Mechanisms of Rho GTPases in regulating tumor proliferation, migration and invasion.

The occurrence of most cancers is due to the clonal proliferation of tumor cells, immune evasion, and the ability to spread to other body parts. Rho GTPases, a family of small GTPases, are key regulators of cytoskeleton reorganization and cell polarity. Additionally, Rho GTPases are key proteins that induce the proliferation and metastasis of tumor cells. This review focuses on the complex regulatory mechanisms of Rho GTPases, exploring their critical role in promoting tumor cell proliferation and dissemination. Regarding tumor cell proliferation, attention is given to the role of Rho GTPases in regulating the cell cycle and mitosis. In terms of tumor cell dissemination, the focus is on the role of Rho GTPases in regulating cell migration and invasion. Overall, this review elucidates the mechanisms of Rho GTPases members in the development of tumor cells, aiming to provide theoretical references for the treatment of mammalian tumor diseases and related applications.

DiffPhys: Enhancing Signal-to-Noise Ratio in Remote Photoplethysmography Signal Using a Diffusion Model Approach.

Remote photoplethysmography (rPPG) is an emerging non-contact method for monitoring cardiovascular health based on facial videos. The quality of the captured videos largely determines the efficacy of rPPG in this application. Traditional rPPG techniques, while effective for heart rate (HR) estimation, often produce signals with an inadequate signal-to-noise ratio (SNR) for reliable vital sign measurement due to artifacts like head motion and measurement noise. Another pivotal factor is the overlooking of the inherent properties of signals generated by rPPG (rPPG-signals). To address these limitations, we introduce DiffPhys, a novel deep generative model particularly designed to enhance the SNR of rPPG-signals. DiffPhys leverages the conditional diffusion model to learn the distribution of rPPG-signals and uses a refined reverse process to generate rPPG-signals with a higher SNR. Experimental results demonstrate that DiffPhys elevates the SNR of rPPG-signals across within-database and cross-database scenarios, facilitating the extraction of cardiovascular metrics such as HR and HRV with greater precision. This enhancement allows for more accurate monitoring of health conditions in non-clinical settings.

Biclustering for Epi-Transcriptomic Co-functional Analysis.

Dynamic and reversible N6-methyladenosine (m6A) modifications are associated with many essential cellular functions as well as physiological and pathological phenomena. In-depth study of m6A co-functional patterns in epi-transcriptomic data may help to understand its complex regulatory mechanisms. In this chapter, we describe several biclustering mining algorithms for epi-transcriptomic data to discover potential co-functional patterns. The concepts and computational methods discussed in this chapter will be particularly useful for researchers working in related fields. We also aim to introduce new deep learning techniques into the field of co-functional analysis of epi-transcriptomic data.

Comparison of the applicability of seven calculation equations of glomerular filtration rate among elderly people in China.

BACKGROUND: At present, estimated glomerular filtration rate (eGFR) remains the most frequently utilized parameter in the evaluation of kidney injury severity. Numerous equations have been formulated based on serum creatinine (Scr) or serum cystatin C (Cysc) levels. However, there is a lack of consensus regarding the efficacy of these equations in assessing eGFR, particularly for elderly individuals in China. This study aimed to evaluate the applicability of the MDRD, MDRDc, CKD-EPI series, BIS1, and FAS equations within the Chinese elderly population. METHODS: A cohort of 298 elderly patients with measured GFR (mGFR) was enrolled. The patients were categorized into three subgroups based on their mGFR levels. The eGFR performance was examined, taking into account bias, interquartile range (IQR), accuracy P30, and root-mean-square error (RMSE). Bland-Altman plots were employed to verify the validity of eGFR. RESULTS: The participants had a median age of 71 years, with 167 (56.0%) being male. Overall, no significant differences in bias were observed among the seven equations (P > 0.05). In terms of IQR, P30, and RMSE, the BIS1 equation demonstrated superior accuracy (14.61, 72.1%, and 13.53, respectively). When mGFR < 30 ml/min/1.73 m2, all equations underestimated the true GFR, with the highest accuracy reaching only 59%. Bland-Altman plots indicated that the BIS1 equation exhibited the highest accuracy, featuring a 95% confidence interval (CI) width of 52.37. CONCLUSIONS: This study suggested that the BIS1 equation stands out as the most applicable for estimating GFR in Chinese elderly patients with normal renal function or only moderate decline. 2020NL-085-03, 2020.08.10, retrospectively registered.

