Exercise-Mediated Protection against Air Pollution-Induced Immune Damage: Mechanisms, Challenges, and Future Directions.

Air pollution, a serious risk factor for human health, can lead to immune damage and various diseases. Long-term exposure to air pollutants can trigger oxidative stress and inflammatory responses (the main sources of immune impairment) in the body. Exercise has been shown to modulate anti-inflammatory and antioxidant statuses, enhance immune cell activity, as well as protect against immune damage caused by air pollution. However, the underlying mechanisms involved in the protective effects of exercise on pollutant-induced damage and the safe threshold for exercise in polluted environments remain elusive. In contrast to the extensive research on the pathogenesis of air pollution and the preventive role of exercise in enhancing fitness, investigations into exercise resistance to injury caused by air pollution are still in their infancy. In this review, we analyze evidence from humans, animals, and cell experiments on the combined effects of exercise and air pollution on immune health outcomes, with an emphasis on oxidative stress, inflammatory responses, and immune cells. We also propose possible mechanisms and directions for future research on exercise resistance to pollutant-induced damage in the body. Furthermore, we suggest strengthening epidemiological studies at different population levels and investigations on immune cells to guide how to determine the safety thresholds for exercise in polluted environments.

Prediction Model for Tight Gas Wells with Time-Dependent Mechanism and Stress Sensitivity Effect.

In the production process of tight gas wells, reservoir fluid distribution and gas-water relative permeability vary with time. However, traditional models fail to handle the time-dependent mechanism and stress sensitivity effect in the reservoir, leading to significant errors in the dynamic analysis results. To address this issue, this article presents a prediction model for fractured well production in tight gas reservoirs. It is based on a three-dimensional embedded discrete fracture model (EDFM), which considers the influences of the time-dependent mechanism and stress-dependent reservoir permeability. Transient flow equations are treated by using the finite volume method to obtain the solution of the model. The accuracy and reliability of the model are verified by comparison with the results of the commercial simulator Eclipse and the field application. Based on the model's solution, this study emphasizes the analysis of the impact of the time-dependent mechanism and reservoir stress sensitivity on gas well productivity. Simulation results show that the time-dependent relative permeability curve can decrease the level of irreducible water saturation and promote the migration of irreducible water, resulting in an increase in water permeability and a decrease in gas permeability. This effect will reduce the period of stable gas production and increase the level of water production. Besides, reservoir stress sensitivity will reduce daily water production and accelerate gas well decline. It is necessary to control the production pressure difference reasonably during the production process to effectively reduce the negative impact of stress sensitivity effects. The results indicate that when the relative permeability curve and the reservoir permeability are constant, the real gas production capacity of the reservoir will be strengthened. The application of field case studies shows that the theoretical model exhibits stronger adaptability, achieves better fitting results, and can guide the compilation and adjustment of development plans for water-bearing tight gas reservoirs. These findings provide insights into understanding the effects of the time-dependent mechanism on gas production rates in tight gas reservoirs. Furthermore, this study offers useful guidance for the prediction of field-scale gas production.

Decomposition and humification process of domestic biodegradable waste by black soldier fly (Hermetia illucens L.) larvae from the perspective of dissolved organic matter.

Black soldier fly larvae (Hermitia illucens L.) (BSFL) bioconversion is a promising technology for domestic biodegradable waste (DBW) management and resource recovery. However, little is known about the DBW biodegradation during the BSFL bioconversion from the perspective of dissolved organic matter (DOM). In the current study, field tests were conducted on a full-scale BSFL bioconversion facility with treatment capacity of 15 tons DBW/day. Composition of DOM in DBW were investigated by spectral analysis (UV-vis, fluorescence, and Fourier Transform Infrared spectroscopy (FT-IR)), coupled with enzyme activity analysis. After BSFL bioconversion, DOM concentrations, total carbon and total nitrogen in residues decreased by 51.5%, 18.3% and 19.9%, respectively. Meanwhile, enzymes like catalase, lipase, protease, sucrase, urease and cellulase significantly increased (9.28%-56.3%). The specific UV absorbance at 254 nm and 280 nm (SUVA254, SUVA280), the area at 226-400 nm (A226-400) and slope in the 280-400 nm region (S280-400) of DOM increased by 230%, 186%, 143% and 398%, respectively. Moreover, the characteristic peaks at 1636, 1077 and 1045 cm-1 in FT-IR increased continuously, with a significant decrease in peak at 1124 and 1572 cm-1. DOM spectral data show that BSFL decomposed the carboxylic, cellulose and aliphatic components, resulting in the increase of oxygen-containing functional groups (e.g., hydroxyl, carboxyl, carbonyl) and aromatic compounds. Furthermore, fluorescence profiles show that Region Ⅰ, Ⅱ (aromatic proteins) and Ⅳ (soluble microbial byproducts) decreased while Region Ⅴ (humic-like substances) increased significantly. Humification index increased by 122% while biological index decreased by 18.0%, indicating a significant increase in degree of humification and stabilization of the residues. The current evidence provides a theoretical basis for technical re-innovation and improving economic potential of BSFL technology.

