Targeting histone deacetylase in cardiac diseases.

Histone deacetylases (HDAC) catalyze the removal of acetylation modifications on histones and non-histone proteins, which regulates gene expression and other cellular processes. HDAC inhibitors (HDACi), approved anti-cancer agents, emerge as a potential new therapy for heart diseases. Cardioprotective effects of HDACi are observed in many preclinical animal models of heart diseases. Genetic mouse models have been developed to understand the role of each HDAC in cardiac functions. Some of the findings are controversial. Here, we provide an overview of how HDACi and HDAC impact cardiac functions under physiological or pathological conditions. We focus on in vivo studies of zinc-dependent classical HDACs, emphasizing disease conditions involving cardiac hypertrophy, myocardial infarction (MI), ischemic reperfusion (I/R) injury, and heart failure. In particular, we review how non-biased omics studies can help our understanding of the mechanisms underlying the cardiac effects of HDACi and HDAC.

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer.

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

Multi-omic validation of the cuproptosis-sphingolipid metabolism network: modulating the immune landscape in osteosarcoma.

Background: The current understanding of the mechanisms by which metal ion metabolism promotes the progression and drug resistance of osteosarcoma remains incomplete. This study aims to elucidate the key roles and mechanisms of genes involved in cuproptosis-related sphingolipid metabolism (cuproptosis-SPGs) in regulating the immune landscape, tumor metastasis, and drug resistance in osteosarcoma cells. Methods: This study employed multi-omics approaches to assess the impact of cuproptosis-SPGs on the prognosis of osteosarcoma patients. Lasso regression analysis was utilized to construct a prognostic model, while multivariate regression analysis was applied to identify key core genes and generate risk coefficients for these genes, thereby calculating a risk score for each osteosarcoma patient. Patients were then stratified into high-risk and low-risk groups based on their risk scores. The ESTIMATE and CIBERSORT algorithms were used to analyze the level of immune cell infiltration within these risk groups to construct the immune landscape. Single-cell analysis was conducted to provide a more precise depiction of the expression patterns of cuproptosis-SPGs among immune cell subtypes. Finally, experiments on osteosarcoma cells were performed to validate the role of the cuproptosis-sphingolipid signaling network in regulating cell migration and apoptosis. Results: In this study, seven cuproptosis-SPGs were identified and used to construct a prognostic model for osteosarcoma patients. In addition to predicting survival, the model also demonstrated reliability in forecasting the response to chemotherapy drugs. The results showed that a high cuproptosis-sphingolipid metabolism score was closely associated with reduced CD8 T cell infiltration and indicated poor prognosis in osteosarcoma patients. Cellular functional assays revealed that cuproptosis-SPGs regulated the LC3B/ERK signaling pathway, thereby triggering cell death and impairing migration capabilities in osteosarcoma cells. Conclusion: The impact of cuproptosis-related sphingolipid metabolism on the survival and migration of osteosarcoma cells, as well as on CD8 T cell infiltration, highlights the potential of targeting copper ion metabolism as a promising strategy for osteosarcoma patients.

Mitophagy and clear cell renal cell carcinoma: insights from single-cell and spatial transcriptomics analysis.

Background: Clear Cell Renal Cell Carcinoma (ccRCC) is the most common type of kidney cancer, characterized by high heterogeneity and complexity. Recent studies have identified mitochondrial defects and autophagy as key players in the development of ccRCC. This study aims to delve into the changes in mitophagic activity within ccRCC and its impact on the tumor microenvironment, revealing its role in tumor cell metabolism, development, and survival strategies. Methods: Comprehensive analysis of ccRCC tumor tissues using single cell sequencing and spatial transcriptomics to reveal the role of mitophagy in ccRCC. Mitophagy was determined to be altered among renal clear cells by gene set scoring. Key mitophagy cell populations and key prognostic genes were identified using NMF analysis and survival analysis approaches. The role of UBB in ccRCC was also demonstrated by in vitro experiments. Results: Compared to normal kidney tissue, various cell types within ccRCC tumor tissues exhibited significantly increased levels of mitophagy, especially renal clear cells. Key genes associated with increased mitophagy levels, such as UBC, UBA52, TOMM7, UBB, MAP1LC3B, and CSNK2B, were identified, with their high expression closely linked to poor patient prognosis. Particularly, the ubiquitination process involving the UBB gene was found to be crucial for mitophagy and its quality control. Conclusion: This study highlights the central role of mitophagy and its regulatory factors in the development of ccRCC, revealing the significance of the UBB gene and its associated ubiquitination process in disease progression.

A cross-omics data analysis strategy for metabolite-microbe pair identification.

