Grimon: graphical interface to visualize multi-omics networks

Delving into gene-set multiplex networks facilitated by a k-nearest neighbor-based measure of similarity

Gene sets are functional units for living cells. Previously, limited studies investigated the complex relations among gene sets, but documents about their altering patterns across biological conditions still need to be prepared. In this study, we adopted and modified a classical k-nearest neighbor-based association function to detect inter-gene-set similarities. Based on this method, we built multiplex networks of gene sets for the first time; these networks contain layers of gene sets corresponding to different populations of cells. The context-based multiplex networks can capture meaningful biological variation and have considerable differences from knowledge-based networks of gene sets built on Jaccard similarity, as demonstrated in this study. Furthermore, at the scale of individual gene sets, the structural coefficients of gene sets (multiplex PageRank centrality, clustering coefficient, and participation coefficient) disclose the diversity of gene sets from the perspective of structural properties and make it easier to identify unique gene sets. In gene set enrichment analysis (GSEA), each gene set is treated independently, and its contextual and relational attributes are ignored. The structural coefficients of gene sets can supplement GSEA with information about the overall picture of gene sets, promoting the constructive reorganization of the enriched terms and helping researchers better prioritize and select gene sets.

Ambient 1,2-propanediol exposure accelerates the degradation of lipids and amino acids in milk via allosteric effects and affects the utilization of nutrients containing amide bond

The scandal of detecting 1, 2-propanediol (PL) in milk brought a crisis to the trust of consumers in the dairy industry, and the potential toxicity of PL has aroused the public concern about dietary exposure. A total of 200 pasteurized milk samples were collected from 15 regions, and the quantity of PL ranged between 0 and 0.31 g kg^-1. Pseudo-targeted quantitative metabolomics integrated with proteomics demonstrated that PL enhanced the reduction of κ-casein, β-casein, and 107 substances (41 amines and 66 amides) containing amide bonds. Pathway enrichment and topological analysis indicated that PL induced the metabolism of lipids, amino acids, oligosaccharide nucleotides, and alkaloids by accelerating the rate of nucleophilic reaction, and acetylcholinesterase, sarcosine oxidase, and prolyl 4-hydroxylase were determined as the vital enzymes related to the degradation of above nutrients. The results of molecular simulation calculation illustrated that the number of hydrogen bonds between acetylcholinesterase, sarcosine oxidase, and substrate increased to 2 and 3, respectively, while the position of hydrogen bonds between prolyl 4-hydroxylase and proline was shifted, indicating the change of conformation and the enhancement of hydrogen bond force were essential factors for the up-regulation of enzyme activity. This study first revealed the mechanism of deposition and transformation of PL in milk, which contributed to the knowledge of the quality control of milk and provided vital indicators to evaluate the adverse risks of PL in dairy products.

UHPLC-Q-Orbitrap-Based Integrated Lipidomics and Proteomics Reveal Propane-1,2-diol Exposure Accelerating Degradation of Lipids via the Allosteric Effect and Reducing the Nutritional Value of Milk

The scandal of detecting the flavoring solvent propane-1,2-diol (PD) in milk has brought a crisis to the trust of consumers in the dairy industry, while its deposition and transformation are still indistinct. Pseudo-targeted lipidomics revealed that PD accelerated the degradation of glycerolipid (33,638.3 ± 28.9 to 104,54.2 ± 28.4 mg kg^-1), phosphoglyceride (467.4 ± 8.2 to 56.6 ± 4.2 mg kg^-1), and sphingolipids (11.4 ± 0.3 to 0.7 ± 0.2 mg kg^-1), which extremely decreased the milk quality. Recoveries and relative standard deviations (RSDs) of the established method were 85.0-109.9 and 0.1-14.9%, respectively, indicating that the approach was credible. Protein-lipid interactions demonstrated that 10 proteins originating from fat globules were upregulated significantly and the activities of 7 enzymes related to lipid degradation were improved. Diacylglycerol cholinephosphotransferase was the only enzyme with decreased activity, and the molecular docking results indicated that PD adjusted its activity through regulating the conformation of the active center and weakening the hydrogen bond force between the enzyme and substrate. This study firstly revealed the mechanism of deposition and transformation of PD in milk, which contributed to the knowledge on the milk quality control and provided key indicators to evaluate the adverse risks of PD in dairy products.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Discovering Biomarkers for Non-Alcoholic Steatohepatitis Patients with and without Hepatocellular Carcinoma Using Fecal Metaproteomics

High-calorie diets lead to hepatic steatosis and to the development of non-alcoholic fatty liver disease (NAFLD), which can evolve over many years into the inflammatory form of non-alcoholic steatohepatitis (NASH), posing a risk for the development of hepatocellular carcinoma (HCC). Due to diet and liver alteration, the axis between liver and gut is disturbed, resulting in gut microbiome alterations. Consequently, detecting these gut microbiome alterations represents a promising strategy for early NASH and HCC detection. We analyzed medical parameters and the fecal metaproteome of 19 healthy controls, 32 NASH patients, and 29 HCC patients, targeting the discovery of diagnostic biomarkers. Here, NASH and HCC resulted in increased inflammation status and shifts within the composition of the gut microbiome. An increased abundance of kielin/chordin, E3 ubiquitin ligase, and nucleophosmin 1 represented valuable fecal biomarkers, indicating disease-related changes in the liver. Although a single biomarker failed to separate NASH and HCC, machine learning-based classification algorithms provided an 86% accuracy in distinguishing between controls, NASH, and HCC. Fecal metaproteomics enables early detection of NASH and HCC by providing single biomarkers and machine learning-based metaprotein panels.