Cell cycle arrest explains the observed bulk 3D genomic alterations in response to long-term heat shock in K562 cells.

Heat shock is a common environmental stress, although the response of the nucleus to it remains controversial in mammalian cells. Acute reaction and chronic adaptation to environmental stress may have distinct internal rewiring in the gene regulation networks. However, this difference remains largely unexplored. Here, we report that chromatin conformation and chromatin accessibility respond differently in short- and long-term heat shock in human K562 cells. We found that chromatin conformation in K562 cells was largely stable in response to short-term heat shock, whereas it showed clear and characteristic changes after long-term heat treatment with little alteration in chromatin accessibility during the whole process. We further show in silico and experimental evidence strongly suggesting that changes in chromatin conformation may largely stem from an accumulation of cells in the M stage of the cell cycle in response to heat shock. Our results represent a paradigm shift away from the controversial view of chromatin response to heat shock and emphasize the necessity of cell cycle analysis when interpreting bulk Hi-C data.

DeNOPA: decoding nucleosome positions sensitively with sparse ATAC-seq data.

As the basal bricks, the dynamics and arrangement of nucleosomes orchestrate the higher architecture of chromatin in a fundamental way, thereby affecting almost all nuclear biology processes. Thanks to its rather simple protocol, assay for transposase-accessible chromatin using sequencing (ATAC)-seq has been rapidly adopted as a major tool for chromatin-accessible profiling at both bulk and single-cell levels; however, to picture the arrangement of nucleosomes per se remains a challenge with ATAC-seq. In the present work, we introduce a novel ATAC-seq analysis toolkit, named decoding nucleosome organization profile based on ATAC-seq data (deNOPA), to predict nucleosome positions. Assessments showed that deNOPA outperformed state-of-the-art tools with ultra-sparse ATAC-seq data, e.g. no more than 0.5 fragment per base pair. The remarkable performance of deNOPA was fueled by the short fragment reads, which compose nearly half of sequenced reads in the ATAC-seq libraries and are commonly discarded by state-of-the-art nucleosome positioning tools. However, we found that the short fragment reads enrich information on nucleosome positions and that the linker regions were predicted by reads from both short and long fragments using Gaussian smoothing. Last, using deNOPA, we showed that the dynamics of nucleosome organization may not directly couple with chromatin accessibility in the cis-regulatory regions when human cells respond to heat shock stimulation. Our deNOPA provides a powerful tool with which to analyze the dynamics of chromatin at nucleosome position level with ultra-sparse ATAC-seq data.

Exercise Promotes Bone Marrow Microenvironment by Inhibiting Adipsin in Diet-Induced Male Obese Mice.

Obesity is a growing global epidemic linked to many diseases, including diabetes, cardiovascular diseases, and musculoskeletal disorders. Exercise can improve bone density and decrease excess bone marrow adipose tissue (BMAT) in obese individuals. However, the mechanism of exercise regulating bone marrow microenvironment remains unclear. This study examines how exercise induces bone marrow remodeling in diet-induced obesity. We employed unbiased RNA-Seq to investigate the effect of exercise on the bone marrow of diet-induced obese male mice. Bone mesenchymal stem cells (BMSCs) were isolated to explore the regulatory effects of exercise in vitro. Our data demonstrated that exercise could slow down the progression of obesity and improve trabecular bone density. RNA-seq data revealed that exercise inhibited secreted phosphoprotein 1 (Spp1), which was shown to mediate bone resorption through mechanosensing mechanisms. Interactome analysis of Spp1 using the HINT database showed that Spp1 interacted with the adipokine adipsin. Moreover, exercise decreased BMAT, which induced osteoclast differentiation and promoted bone loss. Our study reveals that exercise improves the bone marrow microenvironment by at least partially inhibiting the adipsin-Spp1 signaling pathway so as to inhibit the alternative complement system from activating osteoclasts in diet-induced obese mice.

