Proceedings of the EuBIC-MS developers meeting 2023.

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods.

BACKGROUND: Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. RESULTS: In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. CONCLUSIONS: Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.

Hepatotoxicity of oral exposure to 2-methyl-4-nitroaniline: toxicity prediction and in vivo evaluation.

2-Methyl-4-nitroaniline (MNA), an intermediate in the synthesis of azo dyes, is widely distributed in various environmental media and organisms. Although there is speculation regarding MNA's potential to be hepatotoxic, the underlying mechanisms of its hepatotoxicity and its definitive diagnostic process remain largely unexplored. In this research. In the present study, we initially predicted the toxicity and possible toxic effect pathways of MNA using ProTox-II, and found that MNA binds to the PPARγ receptor (binding energy －6.118 kcal/mol) with a potential PPARγ agonist effect. Subsequently, in vivo exposure evaluation was conducted on Wistar rats to assess the impact of MNA after a 90-day exposure period, by detecting serum biochemical indexes, hematological indexes, urinary indexes, inflammatory factors, liver histopathological observations and liver tissue PPARγ mRNA expression. The results showed that MNA causes liver function abnormalities, liver histopathological changes and inflammatory response, along with a pronounced increase in PPARγ mRNA levels. This study suggests that the hepatotoxic mechanism of MNA may be related to its possible upregulation of PPARγ expression, increased liver dysfunction and inflammatory responses. Based on these results, the benchmark dose lower limit (BMDL) of 1.503 mg/kg for male Wistar rats was also established, providing a vital benchmark for determining the safety threshold of MNA. Our data highlight the hepatotoxic mechanism of MNA and contribute to a better understanding of its potential etiological diagnosis.

Assessing the genotoxicity of N-nitrosodiethylamine with three in vivo endpoints in male Big Blue® transgenic and wild-type C57BL/6N mice.

The detection of N-nitrosamines in drug products has raised global regulatory interest in recent years due to the carcinogenic potential of some nitrosamines in animals and a need to identify a testing strategy has emerged. Ideally, methods used would allow for the use of quantitative analysis of dose-response data from in vivo genotoxicity assays to determine a compound-specific acceptable intake for novel nitrosamines without sufficient carcinogenicity data. In a previous study we compared the dose-response relationships of N-nitrosodiethylamine (NDEA) in three in vivo genotoxicity endpoints in rats. Here we report a comparison of NDEA's genotoxicity profile in mice. Big Blue® mice were administered NDEA at doses of 0.001, 0.01, 0.1, 1 and 3 mg/kg/day by oral gavage for 28 days followed by 3 days of expression. Statistically significant increases in the NDEA induced mutations were detected by both the transgenic rodent mutation assay (TGR) using the cII endpoint and by duplex sequencing in the liver but not bone marrow of mice. In addition, administration of NDEA for two consecutive days in male C57BL/6N mice caused elevated DNA damage levels in the liver as measured by % tail DNA in comet assay. The benchmark dose (BMD) analysis shows a BMDL50 of 0.03, 0.04 and 0.72 mg/kg/day for TGR, duplex sequencing and comet endpoints, respectively. Overall, this study demonstrated a similar genotoxicity profile of NDEA between mice and rats and provides a reference that can be used to compare the potential potency of other novel nitrosamines for the induction of gene mutations.

Data Set and Benchmark (MedGPTEval) to Evaluate Responses From Large Language Models in Medicine: Evaluation Development and Validation.

Background: Large language models (LLMs) have achieved great progress in natural language processing tasks and demonstrated the potential for use in clinical applications. Despite their capabilities, LLMs in the medical domain are prone to generating hallucinations (not fully reliable responses). Hallucinations in LLMs' responses create substantial risks, potentially threatening patients' physical safety. Thus, to perceive and prevent this safety risk, it is essential to evaluate LLMs in the medical domain and build a systematic evaluation. Objective: We developed a comprehensive evaluation system, MedGPTEval, composed of criteria, medical data sets in Chinese, and publicly available benchmarks. Methods: First, a set of evaluation criteria was designed based on a comprehensive literature review. Second, existing candidate criteria were optimized by using a Delphi method with 5 experts in medicine and engineering. Third, 3 clinical experts designed medical data sets to interact with LLMs. Finally, benchmarking experiments were conducted on the data sets. The responses generated by chatbots based on LLMs were recorded for blind evaluations by 5 licensed medical experts. The evaluation criteria that were obtained covered medical professional capabilities, social comprehensive capabilities, contextual capabilities, and computational robustness, with 16 detailed indicators. The medical data sets include 27 medical dialogues and 7 case reports in Chinese. Three chatbots were evaluated: ChatGPT by OpenAI; ERNIE Bot by Baidu, Inc; and Doctor PuJiang (Dr PJ) by Shanghai Artificial Intelligence Laboratory. Results: Dr PJ outperformed ChatGPT and ERNIE Bot in the multiple-turn medical dialogues and case report scenarios. Dr PJ also outperformed ChatGPT in the semantic consistency rate and complete error rate category, indicating better robustness. However, Dr PJ had slightly lower scores in medical professional capabilities compared with ChatGPT in the multiple-turn dialogue scenario. Conclusions: MedGPTEval provides comprehensive criteria to evaluate chatbots by LLMs in the medical domain, open-source data sets, and benchmarks assessing 3 LLMs. Experimental results demonstrate that Dr PJ outperforms ChatGPT and ERNIE Bot in social and professional contexts. Therefore, such an assessment system can be easily adopted by researchers in this community to augment an open-source data set.