Given the pivotal roles of metabolomics and microbiomics, numerous data mining approaches aim to uncover their intricate connections. However, the complex many-to-many associations between metabolome-microbiome profiles yield numerous statistically significant but biologically unvalidated candidates. To address these challenges, we introduce BiOFI, a strategic framework for identifying metabolome-microbiome correlation pairs (Bi-Omics). BiOFI employs a comprehensive scoring system, incorporating intergroup differences, effects on feature correlation networks, and organism abundance. Meanwhile, it establishes a built-in database of metabolite-microbe-KEGG functional pathway linking relationships. Furthermore, BiOFI can rank related feature pairs by combining importance scores and correlation strength. Validation on a dataset of cesarean-section infants confirms the strategy's validity and interpretability. The BiOFI R package is freely accessible at https://github.com/chentianlu/BiOFI.

Metabolomics in sturgeon research: a mini-review.

Sturgeons are ancient fish, with 27 species distributed in the Northern Hemisphere. This review first touches upon the significance of sturgeons in the context of their biological, ecological, and economic importance, highlighting their status as "living fossils" and the challenges they face in genomic research due to their diverse chromosome numbers. This review then discusses how omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have been used in sturgeon research, which so far has only been done on Acipenser species. It focuses on metabolomics as a way to better understand how sturgeons work and how they react to their environment. Specific studies in sturgeon metabolomics are cited, showing how metabolomics has been used to investigate various aspects of sturgeon biology, such as growth, reproduction, stress responses, and nutrition. These studies demonstrate the potential of metabolomics in improving sturgeon aquaculture practices and conservation efforts. Overall, the review suggests that metabolomics, as a relatively new scientific tool, has the potential to enhance our understanding of sturgeon biology and aid in their conservation and sustainable aquaculture, contributing to global food security efforts.

Nutraceutical prospects of genetically engineered cyanobacteria- technological updates and significance.

Genetically engineered cyanobacterial strains that have improved growth rate, biomass productivity, and metabolite productivity could be a better option for sustainable bio-metabolite production. The global demand for biobased metabolites with nutraceuticals and health benefits has increased due to their safety and plausible therapeutic and nutritional utility. Cyanobacteria are solar-powered green cellular factories that can be genetically tuned to produce metabolites with nutraceutical and pharmaceutical benefits. The present review discusses biotechnological endeavors for producing bioprospective compounds from genetically engineered cyanobacteria and discusses the challenges and troubleshooting faced during metabolite production. This review explores the cyanobacterial versatility, the use of engineered strains, and the techno-economic challenges associated with scaling up metabolite production from cyanobacteria. Challenges to produce cyanobacterial bioactive compounds with remarkable nutraceutical values have been discussed. Additionally, this review also summarises the challenges and future prospects of metabolite production from genetically engineered cyanobacteria as a sustainable approach.

PPARγ/NF-κB axis contributes to cold-induced resolution of experimental colitis and preservation of intestinal barrier.

BACKGROUND: Environmental stress is a significant contributor to the development of inflammatory bowel disease (IBD). The involvement of temperature stimulation in the development of IBD remains uncertain. Our preliminary statistical data suggest that the prevalence of IBD is slightly lower in colder regions compared to non-cold regions. The observation indicates that temperature changes may play a key role in the occurrence and progression of IBD. Here, we hypothesized that cold stress has a protective effect on IBD. METHODS: The cold exposure model for mice was placed in a constant temperature and humidity chamber, maintained at a temperature of 4 °C. Colitis models were induced in the mice using TNBS or DSS. To promote the detection methods more clinically, fluorescence confocal endoscopy was used to observe the mucosal microcirculation status of the colon in the live model. Changes in the colonic wall of the mice were detected using 9.4 T Magnetic Resonance Imaging (MRI) imaging and in vivo fluorescence imaging. Hematoxylin and eosin (H&E) and Immunofluorescence (IF) staining confirmed the pathological alterations in the colons of sacrificed mice. Molecular changes at the protein level were assessed through Western blotting and Enzyme-Linked Immunosorbent Assay (ELISA) assays. RNA sequencing (RNA-seq) and metabolomics (n = 18) were jointly analyzed to investigate the biological changes in the colon of mice treated by cold exposure. RESULTS: Cold exposure decreased the pathologic and disease activity index scores in a mouse model. Endomicroscopy revealed that cold exposure preserved colonic mucosal microcirculation, and 9.4 T MRI imaging revealed alleviation of intestinal wall thickness. In addition, the expression of the TLR4 and PP65 proteins was downregulated and epithelial cell junctions were strengthened after cold exposure. Intriguingly, we found that cold exposure reversed the decrease in ZO-1 and occludin protein levels in dextran sulfate sodium (DSS)- and trinitrobenzenesulfonic acid-induced colitis mouse models. Multi-omics analysis revealed the biological landscape of DSS-induced colitis under cold exposure and identified that the peroxisome proliferator-activated receptor (PPAR) signaling pathway mediates the effects of cold on colitis. Subsequent administration of rosiglitazone (PPAR agonist) enhanced the protective effect of cold exposure on colitis, whereas GW9662 (PPAR antagonist) administration mitigated these protective effects. Overall, cold exposure ameliorated the progression of mouse colitis through the PPARγ/NF-κB signaling axis and preserved the intestinal mucosal barrier. CONCLUSION: Our study provides a mechanistic link between intestinal inflammation and cold exposure, providing a theoretical framework for understanding the differences in the prevalence of IBD between the colder regions and non-cold regions, and offering new insights into IBD therapy.