Genome-Wide Association Study for Screening and Identifying Potential Shin Color Loci in Ducks

Shin color diversity is a widespread phenomenon in birds. In this study, ducks were assessed to identify candidate genes for yellow, black, and spotted tibiae. For this purpose, we performed whole-genome resequencing of an F2 population consisting of 275 ducks crossed between Runzhou crested-white ducks and Cherry Valley ducks. We obtained 12.6 Mb of single nucleotide polymorphism (SNP) data, and the three shin colors were subsequently genotyped. Genome-wide association studies (GWASs) were performed to identify candidate and potential SNPs for the three shin colors. According to the results, 2947 and 3451 significant SNPs were associated with black and yellow shins, respectively, and six potential SNPs were associated with spotted shins. Based on the SNP annotations, the MITF, EDNRB2, POU family members, and the SLC superfamily were the candidate genes regulating pigmentation. In addition, the isoforms of EDNRB2, TYR, TYRP1, and MITF-M were significantly different between the black and yellow tibiae. MITF and EDNRB2 may have synergistic roles in the regulation of melanin synthesis, and their mutations may lead to phenotypic differences in the melanin deposition between individuals. This study provides new insights into the genetic factors that may influence tibia color diversity in birds.

Multi-Omics Characterization of Type 2 Diabetes Mellitus-Induced Cognitive Impairment in the db/db Mouse Model

Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder frequently accompanied by cognitive impairment. Contributing factors such as modern lifestyle, genetic predisposition, and gene environmental interactions have been postulated, but the pathogenesis remains unclear. In this study, we attempt to investigate the potential mechanisms and interventions underlying T2DM-induced cognitive deficits from the brain–gut axis perspective. A combined analysis of the brain transcriptome, plasma metabolome, and gut microbiota in db/db mice with cognitive decline was conducted. Transcriptome analysis identified 222 upregulated gene sets and 85 downregulated gene sets, mainly related to mitochondrial respiratory, glycolytic, and inflammation. In metabolomic analysis, a total of 75 significantly altered metabolites were identified, correlated with disturbances of glucose, lipid, bile acid, and steroid metabolism under disease state. Gut microbiota analysis suggested that the species abundance and diversity of db/db mice were significantly increased, with 23 significantly altered genus detected. Using the multi-omics integration, significant correlations among key genes (n = 33), metabolites (n = 41), and bacterial genera (n = 21) were identified. Our findings suggest that disturbed circulation and brain energy metabolism, especially mitochondrial-related disturbances, may contribute to cognitive impairment in db/db mice. This study provides novel insights into the functional interactions among the brain, circulating metabolites, and gut microbiota.

A data science approach to drug safety: Semantic and visual mining of adverse drug events from clinical trials of pain treatments

Clinical trials are the basis of Evidence-Based Medicine. Trial results are reviewed by experts and consensus panels for producing meta-analyses and clinical practice guidelines. However, reviewing these results is a long and tedious task, hence the meta-analyses and guidelines are not updated each time a new trial is published. Moreover, the independence of experts may be difficult to appraise. On the contrary, in many other domains, including medical risk analysis, the advent of data science, big data and visual analytics allowed moving from expert-based to fact-based knowledge. Since 12 years, many trial results are publicly available online in trial registries. Nevertheless, data science methods have not yet been applied widely to trial data. In this paper, we present a platform for analyzing the safety events reported during clinical trials and published in trial registries. This platform is based on an ontological model including 582 trials on pain treatments, and uses semantic web technologies for querying this dataset at various levels of granularity. It also relies on a 26-dimensional flower glyph for the visualization of the Adverse Drug Events (ADE) rates in 13 categories and 2 levels of seriousness. We illustrate the interest of this platform through several use cases and we were able to find back conclusions that were initially found during meta-analyses. The platform was presented to four experts in drug safety, and is publicly available online, with the ontology of pain treatment ADE.

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction

The diversity in our genome is crucial to understanding the demographic history of worldwide populations. However, we have yet to know whether subtle genetic differences within a population can be disentangled, or whether they have an impact on complex traits. Here we apply dimensionality reduction methods (PCA, t-SNE, PCA-t-SNE, UMAP, and PCA-UMAP) to biobank-derived genomic data of a Japanese population (n = 169,719). Dimensionality reduction reveals fine-scale population structure, conspicuously differentiating adjacent insular subpopulations. We further enluciate the demographic landscape of these Japanese subpopulations using population genetics analyses. Finally, we perform phenome-wide polygenic risk score (PRS) analyses on 67 complex traits. Differences in PRS between the deconvoluted subpopulations are not always concordant with those in the observed phenotypes, suggesting that the PRS differences might reflect biases from the uncorrected structure, in a trait-dependent manner. This study suggests that such an uncorrected structure can be a potential pitfall in the clinical application of PRS.