Systems Toxicology: Beyond Animal Models

Toxicology – much like the rest of biology – is undergoing a profound change as new technologies begin to offer a more systems oriented view of cellular physiology. For toxicology in particular, this means moving away from black-box animal models that provide limited information about mechanisms of toxicity towards the use of in vitro approaches which can both expedite hazard assessment while at the same time providing a more data –rich insight into toxic effects at the molecular level. One motivator of this shift is Green Toxciology, which seeks to support the Green Chemistry movement. In order for this approach to succeed, it will require two separate but parallel efforts. The first is an Integrated Testing Strategy which seeks to use machine learning and data mining techniques to combine QSARs and in vitro tests in the most efficient way possible to accurately estimate hazard, which is discussed both theoretically and demonstrated practically with the example of skin sensitization. Secondly, toxicology will require new approaches that exploit the insights of network biology to look at toxic mechanisms from a systems perspective. The theoretical concept of a Pathway of Toxicity is outlined, and an example of how to extract a suggested Pathway of Toxicity is given, using a Weighted Gene Correlation Network Analysis of a small microarray study of MPTP toxicity combined with text-mining and other high-throughput data to suggest novel candidate transcription factors and proteins. In conclusion, it discusses some of the current limitations of another promising –omics technology, metabolomics

Weighted Gene Correlation Network Analysis (WGCNA) Reveals Novel Transcription Factors Associated With Bisphenol A Dose-Response

Despite Bisphenol-A (BPA) being subject to extensive study, a thorough understanding of molecular mechanism remains elusive. Here we show that using weighted gene correlation network analysis (WGCNA), which takes advantage of a graph theoretical approach to understanding correlations amongst genes and grouping genes into modules that typically have co-ordinated biological functions and regulatory mechanisms, that despite some commonality in altered genes, there is minimal overlap between BPA and estrogen in terms of network topology. We confirmed previous findings that ZNF217 and TFAP2C are involved in the estrogen pathway, and are implicated in BPA as well, although for BPA they appear to be active in the absence of canonical estrogen-receptor driven gene expression. Furthermore, our study suggested that PADI4 and RACK7/ZMYNDB8 may be involved in the overlap in gene expression between estradiol and BPA. Lastly, we demonstrated that even at low doses there are unique transcription factors that appear to be driving the biology of BPA, such as SREBF1. Overall, our data is consistent with other reports that BPA leads to subtle gene changes rather than profound aberrations of a conserved estrogen signaling (or other) pathways

Adaptation of the Systematic Review Framework to the Assessment of Toxicological Test Methods: Challenges and Lessons Learned With the Zebrafish Embryotoxicity Test

Systematic review methodology is a means of addressing specific questions through structured, consistent, and transparent examinations of the relevant scientific evidence. This methodology has been used to advantage in clinical medicine, and is being adapted for use in other disciplines. Although some applications to toxicology have been explored, especially for hazard identification, the present preparatory study is, to our knowledge, the first attempt to adapt it to the assessment of toxicological test methods. As our test case, we chose the zebrafish embryotoxicity test (ZET) for developmental toxicity and its mammalian counterpart, the standard mammalian prenatal development toxicity study, focusing the review on how well the ZET predicts the presence or absence of chemical-induced prenatal developmental toxicity observed in mammalian studies. An interdisciplinary team prepared a systematic review protocol and adjusted it throughout this piloting phase, where needed. The final protocol was registered and will guide the main study (systematic review), which will execute the protocol to comprehensively answer the review question. The goal of this preparatory study was to translate systematic review methodology to the assessment of toxicological test method performance. Consequently, it focused on the methodological issues encountered, whereas the main study will report substantive findings. These relate to numerous systematic review steps, but primarily to searching and selecting the evidence. Applying the lessons learned to these challenges can improve not only our main study, but may also be helpful to others seeking to use systematic review methodology to compare toxicological test methods. We conclude with a series of recommendations that, if adopted, would help improve the quality of the published literature, and make conducting systematic reviews of toxicological studies faster and easier over time

Pan-cancer interrogation of MUTYH variants reveals biallelic inactivation and defective base excision repair across a spectrum of solid tumors

Purpose Biallelic germline pathogenic variants of the base excision repair (BER) pathway gene MUTYH predispose to colorectal cancer (CRC) and other cancers. The possible association of heterozygous variants with broader cancer susceptibility remains uncertain. This study investigated the prevalence and consequences of pathogenic MUTYH variants and MUTYH loss of heterozygosity (LOH) in a large pan-cancer analysis. Materials and Methods Data from 354,366 solid tumor biopsies that were sequenced as part of routine clinical care were analyzed using a validated algorithm to distinguish germline from somatic MUTYH variants. Results Biallelic germline pathogenic MUTYH variants were identified in 119 tissue biopsies. Most were CRCs and showed increased tumor mutational burden (TMB) and a mutational signature consistent with defective BER (COSMIC Signature SBS18). Germline heterozygous pathogenic variants were identified in 5,991 biopsies and their prevalence was modestly elevated in some cancer types. About 12% of these cancers (738 samples: including adrenal gland cancers, pancreatic islet cell tumors, nonglioma CNS tumors, GI stromal tumors, and thyroid cancers) showed somatic LOH for MUTYH, higher rates of chromosome 1p loss (where MUTYH is located), elevated genomic LOH, and higher COSMIC SBS18 signature scores, consistent with BER deficiency. Conclusion This analysis of MUTYH alterations in a large set of solid cancers suggests that in addition to the established role of biallelic pathogenic MUTYH variants in cancer predisposition, a broader range of cancers may possibly arise in MUTYH heterozygotes via a mechanism involving somatic LOH at the MUTYH locus and defective BER. However, the effect is modest and requires confirmation in additional studies before being clinically actionable