Corrigendum: Transcriptome-wide analyses of early immune responses in lumpfish leukocytes upon stimulation with poly(I:C).

[This corrects the article DOI: 10.3389/fimmu.2023.1198211.].

DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification.

Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies in meaningfully integrating these diverse views. Existing methods often require the same type of data from all views (cross-sectional data only or longitudinal data only) or do not consider any class outcome in the integration method, which presents limitations. To overcome these limitations, we have developed a pipeline that harnesses the power of statistical and deep learning methods to integrate cross-sectional and longitudinal data from multiple sources. In addition, it identifies key variables that contribute to the association between views and the separation between classes, providing deeper biological insights. This pipeline includes variable selection/ranking using linear and nonlinear methods, feature extraction using functional principal component analysis and Euler characteristics, and joint integration and classification using dense feed-forward networks for cross-sectional data and recurrent neural networks for longitudinal data. We applied this pipeline to cross-sectional and longitudinal multiomics data (metagenomics, transcriptomics and metabolomics) from an inflammatory bowel disease (IBD) study and identified microbial pathways, metabolites and genes that discriminate by IBD status, providing information on the etiology of IBD. We conducted simulations to compare the two feature extraction methods.

An Integrated Multi-omics prediction model for stroke recurrence based on Lnet transformer layer and dynamic weighting mechanism.

BACKGROUND AND OBJECTIVE: Stroke is a disease with high mortality and disability. Importantly, the fatality rate demonstrates a significant increase among patients afflicted by recurrent strokes compared to those experiencing their initial stroke episode. Currently, the existing research encounters three primary challenges. The first is the lack of a reliable, multi-omics image dataset related to stroke recurrence. The second is how to establish a high-performance feature extraction model and eliminate noise from continuous magnetic resonance imaging (MRI) data. The third is how to integration multi-omics data and dynamically weighted for different omics data. METHODS: We systematically compiled MRI and conventional detection data from a cohort comprising 737 stroke patients and established PSTSZC, a multi-omics dataset for predicting stroke recurrence. We introduced the first-ever Integrated Multi-omics Prediction Model for Stroke Recurrence, MPSR, which is based on ResNet, Lnet-transformer, LSTM and dynamically weighted DNN. The MPSR model comprises two principal modules, the Feature Extraction Module, and the Integrated Multi-Omics Prediction Module. In the Feature Extraction module, we proposed a novel Lnet regularization layer, which effectively addresses noise issues in MRI data. In the Integrated Multi-omics Prediction Module, we propose a dynamic weighted mechanism based on evaluators, which mitigates the noise impact brought about by low-performance omics. RESULTS: We compared seven single-omics models and six state-of-the-art multi-omics stroke recurrence models. The experimental results demonstrate that the MPSR model exhibited superior performance. The accuracy, AUROC, specificity, and sensitivity of the MPSR model can reach 0.96, 0.97, 1, and 0.94, respectively, which is higher than the results of contrast model. CONCLUSION: MPSR is the first available high-performance multi-omics prediction model for stroke recurrence. We assert that the MPSR model holds the potential to function as a valuable tool in assisting clinicians in accurately diagnosing individuals with a predisposition to stroke recurrence.

Advances in Physalis molecular research: applications in authentication, genetic diversity, phylogenetics, functional genes, and omics.

