Prospects and challenges of multi-omics data integration in toxicology

Comprehensive applications of the artificial intelligence technology in new drug research and development

Purpose

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods

Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results

In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion

Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Time-course cross-species transcriptomics reveals conserved hepatotoxicity pathways induced by repeated administration of cyclosporine A

Cyclosporine A (CsA) has shown efficacy against immunity-related diseases despite its toxicity in various organs, including the liver, emphasizing the need to elucidate its underlying hepatotoxicity mechanism. This study aimed to capture the alterations in genome-wide expression over time and the subsequent perturbations of corresponding pathways across species. Six data from humans, mice, and rats, including animal liver tissue, human liver microtissues, and two liver cell lines exposed to CsA toxic dose, were used. The microtissue exposed to CsA for 10 d was analyzed to obtain dynamically differentially expressed genes (DEGs). Single-time points data at 1, 3, 5, 7, and 28 d of different species were used to provide additional evidence. Using liver microtissue-based longitudinal design, DEGs that were consistently up- or down-regulated over time were captured, and the well-known mechanism involved in CsA toxicity was elucidated. Thirty DEGs that consistently changed in longitudinal data were also altered in 28-d rat in-house data with concordant expression. Some genes (e.g. TUBB2A, PLIN2, APOB) showed good concordance with identified DEGs in 1-d and 7-d mouse data. Pathway analysis revealed up-regulations of protein processing, asparagine N-linked glycosylation, and cargo concentration in the endoplasmic reticulum. Furthermore, the down-regulations of pathways related to biological oxidations and metabolite and lipid metabolism were elucidated. These pathways were also enriched in single-time-point data and conserved across species, implying their biological significance and generalizability. Overall, the human organoids-based longitudinal design coupled with cross-species validation provides temporal molecular change tracking, aiding mechanistic elucidation and biologically relevant biomarker discovery.

Multi-omics integration analysis: Tools and applications in environmental toxicology

Nowadays, traditional single-omics study is not enough to explain the causality between molecular alterations and toxicity endpoints for environmental pollutants. With the development of high-throughput sequencing technology and high-resolution mass spectrometry technology, the integrative analysis of multi-omics has become an efficient strategy to understand holistic biological mechanisms and to uncover the regulation network in specific biological processes. This review summarized sample preparation methods, integration analysis tools and the application of multi-omics integration analyses in environmental toxicology field. Currently, omics methods have been widely applied being as the sensitivity of early biological response, especially for low-dose and long-term exposure to environmental pollutants. Integrative omics can reveal the overall changes of genes, proteins, and/or metabolites in the cells, tissues or organisms, which provide new insights into revealing the overall toxicity effects, screening the toxic targets, and exploring the underlying molecular mechanism of pollutants.

Integrated transcriptomics and metabolomics reveal the mechanism of polystyrene nanoplastics toxicity to mice

Microplastic (MP) are an emerging environmental pollutant, which has toxic effects on organisms, and it has received extensive attention currently. Studying the transcriptomic and metabolic responses of mice to nanoplastic-contaminated water is critical for understanding molecular-level toxicity of nanoplastics (NPs), but there are few studies on this topic. To analyze the effects of different concentrations of polystyrene (PS) nanoplastic-contaminated water on mice at the transcriptome and metabolism of spleens to study the molecular toxicity. Here, testing of histopathology of spleen of female mice was performed after drinking water containing 0.1 μm PS-NPs (1 mg/mL and 50 mg/mL) at different concentrations for 49 days, respectively. The spleen tissue samples were subjected to metabolome and transcriptome sequencing. Four differentially expressed genes were randomly chosen for qRT-PCR to confirm the correctness of transcriptome sequencing. Common Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that a large number of differential genes and differential metabolites mainly focused on immune, inflammation, neurodegenerative disease, cardiovascular disease, nervous, etc. in the organism systems module; lipid, amino acid, taurine and hypotaurine metabolisms, etc. in the metabolism module; signaling translation, signaling molecules and interaction, and neuroactive ligand-receptor interaction, etc. in the environmental information processing. The results showed that pathway analysis at transcriptome and metabolome levels confirmed that the immune system of mice was affected after drinking water contaminated with polystyrene nanoplastics.

Perfluorohexanesulfonic Acid (PFHxS) Impairs Lipid Homeostasis in Zebrafish Larvae through Activation of PPARα

Perfluorohexanesulfonic acid (PFHxS), an emerging short-chain per- and polyfluoroalkyl substance, has been frequently detected in aquatic environments. Adverse outcome pathway studies have shown that perfluorinated compounds impair lipid homeostasis through peroxisome proliferator activated receptors (PPARs). However, many of these studies were performed at high concentrations and may thus be a result of overt toxicity. To better characterize the molecular and key events of PFHxS to biota, early life-stage zebrafish (Danio rerio) were exposed to concentrations detected in the environment (0.01, 0.1, 1, and 10 μg/L). Lipidomic and transcriptomic evaluations were integrated to predict potential molecular targets. PFHxS significantly impaired lipid homeostasis by the dysregulation of glycerophospholipids, fatty acyls, glycerolipids, sphingolipids, prenol lipids, and sterol lipids. Informatic analyses of the lipidome and transcriptome indicated alterations of the PPAR signaling pathway, with downstream changes to retinol, linoleic acid, and glycerophospholipid metabolism. To assess the role of PPARs, potential binding of PFHxS to PPARs was predicted and animals were coexposed to a PPAR antagonist (GW6471). Molecular simulation indicated PFHxS had a 27.1% better binding affinity than oleic acid, an endogenous agonist of PPARα. Antagonist coexposures rescued impaired glycerophosphocholine concentrations altered by PFHxS. These data indicate PPARα activation may be an important molecular initiating event for PFHxS.

