Yeast biological networks unfold the interplay of antioxidants, genome and phenotype, and reveal a novel regulator of the oxidative stress response

Species Differences in Response to Binding Interactions of Bisphenol A and its Analogs with the Modeled Estrogen Receptor 1 and In Vitro Reporter Gene Assay in Human and Zebrafish

Adverse impacts associated with the interactions of numerous endocrine-disruptor chemicals (EDCs) with estrogen receptor 1 play a pivotal role in reproductive dysfunction. The predictive studies on these interactions thus are crucial in the risk assessment of EDCs but rely heavily on the accuracy of specific protein structure in three dimensions. As the three-dimensional (3D) structure of zebrafish estrogen receptor 1 (zEsr1) is not available, the 3D structure of zEsr1 ligand-binding domain (zEsr1-LBD) was generated using MODELLER and its quality was assessed by the PROCHECK, ERRAT, ProSA, and Verify-3D tools. After the generated model was verified as reliable, bisphenol A and its analogs were docked on the zEsr1-LBD and human estrogen receptor 1 ligand-binding domain (hESR1-LBD) using the Discovery Studio and Autodock Vina programs. The molecular dynamics followed by molecular docking were simulated using the Nanoscale Molecular Dynamics program and compared to those of the in vitro reporter gene assays. Some chemicals were bound with an orientation similar to that of 17β-estradiol in both models and in silico binding energies showed moderate or high correlations with in vitro results (0.33 ≤ r²  ≤ 0.71). Notably, hydrogen bond occupancy during molecular dynamics simulations exhibited a high correlation with in vitro results (r²  ≥ 0.81) in both complexes. These results show that the combined in silico and in vitro approaches is a valuable tool for identifying EDCs in different species, facilitating the assessment of EDC-induced reproductive toxicity. Environ Toxicol Chem 2022;41:2431-2443. © 2022 SETAC.

Global connectivity in genome-scale metabolic networks revealed by comprehensive FBA-based pathway analysis

Background

Graph-based analysis (GBA) of genome-scale metabolic networks has revealed system-level structures such as the bow-tie connectivity that describes the overall mass flow in a network. However, many pathways obtained by GBA are biologically impossible, making it difficult to study how the global structures affect the biological functions of a network. New method that can calculate the biologically relevant pathways is desirable for structural analysis of metabolic networks.

Results

Here, we present a new method to determine the bow-tie connectivity structure by calculating possible pathways between any pairs of metabolites in the metabolic network using a flux balance analysis (FBA) approach to ensure that the obtained pathways are biologically relevant. We tested this method with 15 selected high-quality genome-scale metabolic models from BiGG database. The results confirmed the key roles of central metabolites in network connectivity, locating in the core part of the bow-tie structure, the giant strongly connected component (GSC). However, the sizes of GSCs revealed by GBA are significantly larger than those by FBA approach. A great number of metabolites in the GSC from GBA actually cannot be produced from or converted to other metabolites through a mass balanced pathway and thus should not be in GSC but in other subsets of the bow-tie structure. In contrast, the bow-tie structural classification of metabolites obtained by FBA is more biologically relevant and suitable for the study of the structure-function relationships of genome scale metabolic networks.

Conclusions

The FBA based pathway calculation improve the biologically relevant classification of metabolites in the bow-tie connectivity structure of the metabolic network, taking us one step further toward understanding how such system-level structures impact the biological functions of an organism.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02357-1.

Prognostic role of mitochondrial pyruvate carrier in isocitrate dehydrogenase-mutant glioma

OBJECTIVE Gliomas are one of the most common types of primary brain tumors. Recent studies have supported the importance of key genetic alterations, including isocitrate dehydrogenase (IDH) mutations and 1p19q codeletion, in glioma prognosis. Mutant IDH produces 2-hydroxyglutarate from α-ketoglutarate, a key metabolite of the Krebs cycle. The mitochondrial pyruvate carrier (MPC) is composed of MPC1 and MPC2 subunits and is functionally essential for the Krebs cycle. The authors sought to explore the impact of MPC1 and MPC2 expression on patient prognosis. METHODS Genomic and clinical data in patients with lower-grade glioma (WHO grades II and III) from The Cancer Genome Atlas (TCGA) were evaluated using Kaplan-Meier analysis and hazards modeling. Validation was conducted with additional data sets, including glioblastoma. RESULTS A total of 286 patients with lower-grade glioma (mean age 42.7 ± 13.5 years, 55.6% males) included 54 cases of IDH-wild type (18.9%); 140 cases of IDH-mutant, 1p19q-intact (49.0%); and 85 cases of IDH-mutant, 1p19q-codeleted (29.7%) tumors. Kaplan-Meier analysis showed that an MPC1 z-score > 0 distinguished better survival, particularly in IDH-mutant (p < 0.01) but not IDH-wild type tumors. Conversely, an MPC2 z-score > 0 identified worsened survival, particularly in IDH-mutant (p < 0.01) but not IDH-wild type tumors. Consistently, neither MPC1 nor MPC2 was predictive in a glioblastoma data set containing 5% IDH-mutant cases. Within the IDH-stratified lower-grade glioma data set, MPC1 status distinguished improved survival in 1p19q-codeleted tumors (p < 0.05), whereas MPC2 expression delineated worsened survival in 1p19q-intact tumors (p < 0.01). A hazards model identified IDH and 1p19q status, age (p = 0.01, HR = 1.03), Karnofsky Performance Scale (KPS) score (p = 0.03, HR = 0.97), and MPC1 (p = 0.003, HR = 0.52) but not MPC2 (p = 0.38) as key variables affecting overall survival. Further validation confirmed MPC1 as an independent predictor of lower-grade glioma. A clinical risk score using IDH and 1p19q status, age, KPS score, and MPC1 and MPC2 z-scores defined 4 risk categories for lower-grade glioma; this score was validated using a secondary glioma data set. CONCLUSIONS These results support the importance of MPC, especially MPC1, in improving prognostication of IDH-mutant tumors. The generation of a risk score system directly translates this finding to clinical application; however, further research to improve the molecular understanding of the role of MPC in the metabologenomic regulation of gliomas is warranted.

