In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass ( Micropterus salmoides)

Defined cellular reprogramming of androgen receptor-active prostate cancer to neuroendocrine prostate cancer

Neuroendocrine prostate cancer (NEPC) arises primarily through neuroendocrine transdifferentiation (NEtD) as an adaptive mechanism of therapeutic resistance. Models to define the functional effects of putative drivers of this process on androgen receptor (AR) signaling and NE cancer lineage programs are lacking. We adapted a genetically defined strategy from the field of cellular reprogramming to directly convert AR-active prostate cancer (ARPC) to AR-independent NEPC using candidate factors. We delineated critical roles of the pioneer factors ASCL1 and NeuroD1 in NEtD and uncovered their abilities to silence AR expression and signaling by remodeling chromatin at the somatically acquired AR enhancer and global AR binding sites with enhancer activity. We also elucidated the dynamic temporal changes in the transcriptomic and epigenomic landscapes of cells undergoing acute lineage conversion from ARPC to NEPC which should inform future therapeutic development. Further, we distinguished the activities of ASCL1 and NeuroD1 from the inactivation of RE-1 silencing transcription factor (REST), a master suppressor of a major neuronal gene program, in establishing a NEPC lineage state and in modulating the expression of genes associated with major histocompatibility complex class I (MHC I) antigen processing and presentation. These findings provide important, clinically relevant insights into the biological processes driving NEtD of prostate cancer.

Spatial proteomics of skeletal muscle using thin cryosections reveals metabolic adaptation at the muscle-tendon transition zone

Morphological studies of skeletal muscle tissue provide insights into the architecture of muscle fibers, the surrounding cells, and the extracellular matrix (ECM). However, a spatial proteomics analysis of the skeletal muscle including the muscle-tendon transition zone is lacking. Here, we prepare cryotome muscle sections of the mouse soleus muscle and measure each slice using short liquid chromatography-mass spectrometry (LC-MS) gradients. We generate 3,000 high-resolution protein profiles that serve as the basis for a network analysis to reveal the complex architecture of the muscle-tendon junction. Among the protein profiles that increase from muscle to tendon, we find proteins related to neuronal activity, fatty acid biosynthesis, and the renin-angiotensin system (RAS). Blocking the RAS in cultured mouse tenocytes using losartan reduces the ECM synthesis. Overall, our analysis of thin cryotome sections provides a spatial proteome of skeletal muscle and reveals that the RAS acts as an additional regulator of the matrix within muscle-tendon junctions.

A Data-Driven Computational Framework for Assessing the Risk of Placental Exposure to Environmental Chemicals

A computational framework based on placental gene networks was proposed in this work to improve the accuracy of the placental exposure risk assessment of environmental compounds. The framework quantitatively characterizes the ability of compounds to cross the placental barrier by systematically considering the interaction and pathway-level information on multiple placental transporters. As a result, probability scores were generated for 307 compounds crossing the placental barrier based on this framework. These scores were then used to categorize the compounds into different levels of transplacental transport range, creating a gradient partition. These probability scores not only facilitated a more intuitive understanding of a compound's ability to cross the placental barrier but also provided valuable information for predicting potential placental disruptors. Compounds with probability scores greater than 90% were considered to have significant transplacental transport potential, whereas those with probability scores less than 80% were classified as unlikely to cross the placental barrier. Furthermore, external validation set results showed that the probability score could accurately predict the compounds known to cross the placental barrier. In conclusion, the computational framework proposed in this study enhances the intuitive understanding of the ability of compounds to cross the placental barrier and opens up new avenues for assessing the placental exposure risk of compounds.

Identifying transcriptomic indicators of tertiary treated municipal effluent in longnose dace (Rhinichthys cataractae) caged under semi-controlled conditions in experimental raceways

To evaluate effects of tertiary treated wastewater treatment plant effluent (MWWE) on transcriptomic responses in longnose dace (Rhinichthys cataractae; LND) we conducted a semi-controlled study in experimental raceways (Advancing Canadian Water Assets facility) imbedded in the Pine Creek treatment plant (Calgary, AB). LND collected from a reference site in the Bow River (REF) were caged in raceways containing either 5 % Pine Creek effluent (PC) or Bow River water (BR; control) over 28 d. Liver transcriptomes were analyzed in males and females sampled on days 7, 14 and 28 from BR and PC, and compared to REF fish on day 0. Concurrent with the caging, selected environmental substances of concern were analyzed in the BR and PC. Significantly different unigenes (SDUs) in females (vs males) within both BR and PC raceways increased over time and compared to REF fish. Moreover, SDUs in females and males within the same treatment (i.e., BR, PC) showed a temporal increase as well as compared to REF fish. Time was the dominant factor affecting SDUs, whereas sex and treatment had less of an impact on the transcriptome profiling. Gene Set Enrichment Analysis of BR vs PC over time revealed effects on genes involved in growth, metabolism of carbohydrates and lipids, and immune system on day 7; however, by day 28, 80-100 % of the transcripts localized to enriched biomarkers were associated with tissue immune responses in both sexes. Exposure to 5 % effluent had significant effects on female liver somatic index but no effects were observed on other phenotypic health indices in either sex. BR was used as the source of reference water, but analyses showed trace amounts of ESOCs. Analyses did not point towards definitive response patterns that could be used in field-based ecotoxicogenomic studies on the impacts of well-treated MWWE but suggested compromised adaptive immune responses.

