Extracting Chromosome Structural Information as One-Dimensional Metrics and Integrating Them with Epigenomics.

Hi-C is a powerful method for obtaining genome-wide chromosomal structural information. The typical Hi-C analysis utilizes a two-dimensional (2D) contact matrix, which poses challenges for quantitative comparisons, visualizations, and integrations across multiple datasets. Here, we present a protocol for extracting one-dimensional (1D) features from chromosome structure data by HiC1Dmetrics. Leveraging these 1D features enables integrated analysis of Hi-C and epigenomic data.

Exploring Cohesin-Related Multiomics Information in the Web Browser.

Cohesin is a protein complex that plays a key role in regulating chromosome structure and gene expression. While next-generation sequencing technologies have provided extensive information on various aspects of cohesin, integrating and exploring the vast datasets associated with cohesin are not straightforward. CohesinDB ( https://cohesindb.iqb.u-tokyo.ac.jp ) offers a web-based interface for browsing, searching, analyzing, visualizing, and downloading comprehensive multiomics cohesin information in human cells. In this protocol, we introduce how to utilize CohesinDB to facilitate research on transcriptional regulation and chromatin organization.

Networks as tools for defining emergent properties of microbiomes and their stability.

The potential promise of the microbiome to ameliorate a wide range of societal and ecological challenges, from disease prevention and treatment to the restoration of entire ecosystems, hinges not only on microbiome engineering but also on the stability of beneficial microbiomes. Yet the properties of microbiome stability remain elusive and challenging to discern due to the complexity of interactions and often intractable diversity within these communities of bacteria, archaea, fungi, and other microeukaryotes. Networks are powerful tools for the study of complex microbiomes, with the potential to elucidate structural patterns of stable communities and generate testable hypotheses for experimental validation. However, the implementation of these analyses introduces a cascade of dichotomies and decision trees due to the lack of consensus on best practices. Here, we provide a road map for network-based microbiome studies with an emphasis on discerning properties of stability. We identify important considerations for data preparation, network construction, and interpretation of network properties. We also highlight remaining limitations and outstanding needs for this field. This review also serves to clarify the varying schools of thought on the application of network theory for microbiome studies and to identify practices that enhance the reproducibility and validity of future work. Video Abstract.

Overexpression of SLC2A1, ALDOC, and PFKFB4 in the glycolysis pathway drives strong drug resistance in 3D HeLa tumor cell spheroids.

The 3D multicellular tumor spheroid (MTS) model exhibits enhanced fidelity in replicating the tumor microenvironment and demonstrates exceptional resistance to clinical drugs compared to the 2D monolayer model. In this study, we used multiomics (transcriptome, proteomics, and metabolomics) tools to explore the molecular mechanisms and metabolic differences of the two culture models. Analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathways revealed that the differentially expressed genes between the two culture models were mainly enriched in cellular components and biological processes associated with extracellular matrix, extracellular structural organization, and mitochondrial function. An integrated analysis of three omics data revealed 11 possible drug resistance targets. Among these targets, seven genes, AKR1B1, ALDOC, GFPT2, GYS1, LAMB2, PFKFB4, and SLC2A1, exhibited significant upregulation. Conversely, four genes, COA7, DLD, IFNGR1, and QRSL1, were significantly downregulated. Clinical prognostic analysis using the TCGA survival database indicated that high-expression groups of SLC2A1, ALDOC, and PFKFB4 exhibited a significant negative correlation with patient survival. We further validated their involvement in chemotherapy drug resistance, indicating their potential significance in improving prognosis and chemotherapy outcomes. These results provide valuable insights into potential therapeutic targets that can potentially enhance treatment efficacy and patient outcomes.

Multimodal functional deep learning for multiomics data.

With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.

Unveiling responses and mechanisms of spice crop chive exposure to three typical pesticides using metabolomics combined with transcriptomics, physiology and biochemistry.

