Analyzing high dimensional toxicogenomic data using consensus clustering

Acute impact of salinity and C/N ratio on the formation and properties of soluble microbial products from activated sludge

The present study investigated the shock of NaCl and C/N ratio on properties of soluble microbial products (SMPs), focusing on their sized fractions. The results indicated that the NaCl stress increased the content of biopolymers, humic substances, building blocks, and LMW substances in SMPs, while the addition of 40 g NaCl L^-1 significantly changed their relative abundance in SMPs. The acute impact of both N-rich and N-deficient conditions accelerated the secretion of SMPs, but the characteristics of LMW substances differed. Meanwhile, the bio-utilization of SMPs has been enhanced with the increase of NaCl dosage but decreased with the increase of the C/N ratio. The mass balance of sized fractions in SMPs + EPS could be set up when NaCl dosage <10 g/L and C/N ratio >5, which indicates the hydrolysis of sized fractions in EPS mainly compensated for their increase/reduction in SMPs. Besides, the results of the toxic assessment indicated that the oxidative damage caused by the NaCl shock was an important factor affecting the property of SMPs, and the abnormal expression of DNA transcription cannot be neglected for bacteria metabolisms with the change of C/N ratio.

Unsupervised Algorithms for Microarray Sample Stratification

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.

Extending a Tandem Mass Spectral Library to Include MS2 Spectra of Fragment Ions Produced In-Source and MSn Spectra

Tandem mass spectral library searching is finding increased use as an effective means of determining chemical identity in mass spectrometry-based omics studies. We previously reported on constructing a tandem mass spectral library that includes spectra for multiple precursor ions for each analyte. Here we report our method for expanding this library to include MS² spectra of fragment ions generated during the ionization process (in-source fragment ions) as well as MS³ and MS⁴ spectra. These can assist the chemical identification process. A simple density-based clustering algorithm was used to cluster all significant precursor ions from MS¹ scans for an analyte acquired during an infusion experiment. The MS² spectra associated with these precursor ions were grouped into the same precursor clusters. Subsequently, a new top-down hierarchical divisive clustering algorithm was developed for clustering the spectra from fragmentation of ions in each precursor cluster, including the MS² spectra of the original precursors and of the in-source fragments as well as the MSⁿ spectra. This algorithm starts with all the spectra of one precursor in one cluster and then separates them into sub-clusters of similar spectra based on the fragment patterns. Herein, we describe the algorithms and spectral evaluation methods for extending the library. The new library features were demonstrated by searching the high resolution spectra of E. coli extracts against the extended library, allowing identification of compounds and their in-source fragment ions in a manner that was not possible before. Graphical Abstract ᅟ.

A Quantitative Toxicogenomics Assay for High-throughput and Mechanistic Genotoxicity Assessment and Screening of Environmental Pollutants

The ecological and health concern of mutagenicity and carcinogenicity potentially associated with an overwhelmingly large and ever-increasing number of chemicals demands for cost-effective and feasible method for genotoxicity screening and risk assessment. This study proposed a genotoxicity assay using GFP-tagged yeast reporter strains, covering 38 selected protein biomarkers indicative of all the seven known DNA damage repair pathways. The assay was applied to assess four model genotoxic chemicals, eight environmental pollutants and four negative controls across six concentrations. Quantitative molecular genotoxicity end points were derived based on dose response modeling of a newly developed integrated molecular effect quantifier, Protein Effect Level Index (PELI). The molecular genotoxicity end points were consistent with multiple conventional in vitro genotoxicity assays, as well as with in vivo carcinogenicity assay results. Further more, the proposed genotoxicity end point PELI values quantitatively correlated with both comet assay in human cell and carcinogenicity potency assay in mice, providing promising evidence for linking the molecular disturbance measurements to adverse outcomes at a biological relevant level. In addition, the high-resolution DNA damaging repair pathway alternated protein expression profiles allowed for chemical clustering and classification. This toxicogenomics-based assay presents a promising alternative for fast, efficient and mechanistic genotoxicity screening and assessment of drugs, foods, and environmental contaminants.

