Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

De Novo multi-omics pathway analysis (DMPA) designed for prior data independent inference of cell signaling pathways

New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into network modules and pathways. DMPA was validated with published omics data and was found accurate in discovering reported molecular associations in transcriptome, interactome, phosphoproteome, methylome, and metabolomics data and signaling pathways in multi-omics data. DMPA was benchmarked against module discovery and multi-omics integration methods and outperformed previous methods in module and pathway discovery especially when applied to datasets with relatively low sample sizes. Transcription factor, kinase, subcellular location and function prediction algorithms were devised for transcriptome, phosphoproteome and interactome modules and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected the predicted function and its direction in validation experiments.

Web-based multi-omics integration using the Analyst software suite

The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface.

Multi-omic integration of microbiome data for identifying disease-associated modules

Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.

Plant genomic resources at National Genomics Data Center: assisting in data-driven breeding applications

Genomic data serve as an invaluable resource for unraveling the intricacies of the higher plant systems, including the constituent elements within and among species. Through various efforts in genomic data archiving, integrative analysis and value-added curation, the National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), has successfully established and currently maintains a vast amount of database resources. This dedicated initiative of the NGDC facilitates a data-rich ecosystem that greatly strengthens and supports genomic research efforts. Here, we present a comprehensive overview of central repositories dedicated to archiving, presenting, and sharing plant omics data, introduce knowledgebases focused on variants or gene-based functional insights, highlight species-specific multiple omics database resources, and briefly review the online application tools. We intend that this review can be used as a guide map for plant researchers wishing to select effective data resources from the NGDC for their specific areas of study.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-023-00134-4.

Multi-omics data integration for the identification of biomarkers for bull fertility

Bull fertility is an important economic trait, and the use of subfertile semen for artificial insemination decreases the global efficiency of the breeding sector. Although the analysis of semen functional parameters can help to identify infertile bulls, no tools are currently available to enable precise predictions and prevent the commercialization of subfertile semen. Because male fertility is a multifactorial phenotype that is dependent on genetic, epigenetic, physiological and environmental factors, we hypothesized that an integrative analysis might help to refine our knowledge and understanding of bull fertility. We combined -omics data (genotypes, sperm DNA methylation at CpGs and sperm small non-coding RNAs) and semen parameters measured on a large cohort of 98 Montbéliarde bulls with contrasting fertility levels. Multiple Factor Analysis was conducted to study the links between the datasets and fertility. Four methodologies were then considered to identify the features linked to bull fertility variation: Logistic Lasso, Random Forest, Gradient Boosting and Neural Networks. Finally, the features selected by these methods were annotated in terms of genes, to conduct functional enrichment analyses. The less relevant features in -omics data were filtered out, and MFA was run on the remaining 12,006 features, including the 11 semen parameters and a balanced proportion of each type of-omics data. The results showed that unlike the semen parameters studied the-omics datasets were related to fertility. Biomarkers related to bull fertility were selected using the four methodologies mentioned above. The most contributory CpGs, SNPs and miRNAs targeted genes were all found to be involved in development. Interestingly, fragments derived from ribosomal RNAs were overrepresented among the selected features, suggesting roles in male fertility. These markers could be used in the future to identify subfertile bulls in order to increase the global efficiency of the breeding sector.

iCluF: an unsupervised iterative cluster-fusion method for patient stratification using multiomics data

Motivation

Patient stratification is crucial for the effective treatment or management of heterogeneous diseases, including cancers. Multiomic technologies facilitate molecular characterization of human diseases; however, the complexity of data warrants the need for the development of robust data integration tools for patient stratification using machine-learning approaches.

Results

iCluF iteratively integrates three types of multiomic data (mRNA, miRNA, and DNA methylation) using pairwise patient similarity matrices built from each omic data. The intermediate omic-specific neighborhood matrices implement iterative matrix fusion and message passing among the similarity matrices to derive a final integrated matrix representing all the omics profiles of a patient, which is used to further cluster patients into subtypes. iCluF outperforms other methods with significant differences in the survival profiles of 8581 patients belonging to 30 different cancers in TCGA. iCluF also predicted the four intrinsic subtypes of Breast Invasive Carcinomas with adjusted rand index and Fowlkes-Mallows scores of 0.72 and 0.83, respectively. The Gini importance score showed that methylation features were the primary decisive players, followed by mRNA and miRNA to identify disease subtypes. iCluF can be applied to stratify patients with any disease containing multiomic datasets.

Availability and implementation

Source code and datasets are available at https://github.com/GudaLab/iCluF_core.

Genomic data integration tutorial, a plant case study

BACKGROUND

The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts.

RESULTS

To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration.

CONCLUSION

The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. The PathIntegrate Python package is available at https://github.com/cwieder/PathIntegrate.

In utero exposure to ionizing radiation and metabolic regulation: perspectives for future multi- and trans-generation effects studies

PURPOSE

The radiation protection community has been particularly attentive to the risks of delayed effects on offspring from low dose or low dose-rate exposures to ionizing radiation. Despite this, the current epidemiologic studies and scientific data are still insufficient to provide the necessary evidence for improving risk assessment guidelines. This literature review aims to inform future studies on multigenerational and transgenerational effects. It primarily focuses on animal studies involving in utero exposure and discusses crucial elements for interpreting the results. These elements include in utero exposure scenarios relative to the developmental stages of the embryo/fetus, and the primary biological mechanisms responsible for transmitting heritable or hereditary effects to future generations. The review addresses several issues within the contexts of both multigenerational and transgenerational effects, with a focus on hereditary perspectives.