Towards characterization of cell culture conditions for reliable proteomic analysis: in vitro studies on A549, differentiated THP-1, and NR8383 cell lines

Proteomic investigations result in high dimensional datasets, but integration or comparison of different studies is hampered by high variances due to different experimental setups. In addition, cell culture conditions can have a huge impact on the outcome. This study systematically investigates the impact of experimental parameters on the proteomic profiles of commonly used cell lines-A549, differentiated THP-1 macrophage-like cells, and NR8383-for toxicity studies. The work focuses on analyzing the influence at the proteome level of cell culture setup involving different vessels, cell passage numbers, and post-differentiation harvesting time, aiming to improve the reliability of proteomic analyses for hazard assessment. Mass-spectrometry-based proteomics was utilized for accurate protein quantification by means of a label-free approach. Our results showed that significant proteome variations occur when cells are cultivated under different setups. Further analysis of these variations revealed their association to specific cellular pathways related to protein misfolding, oxidative stress, and proteasome activity. Conversely, the influence of cell passage numbers on the proteome is minor, suggesting a reliable range for conducting reproducible biological replicates. Notable, substantial proteome alterations occur over-time post-differentiation of dTHP-1 cells, particularly impacting pathways crucial for macrophage function. This finding is key for the interpretation of experimental results. These results highlight the need for standardized culture conditions in proteomic-based evaluations of treatment effects to ensure reliable results, a prerequisite for achieving regulatory acceptance of proteomics data.

Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

IMPORTANCE

We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

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.

Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study

The wide variation of nanomaterial (NM) characters (size, shape, and properties) and the related impacts on living organisms make it virtually impossible to assess their safety; the need for modeling has been urged for long. We here investigate the custom-designed 1-10% Fe-doped CuO NM library. Effects were assessed using the soil ecotoxicology model Enchytraeus crypticus (Oligochaeta) in the standard 21 days plus its extension (49 days). Results showed that 10%Fe-CuO was the most toxic (21 days reproduction EC50 = 650 mg NM/kg soil) and Fe3O4 NM was the least toxic (no effects up to 3200 mg NM/kg soil). All other NMs caused similar effects to E. crypticus (21 days reproduction EC50 ranging from 875 to 1923 mg NM/kg soil, with overlapping confidence intervals). Aiming to identify the key NM characteristics responsible for the toxicity, machine learning (ML) modeling was used to analyze the large data set [9 NMs, 68 descriptors, 6 concentrations, 2 exposure times (21 and 49 days), 2 endpoints (survival and reproduction)]. ML allowed us to separate experimental related parameters (e.g., zeta potential) from particle-specific descriptors (e.g., force vectors) for the best identification of important descriptors. We observed that concentration-dependent descriptors (environmental parameters, e.g., zeta potential) were the most important under standard test duration (21 day) but not for longer exposure (closer representation of real-world conditions). In the longer exposure (49 days), the particle-specific descriptors were more important than the concentration-dependent parameters. The longer-term exposure showed that the steepness of the concentration-response decreased with an increased Fe content in the NMs. Longer-term exposure should be a requirement in the hazard assessment of NMs in addition to the standard in OECD guidelines for chemicals. The progress toward ML analysis is desirable given its need for such large data sets and significant power to link NM descriptors to effects in animals. This is beyond the current univariate and concentration-response modeling analysis.

Advancing Nanomaterial Toxicology Screening Through Efficient and Cost-Effective Quantitative Proteomics

Proteomic investigations yield high-dimensional datasets, yet their application to large-scale toxicological assessments is hindered by reproducibility challenges due to fluctuating measurement conditions. To address these limitations, this study introduces an advanced tandem mass tag (TMT) labeling protocol. Although labeling approaches shorten data acquisition time by multiplexing samples compared to traditional label-free quantification (LFQ) methods in general, the associated costs may surge significantly with large sample sets, for example, in toxicological screenings. However, the introduced advanced protocol offers an efficient, cost-effective alternative, reducing TMT reagent usage (by a factor of ten) and requiring minimal biological material (1 µg), while demonstrating increased reproducibility compared to LFQ. To demonstrate its effectiveness, the advanced protocol is employed to assess the toxicity of nine benchmark nanomaterials (NMs) on A549 lung epithelial cells. While LFQ measurements identify 3300 proteins, they proved inadequate to reveal NM toxicity. Conversely, despite detecting 2600 proteins, the TMT protocol demonstrates superior sensitivity by uncovering alterations induced by NM treatment. In contrast to previous studies, the introduced advanced protocol allows simultaneous and straightforward assessment of multiple test substances, enabling prioritization, ranking, and grouping for hazard evaluation. Additionally, it fosters the development of New Approach Methodologies (NAMs), contributing to innovative methodologies in toxicological research.

Exposure to virgin and marine incubated microparticles of biodegradable and conventional polymers modulates the hepatopancreas transcriptome of Mytilus galloprovincialis

Biodegradable polymers have been proposed as an alternative to conventional plastics to mitigate the impact of marine litter, but the research investigating their toxicity is still in its infancy. This study evaluates the potential ecotoxicological effects of both virgin and marine-incubated microparticles (MPs), at environmentally relevant concentration (0.1 mg/l), made of different biodegradable polymers (Polycaprolactone, Mater-Bi, cellulose) and conventional polymers (Polyethylene) on Mytilus galloprovincialis by using transcriptomics. This approach is increasingly being used to assess the effects of pollutants on organisms, obtaining data on numerous biological pathways simultaneously. Whole hepatopancreas de novo transcriptome sequencing was performed, individuating 972 genes differentially expressed across experimental groups compared to the control. Through the comparative transcriptomic profiling emerges that the preponderant effect is attributable to the marine incubation of MPs, especially for incubated polycaprolactone (731 DEGs). Mater-Bi and cellulose alter the smallest number of genes and biological processes in the mussel hepatopancreas. All microparticles, regardless of their polymeric composition, dysregulated innate immunity, and fatty acid metabolism biological processes. These findings highlight the necessity of considering the interactions of MPs with the environmental factors in the marine ecosystem when performing ecotoxicological evaluations. The results obtained contribute to fill current knowledge gaps regarding the potential environmental impacts of biodegradable polymers.