Temporal evolution reveals bifurcated lineages in aggressive neuroendocrine small cell prostate cancer trans-differentiation

Trans-differentiation from an adenocarcinoma to a small cell neuroendocrine state is associated with therapy resistance in multiple cancer types. To gain insight into the underlying molecular events of the trans-differentiation, we perform a multi-omics time course analysis of a pan-small cell neuroendocrine cancer model (termed PARCB), a forward genetic transformation using human prostate basal cells and identify a shared developmental, arc-like, and entropy-high trajectory among all transformation model replicates. Further mapping with single cell resolution reveals two distinct lineages defined by mutually exclusive expression of ASCL1 or ASCL2. Temporal regulation by groups of transcription factors across developmental stages reveals that cellular reprogramming precedes the induction of neuronal programs. TFAP4 and ASCL1/2 feedback are identified as potential regulators of ASCL1 and ASCL2 expression. Our study provides temporal transcriptional patterns and uncovers pan-tissue parallels between prostate and lung cancers, as well as connections to normal neuroendocrine cell states.

A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity

Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo-based classifiers achieve accuracy greater than 75% when tested for invitro to in vivoextrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDCevokedmetabolic diseases.

Biological networks across scales

Many biological systems across scales of size and complexity exhibit a time-varying complex network structure that emerges and self-organizes as a result of interactions with the environment. Network interactions optimize some intrinsic cost functions that are unknown and involve for example energy efficiency, robustness, resilience, and frailty. A wide range of networks exist in biology, from gene regulatory networks important for organismal development, protein interaction networks that govern physiology and metabolism, and neural networks that store and convey information to networks of microbes that form microbiomes within hosts, animal contact networks that underlie social systems, and networks of populations on the landscape connected by migration. Increasing availability of extensive (big) data is amplifying our ability to quantify biological networks. Similarly, theoretical methods that describe network structure and dynamics are being developed. Beyond static networks representing snapshots of biological systems, collections of longitudinal data series can help either at defining and characterizing network dynamics over time or analyzing the dynamics constrained to networked architectures. Moreover, due to interactions with the environment and other biological systems, a biological network may not be fully observable. Also, subnetworks may emerge and disappear as a result of the need for the biological system to cope with for example invaders or new information flows. The confluence of these developments renders tractable the question of how the structure of biological networks predicts and controls network dynamics. In particular, there may be structural features that result in homeostatic networks with specific higher-order statistics (e.g., multifractal spectrum), which maintain stability over time through robustness and/or resilience to perturbation. Alternative, plastic networks may respond to perturbation by (adaptive to catastrophic) shifts in structure. Here, we explore the opportunity for discovering universal laws connecting the structure of biological networks with their function, positioning them on the spectrum of time-evolving network structure, i.e. dynamics of networks, from highly stable to exquisitely sensitive to perturbation. If such general laws exist, they could transform our ability to predict the response of biological systems to perturbations-an increasingly urgent priority in the face of anthropogenic changes to the environment that affect life across the gamut of organizational scales.

Overview of biological effects of Quercetin on ovary

Over the last few decades, using natural products has been increased to treat different diseases. Today, great attention has been pointed toward the usage of natural products such as flavonoids, especially Quercetin (QUR), in the treatment of diseases. QUR as a natural antioxidant has been traditionally used to prevent or treat a variety of diseases such as cancer, cardiovascular disease, polycystic ovary syndrome (PCOS), obesity, chronic inflammation, and reproductive system dysfunction. Several studies demonstrated that QUR acts as an anti-inflammatory, anti-apoptotic, antioxidant, and anticancer agent. With this in view, in this study, we intended to describe an overview of the biological effects of QUR on the ovary. QUR improves the quality of oocytes and embryos. It affects the proliferation and apoptosis and decreases the oxidative stress in granulosa cells (GCs). Furthermore, QUR can be used as a complementary and alternative therapy in ovarian cancer and it has beneficial effects in the treatment of PCOS patients. It seems that QUR as a supplementary factor has different activities for the treatment of different disorders and it also has bidirectional activities. However, further investigations are needed for understanding the efficacy of QUR in the treatment and improvement of gynecological patients.

