AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.

deepFPlearn +: enhancing toxicity prediction across the chemical universe using graph neural networks.

SUMMARY: Sophisticated approaches for the in silico prediction of toxicity are required to support the risk assessment of chemicals. The number of chemicals on the global chemical market and the speed of chemical innovation stand in massive contrast to the capacity for regularizing chemical use. We recently proved our ready-to-use application deepFPlearn as a suitable approach for this task. Here, we present its extension deepFPlearn+ incorporating (i) a graph neural network to feed our AI with a more sophisticated molecular structure representation and (ii) alternative train-test splitting strategies that involve scaffold structures and the molecular weights of chemicals. We show that the GNNs outperform the previous model substantially and that our models can generalize on unseen data even with a more robust and challenging test set. Therefore, we highly recommend the application of deepFPlearn+ on the chemical inventory to prioritize chemicals for experimental testing or any chemical subset of interest in monitoring studies. AVAILABILITY AND IMPLEMENTATION: The software is compatible with python 3.6 or higher, and the source code can be found on our GitHub repository: https://github.com/yigbt/deepFPlearn. The data underlying this article are available in Zenodo, and can be accessed with the link below: https://zenodo.org/record/8146252. Detailed installation guides via Docker, Singularity, and Conda are provided within the repository for operability across all operating systems.

Alzheimer-related genes show accelerated evolution.

Alzheimer's disease (AD) is a neurodegenerative disorder of unknown cause with complex genetic and environmental traits. While AD is extremely prevalent in human elderly, it hardly occurs in non-primate mammals and even non-human-primates develop only an incomplete form of the disease. This specificity of AD to human clearly implies a phylogenetic aspect. Still, the evolutionary dimension of AD pathomechanism remains difficult to prove and has not been established so far. To analyze the evolutionary age and dynamics of AD-associated-genes, we established the AD-associated genome-wide RNA-profile comprising both protein-coding and non-protein-coding transcripts. We than applied a systematic analysis on the conservation of splice-sites as a measure of gene-structure based on multiple alignments across vertebrates of homologs of AD-associated-genes. Here, we show that nearly all AD-associated-genes are evolutionarily old and did not originate later in evolution than not-AD-associated-genes. However, the gene-structures of loci, that exhibit AD-associated changes in their expression, evolve faster than the genome at large. While protein-coding-loci exhibit an enhanced rate of small changes in gene structure, non-coding loci show even much larger changes. The accelerated evolution of AD-associated-genes indicates a more rapid functional adaptation of these genes. In particular AD-associated non-coding-genes play an important, as yet largely unexplored, role in AD. This phylogenetic trait indicates that recent adaptive evolution of human brain is causally involved in basic principles of neurodegeneration. It highlights the necessity for a paradigmatic change of our disease-concepts and to reconsider the appropriateness of current animal-models to develop disease-modifying strategies that can be translated to human.

multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data.

BACKGROUND: Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well. In recent years the call for a combined analysis of multiple omics layers became prominent, giving rise to a few multi-omics enrichment tools. Each of these has its own drawbacks and restrictions regarding its universal application. RESULTS: Here, we present the multiGSEA package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layers. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. multiGSEA supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs. CONCLUSIONS: With multiGSEA we introduce a highly versatile tool for multi-omics pathway integration that minimizes previous restrictions in terms of omics layer selection, pathway database availability, organism selection and the mapping of omics feature identifiers. multiGSEA is publicly available under the GPL-3 license at https://github.com/yigbt/multiGSEA and at bioconductor: https://bioconductor.org/packages/multiGSEA .

Prospects and challenges of multi-omics data integration in toxicology.

Exposure of cells or organisms to chemicals can trigger a series of effects at the regulatory pathway level, which involve changes of levels, interactions, and feedback loops of biomolecules of different types. A single-omics technique, e.g., transcriptomics, will detect biomolecules of one type and thus can only capture changes in a small subset of the biological cascade. Therefore, although applying single-omics analyses can lead to the identification of biomarkers for certain exposures, they cannot provide a systemic understanding of toxicity pathways or adverse outcome pathways. Integration of multiple omics data sets promises a substantial improvement in detecting this pathway response to a toxicant, by an increase of information as such and especially by a systemic understanding. Here, we report the findings of a thorough evaluation of the prospects and challenges of multi-omics data integration in toxicological research. We review the availability of such data, discuss options for experimental design, evaluate methods for integration and analysis of multi-omics data, discuss best practices, and identify knowledge gaps. Re-analyzing published data, we demonstrate that multi-omics data integration can considerably improve the confidence in detecting a pathway response. Finally, we argue that more data need to be generated from studies with a multi-omics-focused design, to define which omics layers contribute most to the identification of a pathway response to a toxicant.