Effects of ambient UVB light on Pacific oyster Crassostrea gigas mantle tissue based on multivariate data

Ambient ultraviolet radiation (UVB) from solar and artificial light presents serious environmental risks to aquatic ecosystems. The Pacific oyster, Crassostrea gigas, perceives changes in the external environment primarily through its mantle tissue, which contains many nerve fibers and tentacles. Changes within the mantles can typically illustrate the injury of ambient UVB. In this study, a comprehensive analysis of phenotypic, behavioral, and physiological changes demonstrated that extreme UVB radiation (10 W/m²) directly suppressed the behavioral activities of C. gigas. Conversely, under ambient UVB radiation (5 W/m²), various physiological processes exhibited significant alterations in C. gigas, despite the behavior remaining relatively unaffected. Using mathematical model analysis, the integrated analysis of the full-length transcriptome, proteome, and metabolome showed that ambient UVB significantly affected the metabolic processes (saccharide, lipid, and protein metabolism) and cellular biology processes (autophagy, apoptosis, oxidative stress) of the C. gigas mantle. Subsequently, using Procrustes analysis and Pearson correlation analysis, the association between multi-omics data and physiological changes, as well as their biomarkers, revealed the effect of UVB on three crucial biological processes: activation of autophagy signaling (key factors: Ca2+, LC3B, BECN1, caspase-7), response to oxidative stress (reactive oxygen species, heat shock 70, cytochrome c oxidase), and recalibration of energy metabolism (saccharide, succinic acid, translation initiation factor IF-2). These findings offer a fresh perspective on the integration of multi-data from non-model animals in ambient UVB risk assessment.

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. PathIntegrate is available as an open-source Python package.

Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity

The escalating challenges posed by metal(loid) toxicity in agricultural ecosystems, exacerbated by rapid climate change and anthropogenic pressures, demand urgent attention. Soil contamination is a critical issue because it significantly impacts crop productivity. The widespread threat of metal(loid) toxicity can jeopardize global food security due to contaminated food supplies and pose environmental risks, contributing to soil and water pollution and thus impacting the whole ecosystem. In this context, plants have evolved complex mechanisms to combat metal(loid) stress. Amid the array of innovative approaches, omics, notably transcriptomics, proteomics, and metabolomics, have emerged as transformative tools, shedding light on the genes, proteins, and key metabolites involved in metal(loid) stress responses and tolerance mechanisms. These identified candidates hold promise for developing high-yielding crops with desirable agronomic traits. Computational biology tools like bioinformatics, biological databases, and analytical pipelines support these omics approaches by harnessing diverse information and facilitating the mapping of genotype-to-phenotype relationships under stress conditions. This review explores: (1) the multifaceted strategies that plants use to adapt to metal(loid) toxicity in their environment; (2) the latest findings in metal(loid)-mediated transcriptomics, proteomics, and metabolomics studies across various plant species; (3) the integration of omics data with artificial intelligence and high-throughput phenotyping; (4) the latest bioinformatics databases, tools and pipelines for single and/or multi-omics data integration; (5) the latest insights into stress adaptations and tolerance mechanisms for future outlooks; and (6) the capacity of omics advances for creating sustainable and resilient crop plants that can thrive in metal(loid)-contaminated environments.

Protein kinase D drives the secretion of invasion mediators in triple-negative breast cancer cell lines

The protein kinase D (PKD) family members regulate the fission of cargo vesicles at the Golgi complex and play a pro-oncogenic role in triple-negative breast cancer (TNBC). Whether PKD facilitates the secretion of tumor-promoting factors in TNBC, however, is still unknown. Using the pharmacological inhibition of PKD activity and siRNA-mediated depletion of PKD2 and PKD3, we identified the PKD-dependent secretome of the TNBC cell lines MDA-MB-231 and MDA-MB-468. Mass spectrometry-based proteomics and antibody-based assays revealed a significant downregulation of extracellular matrix related proteins and pro-invasive factors such as LIF, MMP-1, MMP-13, IL-11, M-CSF and GM-CSF in PKD-perturbed cells. Notably, secretion of these proteins in MDA-MB-231 cells was predominantly controlled by PKD2 and enhanced spheroid invasion. Consistently, PKD-dependent secretion of pro-invasive factors was more pronounced in metastatic TNBC cell lines. Our study thus uncovers a novel role of PKD2 in releasing a pro-invasive secretome.

A novel multi-omics data analysis of dose-dependent and temporal changes in regulatory pathways due to chemical perturbation: a case study on caffeine

Comprehensive analysis of multi-omics data can reveal alterations in regulatory pathways induced by cellular exposure to chemicals by characterizing biological processes at the molecular level. Data-driven omics analysis, conducted in a dose-dependent or dynamic manner, can facilitate comprehending toxicity mechanisms. This study introduces a novel multi-omics data analysis designed to concurrently examine dose-dependent and temporal patterns of cellular responses to chemical perturbations. This analysis, encompassing preliminary exploration, pattern deconstruction, and network reconstruction of multi-omics data, provides a comprehensive perspective on the dynamic behaviors of cells exposed to varying levels of chemical stimuli. Importantly, this analysis is adaptable to any number of omics layers, including site-specific phosphoproteomics. We implemented this analysis on multi-omics data obtained from HepG2 cells exposed to a range of caffeine doses over varying durations and identified six response patterns, along with their associated biomolecules and pathways. Our study demonstrates the effectiveness of the proposed multi-omics data analysis in capturing multidimensional patterns of cellular response to chemical perturbation, enhancing understanding of pathway regulation for chemical risk assessment.

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.

Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state-of-the-art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps to identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery and network inference algorithms applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

The phosphoproteome is a first responder in tiered cellular adaptation to chemical stress followed by proteomics and transcriptomics alteration

Next-generation risk assessment (NGRA) for environmental chemicals involves a weight of evidence (WoE) framework integrating a suite of new approach methodologies (NAMs) based on points of departure (PoD) obtained from in vitro assays. Among existing NAMs, the omic-based technologies are of particular importance based on the premise that any apical endpoint change indicative of impaired health must be underpinned by some alterations at the omics level, such as transcriptome, proteome, metabolome, epigenome and genome. Transcriptomic assay plays a leading role in providing relatively conservative PoDs compared with apical endpoints. However, it is unclear whether and how parameters measured with other omics techniques predict the cellular response to chemical perturbations, especially at exposure levels below the transcriptomically defined PoD. Multi-omics coverage may provide additional sensitive or confirmative biomarkers to complement and reduce the uncertainty in safety decisions made using targeted and transcriptomics assays. In the present study, we conducted multi-omics studies of transcriptomics, proteomics and phosphoproteomics on two prototype compounds, coumarin and 2,4-dichlorophenoxyacetic acid (2,4-D), with multiple chemical concentrations and time points, to understand the sensitivity of the three omics techniques in response to chemically-induced changes in HepG2. We demonstrated that, phosphoproteomics alterations occur not only earlier in time, but also more sensitive to lower concentrations than proteomics and transcriptomics when the HepG2 cells were exposed to various chemical treatments. The phosphoproteomics changes appear to approach maximum when the transcriptomics alterations begin to initiate. Therefore, it is proximal to the very early effects induced by chemical exposure. We concluded that phosphoproteomics can be utilized to provide a more complete coverage of chemical-induced cellular alteration and supplement transcriptomics-based health safety decision making.

