Gas6-Axl signal promotes indoor VOCs exposure-induced pulmonary fibrosis via pulmonary microvascular endothelial cells-fibroblasts cross-talk.

Volatile organic compounds (VOCs) as environmental pollutants were associated with respiratory diseases. Pulmonary fibrosis (PF) was characterized by an increase of extracellular matrix, leading to deterioration of lung function. The adverse effects on lung and the potential mechanism underlying VOCs induced PF had not been elucidated clearly. In this study, the indoor VOCs exposure mouse model along with an ex vivo biosensor assay was established. Based on scRNA-seq analysis, the adverse effects on lung and potential molecular mechanism were studied. Herein, the results showed that VOCs exposure from indoor decoration contributed to decreased lung function and facilitated pulmonary fibrosis in mice. Then, the whole lung cell atlas after VOCs exposure and the heterogeneity of fibroblasts were revealed. We explored the molecular interactions among various pulmonary cells, suggesting that endothelial cells contributed to fibroblasts activation in response to VOCs exposure. Mechanistically, pulmonary microvascular endothelial cells (MPVECs) secreted Gas6 after VOCs-induced PANoptosis phenotype, bound to the Axl in fibroblasts, and then activated fibroblasts. Moreover, Atf3 as the key gene negatively regulated PANoptosis phenotype to ameliorate fibrosis induced by VOCs exposure. These novel findings provided a new perspective about MPVECs could serve as the initiating factor of PF induced by VOCs exposure.

Hub gene ELK3-mediated reprogramming lipid metabolism regulates phenotypic switching of pulmonary artery smooth muscle cells to develop pulmonary arterial hypertension induced by PM2.5.

Fine particulate matter (PM2.5) as an environmental pollutant is related with respiratory and cardiovascular diseases. Pulmonary arterial hypertension (PAH) was characterized by incremental pulmonary artery pressure and pulmonary arterial remodeling, leading to right ventricular hypertrophy, and finally cardiac failure and death. The adverse effects on pulmonary artery and the molecular biological mechanism underlying PM2.5-caused PAH has not been elaborated clearly. In the current study, the ambient PM2.5 exposure mice model along with HPASMCs models were established. Based on bioinformatic methods and machine learning algorithms, the hub genes in PAH were screened and then adverse effects on pulmonary artery and potential mechanism was studied. Our results showed that chronic PM2.5 exposure contributed to increased pulmonary artery pressure, pulmonary arterial remodeling and right ventricular hypertrophy in mice. In vitro, PM2.5 induced phenotypic switching in HPASMCs, which served as the early stage of PAH. In mechanism, we investigated that PM2.5-mediated mitochondrial dysfunction could induce phenotypic switching in HPASMCs, which was possibly through reprogramming lipid metabolism. Next, we used machine learning algorithm to identify ELK3 as potential hub gene for mitochondrial fission. Besides, the effect of DNA methylation on ELK3 was further detected in HPASMCs after PM2.5 exposure. The results provided novel directions for protection of pulmonary vasculature injury, against adverse environmental stimuli. This work also provided a new idea for the prevention of PAH, as well as provided experimental evidence for the targeted therapy of PAH.

Comprehensive Analysis of lncRNA-mRNA Expression Profiles in Depression-like Responses of Mice Related to Polystyrene Nanoparticle Exposure.

Plastics in the environment can break down into nanoplastics (NPs), which pose a potential threat to public health. Studies have shown that the nervous system constitutes a significant target for nanoplastics. However, the potential mechanism behind nanoplastics' neurotoxicity remains unknown. This study aimed to investigate the role of lncRNA in the depressive-like responses induced by exposure to 25 nm polystyrene nanoplastics (PS NPs). Forty mice were divided into four groups administered doses of 0, 10, 25, and 50 mg/kg via gavage for 6 months. After conducting behavioral tests, RNA sequencing was used to detect changes in mRNAs, miRNAs, and lncRNAs in the prefrontal cortex of the mice in the 0 and 50 mg/kg PS NPs groups. The results revealed that mice exposed to chronic PS NPs developed depressive-like responses in a dose-dependent manner. It was demonstrated that 987 mRNAs, 29 miRNAs, and 116 lncRNAs were significantly different between the two groups. Then, a competing endogenous RNA (ceRNA) network containing 6 lncRNAs, 18 miRNAs, and 750 mRNAs was constructed. Enrichment results suggested that PS NPs may contribute to the onset of depression-like responses through the activation of axon guidance, neurotrophin-signaling pathways, and dopaminergic synapses. This study provided evidence of the molecular relationship between PS NPs and depression-like responses.

Site-specific Atg13 methylation-mediated autophagy regulates epithelial inflammation in PM2.5-induced pulmonary fibrosis.