Subchronic oral toxic effects of 2,4-dinitroaniline in wistar rats: A comprehensive toxicity evaluation.

2,4-dinitroaniline (2,4-D), a widely used dye intermediate, is one of the typical pollutants, and its potential health risks and toxicity are still largely unknown. To explore its subchronic oral toxicity, Wistar rats (equal numbers of males and females) were used as test animals, and a 90-day oral dosing experiment was conducted, divided into control group, low-dose group (0.055 mg/kg), medium-dose group (0.22 mg/kg), medium-high dose group (0.89 mg/kg), and high-dose group (3.56 mg/kg). The body weight data, clinical appearance, and drug reactions of each test rat within 90 days of dosing were recorded; morning urine samples were collected four times to test for eight urinary indicators; blood samples were collected to test for nineteen hematological indicators and sixteen biochemical indicators; tissue samples were collected for pathological analysis; moreover, the no-observed-adverse-effect level (NOAEL) was determined, and the benchmark dose method was used to support this determination and provide a statistical estimate of the dose corresponding. The results indicated that the chronic toxicity of 2,4-dinitroaniline showed certain gender differences, with the eyes, liver, and kidneys being the main potential target organs of toxicity. Moreover, the subchronic oral NOAEL for 2,4-dinitroaniline was determined to be 0.22 mg/kg body weight (0.22 mg/kg for males and 0.89 mg/kg for females), and a preliminary calculation of the safe exposure limit for human was 0.136 mg/kg. The research results greatly enriched the safety evaluation data of 2,4-dinitroaniline, contributing to a robust scientific foundation for the development of informed safety regulations and public health precautions.

A gold standard dataset and evaluation of methods for lineage abundance estimation from wastewater.

During the SARS-CoV-2 pandemic, genome-based wastewater surveillance sequencing has been a powerful tool for public health to monitor circulating and emerging viral variants. As a medium, wastewater is very complex because of its mixed matrix nature, which makes the deconvolution of wastewater samples more difficult. Here we introduce a gold standard dataset constructed from synthetic viral control mixtures of known composition, spiked into a wastewater RNA matrix and sequenced on the Oxford Nanopore Technologies platform. We compare the performance of eight of the most commonly used deconvolution tools in identifying SARS-CoV-2 variants present in these mixtures. The software evaluated was primarily chosen for its relevance to the CDC wastewater surveillance reporting protocol, which until recently employed a pipeline that incorporates results from four deconvolution methods: Freyja, kallisto, Kraken 2/Bracken, and LCS. We also tested Lollipop, a deconvolution method used by the Swiss SARS-CoV-2 Sequencing Consortium, and three additional methods not used in the C-WAP pipeline: lineagespot, Alcov, and VaQuERo. We found that the commonly used software Freyja outperformed the other CDC pipeline tools in correct identification of lineages present in the control mixtures, and that the VaQuERo method was similarly accurate, with minor differences in the ability of the two methods to avoid false negatives and suppress false positives. Our results also provide insight into the effect of the tiling primer scheme and wastewater RNA extract matrix on viral sequencing and data deconvolution outcomes.

Approaches to Backbone Flexibility in Protein-Protein Docking.