CONCLUSIONS

Knowledge consolidation in the field of Developmental Origins of Health and Disease (DOHaD) has led us to propose a new study strategy. This strategy aims to address the transgenerational effects of in utero exposure to low dose and low dose-rate radiation. Within this concept, there is a possibility that disruption of epigenetic programming in embryonic and fetal cells may occur. This disruption could lead to metabolic dysfunction, which in turn may cause abnormal responses to future environmental challenges, consequently increasing disease risk. Lastly, we discuss methodological limitations in our studies. These limitations are related to cohort size, follow-up time, model radiosensitivity, and analytical techniques. We propose scientific and analytical strategies for future research in this field.

Unraveling phenotypic variance in metabolic syndrome through multi-omics

Complex multi-omics effects drive the clustering of cardiometabolic risk factors, underscoring the imperative to comprehend how individual and combined omics shape phenotypic variation. Our study partitions phenotypic variance in metabolic syndrome (MetS), blood glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and blood pressure through genome, transcriptome, metabolome, and exposome (i.e., lifestyle exposome) analyses. Our analysis included a cohort of 62,822 unrelated individuals with white British ancestry, sourced from the UK biobank. We employed linear mixed models to partition phenotypic variance using the restricted maximum likelihood (REML) method, implemented in MTG2 (v2.22). We initiated the analysis by individually modeling omics, followed by subsequent integration of pairwise omics in a joint model that also accounted for the covariance and interaction between omics layers. Finally, we estimated the correlations of various omics effects between the phenotypes using bivariate REML. Significant proportions of the MetS variance were attributed to distinct data sources: genome (9.47%), transcriptome (4.24%), metabolome (14.34%), and exposome (3.77%). The phenotypic variances explained by the genome, transcriptome, metabolome, and exposome ranged from 3.28% for GLU to 25.35% for HDL-C, 0% for GLU to 19.34% for HDL-C, 4.29% for systolic blood pressure (SBP) to 35.75% for TG, and 0.89% for GLU to 10.17% for HDL-C, respectively. Significant correlations were found between genomic and transcriptomic effects for TG and HDL-C. Furthermore, significant interaction effects between omics data were detected for both MetS and its components. Interestingly, significant correlation of omics effect between the phenotypes was found. This study underscores omics' roles, interaction effects, and random-effects covariance in unveiling phenotypic variation in multi-omics domains.

Multi-omics approaches in psychoneuroimmunology and health research: Conceptual considerations and methodological recommendations

The field of psychoneuroimmunology (PNI) has grown substantially in both relevance and prominence over the past 40 years. Notwithstanding its impressive trajectory, a majority of PNI studies are still based on a relatively small number of analytes. To advance this work, we suggest that PNI, and health research in general, can benefit greatly from adopting a multi-omics approach, which involves integrating data across multiple biological levels (e.g., the genome, proteome, transcriptome, metabolome, lipidome, and microbiome/metagenome) to more comprehensively profile biological functions and relate these profiles to clinical and behavioral outcomes. To assist investigators in this endeavor, we provide an overview of multi-omics research, highlight recent landmark multi-omics studies investigating human health and disease risk, and discuss how multi-omics can be applied to better elucidate links between psychological, nervous system, and immune system activity. In doing so, we describe how to design high-quality multi-omics studies, decide which biological samples (e.g., blood, stool, urine, saliva, solid tissue) are most relevant, incorporate behavioral and wearable sensing data into multi-omics research, and understand key data quality, integration, analysis, and interpretation issues. PNI researchers are addressing some of the most interesting and important questions at the intersection of psychology, neuroscience, and immunology. Applying a multi-omics approach to this work will greatly expand the horizon of what is possible in PNI and has the potential to revolutionize our understanding of mind-body medicine.

Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques

Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.

Resveratrol-Laden Nano-Systems in the Cancer Environment: Views and Reviews

The genesis of cancer is a precisely organized process in which normal cells undergo genetic alterations that cause the cells to multiply abnormally, colonize, and metastasize to other organs such as the liver, lungs, colon, and brain. Potential drugs that could modify these carcinogenic pathways are the ones that will be used in clinical trials as anti-cancer drugs. Resveratrol (RES) is a polyphenolic natural antitoxin that has been utilized for the treatment of several diseases, owing to its ability to scavenge free radicals, control the expression and activity of antioxidant enzymes, and have effects on inflammation, cancer, aging, diabetes, and cardioprotection. Although RES has a variety of pharmacological uses and shows promising applications in natural medicine, its unpredictable pharmacokinetics compromise its therapeutic efficacy and prevent its use in clinical settings. RES has been encapsulated into various nanocarriers, such as liposomes, polymeric nanoparticles, lipidic nanocarriers, and inorganic nanoparticles, to address these issues. These nanocarriers can modulate drug release, increase bioavailability, and reach therapeutically relevant plasma concentrations. Studies on resveratrol-rich nano-formulations in various cancer types are compiled in the current article. Studies relating to enhanced drug stability, increased therapeutic potential in terms of pharmacokinetics and pharmacodynamics, and reduced toxicity to cells and tissues are the main topics of this research. To keep the readers informed about the current state of resveratrol nano-formulations from an industrial perspective, some recent and significant patent literature has also been provided. Here, the prospects for nano-formulations are briefly discussed, along with machine learning and pharmacometrics methods for resolving resveratrol's pharmacokinetic concerns.