Fine particulate matters (PM2.5) increased the risk of pulmonary fibrosis. However, the regulatory mechanisms of lung epithelium in pulmonary fibrosis remained elusive. Here we developed PM2.5-exposure lung epithelial cells and mice models to investigate the role of autophagy in lung epithelia mediating inflammation and pulmonary fibrosis. PM2.5 exposure induced autophagy in lung epithelial cells and then drove pulmonary fibrosis by activation of NF-κB/NLRP3 signaling pathway. PM2.5-downregulated ALKBH5 protein expression promotes m6A modification of Atg13 mRNA at site 767 in lung epithelial cells. Atg13-mediated ULK complex positively regulated autophagy and inflammation in epithelial cells with PM2.5 treatment. Knockout of ALKBH5 in mice further accelerated ULK complex-regulated autophagy, inflammation and pulmonary fibrosis. Thus, our results highlighted that site-specific m6A methylation on Atg13 mRNA regulated epithelial inflammation-driven pulmonary fibrosis in an autophagy-dependent manner upon PM2.5 exposure, and it provided target intervention strategies towards PM2.5-induced pulmonary fibrosis.

An interprovincial input-output database distinguishing firm ownership in China from 1997 to 2017.

Input-Output (IO) data describing supply-demand relationships between buyers and sellers for goods and services within an economy have been used not only in economics but also in scientific, environmental, and interdisciplinary research. However, most conventional IO data are highly aggregated, resulting in challenges for researchers and practitioners who face complex issues in large countries such as China, where firms within the same IO sector may have significant differences in technologies across subnational regions and different ownerships. This paper is the first attempt to compile China's interprovincial IO (IPIO) tables with separate information for mainland China-, Hong Kong, Macau, Taiwan-, and foreign-owned firms inside each province/industry pair. To do this, we collect relevant Chinese economic census data, firm surveys, product level Custom trade statistics, and firm value-added tax invoices and consistently integrate them into a 42-sector, 31-province IO account covering 5 benchmark years between 1997-2017. This work provides a solid foundation for a diverse range of innovative IO-based research in which firm heterogeneity information about location and ownership matters.

Moderate physical activity against effects of short-term PM2.5 exposure on BP via myokines-induced inflammation.

Exposure to PM2.5 increases blood pressure (BP) and cardiovascular morbidity and mortality. We conducted a randomized controlled panel study in Shijiazhuang, China among 55 healthy college students randomly assigned to either the control (CON) or SPORTS group with intervention of 2000 m jogging in 20 min for 3 times in 4 days, and 3-round health examinations from November 15, 2020 to December 6, 2020. We aimed to evaluate whether moderate physical activity (PA) protected BP health against PM2.5 exposure and explore potential mechanisms through myokines and inflammation. Individual PM2.5 exposure was calculated based on outdoor and indoor PM2.5 concentration monitoring data as well as time-activity diary of each subject. In the CON group, the exposure-response curve for SBP was linear with a threshold concentration of approximately 31 µg/m3, while an increment of SBP level was 4.38 mm Hg (95%CI: 0.17 mm Hg, 8.59 mm Hg) at lag03 for each 10-µg/m3 increase in PM2.5, using linear mixed-effect models. For inflammatory indicators, PM2.5 exposure was associated with significant increases in eosinophil counts and proportion in CON group, but decreases in MCP-1 and TNF-α in SPORTS group. Meanwhile, higher myokines including CLU and IL-6 were observed in SPORTS group compared to the CON group. Further mediation analyses revealed that eosinophil counts mediated the elevated BP in CON group, whereas MCP-1 and TNF-α were also crucial mediating cytokines for the SPORTS group, as well as CLU and IL-6 acted as mediators on BP and inflammation indicators in SPORTS group. This study suggests that moderate PA could counteract the elevated BP induced by PM2.5 exposure via myokines-suppressed inflammation pathways.

Shearlet Enhanced Snapshot Compressive Imaging.

Snapshot compressive imaging (SCI) is a promising approach to capture high-dimensional data with low dimensional sensors. With modest modifications to off-the-shelf cameras, SCI cameras encode multiple frames into a single measurement frame. These correlated frames can then be retrieved by reconstruction algorithms. Existing reconstruction algorithms suffer from low speed or low fidelity. In this paper, we propose a novel reconstruction algorithm, namely, Shearlet enhanced Snapshot Compressive Imaging (SeSCI), which exploits the sparsity of the image representation in both frequency domain and shearlet domain. Towards this end, we first derive our SeSCI algorithm under the alternating direction method of multipliers (ADMM) framework. We then propose an efficient solution of SeSCI algorithm. Moreover, we prove that the improved SeSCI algorithm converges to a fixed point. Experimental results on both synthetic data and real data captured by SCI cameras demonstrate the significant advantages of SeSCI, which outperforms the conventional algorithms by more than 2dB in PSNR. At the same time, the SeSCI achieves a speed-up more than 100× over the state-of-the-art algorithm.