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Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making. A knowledge graph platform integrates proteomics with other omics data and biomedical databases.

Human skin provides both physical integrity and immunological protection from the external environment using functionally distinct layers, cell types and extracellular matrix. Despite its central role in human health and disease, the constituent proteins of skin have not been systematically characterized. Here, we combine advanced tissue dissection methods, flow cytometry and state-of-the-art proteomics to describe a spatially-resolved quantitative proteomic atlas of human skin. We quantify 10,701 proteins as a function of their spatial location and cellular origin. The resulting protein atlas and our initial data analyses demonstrate the value of proteomics for understanding cell-type diversity within the skin. We describe the quantitative distribution of structural proteins, known and previously undescribed proteins specific to cellular subsets and those with specialized immunological functions such as cytokines and chemokines. We anticipate that this proteomic atlas of human skin will become an essential community resource for basic and translational research ( https://skin.science/ ). The human skin is a highly complex organ comprising multiple tissue layers and diverse cell types. Here, the authors present a spatially-resolved quantitative proteomic atlas of the healthy human skin, characterizing the protein profiles of four skin layers and nine cell types.

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these. Imputation in mass spectrometry-based proteomics is a recurrent step of importance for downstream analysis. Here, the authors offer an extensive comparison workflow of 27 established with three new scalable, fast and performant methods from deep learning for large and high-dimensional data.

Plasma and serum are rich sources of information regarding an individual's health state, and protein tests inform medical decision making. Despite major investments, few new biomarkers have reached the clinic. Mass spectrometry (MS)‐based proteomics now allows highly specific and quantitative readout of the plasma proteome. Here, we employ Plasma Proteome Profiling to define quality marker panels to assess plasma samples and the likelihood that suggested biomarkers are instead artifacts related to sample handling and processing. We acquire deep reference proteomes of erythrocytes, platelets, plasma, and whole blood of 20 individuals (> 6,000 proteins), and compare serum and plasma proteomes. Based on spike‐in experiments, we determine sample quality‐associated proteins, many of which have been reported as biomarker candidates as revealed by a comprehensive literature survey. We provide sample preparation guidelines and an online resource ( www.plasmaproteomeprofiling.org ) to assess overall sample‐related bias in clinical studies and to prevent costly miss‐assignment of biomarker candidates. Synopsis This study describes marker panels for the systematic assessment of plasma samples that report on erythrocyte lysis, platelet contamination and coagulation. Marker panels can assess entire clinical studies or individual samples for quality issues and allow evaluation of biomarker candidates. Deep reference proteome data (> 6,000 proteins) from erythrocytes, platelets, platelet‐rich plasma, platelet‐free plasma and whole blood from 20 individuals, distilled in three quality marker panels of 30 proteins each. The influence of blood sampling equipment and sample processing protocols on the plasma proteome is compared. The www.plasmaproteomeprofiling.org website allows the automated quality assessment of single samples and clinical studies. A general guideline for minimizing pre‐analytical variations in future clinical studies is proposed. Evaluation of 210 biomarker studies reveal that about 50% of them report proteins of the quality marker panels, indicating potential sample handling issues. Graphical Abstract This study describes marker panels for the systematic assessment of plasma samples that report on erythrocyte lysis, platelet contamination and coagulation. Marker panels can assess entire clinical studies or individual samples for quality issues and allow evaluation of biomarker candidates.

