Ascorbic acid is among the most abundant antioxidants in the lung, where it likely plays a key role in the mechanism by which particulate air pollution initiates a biological response. Because ascorbic acid is a highly redox active species, it engages in a far more complex web of reactions than a typical organic molecule, reacting with oxidants such as the hydroxyl radical as well as redox-active transition metals such as iron and copper. The literature provides a solid outline for this chemistry, but there are large disagreements about mechanisms, stoichiometries and reaction rates, particularly for the transition metal reactions. Here we synthesize the literature, develop a chemical kinetics model, and use seven sets of laboratory measurements to constrain mechanisms for the iron and copper reactions and derive key rate constants. We find that micromolar concentrations of iron(III) and copper(II) are more important sinks for ascorbic acid (both AH2 and AH−) than reactive oxygen species. The iron and copper reactions are catalytic rather than redox reactions, and have unit stoichiometries: Fe(III)/Cu(II) + AH2/AH−  + O2 → Fe(III)/Cu(II) + H2O2 + products. Rate constants are 5.7 × 104 and 4.7 × 104 M−2 s−1 for Fe(III) + AH2/AH− and 7.7 × 104 and 2.8 × 106 M−2 s−1 for Cu(II) + AH2/AH−, respectively.

Extensive exposure to estrogen is generally acknowledged as a risk factor for endometrial cancer. Given that the accumulation of adipocytes also contributes to the increased production of estrogen, in the present study, we evaluated the expression of the fat mass and obesity-associated (FTO) gene in endometrial tumor tissues and further explored the mechanism of how estrogen facilitates FTO nuclear localization and promotes endometrial cancer cell proliferation. Immunohistochemical (IHC) staining assay was used to detect the FTO expression in endometrial tumor samples. Western blotting was performed to investigate the mechanism of estrogen-induced FTO nuclear localization. siRNA was used to knock down ERα and further explore its role in FTO nuclear localization. MTT assay was carried out to determine cell proliferation. We found that FTO was overexpressed in endometrial carcinoma tissues and served as a poor prognostic marker. Additionally, estrogen induced FTO nuclear accumulation via the mTOR signaling pathway and the nuclear localization was ERα-dependent, which contributed to enhanced proliferative activity. Therefore, the present study provides new insight into the mechanisms of estrogen-induced proliferation, implying the possibility of using FTO as a potential therapeutic target for the treatment of endometrial cancer.

No abstract

The molecular mechanism associated with mammalian meiosis has yet to be fully explored, and one of the main reasons for this lack of exploration is that some meiosis-essential genes are still unknown. The profiling of gene expression during spermatogenesis has been performed in previous studies, yet few studies have aimed to find new functional genes. Since there is a huge gap between the number of genes that are able to be quantified and the number of genes that can be characterized by phenotype screening in one assay, an efficient method to rank quantified genes according to phenotypic relevance is of great importance. We proposed to rank genes by the probability of their function in mammalian meiosis based on global protein abundance using machine learning. Here, nine types of germ cells focusing on continual substages of meiosis prophase I were isolated, and the corresponding proteomes were quantified by high-resolution mass spectrometry. By combining meiotic labels annotated from the MGI mouse knockout database and the spermatogenesis proteomics dataset, a supervised machine learning package, FuncProFinder, was developed to rank meiosis-essential candidates. Of the candidates whose functions were unannotated, four of ten genes with the top prediction scores, Zcwpw1, Tesmin, 1700102P08Rik and Kctd19, were validated as meiosis-essential genes by knockout mouse models. Therefore,  mammalian meiosis-essential genes could be efficiently predicted based on the protein abundance dataset, which provides a paradigm for other functional gene mining from a related abundance dataset.

Large-scale epidemiological studies have shown a close correlation between adverse human health effects and exposure to ambient particulate matter (PM). The oxidative potential (OP) of ambient PM has been implicated in inducing toxic effects associated with PM exposure. In particular, reactive oxygen species (ROS), either bound to PM or generated by particulate components in vivo, substantially contribute to the OP and therefore toxicity of PM by lowering antioxidant concentrations in the lung, which can subsequently lead to oxidative stress, inflammation, and disease. Traditional methods for measuring aerosol OP are labor intensive and have poor time resolution, with significant delays between aerosol collection and ROS analysis. These methods may underestimate ROS concentrations in PM because of the potentially short lifetime of some ROS species; therefore, continuous online, highly time-resolved measurement of ROS components in PM is highly advantageous. In this work, we develop a novel online method for measuring aerosol OP based on ascorbic acid chemistry, an antioxidant prevalent in the lung, thus combining the advantages of continuous online measurement with a physiologically relevant assay. The method limit of detection is estimated for a range of atmospherically important chemical components such as Cu­(II) 0.22 ± 0.03 μg m–3, Fe­(II) 47.8 ± 5.5 μg m–3, Fe­(III) 0.63 ± 0.05 μg m–3, and secondary organic aerosol 41.2 ± 6.9 μg m–3, demonstrating that even at this early stage of development, the online method is capable of measuring the OP of PM in polluted urban environments and smog chamber studies.

No abstract

Oxidative potential (OP) has been proposed as a possible integrated metric for particles smaller than 2.5 μm in diameter (PM2.5) to evaluate adverse health outcomes associated with particulate air pollution exposure. Here, we investigate how OP depends on sources and chemical composition and how OP varies by land use type and neighborhood socioeconomic position in the Los Angeles area. We measured OH formation (OPOH), dithiothreitol loss (OPDTT), black carbon, and 52 metals and elements for 54 total PM2.5 samples collected in September 2019 and February 2020. The Positive Matrix Factorization source apportionment model identified four sources contributing to volume-normalized OPOH: vehicular exhaust, brake and tire wear, soil and road dust, and mixed secondary and marine. Exhaust emissions contributed 42% of OPOH, followed by 21% from brake and tire wear. Similar results were observed for the OPDTT source apportionment. Furthermore, by linking measured PM2.5 and OP with census tract level socioeconomic and health outcome data provided by CalEnviroScreen, we found that the most disadvantaged neighborhoods were exposed to both the most toxic particles and the highest particle concentrations. OPOH exhibited the largest inverse social gradients, followed by OPDTT and PM2.5 mass. Finally, OPOH was the metric most strongly correlated with adverse health outcome indicators.

A Ni(ii)-catalyzed asymmetric addition of arylboronic acids to cyclic aldimines and ketimines is reported. Our tropos phosphine-oxazoline biphenyl ligand is crucial for the high catalytic activity, which coordinates to Ni(ii) to form a complex with a single axial configuration. The desired chiral amine products could be prepared with excellent yields (up to 99%) and enantioselectivities (up to 99.8%) under mild reaction conditions.