Novel Time-dependent Multi-omics Integration in Sepsis-associated Liver Dysfunction

The recently developed technologies that allow the analysis of each single omics have provided an unbiased insight into ongoing disease processes. However, it remains challenging to specify the study design for the subsequent integration strategies that can associate sepsis pathophysiology and clinical outcomes. Here, we conducted a time-dependent multi-omics integration (TDMI) in a sepsis-associated liver dysfunction (SALD) model. We successfully deduced the relation of the Toll-like receptor 4 (TLR4) pathway with SALD. Although TLR4 is a critical factor in sepsis progression, it is not specified in single-omics analyses but only in the TDMI analysis. This finding indicates that the TDMI-based approach is more advantageous than single-omics analyses in terms of exploring the underlying pathophysiological mechanism of SALD. Furthermore, TDMI-based approach can be an ideal paradigm for insightful biological interpretations of multi-omics datasets that will potentially reveal novel insights into basic biology, health, and diseases, thus allowing the identification of promising candidates for therapeutic strategies.

Prenatal exposures to endocrine disrupting chemicals: The role of multi-omics in understanding toxicity

Endocrine disrupting chemicals (EDCs) are a diverse group of toxicants detected in populations globally. Prenatal EDC exposures impact birth and childhood outcomes. EDCs work through persistent changes at the molecular, cellular, and organ level. Molecular and biochemical signals or 'omics' can be measured at various functional levels - including the epigenome, transcriptome, proteome, metabolome, and the microbiome. In this narrative review, we introduce each omics and give examples of associations with prenatal EDC exposures. There is substantial research on epigenomic modifications in offspring exposed to EDCs during gestation, and a growing number of studies evaluating the transcriptome, proteome, metabolome, or microbiome in response to these exposures. Multi-omics, integrating data across omics layers, may improve understanding of disrupted function pathways related to early life exposures. We highlight several data integration methods to consider in multi-omics studies. Information from multi-omics can improve understanding of the biological processes and mechanisms underlying prenatal EDC toxicity.

Automated calibration for stability selection in penalised regression and graphical models

Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to [Least Absolute Shrinkage Selection Operator (LASSO)] penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application to multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.

Toxicity effects of pesticides based on zebrafish (Danio rerio) models: Advances and perspectives

Pesticides inevitably enter aquatic environments, posing potential risks to organisms. The common aquatic model organism, zebrafish (Danio rerio), are widely used to evaluate the toxicity of pesticides. In this review, we searched the Web of Science database for articles published between 2012 and 2022, using the keywords "pesticide", "zebrafish", and "toxicity", retrieving 618 publications. Furthermore, we described the main pathways by which pesticides enter aquatic environments and the fate of their residues in these environments. We systematically reviewed the toxicity effects of pesticides on zebrafish, including developmental toxicity, endocrine-disrupting effects, reproductive toxicity, neurotoxicity, immunotoxicity, and genotoxicity. Importantly, we summarized the latest research progress on the toxicity mechanism of pesticides to zebrafish based on omics technologies, including transcriptomics, metabolomics, and microbiomics. Finally, we discussed future research prospects, focusing on the combined exposure of multiple pollutants including pesticides, the risk of multigenerational exposure to pesticides, and the chronic toxicity of aquatic nanopesticides. This review provides essential data support for ecological risk assessments of pesticides in aquatic environments, and has implications for water management in the context of pesticide pollution.

Evolution and impact of high content imaging

The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.

Oxidation is an underappreciated post-translational modification in the regulation of immune responses associated with changes in phosphorylation

Although macrophages are known to be affected by their redox status, oxidation is not yet a well-recognized post-translational modification (PTM) in regulating macrophages and immune cells in general. While it has been described that the redox status of single cysteines in specific proteins is relevant for macrophage functions, global oxidation information is scarce. Hence, we globally assessed the impact of oxidation on macrophage activation using untargeted proteomics and PTM-omics. We exposed THP-1 macrophages to lipopolysaccharide (LPS) for 4 h and 24 h and applied a sequential iodoTMT labeling approach to get information on overall oxidation as well as reversible oxidation of cysteines. Thus, we identified 10452 oxidation sites, which were integratively analyzed with 5057 proteins and 7148 phosphorylation sites to investigate their co-occurance with other omics layers. Based on this integrative analysis, we found significant upregulation of several immune-related pathways, e.g. toll-like receptor 4 (TLR4) signaling, for which 19 proteins, 7 phosphorylation sites, and 39 oxidation sites were significantly affected, highlighting the relevance of oxidations in TLR4-induced macrophage activation. Co-regulation of oxidation and phosphorylation was observed, as evidenced by multiply modified proteins related to inflammatory pathways. Additionally, we observed time-dependent effects, with differences in the dynamics of oxidation sites compared to proteins and phosphorylation sites. Overall, this study highlights the importance of oxidation in regulating inflammatory processes and provides a method that can be readily applied to study the cellular redoxome globally.

Multi-omics clustering for cancer subtyping based on latent subspace learning

The increased availability of high-throughput technologies has enabled biomedical researchers to learn about disease etiology across multiple omics layers, which shows promise for improving cancer subtype identification. Many computational methods have been developed to perform clustering on multi-omics data, however, only a few of them are applicable for partial multi-omics in which some samples lack data in some types of omics. In this study, we propose a novel multi-omics clustering method based on latent sub-space learning (MCLS), which can deal with the missing multi-omics for clustering. We utilize the data with complete omics to construct a latent subspace using PCA-based feature extraction and singular value decomposition (SVD). The data with incomplete multi-omics are then projected to the latent subspace, and spectral clustering is performed to find the clusters. The proposed MCLS method is evaluated on seven different cancer datasets on three levels of omics in both full and partial cases compared to several state-of-the-art methods. The experimental results show that the proposed MCLS method is more efficient and effective than the compared methods for cancer subtype identification in multi-omics data analysis, which provides important references to a comprehensive understanding of cancer and biological mechanisms. AVAILABILITY: The proposed method can be freely accessible at https://github.com/ShangCS/MCLS.