Pesticides are frequently used to control target pests in the production of spice crops such as chives (Allium ascalonicum). However, little information is available on the responses and underlying mechanisms of pesticide exposure in this crop. Our findings revealed that the uptake, transportation, and subcellular distribution of three typical pesticides-the fungicide pyraclostrobin (PAL), insecticide acetamiprid (ATP), and herbicide pendimethalin (PND) in chives, as well as their physiological, biochemical, metabolic, and transcriptomic responses-were dependent on pesticide properties, especially hydrophobicity. The distribution of PAL and PND in chives decreased in the order root > stem > leaf, but the distribution order of ATP was the opposite. The proportion of PAL and PND in the solid phase of the root cells gradually increased, but ATP mainly existed in the cell-soluble component, indicating that the latter had an upward translocation ability and thus mainly accumulated in the leaves. Malondialdehyde levels in chive leaves were not significantly affected by exposure to these pesticides; however, the activities of superoxide dismutase (SOD) and catalase (CAT) in chive leaves increased significantly. Moreover, these pesticides exhibited critical differences in chive responses through the interaction of metabolites and regulation of differentially expressed genes. PAL dramatically influenced five carbohydrate metabolic pathways (34.35 %), disturbing the starch-to-sucrose balance. ATP strongly affected five amino acid (AC) metabolic pathways (33.38 %), enhancing four free amino acid levels. PND notably affected eight fatty acid (FA) metabolic pathways (25.38 %), increasing two unsaturated and decreasing one saturated FA. Simultaneously, PND, ATP, and PND accumulated in the chives could be detoxified through metabolic pathways mediated by cytochrome P450 (P450) and glycosyltransferase (GT)/glutathione S-transferase (GST), producing phase I (7, 4, and 5) and II (11, 13, and 10) metabolites, respectively. This study provides important molecular insights into the responses and underlying mechanisms of spice crop exposure to pesticides.

Advance computational tools for multiomics data learning.

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Multi-omics features of immunogenic cell death in gastric cancer identified by combining single-cell sequencing analysis and machine learning.

Gastric cancer (GC) is a prevalent malignancy with high mortality rates. Immunogenic cell death (ICD) is a unique form of programmed cell death that is closely linked to antitumor immunity and plays a critical role in modulating the tumor microenvironment (TME). Nevertheless, elucidating the precise effect of ICD on GC remains a challenging endeavour. ICD-related genes were identified in single-cell sequencing datasets and bulk transcriptome sequencing datasets via the AddModuleScore function, weighted gene co-expression network (WGCNA), and differential expression analysis. A robust signature associated with ICD was constructed using a machine learning computational framework incorporating 101 algorithms. Furthermore, multiomics analysis, including single-cell sequencing analysis, bulk transcriptomic analysis, and proteomics analysis, was conducted to verify the correlation of these hub genes with the immune microenvironment features of GC and with GC invasion and metastasis. We screened 59 genes associated with ICD and developed a robust ICD-related gene signature (ICDRS) via a machine learning computational framework that integrates 101 different algorithms. Furthermore, we identified five key hub genes (SMAP2, TNFAIP8, LBH, TXNIP, and PIK3IP1) from the ICDRS. Through single-cell analysis of GC tumor s, we confirmed the strong correlations of the hub genes with immune microenvironment features. Among these five genes, LBH exhibited the most significant associations with a poor prognosis and with the invasion and metastasis of GC. Finally, our findings were validated through immunohistochemical staining of a large clinical sample set, and the results further supported that LBH promotes GC cell invasion by activating the epithelial-mesenchymal transition (EMT) pathway.

Integrated multi-omics approaches reveal the neurotoxicity of triclocarban in mouse brain.

