Tissue-specific in vivo genetic toxicity of nine polycyclic aromatic hydrocarbons assessed using the Muta™Mouse transgenic rodent assay

Test batteries to screen chemicals for mutagenic hazard include several endpoints regarded as effective for detecting genotoxic carcinogens. Traditional in vivo methods primarily examine clastogenic endpoints in haematopoietic tissues. Although this approach is effective for identifying systemically distributed clastogens, some mutagens may not induce clastogenic effects; moreover, genotoxic effects may be restricted to the site of contact and/or related tissues. An OECD test guideline for transgenic rodent (TGR) gene mutation assays was released in 2011, and the TGR assays permit assessment of mutagenicity in any tissue. This study assessed the responses of two genotoxicity endpoints following sub-chronic oral exposures of male Muta™Mouse to 9 carcinogenic polycyclic aromatic hydrocarbons (PAHs). Clastogenicity was assessed via induction of micronuclei in peripheral blood, and mutagenicity via induction of lacZ transgene mutations in bone marrow, glandular stomach, small intestine, liver, and lung. Additionally, the presence of bulky PAH-DNA adducts was examined. Five of the 9 PAHs elicited positive results across all endpoints in at least one tissue, and no PAHs were negative or equivocal across all endpoints. All PAHs were positive for lacZ mutations in at least one tissue (sensitivity = 100%), and for 8 PAHs, one or more initial sites of chemical contact (i.e., glandular stomach, liver, small intestine) yielded a greater response than bone marrow. Five PAHs were positive in the micronucleus assay (sensitivity = 56%). Furthermore, all PAHs produced DNA adducts in at least one tissue. The results demonstrate the utility of the TGR assay for mutagenicity assessment, especially for compounds that may not be systemically distributed.</p

Development and application of consensus in silico models for advancing high-throughput toxicological predictions

Computational toxicology models have been successfully implemented to prioritize and screen chemicals. There are numerous in silico (quantitative) structure–activity relationship ([Q]SAR) models for the prediction of a range of human-relevant toxicological endpoints, but for a given endpoint and chemical, not all predictions are identical due to differences in their training sets, algorithms, and methodology. This poses an issue for high-throughput screening of a large chemical inventory as it necessitates several models to cover diverse chemistries but will then generate data conflicts. To address this challenge, we developed a consensus modeling strategy to combine predictions obtained from different existing in silico (Q)SAR models into a single predictive value while also expanding chemical space coverage. This study developed consensus models for nine toxicological endpoints relating to estrogen receptor (ER) and androgen receptor (AR) interactions (i.e., binding, agonism, and antagonism) and genotoxicity (i.e., bacterial mutation, in vitro chromosomal aberration, and in vivo micronucleus). Consensus models were created by combining different (Q)SAR models using various weighting schemes. As a multi-objective optimization problem, there is no single best consensus model, and therefore, Pareto fronts were determined for each endpoint to identify the consensus models that optimize the multiple-criterion decisions simultaneously. Accordingly, this work presents sets of solutions for each endpoint that contain the optimal combination, regardless of the trade-off, with the results demonstrating that the consensus models improved both the predictive power and chemical space coverage. These solutions were further analyzed to find trends between the best consensus models and their components. Here, we demonstrate the development of a flexible and adaptable approach for in silico consensus modeling and its application across nine toxicological endpoints related to ER activity, AR activity, and genotoxicity. These consensus models are developed to be integrated into a larger multi-tier NAM-based framework to prioritize chemicals for further investigation and support the transition to a non-animal approach to risk assessment in Canada

