Autophagy is a highly conserved degradative process through which cells overcome stressful conditions. Inasmuch as faulty autophagy has been associated with aging, neuronal degeneration disorders, diabetes, and fatty liver, autophagy is regarded as a potential therapeutic target. This review summarizes the present state of knowledge concerning the role of zinc in the regulation of autophagy, the role of autophagy in zinc metabolism, and the potential role of autophagy as a mediator of the protective effects of zinc. Data from in vitro studies consistently support the notion that zinc is critical for early and late autophagy. Studies have shown inhibition of early and late autophagy in cells cultured in medium treated with zinc chelators. Conversely, excess zinc added to the medium has shown to potentiate the stimulation of autophagy by tamoxifen, H2O2, ethanol and dopamine. The potential role of autophagy in zinc homeostasis has just begun to be investigated.Increasing evidence indicates that autophagy dysregulation causes significant changes in cellular zinc homeostasis. Autophagy may mediate the protective effect of zinc against lipid accumulation, apoptosis and inflammation by promoting degradation of lipid droplets, inflammasomes, p62/SQSTM1 and damaged mitochondria.Studies with humans and animal models are necessary to determine whether autophagy is influenced by zinc intake.

During the development of an individual from a single cell to prenatal stages to adolescence to adulthood and through the complete life span, humans are exposed to countless environmental and stochastic factors, including estrogenic endocrine disrupting chemicals. Brain cells and neural circuits are likely to be influenced by estrogenic endocrine disruptors (EEDs) because they strongly dependent on estrogens. In this review, we discuss both environmental, epidemiological, and experimental evidence on brain health with exposure to oral contraceptives, hormonal therapy, and EEDs such as bisphenol-A (BPA), polychlorinated biphenyls (PCBs), phthalates, and metalloestrogens, such as, arsenic, cadmium, and manganese. Also we discuss the brain health effects associated from exposure to EEDs including the promotion of neurodegeneration, protection against neurodegeneration, and involvement in various neurological deficits; changes in rearing behavior, locomotion, anxiety, learning difficulties, memory issues, and neuronal abnormalities. The effects of EEDs on the brain are varied during the entire life span and far-reaching with many different mechanisms. To understand endocrine disrupting chemicals mechanisms, we use bioinformatics, molecular, and epidemiologic approaches. Through those approaches, we learn how the effects of EEDs on the brain go beyond known mechanism to disrupt the circulatory and neural estrogen function and estrogen-mediated signaling. Effects on EEDs-modified estrogen and nuclear respiratory factor 1 (NRF1) signaling genes with exposure to natural estrogen, pharmacological estrogen-ethinyl estradiol, PCBs, phthalates, BPA, and metalloestrogens are presented here. Bioinformatics analysis of gene-EEDs interactions and brain disease associations identified hundreds of genes that were altered by exposure to estrogen, phthalate, PCBs, BPA or metalloestrogens. Many genes modified by EEDs are common targets of both 17 β-estradiol (E2) and NRF1. Some of these genes are involved with brain diseases, such as Alzheimer’s Disease (AD), Parkinson’s Disease, Huntington’s Disease, Amyotrophic Lateral Sclerosis, Autism Spectrum Disorder, and Brain Neoplasms. For example, the search of enriched pathways showed that top ten E2 interacting genes in AD—APOE, APP, ATP5A1, CALM1, CASP3, GSK3B, IL1B, MAPT, PSEN2 and TNF—underlie the enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) AD pathway. With AD, the six E2-responsive genes are NRF1 target genes: APBB2, DPYSL2, EIF2S1, ENO1, MAPT, and PAXIP1. These genes are also responsive to the following EEDs: ethinyl estradiol (APBB2, DPYSL2, EIF2S1, ENO1, MAPT, and PAXIP1), BPA (APBB2, EIF2S1, ENO1, MAPT, and PAXIP1), dibutyl phthalate (DPYSL2, EIF2S1, and ENO1), diethylhexyl phthalate (DPYSL2 and MAPT). To validate findings from Comparative Toxicogenomics Database (CTD) curated data, we used Bayesian network (BN) analysis on microarray data of AD patients. We observed that both gender and NRF1 were associated with AD. The female NRF1 gene network is completely ...

Faulty autophagy has been linked to various diseases including neurodegenerative disorders, diabetes, and cancer. Increasing evidence support the notion that activation of autophagy protects against ethanol-induced steatosis and liver injury. Herein, we investigated the role of zinc in autophagy in human hepatoma cells VL-17A exposed or not to ethanol. LC3II/LC3I ratio, p62, and Beclin-1 expression and autophagosomes number were determined in cells incubated in medium containing various concentrations of zinc with or without ethanol. In addition, labile zinc and mRNA expression of metallothionein and the zinc transporters SLC39A8, SLC39A14, and SLC30A10 were evaluated in cells exposed to ethanol and the autophagy inhibitor 3-methyladenine. Zinc depletion caused a significant suppression of autophagy in cells. Conversely, zinc addition to medium stimulated autophagy in cells. Moreover, cotreatment with ethanol and excess zinc (40 μM) had an additive effect on the induction of autophagy. 3-methyadenine treatment decreased labile zinc, but this effect was more pronounced in cells exposed to ethanol. Lastly, ethanol and 3-methyladenine caused significant changes in the expression of metallothionein and zinc transporters. The results from this study support the hypothesis that zinc is critical for autophagy under basal conditions and during ethanol exposure.

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.

We present a combined environmental epidemiologic, genomic, and bioinformatics approach to identify: exposure of environmental chemicals with estrogenic activity; epidemiologic association between endocrine disrupting chemical (EDC) and health effects, such as, breast cancer or endometriosis; and gene-EDC interactions and disease associations. Human exposure measurement and modeling confirmed estrogenic activity of three selected class of environmental chemicals, polychlorinated biphenyls (PCBs), bisphenols (BPs), and phthalates. Meta-analysis showed that PCBs exposure, not Bisphenol A (BPA) and phthalates, increased the summary odds ratio for breast cancer and endometriosis. Bioinformatics analysis of gene-EDC interactions and disease associations identified several hundred genes that were altered by exposure to PCBs, phthalate or BPA. EDCs-modified genes in breast neoplasms and endometriosis are part of steroid hormone signaling and inflammation pathways. All three EDCs–PCB 153, phthalates, and BPA influenced five common genes—CYP19A1, EGFR, ESR2, FOS, and IGF1—in breast cancer as well as in endometriosis. These genes are environmentally and estrogen responsive, altered in human breast and uterine tumors and endometriosis lesions, and part of Mitogen Activated Protein Kinase (MAPK) signaling pathways in cancer. Our findings suggest that breast cancer and endometriosis share some common environmental and molecular risk factors.