Triclocarban (TCC) is an antimicrobial ingredient that commonly incorporated in many household and personal care products, raising public concerns about its potential health risks. Previous research has showed that TCC could cross the blood-brain barrier, but to date our understanding of its potential neurotoxicity at human-relevant concentrations remains lacking. In this study, we observed anxiety-like behaviors in mice with continuous percutaneous exposure to TCC. Subsequently, we combined lipidomic, proteomic, and metabolic landscapes to investigate the underlying mechanisms of TCC-related neurotoxicity. The results showed that TCC exposure dysregulated the proteins involved in endocytosis and neurodegenerative disorders in mouse cerebrum. Brain energy homeostasis was also altered, as evidenced by the perturbation of pyruvate metabolism, TCA cycle, and oxidative phosphorylation, which in turn caused mitochondrial dysfunction. Meanwhile, the changing trends of sphingolipid signaling pathway and overproduction of mitochondrial reactive oxygen species (mROS) could enhance the neural apoptosis. The in vitro approach further demonstrated that TCC exposure promoted apoptosis, accompanied by the overproduction of mROS and alteration in the mitochondrial membrane potential in N2A cells. Together, dysregulated endocytosis, mROS-related mitochondrial dysfunction and neural cell apoptosis are considered to be crucial factors for TCC-induced neurotoxicity, which may contribute to the occurrence and development of neurodegenerative disorders. Our findings provide novel perspectives for the mechanisms of TCC-triggered neurotoxicity.

Mild Blast Exposure Dysregulates Metabolic Pathways and Correlation Networking as Evident from LC-MS-Based Plasma Profiling.

Blast-induced trauma is emerging as a serious threat due to its wide pathophysiology where not only the brain but also a spectrum of organs is being affected. In the present study, we aim to identify the plasma-based metabolic dysregulations along with the associated temporal changes at 5-6 h, day 1 and day 7 post-injury in a preclinical animal model for blast exposure, through liquid chromatography-mass spectrometry (LC-MS). Using significantly advanced metabolomic and statistical bioinformatic platforms, we were able to elucidate better and unravel the complex networks of blast-induced neurotrauma (BINT) and its interlinked systemic effects. Significant changes were evident at 5-6 h with maximal changes at day 1. Temporal analysis also depicted progressive changes which continued till day 7. Significant associations of metabolic markers belonging to the class of amino acids, energy-related molecules, lipids, vitamin, hormone, phenolic acid, keto and histidine derivatives, nucleic acid molecules, uremic toxins, and uronic acids were observed. Also, the present study is the first of its kind where comprehensive, detailed pathway dysregulations of amino acid metabolism and biosynthesis, perturbed nucleotides, lipid peroxidation, and nucleic acid damage followed by correlation networking and multiomics networking were explored on preclinical animal models exposed to mild blast trauma. In addition, markers for systemic changes (renal dysfunction) were also observed. Global pathway predictions of unannotated peaks also presented important insights into BINT pathophysiology. Conclusively, the present study depicts important findings that might help underpin the biological mechanisms of blast-induced brain or systemic trauma.

Visual analysis of multi-omics data.

We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool's interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different "visual channel" of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams.

In silico and in cellulo approaches for functional annotation of human protein splice variants.

The elegance of pre-mRNA splicing mechanisms continues to interest scientists even after over a half century, since the discovery of the fact that coding regions in genes are interrupted by non-coding sequences. The vast majority of human genes have several mRNA variants, coding structurally and functionally different protein isoforms in a tissue-specific manner and with a linkage to specific developmental stages of the organism. Alteration of splicing patterns shifts the balance of functionally distinct proteins in living systems, distorts normal molecular pathways, and may trigger the onset and progression of various pathologies. Over the past two decades, numerous studies have been conducted in various life sciences disciplines to deepen our understanding of splicing mechanisms and the extent of their impact on the functioning of living systems. This review aims to summarize experimental and computational approaches used to elucidate the functions of splice variants of a single gene based on our experience accumulated in the laboratory of interactomics of proteoforms at the Institute of Biomedical Chemistry (IBMC) and best global practices.

Natural hydrogen gas and engineered microalgae prevent acute lung injury in sepsis.