Association Between the Severity of Diabetic Retinopathy and Optical Coherence Tomography Angiography Metrics.

Diabetic retinopathy, the most serious ocular complication of diabetes, imposes a serious economic burden on society. Automatic and objective assessment of vessel changes can effectively manage diabetic retinopathy and prevent blindness. Optical coherence tomography angiography (OCTA) metrics have been confirmed to be used to assess vessel changes. The accuracy and reliability of OCTA metrics are restricted by vessel segmentation methods. In this study, a multi-branch retinal vessel segmentation method is proposed, which is comparable to the segmentation results obtained from the manual segmentation, effectively extracting vessels in low contrast areas and improving the integrity of the extracted vessels. OCTA metrics based on the proposed segmentation method were validated to be reliable for further analysis of the relationship between OCTA metrics and diabetes and the severity of diabetic retinopathy. Changes in vessel morphology are influenced by systemic risk factors. However, there is a lack of analysis of the relationship between OCTA metrics and systemic risk factors. We conducted a cross-sectional study that included 362 eyes of 221 diabetic patients and 1,151 eyes of 587 healthy people. Eight systemic risk factors were confirmed to be closely related to diabetes. After controlling these systemic risk factors, significant OCTA metrics (such as vessel complexity index, vessel diameter index, and mean thickness of retinal nerve fiber layer centered in the macular) were found to be related to diabetic retinopathy and severe diabetic retinopathy. This study provides evidence to support the potential value of OCTA metrics as biomarkers of diabetic retinopathy.

A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.

Recently, tumor immunotherapy based on immune checkpoint inhibitors (ICI) has been introduced and widely adopted for various tumor types. Nevertheless, tumor immunotherapy has a few drawbacks, including significant uncertainty of outcome, the possibility of severe immune-related adverse events for patients receiving such treatments, and the lack of effective biomarkers to determine the ICI treatments' responsiveness. DNA methylation profiles were recently identified as an indicator of the tumor immune microenvironment. They serve as a potential hot spot for predicting responses to ICI treatment for their stability and convenience of measurement by liquid biopsy. We demonstrated the possibility of DNA methylation profiles as a predictor for responses to the ICI treatments at the pan-cancer level by analyzing DNA methylation profiles considered responsive and non-responsive to the treatments. An SVM model was built based on this differential analysis in the pan-cancer levels. The performance of the model was then assessed both at the pan-cancer level and in specific tumor types. It was also compared to the existing gene expression profile-based method. DNA methylation profiles were shown to be predictable for the responses to the ICI treatments in the TCGA cases in pan-cancer levels. The proposed SVM model was shown to have high performance in pan-cancer and specific cancer types. This performance was comparable to that of gene expression profile-based one. The combination of the two models had even higher performance, indicating the potential complementarity of the DNA methylation and gene expression profiles in the prediction of ICI treatment responses.

The asynchronous establishment of chromatin 3D architecture between in vitro fertilized and uniparental preimplantation pig embryos.

BACKGROUND: Pigs are important animals for agricultural and biomedical research, and improvement is needed for use of the assisted reproductive technologies. Determining underlying mechanisms of epigenetic reprogramming in the early stage of preimplantation embryos derived from in vitro fertilization (IVF), parthenogenesis, and androgenesis will not only contribute to assisted reproductive technologies of pigs but also will shed light into early human development. However, the reprogramming of three-dimensional architecture of chromatin in this process in pigs is poorly understood. RESULTS: We generate three-dimensional chromatin profiles for pig somatic cells, IVF, parthenogenesis, and androgenesis preimplantation embryos. We find that the chromosomes in the pig preimplantation embryos are enriched for superdomains, which are more rare in mice. However, p(s) curves, compartments, and topologically associated domains (TADs) are largely conserved in somatic cells and are gradually established during preimplantation embryogenesis in both mammals. In the uniparental pig embryos, the establishment of chromatin architecture is highly asynchronized at all levels from IVF embryos, and a remarkably strong decompartmentalization is observed during zygotic genome activation (ZGA). Finally, chromosomes originating from oocytes always establish TADs faster than chromosomes originating from sperm, both before and during ZGA. CONCLUSIONS: Our data highlight a potential unique 3D chromatin pattern of enriched superdomains in pig preimplantation embryos, an unusual decompartmentalization process during ZGA in the uniparental embryos, and an asynchronized TAD reprogramming between maternal and paternal genomes, implying a severe dysregulation of ZGA in the uniparental embryos in pigs.

