approach allowed us to explore the functions of key genes and the mechanisms of co-morbidity associated with the disease. Subsequently, we integrated protein-protein interaction (PPI) network analysis, gene ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to facilitate drug prediction and molecular docking studies. This study conducted a systematic analysis between two large plasma pQTLs datasets and MS. In the MR analysis, the MR analysis of Icelandic plasma pQTLs and MS identified 88 positive plasma proteins, while the MR analysis of the UK Biobank database pQTLs and MS identified 122 positive plasma proteins. By comparison, uroporphyrinogen III synthase (UROS) and glutathione S-transferase theta 2B (GSTT2B) were found to be the positive proteins shared by the two datasets. After false discovery rate (FDR) correction, signal transducer and activator of transcription 3 (STAT3) was a significantly positive protein in the analysis of Icelandic plasma pQTLs. In the analysis of the UK Biobank database pQTLs, advanced glycosylation end product-specific receptor (AGER), allograft inflammatory factor 1 (AIF1), butyrophilin subfamily 1 member A1 (BTN1A1), cluster of differentiation 58 (CD58), desmoglein 4 (DSG4), ecotropic viral integration site 5 (EVI5), tumor necrosis factor (TNF), and tumor necrosis factor receptor superfamily member 14 (TNFRSF14) were significantly positive proteins. After Bonferroni correction, AGER, CD58, EVI5, and TNF remained significantly positive proteins in the analysis of the UK Biobank database pQTLs. In the Bayesian colocalization analysis, EVI5 (PPH4 = 0.9800), O-GlcNAcase (OGA) (PPH4 = 0.8569), and TNFRSF14 (PPH4 = 0.8904) were the common positive genes in the two analysis methods. In conclusion, EVI5, OGA, and TNFRSF14 may be potential therapeutic targets for MS. Through the comprehensive application of MR analysis and Bayesian colocalization analysis, we have successfully identified that EVI5, OGA, and TNFRSF14 may be key therapeutic targets for MS. These findings may provide a scientific basis for the development of novel immunotherapies, combination treatment regimens, or targeted intervention strategies.

The most common form of genetic heart disease is hypertrophic cardiomyopathy (HCM), which is caused by variants in cardiac sarcomeric genes and leads to abnormal heart muscle thickening. Complications of HCM include heart failure, arrhythmia and sudden cardiac death. The dominant-negative c.1208G>A (p.R403Q) pathogenic variant (PV) in β-myosin ( MYH7 ) is a common and well-studied PV that leads to increased cardiac contractility and HCM onset. In this study we identify an adenine base editor and single-guide RNA system that can efficiently correct this human PV with minimal bystander editing and off-target editing at selected sites. We show that delivery of base editing components rescues pathological manifestations of HCM in induced pluripotent stem cell cardiomyocytes derived from patients with HCM and in a humanized mouse model of HCM. Our findings demonstrate the potential of base editing to treat inherited cardiac diseases and prompt the further development of adenine base editor-based therapies to correct monogenic variants causing cardiac disease. Adenine base editing successfully corrected a MYH7 pathogenic variant that causes hypertrophic cardiomyopathy in human cardiomyocytes and a mouse model of the disease, highlighting the potential of the approach to correct monogenic variants causing cardiac disease.

