Prediction and Characterisation of the System Effects of Aristolochic Acid: A Novel Joint Network Analysis towards Therapeutic and Toxicological Mechanisms

AOP-helpFinder 2.0: Integration of an event-event searches module

To support the use of alternative methods in regulatory assessment of chemical risks, the concept of adverse outcome pathway (AOP) constitutes an important toxicological tool. AOP represents a structured representation of existing knowledge, linking molecular initiating event (MIE) initiated by a prototypical stressor that leads to a cascade of biological key event (KE) to an adverse outcome (AO). Biological information to develop such AOP is very dispersed in various data sources. To increase the chance of capturing relevant existing data to develop a new AOP, the AOP-helpFinder tool was recently implemented to assist researchers to design new AOP. Here, an updated version of AOP-helpFinder proposes novel functionalities. The main one being the implementation of an automatic screening of the abstracts from the PubMed database to identify and extract event-event associations. In addition, a new scoring system was created to classify the identified co-occurred terms (stressor-event or event-event (which represent key event relationships) to help prioritization and support the weight of evidence approach, allowing a global assessment of the strength and reliability of the AOP. Moreover, to facilitate interpretation of the results, visualization options are also proposed. The AOP-helpFinder source code are fully accessible via GitHub, and searches can be performed via a web interface at http://aop-helpfinder-v2.u-paris-sciences.fr/.

The Presence of Testis Determines Aristolochic Acid-Induced Nephrotoxicity in Mice

Aristolochic acid (AA) is notorious for inducing nephrotoxicity, but the influence of sex on AA-induced kidney injury was not clear. This study sought to investigate sex differences in kidney dysfunction and tubular injury induced by AA. Male and female mice were bilaterally orchiectomized and ovariectomized, respectively. Fourteen days after gonadectomy, the mice were intraperitoneally injected with AA (10 mg/kg body weight/day) daily for 2 days and sacrificed 7 days after the first injection. Body weight, kidney function, and tubular structure were assessed. When compared between male and female non-gonadectomized mice, AA-induced body weight loss was greater in male mice than in female mice. Functional and structural damages in male kidneys were markedly induced by AA injection, but kidneys in AA-injected female mice showed no or mild damages. Ovariectomy had no effect on AA-induced nephrotoxic acute kidney injury in female mice. However, orchiectomy significantly reduced body weight loss, kidney dysfunction, and tubular injury in AA-induced nephrotoxicity in male mice. This study has demonstrated that testis causes AA-induced nephrotoxic acute kidney injury.

De novo assembly of a chromosome-level reference genome of the ornamental butterfly Sericinus montelus based on nanopore sequencing and Hi-C analysis

Sericinus montelus (Lepidoptera, Papilionidae, Parnassiinae) is a high-value ornamental swallowtail butterfly species widely distributed in Northern and Central China, Japan, Korea, and Russia. The larval stage of this species feeds exclusively on Aristolochia plants. The Aristolochia species is well known for its high levels of aristolochic acids (AAs), which have been found to be carcinogenic for numerous animals. The swallowtail butterfly is among the few that can feed on these toxic host plants. However, the genetic adaptation of S. montelus to confer new abilities for AA tolerance has not yet been well explored, largely due to the limited genomic resources of this species. This study aimed to present a chromosome-level reference genome for S. montelus using the Oxford Nanopore long-read sequencing, Illumina short-read sequencing, and Hi-C technology. The final assembly was composed of 581.44 Mb with an expected genome size of 619.27 Mb. Further, 99.98% of the bases could be anchored onto 30 chromosomes. The N50 of contigs and scaffolds was 5.74 and 19.12 Mb, respectively. Approximately 48.86% of the assembled genome was suggested to be repeat elements, and 13,720 protein-coding genes were predicted in the current assembly. The phylogenetic analysis indicated that S. montelus diverged from the common ancestor of swallowtails about 58.57-80.46 million years ago. Compared with related species, S. montelus showed a significant expansion of P450 gene family members, and positive selections on eloa, heatr1, and aph1a resulted in the AA tolerance for S. montelus larva. The de novo assembly of a high-quality reference genome for S. montelus provided a fundamental genomic tool for future research on evolution, genome genetics, and toxicology of the swallowtail butterflies.

Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome

Background: The chemical part of the exposome, including drugs, may explain the increase of health effects with outcomes such as infertility, allergies, metabolic disorders, which cannot be only explained by the genetic changes. To better understand how drug exposure can impact human health, the concepts of adverse outcome pathways (AOPs) and AOP networks (AONs), which are representations of causally linked events at different biological levels leading to adverse health, could be used for drug safety assessment.

Methods: To explore the action of drugs across multiple scales of the biological organization, we investigated the use of a network-based approach in the known AOP space. Considering the drugs and their associations to biological events, such as molecular initiating event and key event, a bipartite network was developed. This bipartite network was projected into a monopartite network capturing the event–event linkages. Nevertheless, such transformation of a bipartite network to a monopartite network had a huge risk of information loss. A way to solve this problem is to quantify the network reduction. We calculated two scoring systems, one measuring the uncertainty and a second one describing the loss of coverage on the developed event–event network to better investigate events from AOPs linked to drugs.

Results: This AON analysis allowed us to identify biological events that are highly connected to drugs, such as events involving nuclear receptors (ER, AR, and PXR/SXR). Furthermore, we observed that the number of events involved in a linkage pattern with drugs is a key factor that influences information loss during monopartite network projection. Such scores have the potential to quantify the uncertainty of an event involved in an AON, and could be valuable for the weight of evidence assessment of AOPs. A case study related to infertility, more specifically to “decrease, male agenital distance” is presented.

Conclusion: This study highlights that computational approaches based on network science may help to understand the complexity of drug health effects, with the aim to support drug safety assessment.