A translational silencing function of MCPIP1/Regnase-1 specified by the target site context.

The expression of proteins during inflammatory and immune reactions is coordinated by post-transcriptional mechanisms. A particularly strong suppression of protein expression is exerted by a conserved translational silencing element (TSE) identified in the 3' UTR of NFKBIZ mRNA, which is among the targets of the RNA-binding proteins Roquin-1/2 and MCPIP1/Regnase-1. We present evidence that in the context of the TSE MCPIP1, so far known for its endonuclease activity toward mRNAs specified by distinct stem-loop (SL) structures, also suppresses translation. Overexpression of MCPIP1 silenced translation in a TSE-dependent manner and reduced ribosome occupancy of the mRNA. Correspondingly, MCPIP1 depletion alleviated silencing and increased polysomal association of the mRNA. Translationally silenced NFKBIZ or reporter mRNAs were mostly capped, polyadenylated and ribosome associated. Furthermore, MCPIP1 silenced also cap-independent, CrPV-IRES-dependent translation. This suggests that MCPIP1 suppresses a post-initiation step. The TSE is predicted to form five SL structures. SL4 and 5 resemble target structures reported for MCPIP1 and together were sufficient for MCPIP1 binding and mRNA destabilization. Translational silencing, however, required SL1-3 in addition. Thus the NFKBIZ TSE functions as an RNA element in which sequences adjacent to the site of interaction with MCPIP1 and dispensable for accelerated mRNA degradation extend the functional repertoire of MCPIP1 to translational silencing.

uap: reproducible and robust HTS data analysis.

BACKGROUND: A lack of reproducibility has been repeatedly criticized in computational research. High throughput sequencing (HTS) data analysis is a complex multi-step process. For most of the steps a range of bioinformatic tools is available and for most tools manifold parameters need to be set. Due to this complexity, HTS data analysis is particularly prone to reproducibility and consistency issues. We have defined four criteria that in our opinion ensure a minimal degree of reproducible research for HTS data analysis. A series of workflow management systems is available for assisting complex multi-step data analyses. However, to the best of our knowledge, none of the currently available work flow management systems satisfies all four criteria for reproducible HTS analysis. RESULTS: Here we present uap, a workflow management system dedicated to robust, consistent, and reproducible HTS data analysis. uap is optimized for the application to omics data, but can be easily extended to other complex analyses. It is available under the GNU GPL v3 license at https://github.com/yigbt/uap. CONCLUSIONS: uap is a freely available tool that enables researchers to easily adhere to reproducible research principles for HTS data analyses.

Comparison between transcriptomic responses to short-term stress exposures of a common Holarctic and endemic Lake Baikal amphipods.

BACKGROUND: Lake Baikal is one of the oldest freshwater lakes and has constituted a stable environment for millions of years, in stark contrast to small, transient bodies of water in its immediate vicinity. A highly diverse endemic endemic amphipod fauna is found in one, but not the other habitat. We ask here whether differences in stress response can explain the immiscibility barrier between Lake Baikal and non-Baikal faunas. To this end, we conducted exposure experiments to increased temperature and the toxic heavy metal cadmium as stressors. RESULTS: Here we obtained high-quality de novo transcriptome assemblies, covering mutiple conditions, of three amphipod species, and compared their transcriptomic stress responses. Two of these species, Eulimnogammarus verrucosus and E. cyaneus, are endemic to Lake Baikal, while the Holarctic Gammarus lacustris is a potential invader. CONCLUSIONS: Both Baikal species possess intact stress response systems and respond to elevated temperature with relatively similar changes in their expression profiles. G. lacustris reacts less strongly to the same stressors, possibly because its transcriptome is already perturbed by acclimation conditions.

Specific induction of the unique GPR15 expression in heterogeneous blood lymphocytes by tobacco smoking.