Background: Hydrogen gas and microalgae both exist in the natural environment. We aimed to integrate hydrogen gas and biology nano microalgae together to expand the treatment options in sepsis. Methods: Phosphoproteomics, metabolomics and proteomics data were obtained from mice undergoing cecum ligation and puncture (CLP) and inhalation of hydrogen gas. All omics analysis procedure were accordance with standards. Multi R packages were used in single cell and spatial transcriptomics analysis to identify primary cells expressing targeted genes, and the genes' co-expression relationships in sepsis related lung landscape. Then, network pharmacology method was used to identify candidate drugs. We used hydrophobic-force-driving self-assembly method to construct dihydroquercetin (DQ) nanoparticle. To cooperate with molecular hydrogen, ammonia borane (B) was added to DQ surface. Then, Chlorella vulgaris (C) was used as biological carrier to improve self-assembly nanoparticle. Vivo and vitro experiments were both conducted to evaluate anti-inflammation, anti-ferroptosis, anti-infection and organ protection capability. Results: As a result, we identified Esam and Zo-1 were target phosphorylation proteins for molecular hydrogen treatment in lung. Ferroptosis and glutathione metabolism were two target pathways. Chlorella vulgaris improved the dispersion of DQB and reconstructed morphological features of DQB, formed DQB@C nano-system (size = 307.3 nm, zeta potential = -22mv), with well infection-responsive hydrogen release capability and biosafety. In addition, DQB@C was able to decrease oxidative stress and inflammation factors accumulation in lung cells. Through increasing expression level of Slc7a11/xCT and decreasing Cox2 level to participate with the regulation of ferroptosis. Also, DQB@C played lung and multi organ protection and anti-inflammation roles on CLP mice. Conclusion: Our research proposed DQB@C as a novel biology nano-system with enormous potential on treatment for sepsis related acute lung injury to solve the limitation of hydrogen gas utilization in clinics.

Cancer subtype identification by multi-omics clustering based on interpretable feature and latent subspace learning.

In recent years, multi-omics clustering has become a powerful tool in cancer research, offering a comprehensive perspective on the diverse molecular characteristics inherent to various cancer subtypes. However, most existing multi-omics clustering methods directly integrate heterogeneous features from different omics, which may struggle to deal with the noise or redundancy of multi-omics data and lead to poor clustering results. Therefore, we propose a novel multi-omics clustering method to extract interpretable and discriminative features from various omics before data integration. The clinical information is used to supervise the process of feature extraction based on SHAP (SHapley Additive exPlanation) values. Singular value decomposition (SVD) is then applied to integrate the extracted features of different omics by constructing a latent subspace. Finally, we utilize shared nearest neighbor-based spectral clustering on the latent representation to obtain the clustering result. The proposed method is evaluated on several cancer datasets across three levels of omics, in comparison to several state-of-the-art multi-omics clustering methods. The comparison results demonstrate the superior performance of the proposed method in multi-omics data analysis for cancer subtyping. Additionally, experiments reveal the efficacy of utilizing clinical information based on SHAP values for feature extraction, enhancing the performance of clustering analyses. Moreover, enrichment analysis of the identified gene signatures in different subtypes is also performed to further demonstrate the effectiveness of the proposed method. Availability: The proposed method can be freely accessible at https://github.com/Tianyi-Shi-Tsukuba/Multi-omics-clustering-based-on-SHAP. Data will be made available on request.

Multi-omics analysis reveals a pericyte-associated gene expression signature for predicting prognosis and therapeutic responses in solid cancers.

