Thyroid-Hormone–Disrupting Chemicals: Evidence for Dose-Dependent Additivity or Synergism

Endocrine disruption from environmental contaminants has been linked to a broad spectrum of adverse outcomes. One concern about endocrine-disrupting xenobiotics is the potential for additive or synergistic (i.e., greater-than-additive) effects of mixtures. A short-term dosing model to examine the effects of environmental mixtures on thyroid homeostasis has been developed. Prototypic thyroid-disrupting chemicals (TDCs) such as dioxins, polychlorinated biphenyls (PCBs), and poly-brominated diphenyl ethers have been shown to alter thyroid hormone homeostasis in this model primarily by up-regulating hepatic catabolism of thyroid hormones via at least two mechanisms. Our present effort tested the hypothesis that a mixture of TDCs will affect serum total thyroxine (T(4)) concentrations in a dose-additive manner. Young female Long-Evans rats were dosed via gavage with 18 different polyyhalogenated aromatic hydrocarbons [2 dioxins, 4 dibenzofurans, and 12 PCBs, including dioxin-like and non-dioxin-like PCBs] for 4 consecutive days. Serum total T(4) was measured via radioimmunoassay in samples collected 24 hr after the last dose. Extensive dose–response functions (based on seven to nine doses per chemical) were determined for individual chemicals. A mixture was custom synthesized with the ratio of chemicals based on environmental concentrations. Serial dilutions of this mixture ranged from approximately background levels to 100-fold greater than background human daily intakes. Six serial dilutions of the mixture were tested in the same 4-day assay. Doses of individual chemicals that were associated with a 30% TH decrease from control (ED(30)), as well as predicted mixture outcomes were calculated using a flexible single-chemical-required method applicable to chemicals with differing dose thresholds and maximum-effect asymptotes. The single-chemical data were modeled without and with the mixture data to determine, respectively, the expected mixture response (the additivity model) and the experimentally observed mixture response (the empirical model). A likelihood-ratio test revealed statistically significant departure from dose additivity. There was no deviation from additivity at the lowest doses of the mixture, but there was a greater-than-additive effect at the three highest mixtures doses. At high doses the additivity model underpredicted the empirical effects by 2- to 3-fold. These are the first results to suggest dose-dependent additivity and synergism in TDCs that may act via different mechanisms in a complex mixture. The results imply that cumulative risk approaches be considered when assessing the risk of exposure to chemical mixtures that contain TDCs

Engaging with patient online health information use: a survey of primary health care nurses

Internet health information is used by patients for health care decision making. Research indicates this information is not necessarily disclosed in interactions with health professionals. This study investigated primary health care nurses’ engagement with patient online health information use along with the respondents’ disclosure of online sources to their personal health care provider. A questionnaire was posted to a random sample of 1,000 New Zealand nurses with 630 responses. Half the respondents assessed patients’ online use (n = 324) and had encountered patients who had wrongly interpreted information. Health information quality evaluation activities with patients indicated the need for nursing information literacy skills. A majority of respondents (71%, n = 443) used online sources for personal health information needs; 36.3% (n = 155) of the respondents using online sources did not tell their personal health care provider about information obtained. This study identifies that there are gaps in supporting patient use but more nursing engagement with online sources when compared with earlier studies

IGF1-mediated human embryonic stem cell self-renewal recapitulates the embryonic niche.

Our understanding of the signalling pathways regulating early human development is limited, despite their fundamental biological importance. Here, we mine transcriptomics datasets to investigate signalling in the human embryo and identify expression for the insulin and insulin growth factor 1 (IGF1) receptors, along with IGF1 ligand. Consequently, we generate a minimal chemically-defined culture medium in which IGF1 together with Activin maintain self-renewal in the absence of fibroblast growth factor (FGF) signalling. Under these conditions, we derive several pluripotent stem cell lines that express pluripotency-associated genes, retain high viability and a normal karyotype, and can be genetically modified or differentiated into multiple cell lineages. We also identify active phosphoinositide 3-kinase (PI3K)/AKT/mTOR signalling in early human embryos, and in both primed and naïve pluripotent culture conditions. This demonstrates that signalling insights from human blastocysts can be used to define culture conditions that more closely recapitulate the embryonic niche

Enhanced multiplex genome engineering through co-operative oligonucleotide co-selection

Genome-scale engineering of living organisms requires precise and economical methods to efficiently modify many loci within chromosomes. One such example is the directed integration of chemically synthesized single-stranded deoxyribonucleic acid (oligonucleotides) into the chromosome of Escherichia coli during replication. Herein, we present a general co-selection strategy in multiplex genome engineering that yields highly modified cells. We demonstrate that disparate sites throughout the genome can be easily modified simultaneously by leveraging selectable markers within 500 kb of the target sites. We apply this technique to the modification of 80 sites in the E. coli genome.United States. Dept. of Energy. Genomes To Life (DE-FG02-03ER6344)National Science Foundation (U.S.). Genes and Genomes Systems Cluster (0719344)National Science Foundation (U.S.). Center for Bits and Atoms (0122419)National Science Foundation (U.S.). Synthetic Biology Engineering Research Center (0540879