Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering

Identification of molecular signatures for azole fungicide toxicity in zebrafish embryos by integrating transcriptomics and gene network analysis

Azoles control fungal growth by inhibiting sterol biosynthesis in fungi according to the fungicide resistance action committee. Furthermore, previous studies have highlighted several effects of azole fungicides in fish including endocrine disruption. In this study, we analysed the transcriptome responses of zebrafish embryos exposed to azole fungicides to identify gene expression fingerprints indicating toxic effects such as endocrine disruption induced by sterol biosynthesis inhibition. Firstly, a modified zebrafish embryo toxicity test was conducted following the OECD 236 guideline, exposing embryos to difenoconazole, epoxiconazole, and tebuconazole. After 96 h, RNA was extracted for transcriptome analysis, which revealed concentration-dependent responses for each fungicide. Additionally, overrepresentation analysis of significantly differentially expressed genes revealed biological functions related to sterol biosynthesis and endocrine disruption. A gene set with specific expression patterns was was identified as molecular signature for indicating adverse effects induced by sterol biosynthesis inhibitors in zebrafish embryos. After further validation, the gene expression fingerprints and biomarkers identified in this study may be used in the future to identify endocrine activity of substances under development in a pre-regulatory screening using the zebrafish embryo model.

Anti-HLA serologic response to CD38-targeting desensitization therapy is challenged by peripheral memory B cells in highly sensitized kidney transplant candidates

High human leukocyte antigen (HLA) sensitization limits access to compatible transplantation. New CD38-targeting agents have been shown to reduce anti-HLA antibodies, although with important interpatient variability. Thus, pretreatment identification of responder and nonresponder (NR) patients is needed for treatment decision-making. We analyzed 26 highly sensitized (HS) patients from 2 desensitization trials using anti-CD38 monoclonal antibodies. Hierarchical clustering identified 3 serologic responder groups: high responders, low responders, and NR. Spectral flow cytometry and functional HLA-specific memory B cell (mBC) assessment were first conducted on peripheral blood mononuclear cells and bone marrow samples from 16 patients treated with isatuximab (NCT04294459). Isatuximab effectively depleted bone marrow plasma cells, peripheral CD38-expressing plasmablasts, plasma cells, transitional B cells, and class-switch mBCs, ultimately reducing frequencies of HLA-specific immunoglobulin G (IgG)-producing mBCs. Multidimensional spectral flow cytometry with partial least squares discriminant analysis revealed that pretreatment abundance of specific circulating mBC phenotypes, especially CD38neg class-switch mBCs, accurately distinguished between high serologic responders and low responders or NR (AUC 0.958, 0.860-1.000, P = .009), who also displayed significantly lower frequencies of HLA-specific IgG-producing mBCs (P < .0001). This phenotypical mBC signature predicting response to therapy was validated in an external HS patient cohort (n = 10) receiving daratumumab (NCT04204980). This study identifies critical circulating mBC subset phenotypes that distinguish HS patients with successful serologic responses to CD38-targeting desensitization therapies, potentially guiding treatment decision-making.

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.

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.