The influence of the stroma on cancer progression has been underestimated, particularly the role of vascular pericytes in the tumor microenvironment. Herein, we identified 51 differentially expressed genes in tumor-derived pericytes (TPCs) by analyzing transcriptomic data from TCGA alongside our proteomic data. Using five key TPC-related genes, we constructed a prognostic risk model that accurately predicts prognosis and treatment responses in liver and lung cancers. Enrichment analyses linked these genes to blood vessel remodeling, function, and immune-related pathways. Single-cell RNA sequencing data from the GEO database validated these findings, showing significant upregulation of AKAP12 and RRAS in TPCs. Immunostaining confirmed increased expression of these genes in liver and lung tumors. Depletion of RRAS or AKAP12 in TPCs restored their blood vessel-supporting role. Overall, our findings suggest that TPC-related gene profiles can predict patient outcomes and therapeutic responses in solid cancers, and targeting these profiles could be an improved treatment strategy.

G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery using multi-omics data.

The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases and other biological processes. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/.

Mechanistic study of leukopenia treatment by Qijiao shengbai Capsule via the Bcl2/Bax/CASAPSE3 pathway.

Background: Leukopenia can be caused by chemotherapy, which suppresses bone marrow function and can impact the effectiveness of cancer treatment. Qijiao Shengbai Capsule (QJSB) is commonly used to treat leukopenia, but the specific bioactive components and mechanisms of action are not well understood. Objectives and results: This study aimed to analyze the active ingredients of QJSB and its potential targets for treating leukopenia using network pharmacology and molecular docking. Through a combination of serum pharmacochemistry, multi-omics, network pharmacology, and validation experiments in a murine leukopenia model, the researchers sought to understand how QJSB improves leukopenia. The study identified 16 key components of QJSB that act in vivo to increase the number of white blood cells in leukopenic mice. Multi-omics analysis and network pharmacology revealed that the PI3K-Akt and MAPK signaling pathways are important in the treatment of leukopenia with QJSB. Five specific targets (JUN, FOS, BCl-2, CASPAS-3) were identified as key targets. Conclusion: Validation experiments confirmed that QJSB regulates genes related to cell growth and inhibits apoptosis, suggesting that apoptosis may play a crucial role in leukopenia development and that QJSB may improve immune function by regulating apoptotic proteins and increasing CD4+ T cell count in leukopenic mice.

Systems Biology of Human Microbiome for the Prediction of Personal Glycaemic Response.

The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body's response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome's role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome's function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.

Multi-omics based artificial intelligence for cancer research.

With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogeneity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic view of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. However, the exponential growth of data generated by multi-omics studies present significant analytical challenges. Processing, analyzing, integrating, and interpreting these multi-omics datasets to extract meaningful insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a powerful solution to these challenges, demonstrating exceptional capabilities in deciphering complex patterns and extracting valuable information from large-scale, intricate omics datasets. This review delves into the synergy of AI and multi-omics, highlighting its revolutionary impact on oncology. We dissect how this confluence is reshaping the landscape of cancer research and clinical practice, particularly in the realms of early detection, diagnosis, prognosis, treatment and pathology. Additionally, we elaborate the latest AI methods for multi-omics integration to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. Finally, we discuss the current challenges of data harmonization, algorithm interpretability, and ethical considerations. Addressing these challenges necessitates a multidisciplinary collaboration, paving the promising way for more precise, personalized, and effective treatments for cancer patients.

Computational Methods for Image Analysis in Craniofacial Development and Disease.

Observation is at the center of all biological sciences. Advances in imaging technologies are therefore essential to derive novel biological insights to better understand the complex workings of living systems. Recent high-throughput sequencing and imaging techniques are allowing researchers to simultaneously address complex molecular variations spatially and temporarily in tissues and organs. The availability of increasingly large dataset sizes has allowed for the evolution of robust deep learning models, designed to interrogate biomedical imaging data. These models are emerging as transformative tools in diagnostic medicine. Combined, these advances allow for dynamic, quantitative, and predictive observations of entire organisms and tissues. Here, we address 3 main tasks of bioimage analysis, image restoration, segmentation, and tracking and discuss new computational tools allowing for 3-dimensional spatial genomics maps. Finally, we demonstrate how these advances have been applied in studies of craniofacial development and oral disease pathogenesis.