The plants of the genus Physalis L. have been extensively utilized in traditional and indigenous Chinese medicinal practices for treating a variety of ailments, including dermatitis, malaria, asthma, hepatitis, and liver disorders. The present review aims to achieve a comprehensive and up-to-date investigation of the genus Physalis, a new model crop, to understand plant diversity and fruit development. Several chloroplast DNA-, nuclear ribosomal DNA-, and genomic DNA-based markers, such as psbA-trnH, internal-transcribed spacer (ITS), simple sequence repeat (SSR), random amplified microsatellites (RAMS), sequence-characterized amplified region (SCAR), and single nucleotide polymorphism (SNP), were developed for molecular identification, genetic diversity, and phylogenetic studies of Physalis species. A large number of functional genes involved in inflated calyx syndrome development (AP2-L, MPF2, MPF3, and MAGO), organ growth (AG1, AG2, POS1, and CNR1), and active ingredient metabolism (24ISO, DHCRT, P450-CPL, SR, DUF538, TAS14, and 3ß-HSB) were identified contributing to the breeding of novel Physalis varieties. Various omic studies revealed and functionally identified a series of reproductive organ development-related factors, environmental stress-responsive genes, and active component biosynthesis-related enzymes. The chromosome-level genomes of Physalis floridana Rydb., Physalis grisea (Waterf.) M. Martínez, and Physalis pruinosa L. have been recently published providing a valuable resource for genome editing in Physalis crops. Our review summarizes the recent progress in genetic diversity, molecular identification, phylogenetics, functional genes, and the application of omics in the genus Physalis and accelerates efficient utilization of this traditional herb.

Action of circulating and infiltrating B cells in the immune microenvironment of colorectal cancer by single-cell sequencing analysis.

BACKGROUND: The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer (CRC), one of the most prevalent malignancies worldwide. In this study, multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC. By revealing the heterogeneity and functional differences of B cells in cancer immunity, we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies. AIM: To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC, explore the potential driving mechanism of B cell development, analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules, and search for possible regulatory pathways to promote the anti-tumor effects of B cells. METHODS: A total of 69 paracancer (normal), tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database (https://portal.gdc.cancer.gov/). After the immune cells were sorted by multicolor flow cytometry, the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform, and the data were analyzed using bioinformatics tools such as Seurat. The differences in the number and function of B cell infiltration between tumor and normal tissue, the interaction between B cell subsets and T cells and myeloid cell subsets, and the transcription factor regulatory network of B cell subsets were explored and analyzed. RESULTS: Compared with normal tissue, the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly. Among them, germinal center B cells (GCB) played the most prominent role, with positive clone expansion and heavy chain mutation level increasing, and the trend of differentiation into memory B cells increased. However, the number of plasma cells in the tumor microenvironment decreased significantly, and the plasma cells secreting IgA antibodies decreased most obviously. In addition, compared with the immune microenvironment of normal tissues, GCB cells in tumor tissues became more closely connected with other immune cells such as T cells, and communication molecules that positively regulate immune function were significantly enriched. CONCLUSION: The role of GCB in CRC tumor microenvironment is greatly enhanced, and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level. Meanwhile, GCB has enhanced its association with immune cells in the microenvironment, which plays a positive anti-tumor effect.

The bioinformatics landscape in environmental omics: Lessons from a national ELIXIR survey.

As a research infrastructure with a mission to provide services for bioinformatics, ELIXIR aims to identify and inform its target audiences. Here, we present a survey on a community of researchers studying the environment with omics approaches in Greece, one of the youngest member countries of ELIXIR. Personal interviews followed by quantitative and qualitative analysis were employed to document interactions and practices of the community and to perform a gap analysis for the transition toward multiomics and systems biology. Environmental omics in Greece mostly concerns production of data, in large majority on microbes and non-model organisms. Our survey highlighted (1) the popularity and suitability of targeted hands-on training events; (2) data quality and management issues as important elements for the transition to multiomics, and (3) lack of knowledge and misconceptions regarding interoperability, metadata standards, and pre-registration. The publicly available collected answers represent a valuable resource in view of future strategic planning.

Multi-omics analyses provide insights into the sulfur metabolism of a novel deep-sea sulfate-reducing bacterium.

Sulfate-reducing bacteria (SRB) are ubiquitously distributed across various biospheres and play key roles in global sulfur and carbon cycles. However, few deep-sea SRB have been cultivated and studied in situ, limiting our understanding of the true metabolism of deep-sea SRB. Here, we firstly clarified the high abundance of SRB in deep-sea sediments and successfully isolated a sulfate-reducing bacterium (zrk46) from a cold seep sediment. Our genomic, physiological, and phylogenetic analyses indicate that strain zrk46 is a novel species, which we propose as Pseudodesulfovibrio serpens. We found that supplementation with sulfate, thiosulfate, or sulfite promoted strain zrk46 growth by facilitating energy production through the dissimilatory sulfate reduction, which was coupled to the oxidation of organic matter in both laboratory and deep-sea conditions. Moreover, in situ metatranscriptomic results confirmed that other deep-sea SRB also performed the dissimilatory sulfate reduction, strongly suggesting that SRB may play undocumented roles in deep-sea sulfur cycling.