Proteins are the fundamental organic macromolecules in living systems that play a key role in a variety of biological functions including immunological detection, intracellular trafficking, and signal transduction. The docking of proteins has greatly advanced during recent decades and has become a crucial complement to experimental methods. Protein-protein docking is a helpful method for simulating protein complexes whose structures have not yet been solved experimentally. This chapter focuses on major search tactics along with various docking programs used in protein-protein docking algorithms, which include: direct search, exhaustive global search, local shape feature matching, randomized search, and broad category of post-docking approaches. As backbone flexibility predictions and interactions in high-resolution protein-protein docking remain important issues in the overall optimization context, we have put forward several methods and solutions used to handle backbone flexibility. In addition, various docking methods that are utilized for flexible backbone docking, including ATTRACT, FlexDock, FLIPDock, HADDOCK, RosettaDock, FiberDock, etc., along with their scoring functions, algorithms, advantages, and limitations are discussed. Moreover, what progress in search technology is expected, including not only the creation of new search algorithms but also the enhancement of existing ones, has been debated. As conformational flexibility is one of the most crucial factors affecting docking success, more work should be put into evaluating the conformational flexibility upon binding for a particular case in addition to developing new algorithms to replace the rigid body docking and scoring approach.

Comparative analysis of integrative classification methods for multi-omics data.

Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.

Many-objective ant lion optimizer (MaOALO): A new many-objective optimizer with its engineering applications.

Many-objective optimization (MaO) is an important aspect of engineering scenarios. In many-objective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity. With increase in number of objectives, the process becomes more complex, mainly due to challenges in achieving convergence during population selection. This paper introduces a novel Many-Objective Ant Lion Optimizer (MaOALO), featuring the widely-popular ant lion optimizer algorithm. This method utilizes reference point, niche preserve and information feedback mechanism (IFM), to enhance the convergence and diversity of the population. Extensive experimental tests on five real-world (RWMaOP1- RWMaOP5) optimization problems and standard problem classes, including MaF1-MaF15 (for 5, 9 and 15 objectives), DTLZ1-DTLZ7 (for 8 objectives) has been carried out. It is shown that MaOALO is superior compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA and MaOEA-IH algorithms in terms of GD, IGD, SP, SD, HV and RT metrics. The MaOALO source code is available at: https://github.com/kanak02/MaOALO.

Composite Graph Neural Networks for Molecular Property Prediction.

Graph Neural Networks have proven to be very valuable models for the solution of a wide variety of problems on molecular graphs, as well as in many other research fields involving graph-structured data. Molecules are heterogeneous graphs composed of atoms of different species. Composite graph neural networks process heterogeneous graphs with multiple-state-updating networks, each one dedicated to a particular node type. This approach allows for the extraction of information from s graph more efficiently than standard graph neural networks that distinguish node types through a one-hot encoded type of vector. We carried out extensive experimentation on eight molecular graph datasets and on a large number of both classification and regression tasks. The results we obtained clearly show that composite graph neural networks are far more efficient in this setting than standard graph neural networks.

Navigating the complexities of docking tools with nicotinic receptors and acetylcholine binding proteins in the realm of neonicotinoids.

Molecular docking, pivotal in predicting small-molecule ligand binding modes, struggles with accurately identifying binding conformations and affinities. This is particularly true for neonicotinoids, insecticides whose impacts on ecosystems require precise molecular interaction modeling. This study scrutinizes the effectiveness of prominent docking software (Ledock, ADFR, Autodock Vina, CDOCKER) in simulating interactions of environmental chemicals, especially neonicotinoid-like molecules with nicotinic acetylcholine receptors (nAChRs) and acetylcholine binding proteins (AChBPs). We aimed to assess the accuracy and reliability of these tools in reproducing crystallographic data, focusing on semi-flexible and flexible docking approaches. Our analysis identified Ledock as the most accurate in semi-flexible docking, while Autodock Vina with Vinardo scoring function proved most reliable. However, no software consistently excelled in both accuracy and reliability. Additionally, our evaluation revealed that none of the tools could establish a clear correlation between docking scores and experimental dissociation constants (Kd) for neonicotinoid-like compounds. In contrast, a strong correlation was found with drug-like compounds, bringing to light a bias in considered software towards pharmaceuticals, thus limiting their applicability to environmental chemicals. The comparison between semi-flexible and flexible docking revealed that the increased computational complexity of the latter did not result in enhanced accuracy. In fact, the higher computational cost of flexible docking with its lack of enhanced predictive accuracy, rendered this approach useless for this class of compounds. Conclusively, our findings emphasize the need for continued development of docking methodologies, particularly for environmental chemicals. This study not only illuminates current software capabilities but also underscores the urgency for advancements in computational molecular docking as it is a relevant tool to environmental sciences.

Modeling HepaRG metabolome responses to pyrrolizidine alkaloid exposure for insight into points of departure and modes of action.