IC4R-2.0: Rice Genome Reannotation Using Massive RNA-seq Data.

Genome reannotation aims for complete and accurate characterization of gene models and thus is of critical significance for in-depth exploration of gene function. Although the availability of massive RNA-seq data provides great opportunities for gene model refinement, few efforts have been made to adopt these precious data in rice genome reannotation. Here we reannotate the rice (Oryza sativa L. ssp. japonica) genome based on integration of large-scale RNA-seq data and release a new annotation system IC4R-2.0. In general, IC4R-2.0 significantly improves the completeness of gene structure, identifies a number of novel genes, and integrates a variety of functional annotations. Furthermore, long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) are systematically characterized in the rice genome. Performance evaluation shows that compared to previous annotation systems, IC4R-2.0 achieves higher integrity and quality, primarily attributable to massive RNA-seq data applied in genome annotation. Consequently, we incorporate the improved annotations into the Information Commons for Rice (IC4R), a database integrating multiple omics data of rice, and accordingly update IC4R by providing more user-friendly web interfaces and implementing a series of practical online tools. Together, the updated IC4R, which is equipped with the improved annotations, bears great promise for comparative and functional genomic studies in rice and other monocotyledonous species. The IC4R-2.0 annotation system and related resources are freely accessible at http://ic4r.org/.

Decoding topologically associating domains with ultra-low resolution Hi-C data by graph structural entropy.

Submegabase-size topologically associating domains (TAD) have been observed in high-throughput chromatin interaction data (Hi-C). However, accurate detection of TADs depends on ultra-deep sequencing and sophisticated normalization procedures. Here we propose a fast and normalization-free method to decode the domains of chromosomes (deDoc) that utilizes structural information theory. By treating Hi-C contact matrix as a representation of a graph, deDoc partitions the graph into segments with minimal structural entropy. We show that structural entropy can also be used to determine the proper bin size of the Hi-C data. By applying deDoc to pooled Hi-C data from 10 single cells, we detect megabase-size TAD-like domains. This result implies that the modular structure of the genome spatial organization may be fundamental to even a small cohort of single cells. Our algorithms may facilitate systematic investigations of chromosomal domains on a larger scale than hitherto have been possible.

Characteristic arrangement of nucleosomes is predictive of chromatin interactions at kilobase resolution.

High-throughput chromosome conformation capture (3C) technologies, such as Hi-C, have made it possible to survey 3D genome structure. However, obtaining 3D profiles at kilobase resolution at low cost remains a major challenge. Therefore, we herein present an algorithm for precise identification of chromatin interaction sites at kilobase resolution from MNase-seq data, termed chromatin interaction site detector (CISD), and a CISD-based chromatin loop predictor (CISD_loop) that predicts chromatin-chromatin interactions (CCIs) from low-resolution Hi-C data. We show that the predictions of CISD and CISD_loop overlap closely with chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) anchors and loops, respectively. The validity of CISD/CISD_loop was further supported by a 3C assay at about 5 kb resolution. Finally, we demonstrate that only modest amounts of MNase-seq and Hi-C data are sufficient to achieve ultrahigh resolution CCI maps. Our results suggest that CCIs may result in characteristic nucleosomes arrangement patterns flanking the interaction sites, and our algorithms may facilitate precise and systematic investigations of CCIs on a larger scale than hitherto have been possible.

An efficient and assumption-free method to approximate protein level distribution in the two-states gene expression model.

Stochastic fluctuations at each step of gene expression might influence protein levels distributions across cell populations. However, current methods to model protein distribution of intrinsic gene expression dynamics are either computationally inefficient or rely on ad hoc assumptions, e.g., that the gene is always active. Taking advantage of the simple form of lower-order moments of distribution, we developed an efficient and assumption-free protein distribution approximation method (EFPD), for the two state gene expression model to accurately approximate the distribution. By EFPD, we computed nearly identical intensity of gene expression regulation at mRNA and protein level, implying a profound link between transcription and translation. Finally, by extending EFPD to approximate the distribution of protein level at any arbitrary temporal state, we proposed an explanation for the role of stochastic noise in gene expression in the context of a continuously changing environment. EFPD can be a powerful tool for modeling the particular molecular mechanisms of targeted gene expression pattern.