Purpose: In the peripheral blood, it has been shown that smoking is, to date, the only specific condition leading to an increase in GPR15+ T cells. We, therefore, aimed to characterize GPR15-expressing blood T cells in more detail. Materials and Methods: The whole transcriptome by RNAseq as a proxy for protein expression was analyzed in GPR15+ and GPR15- T cells. A deep immuno-phenotyping was conducted for the identification of T cell subtypes. Results: The expression of GPR15 seemed to be unique, not concomitantly accompanied with the expression of another protein. According to different T cell subtypes, there is no single cell type prominently represented in GPR15+ T cells. The individually different proportions of GPR15+ cells among each GPR15-expressing T cell subtypes in blood were strongly associated with chronic smoking. Indeed, the frequency of GPR15+ T cell subtypes can be effectively used as a highly convincing biomarker for tobacco smoking. Conclusions: While the chronic smoking-induced enrichment of GPR15+ T cells in blood might indicate a systemic inflammation, by the widespread presence in different T cell subtypes, GPR15 could feature a general impact on maintaining the systemic homeostasis to putatively prevent harm from smoking.

Maternal phthalate exposure promotes allergic airway inflammation over 2 generations through epigenetic modifications.

BACKGROUND: Prenatal and early postnatal exposures to environmental factors are considered responsible for the increasing prevalence of allergic diseases. Although there is some evidence for allergy-promoting effects in children because of exposure to plasticizers, such as phthalates, findings of previous studies are inconsistent and lack mechanistic information. OBJECTIVE: We investigated the effect of maternal phthalate exposure on asthma development in subsequent generations and their underlying mechanisms, including epigenetic alterations. METHODS: Phthalate metabolites were measured within the prospective mother-child cohort Lifestyle and Environmental Factors and Their Influence on Newborns Allergy Risk (LINA) and correlated with asthma development in the children. A murine transgenerational asthma model was used to identify involved pathways. RESULTS: In LINA maternal urinary concentrations of mono-n-butyl phthalate, a metabolite of butyl benzyl phthalate (BBP), were associated with an increased asthma risk in the children. Using a murine transgenerational asthma model, we demonstrate a direct effect of BBP on asthma severity in the offspring with a persistently increased airway inflammation up to the F2 generation. This disease-promoting effect was mediated by BBP-induced global DNA hypermethylation in CD4+ T cells of the offspring because treatment with a DNA-demethylating agent alleviated exacerbation of allergic airway inflammation. Thirteen transcriptionally downregulated genes linked to promoter or enhancer hypermethylation were identified. Among these, the GATA-3 repressor zinc finger protein 1 (Zfpm1) emerged as a potential mediator of the enhanced susceptibility for TH2-driven allergic asthma. CONCLUSION: These data provide strong evidence that maternal BBP exposure increases the risk for allergic airway inflammation in the offspring by modulating the expression of genes involved in TH2 differentiation through epigenetic alterations.

Dissecting the genetics of the human transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding loci†.

Genetics of gene expression (eQTLs or expression QTLs) has proved an indispensable tool for understanding biological pathways and pathomechanisms of trait-associated SNPs. However, power of most genome-wide eQTL studies is still limited. We performed a large eQTL study in peripheral blood mononuclear cells of 2112 individuals increasing the power to detect trans-effects genome-wide. Going beyond univariate SNP-transcript associations, we analyse relations of eQTLs to biological pathways, polygenetic effects of expression regulation, trans-clusters and enrichment of co-localized functional elements. We found eQTLs for about 85% of analysed genes, and 18% of genes were trans-regulated. Local eSNPs were enriched up to a distance of 5 Mb to the transcript challenging typically implemented ranges of cis-regulations. Pathway enrichment within regulated genes of GWAS-related eSNPs supported functional relevance of identified eQTLs. We demonstrate that nearest genes of GWAS-SNPs might frequently be misleading functional candidates. We identified novel trans-clusters of potential functional relevance for GWAS-SNPs of several phenotypes including obesity-related traits, HDL-cholesterol levels and haematological phenotypes. We used chromatin immunoprecipitation data for demonstrating biological effects. Yet, we show for strongly heritable transcripts that still little trans-chromosomal heritability is explained by all identified trans-eSNPs; however, our data suggest that most cis-heritability of these transcripts seems explained. Dissection of co-localized functional elements indicated a prominent role of SNPs in loci of pseudogenes and non-coding RNAs for the regulation of coding genes. In summary, our study substantially increases the catalogue of human eQTLs and improves our understanding of the complex genetic regulation of gene expression, pathways and disease-related processes.

The challenge of the application of 'omics technologies in chemicals risk assessment: Background and outlook.