In the field of treatment and prevention of immune-related bone diseases, significant challenges persist, necessitating the urgent exploration of new and effective treatment methods. However, most existing Mendelian randomization (MR) studies are confined to a single analytical approach, which limits the comprehensive understanding of the pathogenesis and potential therapeutic targets of these diseases. In light of this, we propose the hypothesis that genetic variations in specific plasma proteins have a causal relationship with immune-related bone diseases through the MR mechanism, and that key therapeutic targets can be accurately identified using an integrated multi-omic analysis approach. This study comprehensively applied a variety of analytical methods. Firstly, the protein quantitative trait locus (pQTLs) data from two large plasma protein databases and the Genome-Wide Association Study (GWAS) data of nine immune-related bone diseases were used for Mendelian randomization (MR) analysis. At the same time, we employed the Summary-based Mendelian Randomization (SMR) method, combined with the Bayesian colocalization analysis method of coding genes, as well as the Linkage Disequilibrium Score Regression (LDSC) analysis method based on genetic correlation analysis, as methods to verify the genetic association between genes and complex diseases, thus comprehensively obtaining positive results. In addition, a Phenome-wide Association Study (PheWAS) was conducted on significantly positive genes, and their expression patterns in different tissues were also explored. Subsequently, we integrated Protein–Protein Interaction (PPI) network analysis, Gene Ontology (GO) analysis. Finally, based on the above analytical methods, drug prediction and molecular docking studies were carried out with the aim of accurately identifying key therapeutic targets. Through a comprehensive analysis using four methods, namely the Mendelian randomization (MR) analysis study, Summary-based Mendelian Randomization (SMR) analysis study, Bayesian colocalization analysis study, and Linkage Disequilibrium Score Regression (LDSC) analysis study. We found that through MR, SMR, and combined with Bayesian colocalization analysis, an association was found between rheumatoid arthritis (RA) and HDGF. Using the combination of MR and Bayesian colocalization analysis, as well as LDSC analysis, it was concluded that RA was related to CCL19 and TNFRSF14. Based on the methods of MR and Bayesian colocalization, an association was found between GPT and Crohn’s disease-related arthritis, and associations were found between BTN1A1, EVI5, OGA, TNFRSF14 and multiple sclerosis (MS), and associations were found between ICAM5, CCDC50, IL17RD, UBLCP1 and psoriatic arthritis (PsA). Specifically, in the MR analysis of RA, HDGF (P_ivw = 0.0338, OR = 1.0373, 95%CI = 1.0028—1.0730), CCL19 (P_ivw = 0.0004, OR = 0.3885, 95%CI = 0.2299—0.6566), TNFRSF14 (P_ivw = 0.0007, OR = 0.6947, 95%CI = 0.5634—0.8566); in the MR analysis of MS, BTN1A1 (P_ivw = 0.0000, OR = 0.6101, 95%CI = 0.4813—0.7733), EVI5 (P_ivw = 0.0000, OR = 0.3032, 95%CI = 0.1981—0.4642), OGA (P_ivw = 0.0005, OR = 0.4599, 95%CI = 0.2966—0.7131), TNFRSF14 (P_ivw = 0.0002, OR = 0.4026, 95%CI = 0.2505—0.6471); in the MR analysis of PsA, ICAM5 (P_ivw = 0.0281, OR = 1.1742, 95%CI = 1.0174—1.3552), CCDC50 (P_ivw = 0.0092, OR = 0.7359, 95%CI = 0.5843—0.9269), IL17RD (P_ivw = 0.0006, OR = 0.7887, 95%CI = 0.6886—0.9034), UBLCP1 (P_ivw = 0.0021, OR = 0.6901, 95%CI = 0.5448—0.8741); in the MR analysis of Crohn’s disease-related arthritis, GPT (P_ivw = 0.0006, OR = 0.0057, 95%CI = 0.0003—0.1111). In the Bayesian colocalization analysis of RA, HDGF (H4 = 0.8426), CCL19 (H4 = 0.9762), TNFRSF14 (H4 = 0.8016); in the Bayesian colocalization analysis of MS, BTN1A1 (H4 = 0.7660), EVI5 (H4 = 0.9800), OGA (H4 = 0.8569), TNFRSF14 (H4 = 0.8904); in the Bayesian colocalization analysis of PsA, ICAM5 (H4 = 0.9476), CCDC50 (H4 = 0.9091), IL17RD (H4 = 0.9301), UBLCP1 (H4 = 0.8862); in the Bayesian colocalization analysis of Crohn’s disease-related arthritis, GPT (H4 = 0.8126). In the SMR analysis of RA, HDGF (p_SMR = 0.0338, p_HEIDI = 0.0628). In the LDSC analysis of RA, CCL19 (P = 0.0000), TNFRSF14 (P = 0.0258). By comprehensively analyzing plasma proteomic and transcriptomic data, we successfully identified key therapeutic targets for various clinical subtypes of immune-associated bone diseases. Our findings indicate that the significant positive genes associated with RA include HDGF, CCL19, and TNFRSF14; the positive gene linked to Crohn-related arthropathy is GPT; for MS, the positive genes are BTN1A1, EVI5, OGA, and TNFRSF14; and for PsA, the positive genes are ICAM5, CCDC50, IL17RD, and UBLCP1. Through this comprehensive analytical approach, we have screened potential therapeutic targets for different clinical subtypes of immune-related bone diseases. This research not only enhances our understanding of the pathogenesis of these conditions but also provides a solid theoretical foundation for subsequent drug development and clinical treatment, with the potential to yield significant advancements in the management of patients with immune-related bone diseases.