[Research advances of prostaglandin E2 receptor 1 (EP1)].

Prostaglandin E2 (PGE2) is an important lipid molecule derived from arachidonic acid, which regulates a variety of physiological and pathological activities. Based on the inhibition of inflammatory PGE2 production, non-steroidal anti-inflammatory drugs (NSAIDs) are considered as the most commonly used drugs to treat inflammatory diseases and to relieve fever and pain symptoms. PGE2 mediates its functions via four different G protein-coupled receptors, named EP1-EP4. Though the limited distribution and low PGE2 affinity of EP1, it plays important roles in the maintenance of many physiological functions and homeostasis. Moreover, EP1 is widely involved in the inflammatory response, pain perception and multisystem pathological function regulation. In this review, we will briefly summarize the recent advances on the physiological and pathophysiological function of EP1 and its targeted drugs development.

Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning.

Blood oxygen saturation (SpO2) is an essential physiological parameter for evaluating a person's health. While conventional SpO2 measurement devices like pulse oximeters require skin contact, advanced computer vision technology can enable remote SpO2 monitoring through a regular camera without skin contact. In this paper, we propose novel deep learning models to measure SpO2 remotely from facial videos and evaluate them using a public benchmark database, VIPL-HR. We utilize a spatial-temporal representation to encode SpO2 information recorded by conventional RGB cameras and directly pass it into selected convolutional neural networks to predict SpO2. The best deep learning model achieves 1.274% in mean absolute error and 1.71% in root mean squared error, which exceed the international standard of 4% for an approved pulse oximeter. Our results significantly outperform the conventional analytical Ratio of Ratios model for contactless SpO2 measurement. Results of sensitivity analyses of the influence of spatial-temporal representation color spaces, subject scenarios, acquisition devices, and SpO2 ranges on the model performance are reported with explainability analyses to provide more insights for this emerging research field.

Effects of Heat Stress and Lipopolysaccharides on Gene Expression in Chicken Immune Cells.

Prolonged exposure to high temperatures and humidity can trigger heat stress in animals, leading to subsequent immune suppression. Lipopolysaccharides (LPSs) act as upstream regulators closely linked to heat stress, contributing to their immunosuppressive effects. After an initial examination of transcriptome sequencing data from individual samples, 48 genes displaying interactions were found to potentially be associated with heat stress. Subsequently, to delve deeper into this association, we gathered chicken bone marrow dendritic cells (BMDCs). We combined heat stress with lipopolysaccharides and utilized a 48 × 48 Fluidigm IFC quantitative microarray to analyze the patterns of gene changes under various treatment conditions. The results of the study revealed that the combination of heat stress and LPSs in a coinfection led to reduced expressions of CRHR1, MEOX1, and MOV10L1. These differentially expressed genes triggered a pro-inflammatory response within cells via the MAPK and IL-17 signaling pathways. This response, in turn, affected the intensity and duration of inflammation when experiencing synergistic stimulation. Therefore, LPSs exacerbate the immunosuppressive effects of heat stress and prolong cellular adaptation to stress. The combination of heat stress and LPS stimulation induced a cellular inflammatory response through pathways involving cAMP, IL-17, MAPK, and others, consequently leading to decreased expression levels of CRHR1, MEOX1, and MOV10L1.

PDB-BRE: A ligand-protein interaction binding residue extractor based on Protein Data Bank.

Proteins typically exert their biological functions by interacting with other biomolecules or ligands. The study of ligand-protein interactions is crucial in elucidating the biological mechanisms of proteins. Most existing studies have focused on analyzing ligand-protein interactions, and they ignore the additional situational of inserted and modified residues. Besides, the resources often support only a single ligand type and cannot obtain satisfied results in analyzing novel complexes. Therefore, it is important to develop a general analytical tool to extract the binding residues of ligand-protein interactions in complexes fully. In this study, we propose a ligand-protein interaction binding residue extractor (PDB-BRE), which can be used to automatically extract interacting ligand or protein-binding residues from complex three-dimensional (3D) structures based on the RCSB Protein Data Bank (RCSB PDB). PDB-BRE offers a notable advantage in its comprehensive support for analyzing six distinct types of ligands, including proteins, peptides, DNA, RNA, mixed DNA and RNA entities, and non-polymeric entities. Moreover, it takes into account the consideration of inserted and modified residues within complexes. Compared to other state-of-the-art methods, PDB-BRE is more suitable for massively parallel batch analysis, and can be directly applied for downstream tasks, such as predicting binding residues of novel complexes. PDB-BRE is freely available at http://bliulab.net/PDB-BRE.