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments and therapies for patients. Bioinformatics and high-throughput biological data coming from different sources can even be more helpful, because each of these different data chunks can provide alternative, complementary information about a specific biological phenomenon, similar to multiple photos of the same subject taken from different angles. In this context, the integration of bioinformatics and high-throughput biological data gets a pivotal role in running a successful bioinformatics study. In the last decades, data originating from proteomics, metabolomics, metagenomics, phenomics, transcriptomics, and epigenomics have been labelled -omics data, as a unique name to refer to them, and the integration of these omics data has gained importance in all biological areas. Even if this omics data integration is useful and relevant, due to its heterogeneity, it is not uncommon to make mistakes during the integration phases. We therefore decided to present these ten quick tips to perform an omics data integration correctly, avoiding common mistakes we experienced or noticed in published studies in the past. Even if we designed our ten guidelines for beginners, by using a simple language that (we hope) can be understood by anyone, we believe our ten recommendations should be taken into account by all the bioinformaticians performing omics data integration, including experts.

OMICS and Other Advanced Technologies in Mycological Applications

Fungi play many roles in different ecosystems. The precise identification of fungi is important in different aspects. Historically, they were identified based on morphological characteristics, but technological advancements such as polymerase chain reaction (PCR) and DNA sequencing now enable more accurate identification and taxonomy, and higher-level classifications. However, some species, referred to as "dark taxa", lack distinct physical features that makes their identification challenging. High-throughput sequencing and metagenomics of environmental samples provide a solution to identifying new lineages of fungi. This paper discusses different approaches to taxonomy, including PCR amplification and sequencing of rDNA, multi-loci phylogenetic analyses, and the importance of various omics (large-scale molecular) techniques for understanding fungal applications. The use of proteomics, transcriptomics, metatranscriptomics, metabolomics, and interactomics provides a comprehensive understanding of fungi. These advanced technologies are critical for expanding the knowledge of the Kingdom of Fungi, including its impact on food safety and security, edible mushrooms foodomics, fungal secondary metabolites, mycotoxin-producing fungi, and biomedical and therapeutic applications, including antifungal drugs and drug resistance, and fungal omics data for novel drug development. The paper also highlights the importance of exploring fungi from extreme environments and understudied areas to identify novel lineages in the fungal dark taxa.

Integrated lipidomic and transcriptomic analysis reveals clarithromycin-induced alteration of glycerophospholipid metabolism in the cerebral cortex of mice

Clarithromycin (CLA) has been widely used in the treatment of bacterial infection. Research reveals the adverse effects on the central nervous system among patients receiving CLA treatment; whereas, a relevant underlying mechanism remains considerably unclear. According to our research, an integrated lipidomic and transcriptomic analysis was applied to explore the effect of CLA on neurobehavior. CLA treatment caused anxiety-like behaviors dose-dependently during open field as well as elevated plus maze trials on mice. Transcriptomes and LC/MS-MS-based metabolomes were adopted for investigating how CLA affected lipidomic profiling as well as metabolic pathway of the cerebral cortex. CLA exposure greatly disturbed glycerophospholipid metabolism and the carbon chain length of fatty acids. By using whole transcriptome sequencing, we found that CLA significantly downregulated the mRNA expression of CEPT1 and CHPT1, two key enzymes involved in the synthesis of glycerophospholipids, supporting the findings from the lipidomic profiling. Also, CLA causes changes in neuronal morphology and function in vitro, which support the existing findings concerning neurobehavior in vivo. We speculate that altered glycerophospholipid metabolism may be involved in the neurobehavioral effect of CLA. Our findings contribute to understanding the mechanisms of CLA-induced adverse effects on the central nervous system. 1. Clarithromycin treatment caused anxiety-like behavior with dose-dependent response both in the open field and elevated plus maze test in mice; 2. Clarithromycin exposing predominately disturbed the metabolism of glycerophospholipids in the cerebral cortex of mice; 3. Clarithromycin application remarkably attenuated CEPT1 and CHPT1 gene expression, which participate in the last step in the synthesis of glycerophospholipids; 4. The altered glycerophospholipid metabolomics may be involved in the abnormal neurobehavior caused by clarithromycin.

Investigation of the metabolomic crosstalk between liver sinusoidal endothelial cells and hepatocytes exposed to paracetamol using organ-on-chip technology

Organ-on-chip technology is a promising in vitro approach recapitulating human physiology for the study of responses to drug exposure. Organ-on-chip cell cultures have paved new grounds for testing and understanding metabolic dose-responses when evaluating pharmaceutical and environmental toxicity. Here, we present a metabolomic investigation of a coculture of liver sinusoidal endothelial cells (LSECs, SK-HEP-1) with hepatocytes (HepG2/C3a) using advanced organ-on-chip technology. To reproduce the physiology of the sinusoidal barrier, LSECs were separated from hepatocytes by a membrane (culture insert integrated organ-on-chip platform). The tissues were exposed to acetaminophen (APAP), an analgesic drug widely used as a xenobiotic model in liver and HepG2/C3a studies. The differences between the SK-HEP-1, HepG2/C3a monocultures and SK-HEP-1/HepG2/C3a cocultures, treated or not with APAP, were identified from metabolomic profiles using supervised multivariate analysis. The pathway enrichment coupled with metabolite analysis of the corresponding metabolic fingerprints contributed to extracting the specificity of each type of culture and condition. In addition, we analysed the responses to APAP treatment by mapping the signatures with significant modulation of the biological processes of the SK-HEP-1 APAP, HepG2/C3a APAP and SK-HEP-1/HepG2/C3a APAP conditions. Furthermore, our model shows how the presence of the LSECs barrier and APAP first pass can modify the metabolism of HepG2/C3a. Altogether, this study demonstrates the potential of a "metabolomic-on-chip" strategy for pharmaco-metabolomic applications predicting individual response to drugs.

Network model integrated with multi-omic data predicts MBNL1 signals that drive myofibroblast activation

RNA-binding protein muscleblind-like1 (MBNL1) was recently identified as a central regulator of cardiac wound healing and myofibroblast activation. To identify putative MBNL1 targets, we integrated multiple genome-wide screens with a fibroblast network model. We expanded the model to include putative MBNL1-target interactions and recapitulated published experimental results to validate new signaling modules. We prioritized 14 MBNL1 targets and developed novel fibroblast signaling modules for p38 MAPK, Hippo, Runx1, and Sox9 pathways. We experimentally validated MBNL1 regulation of p38 expression in mouse cardiac fibroblasts. Using the expanded fibroblast model, we predicted a hierarchy of MBNL1 regulated pathways with strong influence on αSMA expression. This study lays a foundation to explore the network mechanisms of MBNL1 signaling central to fibrosis.