Metaproteomics reveals parallel utilization of colonic mucin glycans and dietary fibers by the human gut microbiota.

A diet lacking dietary fibers promotes the expansion of gut microbiota members that can degrade host glycans, such as those on mucins. The microbial foraging on mucin has been associated with disruptions of the gut-protective mucus layer and colonic inflammation. Yet, it remains unclear how the co-utilization of mucin and dietary fibers affects the microbiota composition and metabolic activity. Here, we used 14 dietary fibers and porcine colonic and gastric mucins to study the dynamics of mucin and dietary fiber utilization by the human fecal microbiota in vitro. Combining metaproteome and metabolites analyses revealed the central role of the Bacteroides genus in the utilization of complex fibers together with mucin while Akkermansia muciniphila was the main utilizer of sole porcine colonic mucin but not gastric mucin. This study gives a broad overview of the colonic environment in response to dietary and host glycan availability.

Diagnostic and prognostic role of LINC01767 in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a primary contributor to cancer-related mortality on a global scale. However, the underlying molecular mechanisms are still poorly understood. Long noncoding RNAs are emerging markers for HCC diagnosis, prognosis, and therapeutic target. No study of LINC01767 in HCC was published. AIM: To conduct a multi-omics analysis to explore the roles of LINC01767 in HCC for the first time. METHODS: DESeq2 Package was used to analyze different gene expressions. Receiver operating characteristic curves assessed the diagnostic performance. Kaplan-Meier univariate and Cox multivariate analyses were used to perform survival analysis. The least absolute shrinkage and selection operator (LASSO)-Cox was used to identify the prediction model. Subsequent to the validation of LINC01767 expression in HCC fresh frozen tissues through quantitative real time polymerase chain reaction, next generation sequencing was performed following LINC01767 over expression (GSE243371), and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes/Gene Set Enrichment Analysis/ingenuity pathway analysis was carried out. In vitro experiment in Huh7 cell was carried out. RESULTS: LINC01767 was down-regulated in HCC with a log fold change = 1.575 and was positively correlated with the cancer stemness. LINC01767 was a good diagnostic marker with area under the curve (AUC) [0.801, 95% confidence interval (CI): 0.751-0.852, P = 0.0106] and an independent predictor for overall survival (OS) with hazard ratio = 1.899 (95%CI: 1.01-3.58, P = 0.048). LINC01767 nomogram model showed a satisfied performance. The top-ranked regulatory network analysis of LINC01767 showed the regulation of genes participating various pathways. LASSO regression identified the 9-genes model showing a more satisfied performance than 5-genes model to predict the OS with AUC > 0.75. LINC01767 was down-expressed obviously in tumor than para-tumor tissues in our cohort as well as in cancer cell line; the over expression of LINC01767 inhibit cell proliferation and clone formation of Huh7 in vitro. CONCLUSION: LINC01767 was an important tumor suppressor gene in HCC with good diagnostic and prognostic performance.

Plasma and CSF biomarkers of aging and cognitive decline in Caribbean vervets.

INTRODUCTION: Vervets are non-human primates that share high genetic homology with humans and develop amyloid beta (Aß) pathology with aging. We expand current knowledge by examining Aß pathology, aging, cognition, and biomarker proteomics. METHODS: Amyloid immunoreactivity in the frontal cortex and temporal cortex/hippocampal regions from archived vervet brain samples ranging from young adulthood to old age was quantified. We also obtained cognitive scores, plasma samples, and cerebrospinal fluid (CSF) samples in additional animals. Plasma and CSF proteins were quantified with platforms utilizing human antibodies. RESULTS: We found age-related increases in Aß deposition in both brain regions. Bioinformatic analyses assessed associations between biomarkers and age, sex, cognition, and CSF Aß levels, revealing changes in proteins related to immune-related inflammation, metabolism, and cellular processes. DISCUSSION: Vervets are an effective model of aging and early-stage Alzheimer's disease, and we provide translational biomarker data that both align with previous results in humans and provide a basis for future investigations. HIGHLIGHTS: We found changes in immune and metabolic plasma biomarkers associated with age and cognition. Cerebrospinal fluid (CSF) biomarkers revealed changes in cell signaling indicative of adaptative processes. TNFRSF19 (TROY) and Artemin co-localize with Alzheimer's disease pathology. Vervets are a relevant model for translational studies of early-stage Alzheimer's disease.

Bioinformatics and machine learning to support nanomaterial grouping.

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.