Molecular simulation study of feruloyl esterase adsorption on charged surfaces: effects of surface charge density and ionic strength

The surrounding conditions, such as surface charge density and ionic strength, play an important role in enzyme adsorption. The adsorption of a nonmodular type-A feruloyl esterase from Aspergillus niger (AnFaeA) on charged surfaces was investigated by parallel tempering Monte Carlo (PTMC) and all-atom molecular dynamics (AAMD) simulations at different surface charge densities (±0.05 and ±0.16 C·m(-2)) and ionic strengths (0.007 and 0.154 M). The adsorption energy, orientation, and conformational changes were analyzed. Simulation results show that whether AnFaeA can adsorb onto a charged surface is mainly controlled by electrostatic interactions between AnFaeA and the charged surface. The electrostatic interactions between AnFaeA and charged surfaces are weakened when the ionic strength increases. The positively charged surface at low surface charge density and high ionic strength conditions can maximize the utilization of the immobilized AnFaeA. The counterion layer plays a key role in the adsorption of AnFaeA on the negatively charged COOH-SAM. The native conformation of AnFaeA is well preserved under all of these conditions. The results of this work can be used for the controlled immobilization of AnFaeA.

Exploring the metabolic state of microorganisms using metabolomics

Microorganisms depend on their ability to modulate their metabolic composition according to specific circumstances, such as different phases of the growth cycle and circadian rhythms, fluctuations in environmental conditions, as well as experimental perturbations. A thorough understanding of these metabolic adaptations requires the ability to comprehensively identify and quantify the metabolome of bacterial cells in different states. In this review, we present an overview of the diverse metabolomics approaches recently adopted to explore the metabolism of a wide variety of microorganisms. Focusing on a selection of illustrative case studies, we assess the different experimental designs used and explore the major achievements and remaining challenges in the field. We conclude by discussing the important complementary information provided by computational methods such as genome-scale metabolic modeling, which enable an integrated analysis of metabolic state changes in the context of overall cellular physiology.

Role of computational methods in pharmaceutical sciences

Over the past two decades computational methods have eased up the financial and experimental burden of early drug discovery process. The in silico methods have provided support in terms of databases, data mining of large genomes, network analysis, systems biology on the bioinformatics front and structure-activity relationship, similarity analysis, docking, and pharmacophore methods for lead design and optimization. This review highlights some of the applications of bioinformatics and chemoinformatics methods that have enriched the field of drug discovery. In addition, the review also provided insights into the use of free energy perturbation methods for efficiently computing binding energy. These in silico methods are complementary and can be easily integrated into the traditional in vitro and in vivo methods to test pharmacological hypothesis.

Transcriptional profiling in Saccharomyces cerevisiae relevant for predicting alachlor mechanisms of toxicity

Alachlor has been a commonly applied herbicide and is a substance of ecotoxicological concern. The present study aims to identify molecular biomarkers in the eukaryotic model Saccharomyces cerevisiae that can be used to predict potential cytotoxic effects of alachlor, while providing new mechanistic clues with possible relevance for experimentally less accessible eukaryotes. It focuses on genome-wide expression profiling in a yeast population in response to two exposure scenarios exerting effects from slight to moderate magnitude at phenotypic level. In particular, 100 and 264 genes, respectively, were found as differentially expressed on a 2-h exposure of yeast cells to the lowest observed effect concentration (110 mg/L) and the 20% inhibitory concentration (200 mg/L) of alachlor, in comparison with cells not exposed to the herbicide. The datasets of alachlor-responsive genes showed functional enrichment in diverse metabolic, transmembrane transport, cell defense, and detoxification categories. In general, the modifications in transcript levels of selected candidate biomarkers, assessed by quantitative reverse transcriptase polymerase chain reaction, confirmed the microarray data and varied consistently with the growth inhibitory effects of alachlor. Approximately 16% of the proteins encoded by alachlor-differentially expressed genes were found to share significant homology with proteins from ecologically relevant eukaryotic species. The biological relevance of these results is discussed in relation to new insights into the potential adverse effects of alachlor in health of organisms from ecosystems, particularly in worst-case situations such as accidental spills or careless storage, usage, and disposal.