The Urinary Metabolomic Fingerprint in Extremely Preterm Infants on Total Parenteral Nutrition vs. Enteral Feeds

Total Parenteral Nutrition (TPN), which uses intravenous administration of nutrients, minerals and vitamins, is essential for sustaining premature infants until they transition to enteral feeds, but there is limited information on metabolomic differences between infants on TPN and enteral feeds. We performed untargeted global metabolomics on urine samples collected between 23-30 days of life from 314 infants born <29 weeks gestational age from the TOLSURF and PROP cohorts. Principal component analysis across all metabolites showed a separation of infants solely on TPN compared to infants who had transitioned to enteral feeds, indicating global metabolomic differences between infants based on feeding status. Among 913 metabolites that passed quality control filters, 609 varied in abundance between infants on TPN vs. enteral feeds at p < 0.05. Of these, 88% were in the direction of higher abundance in the urine of infants on enteral feeds. In a subset of infants in a longitudinal analysis, both concurrent and delayed changes in metabolite levels were observed with the initiation of enteral feeds. These infants had higher concentrations of essential amino acids, lipids, and vitamins, which are necessary for growth and development, suggesting the nutritional benefit of an enteral feeding regimen.

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

Approach strategies and application of metabolomics to biotechnology in plants

Metabolomics refers to the technology for the comprehensive analysis of metabolites and low-molecular-weight compounds in a biological system, such as cells or tissues. Metabolites play an important role in biological phenomena through their direct involvement in the regulation of physiological mechanisms, such as maintaining cell homeostasis or signal transmission through protein-protein interactions. The current review aims provide a framework for how the integrated analysis of metabolites, their functional actions and inherent biological information can be used to understand biological phenomena related to the regulation of metabolites and how this information can be applied to safety assessments of crops created using biotechnology. Advancement in technology and analytical instrumentation have led new ways to examine the convergence between biology and chemistry, which has yielded a deeper understanding of complex biological phenomena. Metabolomics can be utilized and applied to safety assessments of biotechnology products through a systematic approach using metabolite-level data processing algorithms, statistical techniques, and database development. The integration of metabolomics data with sequencing data is a key step towards improving additional phenotypical evidence to elucidate the degree of environmental affects for variants found in genome associated with metabolic processes. Moreover, information analysis technology such as big data, machine learning, and IT investment must be introduced to establish a system for data extraction, selection, and metabolomic data analysis for the interpretation of biological implications of biotechnology innovations. This review outlines the integrity of metabolomics assessments in determining the consequences of genetic engineering and biotechnology in plants.

The Quality and Utility of Artificial Intelligence in Patient Care

BACKGROUND

Artificial intelligence (AI) is increasingly being used in patient care. In the future, physicians will need to understand not only the basic functioning of AI applications, but also their quality, utility, and risks.

METHODS

This article is based on a selective review of the literature on the principles, quality, limitations, and benefits AI applications in patient care, along with examples of individual applications.

RESULTS

The number of AI applications in patient care is rising, with more than 500 approvals in the United States to date. Their quality and utility are based on a number of interdependent factors, including the real-life setting, the type and amount of data collected, the choice of variables used by the application, the algorithms used, and the goal and implementation of each application. Bias (which may be hidden) and errors can arise at all these levels. Any evaluation of the quality and utility of an AI application must, therefore, be conducted according to the scientific principles of evidence-based medicine-a requirement that is often hampered by a lack of transparency.

CONCLUSION

AI has the potential to improve patient care while meeting the challenge of dealing with an ever-increasing surfeit of information and data in medicine with limited human resources. The limitations and risks of AI applications require critical and responsible consideration. This can best be achieved through a combination of scientific.

Recent advances in mass spectrometry-based computational metabolomics

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

IntLIM 2.0: identifying multi-omic relationships dependent on discrete or continuous phenotypic measurements

Motivation

IntLIM uncovers phenotype-dependent linear associations between two types of analytes (e.g. genes and metabolites) in a multi-omic dataset, which may reflect chemically or biologically relevant relationships.

Results

The new IntLIM R package includes newly added support for generalized data types, covariate correction, continuous phenotypic measurements, model validation and unit testing. IntLIM analysis uncovered biologically relevant gene-metabolite associations in two separate datasets, and the run time is improved over baseline R functions by multiple orders of magnitude.

Availability and implementation

IntLIM is available as an R package with a detailed vignette (https://github.com/ncats/IntLIM) and as an R Shiny app (see Supplementary Figs S1-S6) (https://intlim.ncats.io/).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

RaMP-DB 2.0: a renovated knowledgebase for deriving biological and chemical insight from metabolites, proteins, and genes

MOTIVATION

Functional interpretation of high-throughput metabolomic and transcriptomic results is a crucial step in generating insight from experimental data. However, pathway and functional information for genes and metabolites are distributed among many siloed resources, limiting the scope of analyses that rely on a single knowledge source.