The new challenges in toxicology demand novel and innovative in vitro approaches for deriving points of departure (PODs) and determining the mode of action (MOA) of chemicals. Therefore, the aim of this original study was to couple in vitro studies with untargeted metabolomics to model the concentration-response of extra- and intracellular metabolome data on human HepaRG cells treated for 48 h with three pyrrolizidine alkaloids (PAs): heliotrine, retrorsine and lasiocarpine. Modeling revealed that the three PAs induced various monotonic and, importantly, biphasic curves of metabolite content. Based on unannotated metabolites, the endometabolome was more sensitive than the exometabolome in terms of metabolomic effects, and benchmark concentrations (BMCs) confirmed that lasiocarpine was the most hepatotoxic PA. Regarding its MOA, impairment of lipid metabolism was highlighted at a very low BMC (first quartile, 0.003 µM). Moreover, results confirmed that lasiocarpine targets bile acids, as well as amino acid and steroid metabolisms. Analysis of the endometabolome, based on coupling concentration-response and PODs, gave encouraging results for ranking toxins according to their hepatotoxic effects. Therefore, this novel approach is a promising tool for next-generation risk assessment, readily applicable to a broad range of compounds and toxic endpoints.

Estimation of urinary cadmium benchmark dose thresholds for preschool children in a cadmium-polluted area based on Bayesian model averaging.

Urinary cadmium (U-Cd) values are indicators for determining chronic cadmium toxicity, and previous studies have calculated U-Cd indicators using renal injury biomarkers. However, most of these studies have been conducted in adult populations, and there is a lack of research on U-Cd thresholds in preschool children. We aimed to apply benchmark dose (BMD) analysis to estimate the U-Cd threshold level associated with renal impairment in preschool children in the cadmium-polluted area. 518 preschool children aged 3-5 years were selected by systematic sampling (275 boys, 243 girls). Urinary cadmium and three biomarkers of early renal injury (urinary N-acetyl-ß-D-glucosaminidase, UNAG; urinary ß2-microglobulin, Uß2-MG; urinary retinol-binding protein, URBP) were determined. Bayesian model averaging estimated the BMD and lower confidence interval limit (BMDL) of U-Cd. The medians U-Cd levels in both boys and girls exceeded the recommended national standard threshold (5 µg/g cr) and U-Cd levels were higher in girls than in boys. Urinary N-acetyl-ß-D-glucosaminidase (UNAG) was the most sensitive biomarker of renal effects in preschool children. The overall BMDL5 (BMDL at a benchmark response value of 5) was 2.76 µg/g cr. In the gender analysis, the BMDL5 values were 1.92 µg/g cr for boys and 4.12 µg/g cr for girls. This study shows that the U-Cd threshold (BMDL5) is lower than the national standard (5 µg/g cr) and boys' BMDL5 was lower than the limit set by the European Parliament and Council in 2019 (2 µg/g cr), which provides a reference point for making U-Cd thresholds for preschool children.

PhyloBench: A Benchmark for Evaluating Phylogenetic Programs.

Phylogenetic inference based on protein sequence alignment is a widely used procedure. Numerous phylogenetic algorithms have been developed, most of which have many parameters and options. Choosing a program, options, and parameters can be a nontrivial task. No benchmark for comparison of phylogenetic programs on real protein sequences was publicly available. We have developed PhyloBench, a benchmark for evaluating the quality of phylogenetic inference, and used it to test a number of popular phylogenetic programs. PhyloBench is based on natural, not simulated, protein sequences of orthologous evolutionary domains. The measure of accuracy of an inferred tree is its distance to the corresponding species tree. A number of tree-to-tree distance measures were tested. The most reliable results were obtained using the Robinson-Foulds distance. Our results confirmed recent findings that distance methods are more accurate than maximum likelihood (ML) and maximum parsimony. We tested the bayesian program MrBayes on natural protein sequences and found that, on our datasets, it performs better than ML, but worse than distance methods. Of the methods we tested, the Balanced Minimum Evolution method implemented in FastME yielded the best results on our material. Alignments and reference species trees are available at https://mouse.belozersky.msu.ru/tools/phylobench/ together with a web-interface that allows for a semi-automatic comparison of a user's method with a number of popular programs.

Themis: advancing precision oncology through comprehensive molecular subtyping and optimization.

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.

Hepatotoxicity of silver nanoparticles: Benchmark concentration modeling of an in vitro transcriptomics study in human iPSC-derived hepatocytes.