Hu sheep (HS), a breed of sheep carrying the FecB mutation gene, is known for its “year-round estrus and multiple births” and is an ideal model for studying the high fecundity mechanisms of livestock. Through analyzing and comparing the genomic selection features of Hu sheep and other sheep breeds, we identified a series of candidate genes that may play a role in Hu sheep’s high fecundity mechanisms. In this study, we conducted whole-genome resequencing on six breeds and screened key mutations significantly correlated with high reproductive traits in sheep. Notably, the CC2D1B gene was selected by the fixation index ( F ST ) and the cross-population composite likelihood ratio (XP-CLR) methods in HS and other five breeds. It was worth noting that the CC2D1B gene in HS was different from that in other sheep breeds, and seven missense mutations have been identified. Furthermore, the linkage disequilibrium (LD) analysis revealed a strong linkage disequilibrium in this specific gene region. Subsequently, by performing different grouping based on FecB genotypes in Hu sheep, genome-wide selective signal analysis screened several genes related to reproduction, such as BMPR1B and PPM1K . Besides, F ST analysis identified functional genes related to reproductive traits, including RHEB , HSPA2 , PPP1CC , HVCN1 , and CCDC63 . Additionally, a missense mutation was found in the CCDC63 gene and the haplotype was different between the high reproduction (HR) group and low reproduction (LR) group in HS. In summary, we discovered genetic differentiation among six distinct breeding sheep breeds at the whole genome level. Additionally, we identified a set of genes which were associated with reproductive performance in Hu sheep and visualized how these genes differed in different breeds. These findings laid a theoretical foundation for understanding genetic mechanisms behind high prolific traits in sheep.

Background Colorectal cancer (CRC) incidence is increasing in recent years due to intestinal flora imbalance, making oral probiotics a hotspot for research. However, numerous studies related to intestinal flora regulation ignore its internal mechanisms without in-depth research. Results Here, we developed a probiotic microgel delivery system ( L.r @(SA-CS) 2 ) through the layer-by-layer encapsulation technology of alginate (SA) and chitosan (CS) to improve gut microbiota dysbiosis and enhance anti-tumor therapeutic effect. Short chain fatty acids (SCFAs) produced by L.r have direct anti-tumor effects. Additionally, it reduces harmful bacteria such as Proteobacteria and Fusobacteriota , and through bacteria mutualophy increases beneficial bacteria such as Bacteroidota and Firmicutes which produce butyric acid. By binding to the G protein-coupled receptor 109A (GPR109A) on the surface of colonic epithelial cells, butyric acid can induce apoptosis in abnormal cells. Due to the low expression of GPR109A in colon cancer cells, MK-6892 (MK) can be used to stimulate GPR109A. With increased production of butyrate, activated GPR109A is able to bind more butyrate, which further promotes apoptosis of cancer cells and triggers an antitumor response. Conclusion It appears that the oral administration of L.r @(SA-CS) 2 microgels may provide a treatment option for CRC by modifying the gut microbiota.

Non‐alcoholic fatty liver disease (NAFLD) affects 25% of the population and can progress to cirrhosis with limited treatment options. As the liver secretes most of the blood plasma proteins, liver disease may affect the plasma proteome. Plasma proteome profiling of 48 patients with and without cirrhosis or NAFLD revealed six statistically significantly changing proteins (ALDOB, APOM, LGALS3BP, PIGR, VTN, and AFM), two of which are already linked to liver disease. Polymeric immunoglobulin receptor (PIGR) was significantly elevated in both cohorts by 170% in NAFLD and 298% in cirrhosis and was further validated in mouse models. Furthermore, a global correlation map of clinical and proteomic data strongly associated DPP4, ANPEP, TGFBI, PIGR, and APOE with NAFLD and cirrhosis. The prominent diabetic drug target DPP4 is an aminopeptidase like ANPEP, ENPEP, and LAP3, all of which are up‐regulated in the human or mouse data. Furthermore, ANPEP and TGFBI have potential roles in extracellular matrix remodeling in fibrosis. Thus, plasma proteome profiling can identify potential biomarkers and drug targets in liver disease. Synopsis Applying Plasma Proteome Profiling to liver disease in different human cohorts associated PIGR and ALDOB and other proteins to non‐alcoholic fatty liver disease. Potential biomarkers were validated in a mouse model. Plasma proteome profiling augmented by Boxcar acquisition identified potential biomarkers of human liver diseases. PIGR and ALDOB are associated with NAFLD, among other novel proteins. DPP4, ANPEP, PIGR, APOE, and TGFBI highly correlate with AST, ALT, GGT and ALP. A mouse NAFLD model recapitulated many of the changes seen in humans. Graphical Abstract Applying Plasma Proteome Profiling to liver disease in different human cohorts associated PIGR and ALDOB and other proteins to non‐alcoholic fatty liver disease. Potential biomarkers were validated in a mouse model.