GATA-3 Dose-Dependent Checkpoints in Early T Cell Commitment

GATA-3 expression is crucial for T cell development and peaks during commitment to the T cell lineage, midway through the CD4CD8 (double-negative [DN]) stages 1–3. We used RNA interference and conditional deletion to reduce GATA-3 protein acutely at specific points during T cell differentiation in vitro. Even moderate GATA-3 reduction killed DN1 cells, delayed progression to the DN2 stage, skewed DN2 gene regulation, and blocked appearance of the DN3 phenotype. Although a Bcl-2 transgene rescued DN1 survival and improved DN2 cell generation, it did not restore DN3 differentiation. Gene expression analyses (quantitative PCR, RNA sequencing) showed that GATA-3–deficient DN2 cells quickly upregulated genes, including Spi1 (PU.1) and Bcl11a, and downregulated genes, including Cpa3, Ets1, Zfpm1, Bcl11b, Il9r, and Il17rb with gene-specific kinetics and dose dependencies. These targets could mediate two distinct roles played by GATA-3 in lineage commitment, as revealed by removing wild-type or GATA-3–deficient early T lineage cells from environmental Notch signals. GATA-3 worked as a potent repressor of B cell potential even at low expression levels, so that only full deletion of GATA-3 enabled pro–T cells to reveal B cell potential. The ability of GATA-3 to block B cell development did not require T lineage commitment factor Bcl11b. In prethymic multipotent precursors, however, titration of GATA-3 activity using tamoxifen-inducible GATA-3 showed that GATA-3 inhibits B and myeloid developmental alternatives at different threshold doses. Furthermore, differential impacts of a GATA-3 obligate repressor construct imply that B and myeloid development are inhibited through distinct transcriptional mechanisms. Thus, the pattern of GATA-3 expression sequentially produces B lineage exclusion, T lineage progression, and myeloid-lineage exclusion for commitment

Rhodium(II) Proximity-Labeling Identifies a Novel Target Site on STAT3 for Inhibitors with Potent Anti-Leukemia Activity

Nearly 40 % of children with acute myeloid leukemia (AML) suffer relapse arising from chemoresistance, often involving upregulation of the oncoprotein STAT3 (signal transducer and activator of transcription 3). Herein, rhodium(II)-catalyzed, proximity-driven modification identifies the STAT3 coiled-coil domain (CCD) as a novel ligand-binding site, and we describe a new naphthalene sulfonamide inhibitor that targets the CCD, blocks STAT3 function, and halts its disease-promoting effects in vitro, in tumor growth models, and in a leukemia mouse model, validating this new therapeutic target for resistant AML

The structures underpinning vulnerability: examining landscape-society interactions in a smallholder coffee agroforestry system

Smallholder farmers dependent on rain-fed agriculture are particularly vulnerable to extreme climate events and, therefore, it is necessary to identify adaptive measures that would increase farmer resilience to these shocks. The management options in a low-input system, like forest coffee (Coffea arabica), are limited and there are several factors out of farmers' control driving their vulnerability to changing climatic conditions. These can relate to social structures and landscape factors, which can interact to reduce farmers' adaptive capacity, creating a state of contextual vulnerability. We explored the potential synergies of this interaction across elevation, patch area and shade management gradients for smallholder coffee farms around the UNESCO Yayu Coffee Forest Biosphere Reserve in Ethiopia before, during and immediately following the 2015/16 El Niño. We documented a dramatic collapse in coffee yields across all farms, resulting in coffee incomes 29.5 ± 18.0 % and 19.5 ± 10.0 % of 2014 incomes in 2015 and 2016, respectively. We identified farms at elevations between 1500-1600 m with canopy openness between 40-45 % as being consistently low yielding over our study period. We found these farmers had the highest rates of income diversification and, therefore, were already exhibiting adaptive capacity. Farmers with the largest income losses were spatially concentrated between 1600-1700 m, located in larger patch areas with lower canopy openness. Farmers at this elevation have access to poor infrastructure, restrictions on shade management and reported higher dependence on income from coffee, indicating an interaction of biotic and social factors exacerbating their vulnerability. Unfortunately, due to a nationally declared state of emergency, we were unable to survey farmers on the adaptive measures they undertook; therefore, we are limited in assessing their resilience. However, we do show the importance of considering both biotically and socially-mediated influences for assessing smallholder vulnerability, particularly barriers to diversifying incomes

Benchmark dose analyses of multiple genetic toxicity endpoints permit robust, cross-tissue comparisons of MutaMouse responses to orally delivered benzo[a]pyrene.