RESULTS

RaMP-DB 2.0 is a web interface, relational database, API and R package designed for straightforward and comprehensive functional interpretation of metabolomic and multi-omic data. RaMP-DB 2.0 has been upgraded with an expanded breadth and depth of functional and chemical annotations (ClassyFire, LIPID MAPS, SMILES, InChIs, etc.), with new data types related to metabolites and lipids incorporated. To streamline entity resolution across multiple source databases, we have implemented a new semi-automated process, thereby lessening the burden of harmonization and supporting more frequent updates. The associated RaMP-DB 2.0 R package now supports queries on pathways, common reactions (e.g. metabolite-enzyme relationship), chemical functional ontologies, chemical classes and chemical structures, as well as enrichment analyses on pathways (multi-omic) and chemical classes. Lastly, the RaMP-DB web interface has been completely redesigned using the Angular framework.

AVAILABILITY AND IMPLEMENTATION

The code used to build all components of RaMP-DB 2.0 are freely available on GitHub at https://github.com/ncats/ramp-db, https://github.com/ncats/RaMP-Client/ and https://github.com/ncats/RaMP-Backend. The RaMP-DB web application can be accessed at https://rampdb.nih.gov/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Combined metabolomics and network pharmacology to elucidate the mechanisms of Dracorhodin Perchlorate in treating diabetic foot ulcer rats

Background: Diabetic foot ulcer (DFU) is a severe chronic complication of diabetes, that can result in disability or death. Dracorhodin Perchlorate (DP) is effective for treating DFU, but the potential mechanisms need to be investigated. We aimed to explore the mechanisms underlying the acceleration of wound healing in DFU by the topical application of DP through the combination of metabolomics and network pharmacology. Methods: A DFU rat model was established, and the rate of ulcer wound healing was assessed. Different metabolites were found in the skin tissues of each group, and MetaboAnalyst was performed to analyse metabolic pathways. The candidate targets of DP in the treatment of DFU were screened using network pharmacology. Cytoscape was applied to construct an integrated network of metabolomics and network pharmacology. Moreover, the obtained hub targets were validated using molecular docking. After the topical application of DP, blood glucose, the rate of wound healing and pro-inflammatory cytokine levels were assessed. Results: The levels of IL-1, hs-CRP and TNF-α of the Adm group were significantly downregulated. A total of 114 metabolites were identified. These could be important to the therapeutic effects of DP in the treatment of DFU. Based on the network pharmacology, seven hub genes were found, which were partially consistent with the metabolomics results. We focused on four hub targets by further integrated analysis, namely, PAH, GSTM1, DHFR and CAT, and the crucial metabolites and pathways. Molecular docking results demonstrated that DP was well combined with the hub targets. Conclusion: Our research based on metabolomics and network pharmacology demonstrated that DP improves wound healing in DFU through multiple targets and pathways, and it can potentially be used for DFU treatment.

A comprehensive survey of the approaches for pathway analysis using multi-omics data integration

Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.

Innovative Application of Metabolomics on Bioactive Ingredients of Foods

Metabolomics, as a new omics technology, has been widely accepted by researchers and has shown great potential in the field of nutrition and health in recent years. This review briefly introduces the process of metabolomics analysis, including sample preparation and extraction, derivatization, separation and detection, and data processing. This paper focuses on the application of metabolomics in food-derived bioactive ingredients. For example, metabolomics techniques are used to analyze metabolites in food to find bioactive substances or new metabolites in food materials. Moreover, bioactive substances have been tested in vitro and in vivo, as well as in humans, to investigate the changes of metabolites and the underlying metabolic pathways, among which metabolomics is used to find potential biomarkers and targets. Metabolomics provides a new approach for the prevention and regulation of chronic diseases and the study of the underlying mechanisms. It also provides strong support for the development of functional food or drugs. Although metabolomics has some limitations such as low sensitivity, poor repeatability, and limited detection range, it is developing rapidly in general, and also in the field of nutrition and health. At the end of this paper, we put forward our own insights on the development prospects of metabolomics in the application of bioactive ingredients in food.

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.

The Future of Biomarkers in Veterinary Medicine: Emerging Approaches and Associated Challenges

Simple Summary

In this review we seek to outline the role of new technologies in biomarker discovery, particularly within the veterinary field and with an emphasis on ‘omics’, as well as to examine why many biomarkers-despite much excitement-have not yet made it to clinical practice. Further we emphasise the critical need for close collaboration between clinicians, researchers and funding bodies and the need to set clear goals for biomarker requirements and realistic application in the clinical setting, ensuring that biomarker type, method of detection and clinical utility are compatible, and adequate funding, time and sample size are available for all phases of development.