Despite two decades of research on silver nanoparticle (AgNP) toxicity, a safe threshold for exposure has not yet been established, albeit being critically needed for risk assessment and regulatory decision-making. Traditionally, a point-of-departure (PoD) value is derived from dose response of apical endpoints in animal studies using either the no-observed-adverse-effect level (NOAEL) approach, or benchmark dose (BMD) modeling. To develop new approach methodologies (NAMs) to inform human risk assessment of AgNPs, we conducted a concentration response modeling of the transcriptomic changes in hepatocytes derived from human induced pluripotent stem cells (iPSCs) after being exposed to a wide range concentration (0.01-25 µg/ml) of AgNPs for 24 h. A plausible transcriptomic PoD of 0.21 µg/ml was derived for a pathway related to the mode-of-action (MOA) of AgNPs, and a more conservative PoD of 0.10 µg/ml for a gene ontology (GO) term not apparently associated with the MOA of AgNPs. A reference dose (RfD) could be calculated from either of the PoDs as a safe threshold for AgNP exposure. The current study illustrates the usefulness of in vitro transcriptomic concentration response study using human cells as a NAM for toxicity study of chemicals that lack adequate toxicity data to inform human risk assessment.

Threshold for increased liver weight is protective of other effects in peromyscus exposed to PFNA.

Perfluorononanoic acid (PFNA) is a commercially relevant, long-chain (8 fully fluorinated carbon) perfluorinated carboxylic acid (PFCA). PFNA has limited terrestrial ecotoxicity data and is detected in humans, animals, and the environment. This study is the fourth in a series with the objective of investigating the toxicity of a suite of per- and polyfluoroalkyl substances (PFAS) detected on military installations in a mammal indigenous to North America. Peromyscus leucopus (white-footed mice, ∼25/sex/dose) were exposed via oral gavage to either 0, 0.03, 0.14, 1, or 3 mg PFNA/kg-day for 112 consecutive days (4 weeks pre-mating exposure followed by an additional 12 weeks of exposure after onset of mating). Parental generation animals were assessed for potential reproductive and developmental effects, organ weight changes, thyroid modulation, and immunotoxicity. Pup weight and survival were assessed at postnatal days 0, 1, 4, 7, and 10. Change in liver weight was determined to yield the most sensitive dose response according to benchmark dose analysis, and serves as the most protective point of departure (BMDL = 0.37 mg/kg-d PFNA). Other effects of PFNA exposure included reduced formation of plaque forming cells, which are indicative of functional immune deficits (BMDL = 2.31 mg/kg-d); decreased serum thyroxine (BMDL = 0.93 mg/kg-d) without changes in some other hormones; and increased stillbirths (BMDL = 0.61 mg/kg-d PFNA). Pup weight and survival were not affected by PFNA exposure. Combined with data from previous studies, data from Peromyscus provide a One Health perspective on health effects of PFAS.

Benchmarking NGS-Based HLA Typing Algorithms.

Knowledge of the expected accuracy of HLA typing algorithms is important when choosing between algorithms and when evaluating the HLA typing predictions of an algorithm. This chapter guides the reader through an example benchmarking study that evaluates the performances of four NGS-based HLA typing algorithms as well as outlining factors to consider, when designing and running such a benchmarking study. The code related to this benchmarking workflow can be found at https://github.com/nikolasthuesen/springers-hla-benchmark/ .

Macrophage polarization as a novel endpoint for assessing combined risk of phthalate esters.

Combined exposure to phthalate esters (PAEs) has garnered increasing attention due to potential synergistic effects on human health. This study aimed to develop an in vitro model using human macrophages to evaluate the combined toxicity of PAEs and explore the underlying mechanisms. A high-throughput screening system was engineered by expressing a PPRE-eGFP reporter in THP-1 monocytes to monitor macrophage polarization upon PAEs exposure. Individual PAEs exhibited varied inhibitory effects on M2 macrophage polarization, with mono(2-ethylhexyl) phthalate (MEHP) being the most potent. Isobologram analysis revealed additive interactions when MEHP was combined with other PAEs, resulting in more pronounced suppression of M2 markers compared to individual compounds. Mechanistic studies suggested PAEs may exert effects by modulating PPARγ activity to inhibit M2 polarization. Notably, an equimolar mixture of six PAEs showed additive inhibition of M2 markers. In vivo experiments corroborated the combined hepatotoxic effects, with mice exposed to a PAEs mixture exhibiting reduced liver weight, dyslipidemia, and decreased hepatic M2 macrophages compared to DEHP alone. Transcriptome analysis highlighted disruptions in PPAR signaling, and distinct pathway alterations on cholesterol metabolism in the mixture group. Collectively, these findings underscore the importance of evaluating mixture effects and provide a novel approach for hazard assessment of combined PAEs exposure with implications for environmental health risk assessment.