This survey by the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) highlights that 'omics technologies are generally not yet applied to meet standard information requirements during regulatory hazard assessment. While they are used within weight-of-evidence approaches to investigate substances' modes-of-action, consistent approaches for the generation, processing and interpretation of 'omics data are not applied. To date, no 'omics technology has been standardised or validated. Best practices for performing 'omics studies for regulatory purposes (e.g., microarrays for transcriptome profiling) remain to be established. Therefore, three frameworks for (i) establishing a Good-Laboratory Practice-like context for collecting, storing and curating 'omics data; (ii) 'omics data processing; and (iii) quantitative WoE approaches to interpret 'omics data have been developed, that are presented in this journal supplement. Application of the frameworks will enable between-study comparison of results, which will facilitate the regulatory applicability of 'omics data. The frameworks do not constitute prescriptive protocols precluding any other data analysis method, but provide a baseline for analysis that can be applied to all data allowing ready cross-comparison. Data analysis that does not follow the frameworks can be justified and the resulting data can be compared with the Framework-based common analysis output.

A generic Transcriptomics Reporting Framework (TRF) for 'omics data processing and analysis.

A generic Transcriptomics Reporting Framework (TRF) is presented that lists parameters that should be reported in 'omics studies used in a regulatory context. The TRF encompasses the processes from transcriptome profiling from data generation to a processed list of differentially expressed genes (DEGs) ready for interpretation. Included within the TRF is a reference baseline analysis (RBA) that encompasses raw data selection; data normalisation; recognition of outliers; and statistical analysis. The TRF itself does not dictate the methodology for data processing, but deals with what should be reported. Its principles are also applicable to sequencing data and other 'omics. In contrast, the RBA specifies a simple data processing and analysis methodology that is designed to provide a comparison point for other approaches and is exemplified here by a case study. By providing transparency on the steps applied during 'omics data processing and analysis, the TRF will increase confidence processing of 'omics data, and regulatory use. Applicability of the TRF is ensured by its simplicity and generality. The TRF can be applied to all types of regulatory 'omics studies, and it can be executed using different commonly available software tools.

Framework for the quality assurance of 'omics technologies considering GLP requirements.

'Omics technologies are gaining importance to support regulatory toxicity studies. Prerequisites for performing 'omics studies considering GLP principles were discussed at the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) Workshop Applying 'omics technologies in Chemical Risk Assessment. A GLP environment comprises a standard operating procedure system, proper pre-planning and documentation, and inspections of independent quality assurance staff. To prevent uncontrolled data changes, the raw data obtained in the respective 'omics data recording systems have to be specifically defined. Further requirements include transparent and reproducible data processing steps, and safe data storage and archiving procedures. The software for data recording and processing should be validated, and data changes should be traceable or disabled. GLP-compliant quality assurance of 'omics technologies appears feasible for many GLP requirements. However, challenges include (i) defining, storing, and archiving the raw data; (ii) transparent descriptions of data processing steps; (iii) software validation; and (iv) ensuring complete reproducibility of final results with respect to raw data. Nevertheless, 'omics studies can be supported by quality measures (e.g., GLP principles) to ensure quality control, reproducibility and traceability of experiments. This enables regulators to use 'omics data in a fit-for-purpose context, which enhances their applicability for risk assessment.

Applying 'omics technologies in chemicals risk assessment: Report of an ECETOC workshop.

Prevailing knowledge gaps in linking specific molecular changes to apical outcomes and methodological uncertainties in the generation, storage, processing, and interpretation of 'omics data limit the application of 'omics technologies in regulatory toxicology. Against this background, the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) convened a workshop Applying 'omics technologies in chemicals risk assessment that is reported herein. Ahead of the workshop, multi-expert teams drafted frameworks on best practices for (i) a Good-Laboratory Practice-like context for collecting, storing and curating 'omics data; (ii) the processing of 'omics data; and (iii) weight-of-evidence approaches for integrating 'omics data. The workshop participants confirmed the relevance of these Frameworks to facilitate the regulatory applicability and use of 'omics data, and the workshop discussions provided input for their further elaboration. Additionally, the key objective (iv) to establish approaches to connect 'omics perturbations to phenotypic alterations was addressed. Generally, it was considered promising to strive to link gene expression changes and pathway perturbations to the phenotype by mapping them to specific adverse outcome pathways. While further work is necessary before gene expression changes can be used to establish safe levels of substance exposure, the ECETOC workshop provided important incentives towards achieving this goal.

The primary transcriptome of the major human pathogen Helicobacter pylori.