Endocrine disrupting chemicals and COVID-19 relationships: A computational systems biology approach

BACKGROUND

Patients at high risk of severe forms of COVID-19 frequently suffer from chronic diseases, but other risk factors may also play a role. Environmental stressors, such as endocrine disrupting chemicals (EDCs), can contribute to certain chronic diseases and might aggravate the course of COVID-19.

OBJECTIVES

To explore putative links between EDCs and COVID-19 severity, an integrative systems biology approach was constructed and applied.

METHODS

As a first step, relevant data sets were compiled from major data sources. Biological associations of major EDCs to proteins were extracted from the CompTox database. Associations between proteins and diseases known as important COVID-19 comorbidities were obtained from the GeneCards and DisGeNET databases. Based on these data, we developed a tripartite network (EDCs-proteins-diseases) and used it to identify proteins overlapping between the EDCs and the diseases. Signaling pathways for common proteins were then investigated by over-representation analysis.

RESULTS

We found several statistically significant pathways that may be dysregulated by EDCs and that may also be involved in COVID-19 severity. The Th17 and the AGE/RAGE signaling pathways were particularly promising.

CONCLUSIONS

Pathways were identified as possible targets of EDCs and as contributors to COVID-19 severity, thereby highlighting possible links between exposure to environmental chemicals and disease development. This study also documents the application of computational systems biology methods as a relevant approach to increase the understanding of molecular mechanisms linking EDCs and human diseases, thereby contributing to toxicology prediction.

Endocrine disrupting chemicals and COVID-19 relationships: a computational systems biology approach

Background

Patients at high risk of severe forms of COVID-19 frequently suffer from chronic diseases, but other risk factors may also play a role. Environmental stressors, such as endocrine disrupting chemicals (EDCs), can contribute to certain chronic diseases and might aggravate the course of COVID-19.

Objectives

To explore putative links between EDCs and COVID-19 severity, an integrative systems biology approach was constructed and applied.

Methods

As a first step, relevant data sets were compiled from major data sources. Biological associations of major EDCs to proteins were extracted from the CompTox database. Associations between proteins and diseases known as important COVID-19 comorbidities were obtained from the GeneCards and DisGeNET databases. Based on these data, we developed a tripartite network (EDCs-proteins-diseases) and used it to identify proteins overlapping between the EDCs and the diseases. Signaling pathways for common proteins were then investigated by over-representation analysis.

Results

We found several statistically significant pathways that may be dysregulated by EDCs and that may also be involved in COVID-19 severity. The Th17 and the AGE/RAGE signaling pathways were particularly promising.

Conclusions

Pathways were identified as possible targets of EDCs and as contributors to COVID-19 severity, thereby highlighting possible links between exposure to environmental chemicals and disease development. This study also documents the application of computational systems biology methods as a relevant approach to increase the understanding of molecular mechanisms linking EDCs and human diseases, thereby contributing to toxicology prediction.

The Impact of p53 on Aristolochic Acid I-Induced Gene Expression In Vivo

Exposure to aristolochic acid (AA) is linked to kidney disease and urothelial cancer in humans. The major carcinogenic component of the AA plant extract is aristolochic acid I (AAI). The tumour suppressor p53 is frequently mutated in AA-induced tumours. We previously showed that p53 protects from AAI-induced renal proximal tubular injury, but the underlying mechanism(s) involved remain to be further explored. In the present study, we investigated the impact of p53 on AAI-induced gene expression by treating Trp53(+/+), Trp53(+/-), and Trp53(-/-) mice with 3.5 mg/kg body weight (bw) AAI daily for six days. The Clariom™ S Assay microarray was used to elucidate gene expression profiles in mouse kidneys after AAI treatment. Analyses in Qlucore Omics Explorer showed that gene expression in AAI-exposed kidneys is treatment-dependent. However, gene expression profiles did not segregate in a clear-cut manner according to Trp53 genotype, hence further investigations were performed by pathway analysis with MetaCore™. Several pathways were significantly altered to varying degrees for AAI-exposed kidneys. Apoptotic pathways were modulated in Trp53(+/+) kidneys; whereas oncogenic and pro-survival pathways were significantly altered for Trp53(+/-) and Trp53(-/-) kidneys, respectively. Alterations of biological processes by AAI in mouse kidneys could explain the mechanisms by which p53 protects from or p53 loss drives AAI-induced renal injury in vivo.

The complete chloroplast genome sequence of Aristolochia manshuriensis Kom. (Aristolochiaceae)

Aristolochia manshuriensis is a medicinal plant belonging to the family Aristolichiaceae. In this study, complete chloroplast (cp) genome sequence of A. manshuriensis was characterized through de novo assembly with next-generation sequencing data. The cp genome is 160,182 bp long and has a typical quadripartite organization consisting of a large single-copy (LSC), a small single-copy (SSC), and a pair of inverted repeats (IRs). The cp genome harboured 79 protein-coding genes, 30 tRNA genes, and 4 rRNA genes. Phylogenetic analysis revealed that A. manshuriensis has close relationship with Aristolochia macrophylla.

Systems pharmacology to investigate the interaction of berberine and other drugs in treating polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a common multifactorial endocrine disorder among women of childbearing age. PCOS has various and heterogeneous clinical features apart from its indefinite pathogenesis and mechanism. Clinical drugs for PCOS are multifarious because it only treats separate symptoms. Berberine is an isoquinoline plant alkaloid with numerous biological activities, and it was testified to improve some diseases related to PCOS in animal models and in humans. Systems pharmacology was utilized to predict the potential targets of berberine related to PCOS and the potential drug-drug interaction base on the disease network. In conclusion, berberine is a promising polypharmacological drug for treating PCOS, and for enhancing the efficacy of clinical drugs.