Combining time-resolved transcriptomics and proteomics data for Adverse Outcome Pathway refinement in ecotoxicology

Conventional Environmental Risk Assessment (ERA) of pesticide pollution is based on soil concentrations and apical endpoints, such as the reproduction of test organisms, but has traditionally disregarded information along the organismal response cascade leading to an adverse outcome. The Adverse Outcome Pathway (AOP) framework includes response information at any level of biological organization, providing opportunities to use intermediate responses as a predictive read-out for adverse outcomes instead. Transcriptomic and proteomic data can provide thousands of data points on the response to toxic exposure. Combining multiple omics data types is necessary for a comprehensive overview of the response cascade and, therefore, AOP development. However, it is unclear if transcript and protein responses are synchronized in time or time lagged. To understand if analysis of multi-omics data obtained at the same timepoint reveal one synchronized response cascade, we studied time-resolved shifts in gene transcript and protein abundance in the springtail Folsomia candida, a soil ecotoxicological model, after exposure to the neonicotinoid insecticide imidacloprid. We analyzed transcriptome and proteome data every 12 h up to 72 h after onset of exposure. The most pronounced shift in both transcript and protein abundances was observed after 48 h exposure. Moreover, cross-correlation analyses indicate that most genes displayed the highest correlation between transcript and protein abundances without a time-lag. This demonstrates that a combined analysis of transcriptomic and proteomic data from the same time-point can be used for AOP improvement. This data will promote the development of biomarkers for the presence of neonicotinoid insecticides or chemicals with a similar mechanism of action in soils.

A novel cadmium detoxification pathway in Tri-spine horseshoe crab (Tachypleus tridentatus): A 430-million-years-ago organism

Marine and intertidal heavy metal pollution has been a major concern in recent years. Tachypleus tridentatus has existed on earth for more than 430 million years. It has suffered a sharp decline in population numbers caused by environmental pollution and anthropogenic disturbance for almost 40 years. However, the effects of heavy metal pollution on juvenile T. tridentatus have not been reported. Here we show the mechanism of cadmium (Cd) detoxification in juvenile T. tridentatus using integrated antioxidant indexes and transcriptomic and metabolomic analysis. High Cd²⁺ concentration caused oxidative stress in juvenile T. tridentatus. The hazards increase with increasing Cd²⁺ concentration in juvenile T. tridentatus. Transcriptomics and metabolomics analyses concluded that high Cd²⁺ concentration resulted in the imbalance of glycerophospholipid metabolism in juvenile T. tridentatus to detoxify Cd. Our results offer a rationale for protective measures and further studies of heavy metal stress in T. tridentatus.

Interactive gene identification for cancer subtyping based on multi-omics clustering

Recent advances in multi-omics databases offer the opportunity to explore complex systems of cancers across hierarchical biological levels. Some methods have been proposed to identify the genes that play a vital role in disease development by integrating multi-omics. However, the existing methods identify the related genes separately, neglecting the gene interactions that are related to the multigenic disease. In this study, we develop a learning framework to identify the interactive genes based on multi-omics data including gene expression. Firstly, we integrate different omics based on their similarities and apply spectral clustering for cancer subtype identification. Then, a gene co-expression network is construct for each cancer subtype. Finally, we detect the interactive genes in the co-expression network by learning the dense subgraphs based on the L₁ prosperities of eigenvectors in the modularity matrix. We apply the proposed learning framework on a multi-omics cancer dataset to identify the interactive genes for each cancer subtype. The detected genes are examined by DAVID and KEGG tools for systematic gene ontology enrichment analysis. The analysis results show that the detected genes have relationships to cancer development and the genes in different cancer subtypes are related to different biological processes and pathways, which are expected to yield important references for understanding tumor heterogeneity and improving patient survival.

Metabolomics-based assessment of nanoparticles (nZnO) toxicity in an infaunal marine annelid, the lugworm Arenicola marina (Annelida: Sedentaria)

Nanopollutants such as nZnO gain importance as contaminants of emerging concern due to their high production volume and potential toxicity. Coastal sediments serve as sinks for nanoparticles but the impacts and the toxicity mechanisms of nZnO in sediment-dwelling organisms are not well understood. We used metabolomics to assess the effects of nZnO-contaminated sediments on a benthic ecosystem engineer, an infaunal polychaete Arenicola marina. The worms were exposed to unpolluted (control) sediment or to the sediment spiked with 100 or 1000 μg Zn kg^-1 of nZnO. Oxidative lesions (lipid peroxidation and protein carbonyls) were measured in the body wall as traditional biomarkers of nanopollutant toxicity. Metabolite profiles (including amino acids, tricarboxylic acid (TCA) cycle and urea cycle intermediates) were determined in the body wall and the coelomic fluid. Exposure to nZnO altered metabolism of the lugworms via suppression of the metabolism of gluconeogenic and aromatic amino acids, and altered the TCA cycle likely via suppression of fumarase activity. These metabolic changes may negatively affect carbohydrate metabolism and energy storage, and impair hormonal signaling in the worms. The total pool of free amino acids was depleted in nZnO exposures with potentially negative consequences for osmoregulation and protein synthesis. Exposure to nZnO led to accumulation of the lipid peroxidation products demonstrating high susceptibility of the cellular membranes to nZnO-induced oxidative stress. The nZnO-induced shifts in the metabolite profiles were more pronounced in the coelomic fluid than the body wall. This finding emphasizes the important metabolic role of the coelomic fluid as well as its suitability for assessing the toxic impacts of nZnO and other metabolic disruptors. The metabolic disruptions caused by environmentally relevant concentrations of nZnO can have negative effects on the organisms' fitness impairing growth and reproduction of the populations of marine bioturbators like the lugworms in nanoparticle-polluted sediments.

Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment

Omic-based technologies are of particular interest and importance for hazard identification and health risk characterization of chemicals. Their application in the new approach methodologies (NAMs) anchored on cellular toxicity pathways is based on the premise that any apical health endpoint change must be underpinned by some alterations at the omic levels. In the present study we examined the cellular responses to two chemicals, caffeine and coumarin, by generating and integrating multi-omic data from multi-dose and multi-time point transcriptomic, proteomic and phosphoproteomic experiments. We showed that the methodology presented here was able to capture the complete chain of events from the first chemical-induced changes at the phosphoproteome level, to changes in gene expression, and lastly to changes in protein abundance, each with vastly different points of departure (PODs). In HepG2 cells we found that the metabolism of lipids and general cellular stress response to be the dominant biological processes in response to caffeine and coumarin exposure, respectively. The phosphoproteomic changes were detected early in time, at very low doses and provided a fast, adaptive cellular response to chemical exposure with 7-37-fold lower points of departure comparing to the transcriptomics. Changes in protein abundance were found much less frequently than transcriptomic changes. While challenges remain, our study provides strong and novel evidence supporting the notion that these three omic technologies can be used in an integrated manner to facilitate a more complete understanding of pathway perturbations and POD determinations for risk assessment of chemical exposures.

Development of a quantitative proteomics approach for cyclooxygenases and lipoxygenases in parallel to quantitative oxylipin analysis allowing the comprehensive investigation of the arachidonic acid cascade

Oxylipins derived from the cyclooxygenase (COX) and lipoxygenase (LOX) pathways of the arachidonic acid (ARA) cascade are essential for the regulation of the inflammatory response and many other physiological functions. Comprehensive analytical methods comprised of oxylipin and protein abundance analysis are required to fully understand mechanisms leading to changes within these pathways. Here, we describe the development of a quantitative multi-omics approach combining liquid chromatography tandem mass spectrometry-based targeted oxylipin metabolomics and proteomics. As the first targeted proteomics method to cover these pathways, it enables the quantitative analysis of all human COX (COX-1 and COX-2) and relevant LOX pathway enzymes (5-LOX, 12-LOX, 15-LOX, 15-LOX-2, and FLAP) in parallel to the analysis of 239 oxylipins with our targeted oxylipin metabolomics method from a single sample. The detailed comparison between MRM3 and classical MRM-based detection in proteomics showed increased selectivity for MRM3, while MRM performed better in terms of sensitivity (LLOQ, 16-122 pM vs. 75-840 pM for the same peptides), linear range (up to 1.5-7.4 μM vs. 4-368 nM), and multiplexing capacities. Thus, the MRM mode was more favorable for this pathway analysis. With this sensitive multi-omics approach, we comprehensively characterized oxylipin and protein patterns in the human monocytic cell line THP-1 and differently polarized primary macrophages. Finally, the quantification of changes in protein and oxylipin levels induced by lipopolysaccharide stimulation and pharmaceutical treatment demonstrates its usefulness to study molecular modes of action involved in the modulation of the ARA cascade.

Dynamic Metabolic and Transcriptional Responses of Proteasome-Inhibited Neurons

Proteasome inhibition is associated with parkinsonian pathology in vivo and degeneration of dopaminergic neurons in vitro. We explored here the metabolome (386 metabolites) and transcriptome (3257 transcripts) regulations of human LUHMES neurons, following exposure to MG-132 [100 nM]. This proteasome inhibitor killed cells within 24 h but did not reduce viability for 12 h. Overall, 206 metabolites were changed in live neurons. The early (3 h) metabolome changes suggested a compromised energy metabolism. For instance, AMP, NADH and lactate were up-regulated, while glycolytic and citric acid cycle intermediates were down-regulated. At later time points, glutathione-related metabolites were up-regulated, most likely by an early oxidative stress response and activation of NRF2/ATF4 target genes. The transcriptome pattern confirmed proteostatic stress (fast up-regulation of proteasome subunits) and also suggested the progressive activation of additional stress response pathways. The early ones (e.g., HIF-1, NF-kB, HSF-1) can be considered a cytoprotective cellular counter-regulation, which maintained cell viability. For instance, a very strong up-regulation of AIFM2 (=FSP1) may have prevented fast ferroptotic death. For most of the initial period, a definite life-death decision was not taken, as neurons could be rescued for at least 10 h after the start of proteasome inhibition. Late responses involved p53 activation and catabolic processes such as a loss of pyrimidine synthesis intermediates. We interpret this as a phase of co-occurrence of protective and maladaptive cellular changes. Altogether, this combined metabolomics-transcriptomics analysis informs on responses triggered in neurons by proteasome dysfunction that may be targeted by novel therapeutic intervention in Parkinson's disease.

Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers

Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late-onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell-type enrichment analyses. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA-7 null mice, a mouse model of one of the genes associated with late-onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over-represented across the AD multi-omics data sets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms-metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabolic alterations.

Metabolomics: Going Deeper, Going Broader, Going Further

Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.

Multi-omics data fusion using adaptive GTO guided Non-negative matrix factorization for cancer subtype discovery

BACKGROUND AND OBJECTIVE

Cancer subtype discovery is essential for personalized clinical treatment. With the onset of progressive profile techniques for cancer, a large amount of heterogeneous and high-dimensional transcriptomic, proteomic and genomic datasets are easily accumulated. Integrative clustering of such multi-omics data is crucial to recognize their latent structure and to acknowledge the correlation within and across them. Although the integrative analysis of diversified multi-omics data is informative, it is challenging when multiplicity in data inflicts poor accordance w.r.t. clustering structure. The objective of this work is to develop an effective integrative analysis framework that encapsulates the heterogeneity of various biological mechanisms and predicts homogeneous subgroups of cancer patients.

METHOD

In this paper, improved sparse-joint non-negative matrix factorization (sparse-jNMF) has been devised for the problem of cancer-subtype discovery. The initial points of sparse-jNMF have improved using a novel meta-heuristic algorithm adaptive gorilla troops optimizer (Ada-GTO). Improving the initialization of sparse-jNMF enhances its convergence behavior and further strengthens the clustering performance. In addition, the consensus clustering approach has been adopted to construct a patient-patient similarity matrix for obtaining stable clusters of patient samples.