Abstract

New biomarkers promise to transform veterinary practice through rapid diagnosis of diseases, effective monitoring of animal health and improved welfare and production efficiency. However, the road from biomarker discovery to translation is not always straightforward. This review focuses on molecular biomarkers under development in the veterinary field, introduces the emerging technological approaches transforming this space and the role of ‘omics platforms in novel biomarker discovery. The vast majority of veterinary biomarkers are at preliminary stages of development and not yet ready to be deployed into clinical translation. Hence, we examine the major challenges encountered in the process of biomarker development from discovery, through validation and translation to clinical practice, including the hurdles specific to veterinary practice and to each of the ‘omics platforms–transcriptomics, proteomics, lipidomics and metabolomics. Finally, recommendations are made for the planning and execution of biomarker studies with a view to assisting the success of novel biomarkers in reaching their full potential.

Systems biology of malaria explored with nonhuman primates

"The Primate Malarias" book has been a uniquely important resource for multiple generations of scientists, since its debut in 1971, and remains pertinent to the present day. Indeed, nonhuman primates (NHPs) have been instrumental for major breakthroughs in basic and pre-clinical research on malaria for over 50 years. Research involving NHPs have provided critical insights and data that have been essential for malaria research on many parasite species, drugs, vaccines, pathogenesis, and transmission, leading to improved clinical care and advancing research goals for malaria control, elimination, and eradication. Whilst most malaria scientists over the decades have been studying Plasmodium falciparum, with NHP infections, in clinical studies with humans, or using in vitro culture or rodent model systems, others have been dedicated to advancing research on Plasmodium vivax, as well as on phylogenetically related simian species, including Plasmodium cynomolgi, Plasmodium coatneyi, and Plasmodium knowlesi. In-depth study of these four phylogenetically related species over the years has spawned the design of NHP longitudinal infection strategies for gathering information about ongoing infections, which can be related to human infections. These Plasmodium-NHP infection model systems are reviewed here, with emphasis on modern systems biological approaches to studying longitudinal infections, pathogenesis, immunity, and vaccines. Recent discoveries capitalizing on NHP longitudinal infections include an advanced understanding of chronic infections, relapses, anaemia, and immune memory. With quickly emerging new technological advances, more in-depth research and mechanistic discoveries can be anticipated on these and additional critical topics, including hypnozoite biology, antigenic variation, gametocyte transmission, bone marrow dysfunction, and loss of uninfected RBCs. New strategies and insights published by the Malaria Host-Pathogen Interaction Center (MaHPIC) are recapped here along with a vision that stresses the importance of educating future experts well trained in utilizing NHP infection model systems for the pursuit of innovative, effective interventions against malaria.

An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data

BACKGROUND

Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment.

AIM OF REVIEW

To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.

Network pharmacology combined with metabolomics and lipidomics to reveal the hypolipidemic mechanism of Alismatis rhizoma in hyperlipidemic mice

Alismatis rhizoma (AR), the dried rhizome of Alisma orientale (Sam) Juzep, is effective in treating hyperlipidemia, but the mechanisms involved require further exploration. This study evaluated the hypolipidemic properties of AR using an integrated strategy combining network pharmacology with metabolomics and lipidomics. Firstly, a hyperlipidemia mouse model induced by a high-fat diet was established to evaluate the therapeutic effects of AR. Secondly, plasma metabolomics and lipidomics were used to identify differential metabolites and lipids, and metabolic pathway analysis was performed using MetaboAnalyst. Thirdly, network pharmacology, based on the metabolic profile of AR in vivo, was used to discover potential therapeutic targets. Finally, key targets were obtained through a compound-target-metabolite network, which was verified by molecular docking and quantitative real-time PCR (qPCR). Biochemistry analysis and histological examinations showed that AR exerted hypolipidemic effects on hyperlipidemic mice. Seventy potential biomarkers for the AR treatment of hyperlipidemia were identified by metabolomics and lipidomics, which were mainly involved in lipid metabolism, energy metabolism and amino acid metabolism. Eighteen potentially active compounds were identified in the plasma of mice after oral administration of AR, which were associated with 83 potential therapeutic targets. The PPAR signaling pathway was considered a crucial signaling pathway of AR against hyperlipidemia by KEGG analysis. The joint analysis showed that 6 upstream key targets were regulated by AR, including ALB, TNF, IL1B, MMP9, PPARA and PPARG. Molecular docking showed that active compounds of AR had high binding affinity with these key targets. qPCR further demonstrated that AR could reverse the mRNA expression of these key targets in hyperlipidemic mice. This study integrates network pharmacology with metabolomics and lipidomics to reveal the regulatory effects of AR on endogenous metabolites and validates key therapeutic targets, and represents the most systematic and in-depth study on the hypolipidemic activity of AR.

MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis

In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However, there are great challenges in integrating multi-omics using machine learning methods for cancer subtype classification. In this study, MoGCN, a multi-omics integration model based on graph convolutional network (GCN) was developed for cancer subtype classification and analysis. Genomics, transcriptomics and proteomics datasets for 511 breast invasive carcinoma (BRCA) samples were downloaded from the Cancer Genome Atlas (TCGA). The autoencoder (AE) and the similarity network fusion (SNF) methods were used to reduce dimensionality and construct the patient similarity network (PSN), respectively. Then the vector features and the PSN were input into the GCN for training and testing. Feature extraction and network visualization were used for further biological knowledge discovery and subtype classification. In the analysis of multi-dimensional omics data of the BRCA samples in TCGA, MoGCN achieved the highest accuracy in cancer subtype classification compared with several popular algorithms. Moreover, MoGCN can extract the most significant features of each omics layer and provide candidate functional molecules for further analysis of their biological effects. And network visualization showed that MoGCN could make clinically intuitive diagnosis. The generality of MoGCN was proven on the TCGA pan-kidney cancer datasets. MoGCN and datasets are public available at https://github.com/Lifoof/MoGCN. Our study shows that MoGCN performs well for heterogeneous data integration and the interpretability of classification results, which confers great potential for applications in biomarker identification and clinical diagnosis.