Genetic damage is a key event in tumorigenesis, and chemically induced genotoxic effects are a human health concern. Although genetic toxicity data have historically been interpreted using a qualitative screen-and-bin approach, there is increasing interest in quantitative analysis of genetic toxicity dose-response data. We demonstrate an emerging use of the benchmark dose (BMD)-approach for empirically ranking cross-tissue sensitivity. Using a model environmental carcinogen, we quantitatively examined responses for four genetic damage endpoints over an extended dose range, and conducted cross-tissue sensitivity rankings using BMD100 values and their 90% confidence intervals (CIs). MutaMouse specimens were orally exposed to 11 doses of benzo[a]pyrene. DNA adduct frequency and lacZ mutant frequency (MF) were measured in up to 8 tissues, and Pig-a MF and micronuclei (MN) were assessed in immature (RETs) and mature red blood cells (RBCs). The cross-tissue BMD pattern for lacZ MF is similar to that observed for DNA adducts, and is consistent with an oral route-of-exposure and differences in tissue-specific metabolism and proliferation. The lacZ MF BMDs were significantly correlated with the tissue-matched adduct BMDs, demonstrating a consistent adduct conversion rate across tissues. The BMD CIs, for both the Pig-a and the MN endpoints, overlapped for RETs and RBCs, suggesting comparable utility of both cell populations for protracted exposures. Examination of endpoint-specific response maxima illustrates the difficulty of comparing BMD values for a fixed benchmark response across endpoints. Overall, the BMD-approach permitted robust comparisons of responses across tissues/endpoints, which is valuable to our mechanistic understanding of how benzo[a]pyrene induces genetic damage

Slow progressors to type 1 diabetes lose islet autoantibodies over time, have few islet antigen-specific CD8+ T cells and exhibit a distinct CD95hi B cell phenotype

ims/hypothesis The aim of this study was to characterise islet autoantibody profiles and immune cell phenotypes in slow progressors to type 1 diabetes. Methods Immunological variables were compared across peripheral blood samples obtained from slow progressors to type 1 diabetes, individuals with newly diagnosed or long-standing type 1 diabetes, and healthy individuals. Polychromatic flow cytometry was used to characterise the phenotypic attributes of B and T cells. Islet autoantigen-specific B cells were quantified using an enzyme-linked immunospot (ELISpot) assay and islet autoantigen-specific CD8+ T cells were quantified using peptide–HLA class I tetramers. Radioimmunoassays were used to detect islet autoantibodies. Sera were assayed for various chemokines, cytokines and soluble receptors via ELISAs. Results Islet autoantibodies were lost over time in slow progressors. Various B cell subsets expressed higher levels of CD95 in slow progressors, especially after polyclonal stimulation, compared with the corresponding B cell subsets in healthy donors (p < 0.05). The phenotypic characteristics of CD4+ and CD8+ T cells were similar in slow progressors and healthy donors. Lower frequencies of CD4+ T cells with a central memory phenotype (CD27int, CD127+, CD95int) were observed in slow progressors compared with healthy donors (mean percentage of total CD4+ T cells was 3.00% in slow progressors vs 4.67% in healthy donors, p < 0.05). Autoreactive B cell responses to proinsulin were detected at higher frequencies in slow progressors compared with healthy donors (median no. of spots was 0 in healthy donors vs 24.34 in slow progressors, p < 0.05) in an ELISpot assay. Islet autoantigen-specific CD8+ T cell responses were largely absent in slow progressors and healthy donors. Serum levels of DcR3, the decoy receptor for CD95L, were elevated in slow progressors compared with healthy donors (median was 1087 pg/ml in slow progressors vs 651 pg/ml in healthy donors, p = 0.06). Conclusions/interpretation In this study, we found that slow progression to type 1 diabetes was associated with a loss of islet autoantibodies and a distinct B cell phenotype, consistent with enhanced apoptotic regulation of peripheral autoreactivity via CD95. These phenotypic changes warrant further studies in larger cohorts to determine their functional implications

