Serum metabolome responses induced by long-term inoculation of suspended PM2.5 in chicken

Features of extreme PM 2.5 pollution and its influencing factors: evidence from China

Extreme PM 2.5 pollution has become a significant environmental problem in China in recent years, which is hazardous to human health and daily life. Noticing the importance of investigating the causes of extreme PM 2.5 pollution, this paper classifies cities across China into eight categories (four groups plus two scenarios) based on the generalized extreme value (GEV) distribution using hourly station-level PM 2.5 concentration data, and a series of multi-choice models are employed to assess the probabilities that cities fall into different categories. Various factors such as precursor pollutants and socio-economic factors are considered after controlling for meteorological conditions in each model. It turns out that SO 2 concentration, NO 2 concentration, and population density are the top three factors contributing most to the log ratios. Moreover, in both left- and right-skewed cases, the influence of a one-unit increase of SO 2 concentration on the relative probability of cities falling into different groups shows an increasing trend, while those of NO 2 concentration show a decreasing trend. At the same time, the higher the extreme pollution level, the bigger the effect of SO 2 and NO 2 concentrations on the probability of cities falling into normalized scenarios. The multivariate logit model is used for prediction and policy simulations. In summary, by analyzing the influences of various factors and the heterogeneity of their influence patterns, this paper provides valuable insights in formulating effective emission reduction policies.

Lnc-Clic5 as a sponge for miR-212-5p to inhibit cow barn PM2.5-induced apoptosis in rat alveolar macrophages

Particulate matter 2.5 (PM2.5) is a highly hazardous airborne particulate matter that poses a significant risk to humans and animals. Urban airborne particulate matter contributes to the increased incidence and mortality of respiratory diseases, such as asthma and chronic obstructive pulmonary disease (COPD), in humans. However, the specific mechanism by which PM2.5 affects animals in barn environments is yet to be elucidated. In this study, we investigated the effect of exposure to cow barn PM2.5 on rat alveolar macrophages (NR8383) and found that it induced apoptosis via the miR-212-5p/RASSF1 pathway. We found that lnc-Clic5 expression was downregulated in NR8383 cells exposed to cow barn PM2.5. Lnc-Clic5 plays a competitive endogenous RNA (ceRNA) regulatory role by sponging miR-212-5p to attenuate the regulation of RASSF1. Moreover, lnc-Clic5 overexpression inhibited NR8383 apoptosis by targeting the miR-212-5p/RASSF1 pathway. Co-treatment with miR-212-5p and lnc-Clic5 in the presence of cow barn PM2.5 revealed that lnc-Clic5 reversed NR8383 cell apoptosis induced by PM2.5 when miR-212-5p was overexpressed. These findings contribute to the study of ncRNAs and ceRNAs regulating PM2.5-induced apoptosis in animal farms, provide therapeutic targets for lung macrophage apoptosis, and may be useful for further evaluating the toxicological effects of PM2.5 in farmhouses on the respiratory systems of humans and animals.

Exploring the Microbial Community Structure in the Chicken House Environment by Metagenomic Analysis

The environmental conditions of chicken houses play an important role in the growth and development of these animals. The chicken house is an essential place for the formation of microbial aerosols. Microbial aerosol pollution and transmission can affect human and animal health. In this work, we continuously monitored fine particulate matter (PM2.5) in the chicken house environment for four weeks and studied the microbial community structure in the aerosols of the chicken house environment through metagenomic sequencing. Our results found that bacteria, fungi, viruses, and archaea were the main components of PM2.5 in the chicken house environment, accounting for 89.80%, 1.08%, 2.06%, and 0.49%, respectively. Conditional pathogens are a type of bacteria that poses significant harm to animals themselves and to farm workers. We screened ten common conditional pathogens and found that Staphylococcus had the highest relative abundance, while Clostridium contained the most microbial species, up to 456. Basidiomycetes and Ascomycota in fungi showed dramatic changes in relative abundance, and other indexes showed no significant difference. Virulence factors (VF) are also a class of molecules produced by pathogenic microbes that can cause host diseases. The top five virulence factors were found in four groups: FbpABC, HitABC, colibactin, acinetobactin, and capsule, many of which are used for the iron uptake system. In the PM2.5 samples, eight avian viruses were the most significant discoveries, namely Fowl aviadovirus E, Fowl aviadovirus D, Avian leukosis virus, Avian endogenous retrovirus EAV-HP, Avian dependent parvovirus 1, Fowl adenovus, Fowl aviadovirus B, and Avian sarcoma virus. The above results significantly improve our understanding of the microbial composition of PM2.5 in chicken houses, filling a gap on virus composition; they also indicate a potential threat to poultry and to human health. This work provides an important theoretical basis for animal house environmental monitoring and protection.