Multivariate analysis of NMR-based metabolomic data

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.

Investigating the Postprandial Metabolome after Challenge Tests to Assess Metabolic Flexibility and Dysregulations Associated with Cardiometabolic Diseases

This review focuses on the added value provided by a research strategy applying metabolomics analyses to assess phenotypic flexibility in response to different nutritional challenge tests in the framework of metabolic clinical studies. We discuss findings related to the Oral Glucose Tolerance Test (OGTT) and to mixed meals with varying fat contents and food matrix complexities. Overall, the use of challenge tests combined with metabolomics revealed subtle metabolic dysregulations exacerbated during the postprandial period when comparing healthy and at cardiometabolic risk subjects. In healthy subjects, consistent postprandial metabolic shifts driven by insulin action were reported (e.g., a switch from lipid to glucose oxidation for energy fueling) with similarities between OGTT and mixed meals, especially during the first hours following meal ingestion while differences appeared in a wider timeframe. In populations with expected reduced phenotypic flexibility, often associated with increased cardiometabolic risk, a blunted response on most key postprandial pathways was reported. We also discuss the most suitable statistical tools to analyze the dynamic alterations of the postprandial metabolome while accounting for complexity in study designs and data structure. Overall, the in-depth characterization of the postprandial metabolism and associated phenotypic flexibility appears highly promising for a better understanding of the onset of cardiometabolic diseases.

Application of omics beyond the central dogma in coronary heart disease research: A bibliometric study and literature review

Despite remarkable progress in disease diagnosis and treatment, coronary heart disease (CHD) remains the number one leading cause of death worldwide. Many practical challenges still faced in clinical settings necessitates the pursuit of omics studies to identify alternative/orthogonal biomarkers, as well as to discover novel insights into disease mechanisms. Albeit relatively nascent as compared to the omics frontrunners (genomics, transcriptomics, and proteomics), omics beyond the central dogma (OBCD; e.g., metabolomics, lipidomics, glycomics, and metallomics) have undeniable contributions and prospects in CHD research. In this bibliometric study, we characterised the global trends in publication/citation outputs, collaborations, and research hotspots concerning OBCD-CHD, with a focus on the more prolific fields of metabolomics and lipidomics. As for glycomics and metallomics, there were insufficient publication records on their applications in CHD research for quantitative bibliometrics analysis. Thus, we reviewed their applications in health/disease research in general, discussed and justified their potential in CHD research, and suggested important/promising research avenues. By summarising evidence obtained both quantitatively and qualitatively, this study offers a first and comprehensive picture of OBCD applications in CHD, facilitating the establishment of future research directions.

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.

Causal Considerations Can Inform the Interpretation of Surprising Associations in Medical Registries

An exploratory analysis of registry data from 2437 patients with advanced gastric cancer revealed a surprising association between astrological birth signs and overall survival (OS) with p = 0.01. After dichotomizing or changing the reference sign, p-values <0.05 were observed for several birth signs following adjustments for multiple comparisons. Bayesian models with moderately skeptical priors still pointed to these associations. A more plausible causal model, justified by contextual knowledge, revealed that these associations arose from the astrological sign association with seasonality. This case study illustrates how causal considerations can guide analyses through what would otherwise be a hopeless maze of statistical possibilities.

Ultra-Performance Liquid Chromatography-Q-Exactive Orbitrap-Mass Spectrometry Analysis for Metabolic Communication between Heart and Kidney in Adriamycin-Induced Nephropathy Rats

BACKGROUND/AIMS

Although the adriamycin-induced nephropathy model is frequently employed in the study of nephrotic syndrome and focal segmental glomerulosclerosis, the accompanying myocardial damage has always been a cause for concern. Therefore, there is a great need to study cardiorenal communication in this model.

METHODS

An adriamycin-induced nephropathy model was established via tail vein injection. The levels of the biochemical indicators serum albumin, serum globulin, serum total protein, serum cholesterol, serum creatinine (SCr), urinary protein, and urinary creatinine (UCr) were measured, and histopathological changes in the heart and kidneys were assessed using hematoxylin-eosin staining. Metabolomic changes in the heart, blood, and kidneys were analyzed using the metabolomics method based on ultra-performance liquid chromatography Q-Exactive Orbitrap mass spectrometry.