Genome sequencing of Helicobacter pylori has revealed the potential proteins and genetic diversity of this prevalent human pathogen, yet little is known about its transcriptional organization and noncoding RNA output. Massively parallel cDNA sequencing (RNA-seq) has been revolutionizing global transcriptomic analysis. Here, using a novel differential approach (dRNA-seq) selective for the 5' end of primary transcripts, we present a genome-wide map of H. pylori transcriptional start sites and operons. We discovered hundreds of transcriptional start sites within operons, and opposite to annotated genes, indicating that complexity of gene expression from the small H. pylori genome is increased by uncoupling of polycistrons and by genome-wide antisense transcription. We also discovered an unexpected number of approximately 60 small RNAs including the epsilon-subdivision counterpart of the regulatory 6S RNA and associated RNA products, and potential regulators of cis- and trans-encoded target messenger RNAs. Our approach establishes a paradigm for mapping and annotating the primary transcriptomes of many living species.

Advancing the use of noncoding RNA in regulatory toxicology: Report of an ECETOC workshop.

The European Centre for the Ecotoxicology and Toxicology of Chemicals (ECETOC) organised a workshop to discuss the state-of-the-art research on noncoding RNAs (ncRNAs) as biomarkers in regulatory toxicology and as analytical and therapeutic agents. There was agreement that ncRNA expression profiling data requires careful evaluation to determine the utility of specific ncRNAs as biomarkers. To advance the use of ncRNA in regulatory toxicology, the following research priorities were identified: (1) Conduct comprehensive literature reviews to identify possibly suitable ncRNAs and areas of toxicology where ncRNA expression profiling could address prevailing scientific deficiencies. (2) Develop consensus on how to conduct ncRNA expression profiling in a toxicological context. (3) Conduct experimental projects, including, e.g., rat (90-day) oral toxicity studies, to evaluate the toxicological relevance of the expression profiles of selected ncRNAs. Thereby, physiological ncRNA expression profiles should be established, including the biological variability of healthy individuals. To substantiate the relevance of key ncRNAs for cell homeostasis or pathogenesis, molecular events should be dose-dependently linked with substance-induced apical effects. Applying a holistic approach, knowledge on ncRNAs, 'omics and epigenetics technologies should be integrated into adverse outcome pathways to improve the understanding of the functional roles of ncRNAs within a regulatory context.

A first glimpse at the genome of the Baikalian amphipod Eulimnogammarus verrucosus.

Eulimnogammarus verrucosus is an amphipod endemic to the unique ecosystem of Lake Baikal and serves as an emerging model in ecotoxicological studies. We report here on a survey sequencing of its genome as a first step to establish sequence resources for this species. From a single lane of paired-end sequencing data, we estimated the genome size as nearly 10 Gb and we obtained an overview of the repeat content. At least two-thirds of the genome are non-unique DNA, and a third of the genomic DNA is composed of just five families of repetitive elements, including low-complexity sequences. Attempts to use off-the-shelf assembly tools failed on the available low-coverage data both before and after removal of highly repetitive components. Using a seed-based approach we nevertheless assembled short contigs covering 33 pre-microRNAs and the homeodomain-containing exon of nine Hox genes. The absence of clear evidence for paralogs implies that a genome duplication did not contribute to the large genome size. We furthermore report the assembly of the mitochondrial genome using a new, guided "crystallization" procedure. The initial results presented here set the stage for a more complete sequencing and analysis of this large genome.

Generation of IL-8 and IL-9 producing CD4⁺ T cells is affected by Th17 polarizing conditions and AHR ligands.

The T helper cell subsets Th1, Th2, Th17, and Treg play an important role in immune cell homeostasis, in host defense, and in immunological disorders. Recently, much attention has been paid to Th17 cells which seem to play an important role in the early phase of the adoptive immune response and autoimmune disease. When generating Th17 cells under in vitro conditions the amount of IL-17A producing cells hardly exceeds 20% while the nature of the remaining T cells is poorly characterized. As engagement of the aryl hydrocarbon receptor (AHR) has also been postulated to modulate the differentiation of T helper cells into Th17 cells with regard to the IL-17A expression we ask how far do Th17 polarizing conditions in combination with ligand induced AHR activation have an effect on the production of other T helper cell cytokines. We found that a high proportion of T helper cells cultured under Th17 polarizing conditions are IL-8 and IL-9 single producing cells and that AHR activation results in an upregulation of IL-8 and a downregulation of IL-9 production. Thus, we have identified IL-8 and IL-9 producing T helper cells which are subject to regulation by the engagement of the AHR.