An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization.

BACKGROUND: Nanomaterials (NMs) can be fine-tuned in their properties resulting in a high number of variants, each requiring a thorough safety assessment. Grouping and categorization approaches that would reduce the amount of testing are in principle existing for NMs but are still mostly conceptual. One drawback is the limited mechanistic understanding of NM toxicity. Thus, we conducted a multi-omics in vitro study in RLE-6TN rat alveolar epithelial cells involving 12 NMs covering different materials and including a systematic variation of particle size, surface charge and hydrophobicity for SiO2 NMs. Cellular responses were analyzed by global proteomics, targeted metabolomics and SH2 profiling. Results were integrated using Weighted Gene Correlation Network Analysis (WGCNA). RESULTS: Cluster analyses involving all data sets separated Graphene Oxide, TiO2_NM105, SiO2_40 and Phthalocyanine Blue from the other NMs as their cellular responses showed a high degree of similarities, although apical in vivo results may differ. SiO2_7 behaved differently but still induced significant changes. In contrast, the remaining NMs were more similar to untreated controls. WGCNA revealed correlations of specific physico-chemical properties such as agglomerate size and redox potential to cellular responses. A key driver analysis could identify biomolecules being highly correlated to the observed effects, which might be representative biomarker candidates. Key drivers in our study were mainly related to oxidative stress responses and apoptosis. CONCLUSIONS: Our multi-omics approach involving proteomics, metabolomics and SH2 profiling proved useful to obtain insights into NMs Mode of Actions. Integrating results allowed for a more robust NM categorization. Moreover, key physico-chemical properties strongly correlating with NM toxicity were identified. Finally, we suggest several key drivers of toxicity that bear the potential to improve future testing and assessment approaches.

Genome-wide association analysis on normal hearing function identifies PCDH20 and SLC28A3 as candidates for hearing function and loss.

Hearing loss and individual differences in normal hearing both have a substantial genetic basis. Although many new genes contributing to deafness have been identified, very little is known about genes/variants modulating the normal range of hearing ability. To fill this gap, we performed a two-stage meta-analysis on hearing thresholds (tested at 0.25, 0.5, 1, 2, 4, 8 kHz) and on pure-tone averages (low-, medium- and high-frequency thresholds grouped) in several isolated populations from Italy and Central Asia (total N = 2636). Here, we detected two genome-wide significant loci close to PCDH20 and SLC28A3 (top hits: rs78043697, P = 4.71E-10 and rs7032430, P = 2.39E-09, respectively). For both loci, we sought replication in two independent cohorts: B58C from the UK (N = 5892) and FITSA from Finland (N = 270). Both loci were successfully replicated at a nominal level of significance (P < 0.05). In order to confirm our quantitative findings, we carried out RT-PCR and reported RNA-Seq data, which showed that both genes are expressed in mouse inner ear, especially in hair cells, further suggesting them as good candidates for modulatory genes in the auditory system. Sequencing data revealed no functional variants in the coding region of PCDH20 or SLC28A3, suggesting that variation in regulatory sequences may affect expression. Overall, these results contribute to a better understanding of the complex mechanisms underlying human hearing function.

Hormone Replacement Therapy Associated White Blood Cell DNA Methylation and Gene Expression are Associated With Within-Pair Differences of Body Adiposity and Bone Mass.

The loss of estrogen during menopause causes changes in the female body, with wide-ranging effects on health. Estrogen-containing hormone replacement therapy (HRT) leads to a relief of typical menopausal symptoms, benefits bone and muscle health, and is associated with tissue-specific gene expression profiles. As gene expression is controlled by epigenetic factors (including DNA methylation), many of which are environmentally sensitive, it is plausible that at least part of the HRT-associated gene expression is due to changes in DNA methylation profile. We investigated genome-wide DNA methylation and gene expression patterns of white blood cells (WBCs) and their associations with body composition, including muscle and bone measures of monozygotic (MZ) female twin pairs discordant for HRT. We identified 7,855 nominally significant differentially methylated regions (DMRs) associated with 4,044 genes. Of the genes with DMRs, five (ACBA1, CCL5, FASLG, PPP2R2B, and UHRF1) were also differentially expressed. All have been previously associated with HRT or estrogenic regulation, but not with HRT-associated DNA methylation. All five genes were associated with bone mineral content (BMC), and ABCA1, FASLG, and UHRF1 were also associated with body adiposity. Our study is the first to show that HRT associates with genome-wide DNA methylation alterations in WBCs. Moreover, we show that five differentially expressed genes with DMRs associate with clinical measures, including body fat percentage, lean body mass, bone mass, and blood lipids. Our results indicate that at least part of the known beneficial HRT effects on body composition and bone mass may be regulated by DNA methylation associated alterations in gene expression in circulating WBCs.