Ribosomal RNA 2′O-methylation as a novel layer of inter-tumour heterogeneity in breast cancer

International audienceRecent epitranscriptomics studies unravelled that ribosomal RNA (rRNA) 2′O-methylation is an additional layer of gene expression regulation highlighting the ribosome as a novel actor of translation control. However, this major finding lies on evidences coming mainly, if not exclusively, from cellular models. Using the innovative next-generation RiboMeth-seq technology, we established the first rRNA 2′O-methylation landscape in 195 primary human breast tumours. We uncovered the existence of compulsory/stable sites, which show limited inter-patient variability in their 2′O-methylation level, which map on functionally important sites of the human ribosome structure and which are surrounded by variable sites found from the second nucleotide layers. Our data demonstrate that some positions within the rRNA molecules can tolerate absence of 2′O-methylation in tumoral and healthy tissues. We also reveal that rRNA 2′O-methylation exhibits intra- and inter-patient variability in breast tumours. Its level is indeed differentially associated with breast cancer subtype and tumour grade. Altogether, our rRNA 2′O-methylation profiling of a large-scale human sample collection provides the first compelling evidence that ribosome variability occurs in humans and suggests that rRNA 2′O-methylation might represent a relevant element of tumour biology useful in clinic. This novel variability at molecular level offers an additional layer to capture the cancer heterogeneity and associates with specific features of tumour biology thus offering a novel targetable molecular signature in cancer

Application of a new approach methodology (NAM)-based strategy for genotoxicity assessment of data-poor compounds

The conventional battery for genotoxicity testing is not well suited to assessing the large number of chemicals needing evaluation. Traditional in vitro tests lack throughput, provide little mechanistic information, and have poor specificity in predicting in vivo genotoxicity. New Approach Methodologies (NAMs) aim to accelerate the pace of hazard assessment and reduce reliance on in vivo tests that are time-consuming and resource-intensive. As such, high-throughput transcriptomic and flow cytometry-based assays have been developed for modernized in vitro genotoxicity assessment. This includes: the TGx-DDI transcriptomic biomarker (i.e., 64-gene expression signature to identify DNA damage-inducing (DDI) substances), the MicroFlow® assay (i.e., a flow cytometry-based micronucleus (MN) test), and the MultiFlow® assay (i.e., a multiplexed flow cytometry-based reporter assay that yields mode of action (MoA) information). The objective of this study was to investigate the utility of the TGx-DDI transcriptomic biomarker, multiplexed with the MicroFlow® and MultiFlow® assays, as an integrated NAM-based testing strategy for screening data-poor compounds prioritized by Health Canada’s New Substances Assessment and Control Bureau. Human lymphoblastoid TK6 cells were exposed to 3 control and 10 data-poor substances, using a 6-point concentration range. Gene expression profiling was conducted using the targeted TempO-Seq™ assay, and the TGx-DDI classifier was applied to the dataset. Classifications were compared with those based on the MicroFlow® and MultiFlow® assays. Benchmark Concentration (BMC) modeling was used for potency ranking. The results of the integrated hazard calls indicate that five of the data-poor compounds were genotoxic in vitro, causing DNA damage via a clastogenic MoA, and one via a pan-genotoxic MoA. Two compounds were likely irrelevant positives in the MN test; two are considered possibly genotoxic causing DNA damage via an ambiguous MoA. BMC modeling revealed nearly identical potency rankings for each assay. This ranking was maintained when all endpoint BMCs were converted into a single score using the Toxicological Prioritization (ToxPi) approach. Overall, this study contributes to the establishment of a modernized approach for effective genotoxicity assessment and chemical prioritization for further regulatory scrutiny. We conclude that the integration of TGx-DDI, MicroFlow®, and MultiFlow® endpoints is an effective NAM-based strategy for genotoxicity assessment of data-poor compounds