RESULTS

Compared with the control group, the model group showed significant decreases in serum protein and total protein levels, albumin/globulin ratio, and creatinine clearance rate as well as significant increases in serum cholesterol, SCr, urinary protein, and UCr levels. Significant pathological changes were observed in the renal pathology sections in the model group, including diffusely merged glomerular epithelial cells, inflammatory infiltration, and vacuolated glomerular cells. Additionally, thickened myocardial fibers, swollen nuclei, inflammatory infiltration, and partial myocardial necrosis could be seen in the cardiac pathology sections in the model group. Based on multivariate statistical analysis, a total of 20 differential metabolites associated with 15 metabolic pathways were identified in the heart, 7 differential metabolites with 7 metabolic pathways were identified in the blood, and 16 differential metabolites with 21 metabolic pathways were identified in the kidney. Moreover, 6 common metabolic pathways shared by the heart and kidney were identified: arginine and proline metabolism; arginine biosynthesis; glutathione metabolism; alanine, aspartate, and glutamate metabolism; beta-alanine metabolism; and histidine metabolism. Among these metabolic pathways, alanine, aspartate, and glutamate metabolism was shared by the heart, blood, and kidney. Succinic acid was found to be the key regulatory metabolite in cardiorenal metabolic communication.

CONCLUSION

Six metabolic pathways were found to be involved in cardiorenal metabolic communication in an adriamycin-induced nephropathy model, in which alanine, aspartate, and glutamate metabolism may be the metabolic link between the heart and kidney in the development and maintenance of oxidative stress and inflammation. Succinic acid may serve as a key regulatory metabolic switch or marker of cardiac and renal co-injury, as shown in an adriamycin-induced nephropathy model.

Contribution of Metabolomics to the Understanding of NAFLD and NASH Syndromes: A Systematic Review

Several differential panels of metabolites have been associated with the presence of metabolic syndrome and its related conditions, namely non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). This study aimed to perform a systematic review to summarize the most recent finding in terms of circulating biomarkers following NAFLD/NASH syndromes. Hence, the research was focused on NAFLD/NASH studies analysed by metabolomics approaches. Following Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a systematic search was conducted on the PubMed database. The inclusion criteria were (i) publication date between 2010 and 2021, (ii) presence of the combination of terms: metabolomics and NAFLD/NASH, and (iii) published in a scholarly peer-reviewed journal. Studies were excluded from the review if they were (i) single-case studies, (ii) unpublished thesis and dissertation studies, and (iii) not published in a peer-reviewed journal. Following these procedures, 10 eligible studies among 93 were taken into consideration. The metabolisms of amino acids, fatty acid, and vitamins were significantly different in patients affected by NAFLD and NASH compared to healthy controls. These findings suggest that low weight metabolites are an important indicator for NAFLD/NASH syndrome and there is a strong overlap between NAFLD/NASH and the metabolic syndrome. These findings may lead to new perspectives in early diagnosis, identification of novel biomarkers, and providing novel targets for pharmacological interventions.

Pharmacometabolomics by NMR in Oncology: A Systematic Review

Pharmacometabolomics (PMx) studies aim to predict individual differences in treatment response and in the development of adverse effects associated with specific drug treatments. Overall, these studies inform us about how individuals will respond to a drug treatment based on their metabolic profiles obtained before, during, or after the therapeutic intervention. In the era of precision medicine, metabolic profiles hold great potential to guide patient selection and stratification in clinical trials, with a focus on improving drug efficacy and safety. Metabolomics is closely related to the phenotype as alterations in metabolism reflect changes in the preceding cascade of genomics, transcriptomics, and proteomics changes, thus providing a significant advance over other omics approaches. Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical platforms in metabolomics studies. In fact, since the introduction of PMx studies in 2006, the number of NMR-based PMx studies has been continuously growing and has provided novel insights into the specific metabolic changes associated with different mechanisms of action and/or toxic effects. This review presents an up-to-date summary of NMR-based PMx studies performed over the last 10 years. Our main objective is to discuss the experimental approaches used for the characterization of the metabolic changes associated with specific therapeutic interventions, the most relevant results obtained so far, and some of the remaining challenges in this area.

Advances in functional proteomics to study plant-pathogen interactions

Pathogen infection triggers complex signaling networks in plant cells that ultimately result in either susceptibility or resistance. We have made substantial progress in dissecting many of these signaling events, and it is becoming clear that changes in proteome composition and protein activity are major drivers of plant-microbe interactions. Here, we highlight different approaches to analyze the functional proteomes of hosts and pathogens and discuss how they have been used to further our understanding of plant disease. Global proteome profiling can quantify the dynamics of proteins, posttranslational modifications, and biological pathways that contribute to immune-related outcomes. In addition, emerging techniques such as enzyme activity-based profiling, proximity labeling, and kinase-substrate profiling are being used to dissect biochemical events that operate during infection. Finally, we discuss how these functional approaches can be integrated with other profiling data to gain a mechanistic, systems-level view of plant and pathogen signaling.