Meta-Analysis of Integrated Proteomic and Transcriptomic Data Discerns Structure-Activity Relationship of Carbon Materials with Different Morphologies.

The Fiber Pathogenicity Paradigm (FPP) establishes connections between fiber structure, durability, and disease-causing potential observed in materials like asbestos and synthetic fibers. While emerging nanofibers are anticipated to exhibit pathogenic traits according to the FPP, their nanoscale diameter limits rigidity, leading to tangling and loss of fiber characteristics. The absence of validated rigidity measurement methods complicates nanofiber toxicity assessment. By comprehensively analyzing 89 transcriptomics and 37 proteomics studies, this study aims to enhance carbon material toxicity understanding and proposes an alternative strategy to assess morphology-driven toxicity. Carbon materials are categorized as non-fibrous, high aspect ratio with shorter lengths, tangled, and rigid fibers. Mitsui-7 serves as a benchmark for pathogenic fibers. The meta-analysis reveals distinct cellular changes for each category, effectively distinguishing rigid fibers from other carbon materials. Subsequently, a robust random forest model is developed to predict morphology, unveiling the pathogenicity of previously deemed non-pathogenic NM-400 due to its secondary structures. This study fills a crucial gap in nanosafety by linking toxicological effects to material morphology, in particular regarding fibers. It demonstrates the significant impact of morphology on toxicological behavior and the necessity of integrating morphological considerations into regulatory frameworks.

PROTEOMAS: a workflow enabling harmonized proteomic meta-analysis and proteomic signature mapping.

Toxicological evaluation of substances in regulation still often relies on animal experiments. Understanding the substances' mode-of-action is crucial to develop alternative test strategies. Omics methods are promising tools to achieve this goal. Until now, most attention was focused on transcriptomics, while proteomics is not yet routinely applied in toxicology despite the large number of datasets available in public repositories. Exploiting the full potential of these datasets is hampered by differences in measurement procedures and follow-up data processing. Here we present the tool PROTEOMAS, which allows meta-analysis of proteomic data from public origin. The workflow was designed for analyzing proteomic studies in a harmonized way and to ensure transparency in the analysis of proteomic data for regulatory purposes. It agrees with the Omics Reporting Framework guidelines of the OECD with the intention to integrate proteomics to other omic methods in regulatory toxicology. The overarching aim is to contribute to the development of AOPs and to understand the mode of action of substances. To demonstrate the robustness and reliability of our workflow we compared our results to those of the original studies. As a case study, we performed a meta-analysis of 25 proteomic datasets to investigate the toxicological effects of nanomaterials at the lung level. PROTEOMAS is an important contribution to the development of alternative test strategies enabling robust meta-analysis of proteomic data. This workflow commits to the FAIR principles (Findable, Accessible, Interoperable and Reusable) of computational protocols.

Metabolomics profiling to investigate nanomaterial toxicity in vitro and in vivo.

Nanomaterials (NMs) can be produced in plenty of variants posing several challenges for NM hazard and risk assessment. Metabolomic profiling of NM-treated cells and tissues allows for insights into underlying Mode-of-Action (MoA) and offers several advantages in this context. It supports the description of Adverse Outcome Pathways (AOPs) and, therefore, tailored AOP-based hazard testing strategies. Moreover, it bears great potential for biomarker discovery supporting toxicity prediction. Here, we applied metabolomics profiling to cells treated with four well-selected SiO2 variants, differing in structure, size and surface charge. TiO2 NM-105 served as a benchmark. Responses were studied in vitro in rat lung epithelial cells (RLE-6TN) and alveolar macrophages (NR8383) and compared to in vivo responses in rat lung tissues obtained from in vivo instillation and short-term inhalation studies (STIS). Time- and concentration-dependent changes were observed in both in vitro models but with cell-type specific responses. Overall, the levels of lipids and biogenic amines (BAs) tended to increase in epithelial cells but decreased in macrophages. Many identified metabolites like Met-SO, hydroxy-Pro and spermidine were related to oxidative stress, indicating that oxidative stress contributes to the MoA for the selected NMs. Several biomarker candidates such as Asp, Asn, Ser, Pro, spermidine, putrescine and LysoPCaC16:1 were identified in vitro and verified in vivo. In this study, we successfully applied a metabolomics workflow for in vitro and in vivo samples, which proved to be well suited to identify potential biomarkers, to gain insights into NM structure-activity relationship and into the underlying MoA.