The Baltimore declaration toward the exploration of organoid intelligence

We, the participants of the First Organoid Intelligence Workshop - "Forming an OI Community" (22-24 February 2022), call on the international scientific community to explore the potential of human brain-based organoid cell cultures to advance our understanding of the brain and unleash new forms of biocomputing while recognizing and addressing the associated ethical implications. The term "organoid intelligence" (OI) has been coined to describe this research and development approach (1) in a manner consistent with the term "artificial intelligence" (AI) - used to describe the enablement of computers to perform tasks normally requiring human intelligence. OI has the potential for diverse and far-reaching applications that could benefit humankind and our planet, and which urge the strategic development of OI as a collaborative scientific discipline. OI holds promise to elucidate the physiology of human cognitive functions such as memory and learning. It presents game-changing opportunities in biological and hybrid computing that could overcome significant limitations in silicon-based computing. It offers the prospect of unparalleled advances in interfaces between brains and machines. Finally, OI could allow breakthroughs in modeling and treating dementias and other neurogenerative disorders that cause an immense and growing disease burden globally. Realizing the world-changing potential of OI will require scientific breakthroughs. We need advances in human stem cell technology and bioengineering to recreate brain architectures and to model their potential for pseudo-cognitive capabilities. We need interface breakthroughs to allow us to deliver input signals to organoids, measure output signals, and employ feedback mechanisms to model learning processes. We also need novel machine learning, big data, and AI technologies to allow us to understand brain organoids

Similarities and Differences in Gene Expression Networks Between the Breast Cancer Cell Line Michigan Cancer Foundation-7 and Invasive Human Breast Cancer Tissues

Failure to adequately characterize cell lines, and understand the differences between in vitro and in vivo biology, can have serious consequences on the translatability of in vitro scientific studies to human clinical trials. This project focuses on the Michigan Cancer Foundation-7 (MCF-7) cells, a human breast adenocarcinoma cell line that is commonly used for in vitro cancer research, with over 42,000 publications in PubMed. In this study, we explore the key similarities and differences in gene expression networks of MCF-7 cell lines compared to human breast cancer tissues. We used two MCF-7 data sets, one data set collected by ARCHS4 including 1032 samples and one data set from Gene Expression Omnibus GSE50705 with 88 estradiol-treated MCF-7 samples. The human breast invasive ductal carcinoma (BRCA) data set came from The Cancer Genome Atlas, including 1212 breast tissue samples. Weighted Gene Correlation Network Analysis (WGCNA) and functional annotations of the data showed that MCF-7 cells and human breast tissues have only minimal similarity in biological processes, although some fundamental functions, such as cell cycle, are conserved. Scaled connectivity-a network topology metric-also showed drastic differences in the behavior of genes between MCF-7 and BRCA data sets. Finally, we used canSAR to compute ligand-based druggability scores of genes in the data sets, and our results suggested that using MCF-7 to study breast cancer may lead to missing important gene targets. Our comparison of the networks of MCF-7 and human breast cancer highlights the nuances of using MCF-7 to study human breast cancer and can contribute to better experimental design and result interpretation of study involving this cell line.publishe

Two Good Read-across Practice workshops : Making it work for you!

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Avoiding Regrettable Substitutions : Green Toxicology for Sustainable Chemistry

Green chemistry seeks to design less hazardous chemicals, but many of the efforts to replace chemicals have resulted in so-called “Regrettable Substitutions”, when a chemical with an unknown or unforeseen hazard is used to replace a chemical identified as problematic. Here, we discuss the literature on regrettable substitution and focus on an oft-mentioned case, Bisphenol A, which was replaced with Bisphenol S—and the lessons that can be learned from this history. In particular, we focus on how Green Toxicology can offer a way to make better substitutions.publishe

Integrated testing strategies for safety assessments

Despite the fact that toxicology uses many stand-alone tests, a systematic combination of several information sources very often is required: Examples include: when not all possible outcomes of interest (e.g., modes of action), classes of test substances (applicability domains), or severity classes of effect are covered in a single test; when the positive test result is rare (low prevalence leading to excessive falsepositive results); when the gold standard test is too costly or uses too many animals, creating a need for prioritization by screening. Similarly, tests are combined when the human predictivity of a single test is not satisfactory or when existing data and evidence from various tests will be integrated. Increasingly, kinetic information also will be integrated to make an in vivo extrapolation from in vitro data.Integrated Testing Strategies (ITS) offer the solution to these problems. ITS have been discussed for more than a decade, and some attempts have been made in test guidance for regulations. Despite their obvious potential for revamping regulatory toxicology, however, we still have little guidance on the composition, validation, and adaptation of ITS for different purposes. Similarly, Weight of Evidence and Evidence-based Toxicology approaches require different pieces of evidence and test data to be weighed and combined. ITS also represent the logical way of combining pathway-based tests, as suggested in Toxicology for the 21st Century. This paper describes the state of the art of ITS and makes suggestions as to the definition, systematic combination, and quality assurance of ITS.publishe