First Report of Powdery Mildew Caused by Podosphaera macularis on Humulus scandens in China.

Humulus scandens, (Lour.) Merr., is a climbing herb which are used as traditional Chinese medicine, a raw material for papermaking, making soap, and replacing hops H. lupulus. This herb is distributed in many provinces of China, including Sichuan province. During March and June 2022, powdery mildew was found on leaves of H. scandens in the modern agricultural high-tech demonstration garden of Sichuan Academy of Agricultural Science, Chengdu, Sichuan Province, China. Abundant white or grayish powdery colonies could be seen on the surface of leaves, and 30%-100% of leaf areas were affected. Some of the infected leaves were either chlorotic or senescent. About 90% of the observed plants showed powdery mildew symptoms. Conidiophores (n = 25) were 74.0 to 160.1 µm × 8.7 to 12.7 µm (on average 120.9 × 10.4 µm) and composed of cylindrical foot cells (length 31.9-72.9 µm, average 50.1 µm) and conidia (mostly 10 conidia) in chains. Barrel-shaped conidia with fibrosin bodies (n = 30) were 12.8 to 21.0 µm × 7.9 to 15 µm, on average 16.7 × 11.3 µm, with a length/width ratio of 1.5. Chasmothecia were not found. Based on these morphologic characteristics, the pathogen was initially identified as Podosphaera macularis (Braun and Cook 2012; Mahaffee et al. 2009). To confirm the identification, two isolates (PDLC0315 and PDLC0412) of P. macularis mycelia and conidia were collected, and mycelia and conidia were combined for a single DNA extraction from each isolate. With the total genomic DNA, the sequences of the internal transcribed spacer (ITS), 5.8S rRNA, the 18S and 28S large subunit ribosomal DNA (LSU) (Bradshaw and Tobin 2020; White et al. 1990), were bi-directionally sequenced and deposited in GenBank (ON862625.1 and ON862630.1). The ON862625.1 and ON862630.1 showed 100% similarity with sequences of P. macularis isolate CT1 (MH687414.1). Phylogenetic analyses based on the combined ITS and 28S rDNA sequences indicated that the two specimens, PDLC0315 and PDLC0412 formed a monophyletic clade together with sequences retrieved from Podosphaera macularis CT1 and Head quarter 31 (KX842348.1). The pathogenicity test with the fungus was confirmed by gently pressing the infected leaves onto three healthy wild plants from the same geographical location. Three uninoculated wild plants served as controls. Six inoculated and non-inoculated plants were placed in different growth chambers with a 16-h photoperiod at 22±2°C and 70% of relative humidity. After 10 to 14 days, powdery mildew colonies developed on inoculated plants. Non-inoculated control plants did not show any symptoms. The fungus on inoculated leaves was morphologically identical to that first observed in the garden. As far as we know, this study is the first report of powdery mildew disease in Humulus scandens caused by Podosphaera macularis in China. Rapid expansion and wild distribution of H. scandens could lead to increased powdery mildew risk in outdoor cultivation. Due to the invasive potential of the powdery mildew fungi, this record is important in the context of the range extension of Podosphaera macularis.

FGFICA: Independent Component Analysis of Fusion Genomic Features for Mining Epi-Transcriptome Profiling Data.

Existing studies indicate that in-depth studies of the N6-methyladenosine (m6A) co-methylation patterns in epi-transcriptome profiling data may contribute to understanding its complex regulatory mechanisms. In order to fully utilize the potential features of epi-transcriptome data and consider the advantages of independent component analysis (ICA) in local pattern mining tasks, we propose an ICA algorithm that fuses genomic features (FGFICA) to discover potential functional patterns. FGFICA first extracts and fuses the confidence information, homologous information, and genomic features implied in epi-transcriptome profiling data and then solves the model based on negative entropy maximization. Finally, to mine m6A co-methylation patterns, the probability density of the extracted independent components is estimated. In the experiment, FGFICA extracted 64 m6A co-methylation patterns from our collected MeRIP-seq high-throughput data. Further analysis of some selected patterns revealed that the m6A sites involved in these patterns were highly correlated with four m6A methylases, and these patterns were significantly enriched in some pathways known to be regulated by m6A.

Characteristics of soil N2O emission and N2O-producing microbial communities in paddy fields under elevated CO2 concentrations.