Organochlorine pesticides: Agrochemicals with potent endocrine-disrupting properties in fish

Organochlorine pesticides (OCPs) are persistent environmental contaminants that act as endocrine disruptors and organ system toxicants. These pesticides (e.g. dichlorodiphenyltrichloroethane (DDT), dieldrin, toxaphene, among others) are ranked as some of the most concerning chemicals for human health. These pesticides (1) act as teratogens, (2) are neuroendocrine disruptors, (3) suppress the immune and reproductive systems, and (4) dysregulate lipids and metabolism. Using a computational approach, we revealed enriched endocrine-related pathways in the Comparative Toxicogenomics Database sensitive to this chemical class, and these included reproduction (gonadotropins, estradiol, androgen, steroid biosynthesis, oxytocin), thyroid hormone, and insulin. Insight from the Tox21 and ToxCast programs confirm that these agrochemicals activate estrogen receptors, androgen receptors, and retinoic acid receptors with relatively high affinity, although differences exist in their potency. We propose an adverse outcome pathway for OCPs toxicity in the fish testis as a novel contribution to further understanding of OCP-induced toxicity. Organochlorine pesticides, due to their persistence and high toxicity to aquatic and terrestrial wildlife as well as humans, remain significant agrochemicals of concern.

Experimental Approaches for Characterizing the Endocrine-Disrupting Effects of Environmental Chemicals in Fish

Increasing industrial and agricultural activities have led to a disturbing increase of pollutant discharges into the environment. Most of these pollutants can induce short-term, sustained or delayed impacts on developmental, physiological, and behavioral processes that are often regulated by the endocrine system in vertebrates, including fish, thus they are termed endocrine-disrupting chemicals (EDCs). Physiological impacts resulting from the exposure of these vertebrates to EDCs include abnormalities in growth and reproductive development, as many of the prevalent chemicals are capable of binding the receptors to sex steroid hormones. The approaches employed to investigate the action and impact of EDCs is largely dependent on the specific life history and habitat of each species, and the type of chemical that organisms are exposed to. Aquatic vertebrates, such as fish, are among the first organisms to be affected by waterborne EDCs, an attribute that has justified their wide-spread use as sentinel species. Many fish species are exposed to these chemicals in the wild, for either short or prolonged periods as larvae, adults, or both, thus, studies are typically designed to focus on either acute or chronic exposure at distinct developmental stages. The aim of this review is to provide an overview of the approaches and experimental methods commonly used to characterize the effects of some of the environmentally prevalent and emerging EDCs, including 17 α-ethinylestradiol, nonylphenol, BPA, phthalates, and arsenic; and the pervasive and potential carriers of EDCs, microplastics, on reproduction and growth. In vivo and in vitro studies are designed and employed to elucidate the direct effects of EDCs at the organismal and cellular levels, respectively. In silico approaches, on the other hand, comprise computational methods that have been more recently applied with the potential to replace extensive in vitro screening of EDCs. These approaches are discussed in light of model species, age and duration of EDC exposure.

Genotoxic biomarkers and histological changes in marine medaka (Oryzias melastigma) exposed to 17α-ethynylestradiol and 17β-trenbolone

Endocrine-disrupting pollutants in marine environments have aroused great concern for their adverse effects on the reproduction of marine organisms. This study aimed to seek promising biomarkers for estrogenic/androgenic chemicals. First, two possible male-specific genes, SRY-box containing gene 9a2 (sox9a2) and gonadal soma-derived factor (gsdf), were cloned from marine medaka (Oryzias melastigma). Then the responses of sox9a2, gsdf, choriogenin (chgH and chgL), vitellogenin (vtg1 and vtg2), and cytochrome P450 aromatase (cyp19a and cyp19b) were investigated after exposure to 17α-ethynylestradiol (EE₂) and 17β-trenbolone (TB) at 2, 10, and 50 ng/L. The results showed that gsdf was specifically expressed in the testes and easily induced in the ovaries after TB exposure, indicating that gsdf was a potential biomarker of environmental androgens. ChgL was a useful biomarker of weak estrogen pollution for its high sensitivity to low levels of EE₂. In addition, both EE₂ and TB exposure damaged gonadal structures and inhibited gonadal development.

A de novo transcriptome assembly approach elucidates the dynamics of ovarian maturation in the swordfish (Xiphias gladius)

The Mediterranean swordfish (Xiphias gladius) has been recently classified as overfished and in 2016, the International Commission for the Conservation of the Atlantic Tunas (ICCAT) established a multi-annual management plan to recover this stock. To successfully achieve this goal, knowledge about swordfish biology is needed. To date, few studies on swordfish have been performed and none of them has provided useful insights into the reproductive biology at molecular level. Here we set to characterise the molecular dynamics underlying ovarian maturation by employing a de novo transcriptome assembly approach. Differential gene expression analysis in mature and immature ovaries identified a number of differentially expressed genes associated with biological processes driving ovarian maturation. Focusing on ovarian steroidogenesis and vitellogenin uptake, we depict the molecular dynamics characterizing these processes while a phylogenetic analysis let us identify a candidate vitellogenin receptor. This is the first swordfish transcriptome assembly and these findings provide in-depth understanding of molecular processes describing ovarian maturation. Moreover, the establishment of a publicly available database containing information on the swordfish transcriptome aims to boost research on this species with the long-term of developing more comprehensive and successful stock management plans.