Toxicity mechanisms identification via gene set enrichment analysis of time-series toxicogenomics data: impact of time and concentration

The advance in high-throughput "toxicogenomics" technologies, which allows for concurrent monitoring of cellular responses globally upon exposure to chemical toxicants, presents promises for next-generation toxicity assessment. It is recognized that cellular responses to toxicants have a highly dynamic nature, and exhibit both temporal complexity and dose-response shifts. Most current gene enrichment or pathway analysis lack the recognition of the inherent correlation within time series data, and may potentially miss important pathways or yield biased and inconsistent results that ignore dynamic patterns and time-sensitivity. In this study, we investigated the application of two score metrics for GSEA (gene set enrichment analysis) to rank the genes that consider the temporal gene expression profile. One applies a novel time series CPCA (common principal components analysis) to generate scores for genes based on their contributions to the common temporal variation among treatments for a given chemical at different concentrations. Another one employs an integrated altered gene expression quantifier-TELI (transcriptional effect level index) that integrates altered gene expression magnitude over the exposure time. By comparing the GSEA results using two different ranking metrics for examining the dynamic responses of reporter cells treated with various dose levels of three model toxicants, mitomycin C, hydrogen peroxide, and lead nitrate, the analysis identified and revealed different toxicity mechanisms of these chemicals that exhibit chemical-specific, as well as time-aware and dose-sensitive nature. The ability, advantages, and disadvantages of varying ranking metrics were discussed. These findings support the notion that toxicity bioassays should account for the cells' complex dynamic responses, thereby implying that both data acquisition and data analysis should look beyond simple traditional end point responses.

Colorimetric detection of catalytic reactivity of nanoparticles in complex matrices

There is a need for new methodologies to quickly assess the presence and reactivity of nanoparticles (NPs) in commercial, environmental, and biological samples since current detection techniques require expensive and complex analytical instrumentation. Here, we investigate a simple and portable colorimetric detection assay that assesses the surface reactivity of NPs, which can be used to detect the presence of NPs, in complex matrices (e.g., environmental waters, serum, urine, and in dissolved organic matter) at as low as part per billion (ppb) or ng/mL concentration levels. Surface redox reactivity is a key emerging property related to potential toxicity of NPs with living cells, and is used in our assays as a key surrogate for the presence of NPs and a first tier analytical strategy toward assessing NP exposures. We detect a wide range of metal (e.g., Ag and Au) and oxide (e.g., CeO2, SiO2, VO2) NPs with a diameter range of 5 to 400 nm and multiple capping agents (tannic acid (TA), polyvinylpyrrolidone (PVP), branched polyethylenimine (BPEI), polyethylene glycol (PEG)). This method is sufficiently sensitive (ppb levels) to measure concentrations typically used in toxicological studies, and uses inexpensive, commercially available reagents.

A quantitative toxicogenomics assay reveals the evolution and nature of toxicity during the transformation of environmental pollutants

The incomplete mineralization of contaminants of emerging concern (CECs) during the advanced oxidation processes can generate transformation products that exhibit toxicity comparable to or greater than that of the original contaminant. In this study, we demonstrated the application of a novel, fast, and cost-effective quantitative toxicogenomics-based approach for the evaluation of the evolution and nature of toxicity along the electro-Fenton oxidative degradation of three representative CECs whose oxidative degradation pathways have been relatively well studied, bisphenol A, triclosan, and ibuprofen. The evolution of toxicity as a result of the transformation of parent chemicals and production of intermediates during the course of degradation are monitored, and the quantitative toxicogenomics assay results revealed the dynamic toxicity changes and mechanisms, as well as their association with identified intermediates during the electro-Fenton oxidation process of the selected CECs. Although for the three CECs, a majority (>75%) of the parent compounds disappeared at the 15 min reaction time, the nearly complete elimination of toxicity required a minimal 30 min reaction time, and they seem to correspond to the disappearance of identified aromatic intermediates. Bisphenol A led to a wide range of stress responses, and some identified transformation products containing phenolic or quinone group, such as 1,4-benzoquinone and hydroquinone, likely contributed to the transit toxicity exhibited as DNA stress (genotoxicity) and membrane stress during the degradation. Triclosan is known to cause severe oxidative stress, and although the oxidative damage potential decreased concomitantly with the disappearance of triclosan after a 15 min reaction, the sustained toxicity associated with both membrane and protein stress was likely attributed at least partially to the production of 2,4-dichlorophenol that is known to cause the production of abnormal proteins and affect the cell membrane. Ibuprofen affects the cell transporter function and exhibited significantly high membrane stress related to both membrane structure and function. Oxidative degradation of ibuprofen led to a shift in its toxicity profile from mainly membrane stress to one that exhibited not only sustained membrane stress but also protein stress and DNA stress. The information-rich and high-resolution toxicogenomics results served as "fingerprints" that discerned and revealed the toxicity mechanism at the molecular level among the CECs and their oxidation transformation products. This study demonstrated that the quantitative toxicogenomics assay can serve as a useful tool for remediation technology efficacy assessment and provide guidance about process design and optimization for desired toxicity elimination and risk reduction.