NOABSTRACTTo investigate the health service demands and to analyze influencing factors among elderly people based on a community survey in Guilin, China.A random sampling was used to investigate 366 elderly people in a community using a Health-Care-Needs questionnaire, which was designed by The Western Nursing Alliance research team in China. This survey was used to understand the basic situation, financial condition, health condition, self-care abilities, pension plan, and care services demands of the elderly residing at home. Additionally, this article analyzed the influencing factors contributing to the obtained results.The top 3 nursing needs were security needs (1.61 ± 0.45 points), health education needs (1.54 ± 0.57 points), and respect and self-development needs (1.13 ± 0.64 points). Logistic multifactor regression analysis showed that gender, monthly income, lack of exercise, activities of daily living (ADL) scores, methods of medical payment, and pension plan were independent factors affecting elderly nursing needs.The home-based health services supply for elders did not meet their needs. Therefore, a comprehensive approach considering multifactors such as gender, income, exercise, self-care ability, medical expense payments, and supporting preferences should be considered to address the complex needs of health care.

The adult mammalian heart has limited capacity for regeneration following injury, whereas the neonatal heart can readily regenerate within a short period after birth. To uncover the molecular mechanisms underlying neonatal heart regeneration, we compared the transcriptomes and epigenomes of regenerative and nonregenerative mouse hearts over a 7-d time period following myocardial infarction injury. By integrating gene expression profiles with histone marks associated with active or repressed chromatin, we identified transcriptional programs underlying neonatal heart regeneration, and the blockade to regeneration in later life. Our results reveal a unique immune response in regenerative hearts and a retained embryonic cardiogenic gene program that is active during neonatal heart regeneration. Among the unique immune factors and embryonic genes associated with cardiac regeneration, we identified Ccl24, which encodes a cytokine, and Igf2bp3, which encodes an RNA-binding protein, as previously unrecognized regulators of cardiomyocyte proliferation. Our data provide insights into the molecular basis of neonatal heart regeneration and identify genes that can be modulated to promote heart regeneration.

Background Bacillus pumilus is a Gram-positive and endospore-forming bacterium broadly existing in a variety of environmental niches. Because it produces and secrets many industrially useful enzymes, a lot of studies have been done to understand the underlying mechanisms. Among them, scoC was originally identified as a pleiotropic transcription factor negatively regulating protease production and sporulation in B. subtilis . Nevertheless, its role in B. pumilus largely remains unknown. Results In this study we successfully disrupted scoC gene in B. pumilus BA06 and found increased total extracellular protease activity in scoC mutant strain. Surprisingly, we also found that scoC disruption reduced cell motility possibly by affecting flagella formation. To better understand the underlying mechanism, we performed transcriptome analysis with RNA sequencing. The result showed that more than one thousand genes were alternated at transcriptional level across multiple growth phases, and among them the largest number of differentially expressed genes (DEGs) were identified at the transition time point (12 h) between the exponential growth and the stationary growth phases. In accordance with the altered phenotype, many protease genes especially the aprE gene encoding alkaline protease were transcriptionally regulated. In contrast to the finding in B. subtilis , the aprN gene encoding neutral protease was transcriptionally downregulated in B. pumilus , implicating that scoC plays strain-specific roles. Conclusions The pleiotropic transcription factor ScoC plays multiple roles in various cellular processes in B. pumilus, some of which were previously reported in B. subtilis . The supervising finding is the identification of ScoC as a positive regulator for flagella formation and bacterial motility. Our transcriptome data may provide hints to understand the underlying mechanism.