Multi 'omic data integration: A review of concepts, considerations, and approaches

The application of 'omic techniques including, but not limited to genomics/metagenomics, transcriptomics/meta-transcriptomics, proteomics/meta-proteomics, and metabolomics to generate multiple datasets from a single sample have facilitated hypothesis generation leading to the identification of biological, molecular and ecological functions and mechanisms, as well as associations and correlations. Despite their power and promise, a variety of challenges must be considered in the successful design and execution of a multi-omics study. In this review, various 'omic technologies applicable to single- and meta-organisms (i.e., host + microbiome) are described, and considerations for sample collection, storage and processing prior to data generation and analysis, as well as approaches to data storage, dissemination and analysis are discussed. Finally, case studies are included as examples of multi-omic applications providing novel insights and a more holistic understanding of biological processes.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer

Simple Summary

Cancer, but also its treatment, can lead to a reprogramming of cellular metabolism. These changes are observable in metabolite abundances, which can be unbiasedly measured via mass spectrometry metabolomics. However, even when the metabolome changes strongly, a (mechanistic) interpretation is difficult as metabolite levels do not necessarily directly correspond to pathway activities. Here we measure the changes of the cellular metabolome in colorectal cancer cell lines sensitive and resistant to the ruthenium-based drug BOLD-100/KP1339 and the platinum-based drug oxaliplatin. We map these changes onto a cancer-specific genome-scale metabolic model, which allows us not only to compute intracellular flux distributions, but also to disentangle drug-specific effects from growth differences from differences in metabolic adaptations due to resistance. Specifically, we find that resistance to BOLD-100/KP1339 induces more extensive reprogramming than oxaliplatin, especially with respect to fatty acid and amino acid metabolism.

Abstract

Background: Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. Methods: Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. Results: Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. Conclusions: Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.

A systems approach to enable effective team science from the internal research program of the National Center for Advancing Translational Sciences

The internal research program of the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health aims to fundamentally transform the preclinical translational research process to get more treatments to more people more quickly. The program develops and implements innovative scientific and operational approaches that accelerate and enhance translation across many diverse projects. Cross-disciplinary team science is a defining feature of our organization, with scientists at all levels engaged in multiple research teams. Here, we share our systems approach to nurturing cross-disciplinary team science, which leverages organizational policies, structures, and processes. Policies including the organizational mission statement, principles for ethical conduct of research, performance review criteria, and training program objectives and approaches reinforce the value of team science to achieve the program's scientific goals. Structures including an organizational structure designed around solving translational problems, co-location of employees in a single state-of-the-art scientific facility, and shared-use laboratories, expertise and instrumentation facilitate collaboration. Processes including fluid team assembly, specialized project management, cross-agency partnerships, and decision making based on clear screening criteria and milestones enable effective team assembly and functioning. We share evidence of the impact of these approaches on the science and commercialization of findings and discuss pathways to broad adoption of similar approaches.

Using machine learning approaches for multi-omics data analysis: A review

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

Integration strategies of multi-omics data for machine learning analysis

Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems.

Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.

Urine NMR Metabolomics Profile of Preterm Infants With Necrotizing Enterocolitis Over the First Two Months of Life: A Pilot Longitudinal Case-Control Study

Objective: To investigate changes in the urine metabolome of very low birth weight preterm newborns with necrotizing enterocolitis (NEC) and feed intolerance, we conducted a longitudinal study over the first 2 months of life. The metabolome of NEC newborns was compared with two control groups that did not develop NEC: the first one included preterm babies with feed intolerance, while the second one preterm babies with good feed tolerance.

Methods: Newborns developing NEC within the 3 weeks of life were identified as early onset NEC, while the remaining as late onset NEC. Case-control matching was done according to the gestational age (±1 week), birth weight (± 200 g), and postnatal age. A total of 96 urine samples were collected and analyzed. In newborns with NEC, samples were collected before, during and after the diagnosis over the first 2 months of life, while in controls samples were collected as close as possible to the postnatal age of newborns with NEC. Proton nuclear magnetic resonance (¹H NMR) spectroscopy was used for metabolomic analysis. Data were analyzed by univariate and multivariate statistical analysis.

Results: In all the preterm newborns, urine levels of betaine, glycine, succinate, and citrate positively correlated with postnatal age. Suberate and lactate correlated with postnatal age in preterms with NEC and in controls with food intolerance, while N,N-dimethylglycine (N,N-DMG) correlated only in controls with good digestive tolerance. Preterm controls with feed intolerance showed a progressive significant decrease of N-methylnicotinamide and carnitine. Lactate, betaine, myo-inositol, urea, creatinine, and N,N-dimethylglycine discriminated late-onset NEC from controls with good feed tolerance.

Conclusion: Our findings are discussed in terms of contributions from nutritional and clinical managements of patients and gut microbiota.

Machine learning-integrated omics for the risk and safety assessment of nanomaterials

With the advancement in nanotechnology, we are experiencing transformation in world order with deep insemination of nanoproducts from basic necessities to advanced electronics, health care products and medicines. Therefore, nanoproducts, however, can have negative side effects and must be strictly monitored to avoid negative outcomes. Future toxicity and safety challenges regarding nanomaterial incorporation into consumer products, including rapid addition of nanomaterials with diverse functionalities and attributes, highlight the limitations of traditional safety evaluation tools. Currently, artificial intelligence and machine learning algorithms are envisioned for enhancing and improving the nano-bio-interaction simulation and modeling, and they extend to the post-marketing surveillance of nanomaterials in the real world. Thus, hyphenation of machine learning with biology and nanomaterials could provide exclusive insights into the perturbations of delicate biological functions after integration with nanomaterials. In this review, we discuss the potential of combining integrative omics with machine learning in profiling nanomaterial safety and risk assessment and provide guidance for regulatory authorities as well.