RESULT

The proposed framework has been applied to 4 different real-life multi-omics cancer datasets, namely colon adenocarcinoma, breast-invasive carcinoma, kidney-renal clear-cell carcinoma, and lung adenocarcinoma. The proposed method results in patient clusters with better silhouette scores and cluster purity than classical initialization and similar meta-heuristics for initial point estimation approaches. Survival probabilities estimated using Kaplan-Meier (KM) curve show statistically significant difference (p < 0.05) for the homogenous cancer patient clusters obtained using the proposed method as compared to iCluster. The presented approach further identified the somatic mutations for the classified subgroups, which is beneficial to provide targeted treatments.

CONCLUSION

This paper proposes Ada-GTO guided sparse-jNMF framework for cancer subtype discovery, considering the information from multiple omic features that provide comprehension. The proposed meta-guided framework outperforms all other state-of-the-art counterparts. It also has great potential for obtaining the homogeneous subgroups of other diseases.

Integration of High-Throughput Imaging and Multiparametric Metabolic Profiling Reveals a Mitochondrial Mechanism of Tenofovir Toxicity

Nephrotoxicity is a major cause of kidney disease and failure in drug development, but understanding of cellular mechanisms is limited, highlighting the need for better experimental models and methodological approaches. Most nephrotoxins damage the proximal tubule (PT), causing functional impairment of solute reabsorption and systemic metabolic complications. The antiviral drug tenofovir disoproxil fumarate (TDF) is an archetypal nephrotoxin, inducing mitochondrial abnormalities and urinary solute wasting, for reasons that were previously unclear. Here, we developed an automated, high-throughput imaging pipeline to screen the effects of TDF on solute transport and mitochondrial morphology in human-derived RPTEC/TERT1 cells, and leveraged this to generate realistic models of functional toxicity. By applying multiparametric metabolic profiling-including oxygen consumption measurements, metabolomics, and transcriptomics-we elucidated a highly robust molecular fingerprint of TDF exposure. Crucially, we identified that the active metabolite inhibits complex V (ATP synthase), and that TDF treatment causes rapid, dose-dependent loss of complex V activity and expression. Moreover, we found evidence of complex V suppression in kidney biopsies from humans with TDF toxicity. Thus, we demonstrate an effective and convenient experimental approach to screen for disease relevant functional defects in kidney cells in vitro, and reveal a new paradigm for understanding the pathogenesis of a substantial cause of nephrotoxicity.

Investigation of the Exometabolomic Profiles of Rat Islets of Langerhans Cultured in Microfluidic Biochip

Diabetes mellitus (DM) is a complex disease with high prevalence of comorbidity and mortality. DM is predicted to reach more than 700 million people by 2045. In recent years, several advanced in vitro models and analytical tools were developed to investigate the pancreatic tissue response to pathological situations and identify therapeutic solutions. Of all the in vitro promising models, cell culture in microfluidic biochip allows the reproduction of in-vivo-like micro-environments. Here, we cultured rat islets of Langerhans using dynamic cultures in microfluidic biochips. The dynamic cultures were compared to static islets cultures in Petri. The islets' exometabolomic signatures, with and without GLP1 and isradipine treatments, were characterized by GC-MS. Compared to Petri, biochip culture contributes to maintaining high secretions of insulin, C-peptide and glucagon. The exometabolomic profiling revealed 22 and 18 metabolites differentially expressed between Petri and biochip on Day 3 and 5. These metabolites illustrated the increase in lipid metabolism, the perturbation of the pentose phosphate pathway and the TCA cycle in biochip. After drug stimulations, the exometabolome of biochip culture appeared more perturbed than the Petri exometabolome. The GLP1 contributed to the increase in the levels of glycolysis, pentose phosphate and glutathione pathways intermediates, whereas isradipine led to reduced levels of lipids and carbohydrates.

Studying the Impact of Persistent Organic Pollutants Exposure on Human Health by Proteomic Analysis: A Systematic Review

Persistent organic pollutants (POPs) are organic chemical substances that are widely distributed in environments around the globe. POPs accumulate in living organisms and are found at high concentrations in the food chain. Humans are thus continuously exposed to these chemical substances, in which they exert hepatic, reproductive, developmental, behavioral, neurologic, endocrine, cardiovascular, and immunologic adverse health effects. However, considerable information is unknown regarding the mechanism by which POPs exert their adverse effects in humans, as well as the molecular and cellular responses involved. Data are notably lacking concerning the consequences of acute and chronic POP exposure on changes in gene expression, protein profile, and metabolic pathways. We conducted a systematic review to provide a synthesis of knowledge of POPs arising from proteomics-based research. The data source used for this review was PubMed. This study was carried out following the PRISMA guidelines. Of the 742 items originally identified, 89 were considered in the review. This review presents a comprehensive overview of the most recent research and available solutions to explore proteomics datasets to identify new features relevant to human health. Future perspectives in proteomics studies are discussed.

Computational approaches for network-based integrative multi-omics analysis

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

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.

Effects of nanoplastic exposure on the immunity and metabolism of red crayfish (Cherax quadricarinatus) based on high-throughput sequencing

Previous studies have shown that nanoplastics (NPs) are harmful pollutants that threaten aquatic organisms and ecosystems, however, less research has been conducted on the hazards of NPs for aquaculture animals. In this study, Cherax quadricarinatus was used as an experimental model to evaluate the possible effects of three concentrations (25, 250 and 2500 μg/L) of NPs on red crayfish. The toxicological effects of NPs on this species were investigated based on transcriptomics and microbiome. A total of 67,668 genes were obtained from the transcriptome. The annotation rate of the four major libraries (Nr, KEGG, KOG, Swissprot) was 40.17 %, and the functions of differential genes were mainly related to antioxidant activity, metabolism and immune processes. During the experiment, the activities of superoxide dismutase (SOD) and catalase (CAT) in the high concentration group were significantly decreased, while the concentration of malondialdehyde (MDA) increased after nanoplastics (NPs) exposure, and SOD1, Jafrac1 were significantly reduced at high concentrations. expression is inhibited. The immune genes LYZ and PPO2 were highly expressed at low concentrations and suppressed at high concentrations. After 14 days of exposure to NPs, significant changes in gut microbiota were observed, such as decreased abundances of Actinobacteria, Bacteroidetes, and Firmicutes. NPs compromise host health by inducing changes in microbial communities and the production of beneficial bacterial metabolites. Overall, these results suggest that NPs affect immune-related gene expression and antioxidant enzyme activity in red crayfish and cause redox imbalance in the body, altering the composition and diversity of the gut microbiota.