MYB transcription factor is one of the largest gene families in plants and plays critical roles in plant growth and development, as well as resistance to biotic and abiotic stress. However, the function of MYB genes in pigeon pea (Cajanus cajan) remains largely unknown. Here, 30 R2R3-MYB which involved flavonoid and lignin biosynthesis were identified in the pigeon pea genome and were classified into five groups based on phylogenetic analysis. Simultaneously, another 122 key enzyme genes from biosynthetic pathways of flavonoid and lignin were identified and all of them were mapped on 11 chromosomes with the co-linearity relationship. Among these genes, the intron/exon organization and motif compositions were conserved and they have undergone a strong purifying selection and tandem duplications during evolution. Expression profile analysis demonstrated most of these genes were expressed in different tissues and responded significantly to MeJA, RNA-seq analysis revealed clear details of genes varied with time of induction. Ten key genes from the phenylpropanoid pathway were selected to further verify whether they responded to induction under different abiotic stress conditions (UV-B, cold, heat, salt, drought, and GA3). This study elaborates on potential regulatory relationships between R2R3-MYB genes and some key genes involved in flavonoid and lignin biosynthesis under MeJA treatment, as well as adding to the understanding of improving abiotic stress tolerance and regulating the secondary metabolism in woody crops. A simplified discussion model for the different regulation networks involved with flavonoid and lignin biosynthesis in pigeon pea is proposed.

Background This paper presents quantitative analysis of blood flow shear stress by measuring the carotid arterial wall shear stress (WSS) and the intima-media thickness (IMT) of experimental rabbits fed with high-fat feedstuff on a weekly basis in order to cause atherosclerosis. Methods This study is based on establishing an atherosclerosis model of high-fat rabbits, and measuring the rabbits’ common carotid arterial WSS of the experimental group and control group on a weekly basis. Detailed analysis was performed by using WSS quantification. Results We have demonstrated small significant difference of rabbit carotid artery WSS between the experimental group and the control group (P<0.01) from the 1st week onwards, while the IMT of experimental group had larger differences from 5th week compared with the control group (P<0.05). Next, we have shown that with increasing blood lipids, the rabbit carotid artery shear stress decreases and the rabbit carotid artery IMT goes up. The decrease of shear stress appears before the start of IMT growth. Furthermore, our receiver operator characteristic (ROC) curve analysis showed that when the mean value of shear stress is 1.198 dyne/cm 2 , the rabbit common carotid atherosclerosis fatty streaks sensitivity is 89.8%, and the specificity is 81.3%. The area under the ROC curve is 0.9283. Conclusions All these data goes to show that WSS decreasing to 1.198 dyne/cm 2 can be used as an indicator that rabbit common carotid artery comes into the period of fibrous plaques. In conclusion, our study is able to find and confirm that the decrease of the arterial WSS can predict the occurrence of atherosclerosis earlier, and offer help for positive clinical intervention.

High dimensional biomedical data contained many irrelevant or weakly correlated features, which affected the efficiency of disease diagnosis. This paper presented a feature selection method for high dimensional biomedical data based on the chemotaxis foraging-shuffled frog leaping algorithm (BF-SFLA). The performance of the BF-SFLA-based feature selection method was further improved by introducing chemokines operation and balanced grouping strategies into the shuffled frog leaping algorithm, which maintained the balance between global optimization and local optimization and reduced the possibility of the algorithm falling into local optimization.To evaluate the proposed method’s effectiveness, we employed the K-NN (k-nearest Neighbor) and C4.5 decision tree classification algorithm with a comparative analysis. We compared our proposed approach with improved genetic algorithms, particle swarm optimization, and the basic shuffled frog leaping algorithm. Experimental results showed that the feature selection method based on BF-SFLA obtained a better feature subset, improved classification accuracy and shortened classification time.