The effects of elevated carbon dioxide (CO2) concentration (e[CO2]) on nitrous oxide (N2O) emissions from paddy fields and the microbial processes involved in N2O emissions have recently received much attention. Ammonia-oxidizing microorganisms and denitrifying bacteria dominate the production of N2O in paddy soils. To better understand the dynamics of N2O production under e[CO2], a field experiment was conducted after five years of CO2 fumigation based on three treatments: CK (ambient atmospheric CO2), T1 (CK + increase of 40 ppm per year until 200 ppm), and T2 (CK + 200 ppm). N2O fluxes, soil physicochemical properties, and N2O production potential were quantified during the rice-growth period. The functional gene abundance and community composition of ammonia-oxidizing archaea (AOA) and bacteria (AOB) were analyzed using quantitative polymerase chain reaction (qPCR) and those of ammonia-denitrifying bacteria (nirS- and nirK-type) were analyzed using Illumina MiSeq sequencing. N2O emissions decreased by 173% and 41% under the two e[CO2] treatments during grain filling and milk ripening, respectively (P < 0.05). N2O emissions increased by 279% and 172% in the T2 treatment compared with T1 during the tillering and milk-ripening stages, respectively (P < 0.05). Furthermore, the N2O production potential was significantly higher in the CK treatment than in T1 and T2 during the elongation stage. The N2O production potential and abundance of AOA amoA genes in T1 treatment were significantly lower than those in CK treatment during the high N2O emission phase caused by mid-season drainage (P < 0.05). Although nirK- and nirS-type denitrifying bacteria community structure and diversity did not respond significantly (P > 0.05) to e[CO2], the abundance of nirK-type denitrifying bacteria significantly affected the N2O flux (P < 0.05). Linear regression analysis showed that the N2O production potential, AOA amoA gene abundance, and nirK gene abundance explained 47.2% of the variation in N2O emissions. In addition, soil nitrogen (N) significantly affected the nirK- and nirS-type denitrifier communities. Overall, our results revealed that e[CO2] suppressed N2O emissions, which was closely associated with the abundance of AOA amoA and nirK genes (P < 0.05).

China's terrestrial ecosystem carbon balance during the 20th century: an analysis with a process-based biogeochemistry model.

BACKGROUND: China's terrestrial ecosystems play a pronounced role in the global carbon cycle. Here we combine spatially-explicit information on vegetation, soil, topography, climate and land use change with a process-based biogeochemistry model to quantify the responses of terrestrial carbon cycle in China during the 20th century. RESULTS: At a century scale, China's terrestrial ecosystems have acted as a carbon sink averaging at 96 Tg C yr- 1, with large inter-annual and decadal variabilities. The regional sink has been enhanced due to the rising temperature and CO2 concentration, with a slight increase trend in carbon sink strength along with the enhanced net primary production in the century. The areas characterized by C source are simulated to extend in the west and north of the Hu Huanyong line, while the eastern and southern regions increase their area and intensity of C sink, particularly in the late 20th century. Forest ecosystems dominate the C sink in China and are responsible for about 64% of the total sink. On the century scale, the increase in carbon sinks in China's terrestrial ecosystems is mainly contributed by rising CO2. Afforestation and reforestation promote an increase in terrestrial carbon uptake in China from 1950s. Although climate change has generally contributed to the increase of carbon sinks in terrestrial ecosystems in China, the positive effect of climate change has been diminishing in the last decades of the 20th century. CONCLUSION: This study focuses on the impacts of climate, CO2 and land use change on the carbon cycle, and presents the potential trends of terrestrial ecosystem carbon balance in China at a century scale. While a slight increase in carbon sink strength benefits from the enhanced vegetation carbon uptake in China's terrestrial ecosystems during the 20th century, the increase trend may diminish or even change to a decrease trend under future climate change.

Guitar2: An improved version of the Guitar for sketching the transcriptomic view of RNA-related biological features.

Biological elements, such as genes, exons, coding sequences, are usually expressed as genomic features based on genome-based coordinates. However, the RNA transcription landmarks are usually expressed in the form of RNA-based coordinates. To analyze the association between RNA-related genomic features and RNA transcription landmarks, some tools, such as Guitar, have been developed to convert between these two coordinate systems. However, there remain some issues, such as incomplete transcriptional structures, limitation of transcriptomic view analysis, etc. Therefore, we made corresponding improvements based on Guitar, considered the promoter, 5' cap, and 3' poly-A tail structure in the transcript, standardized the input format of RNA-related genomic features, and finally developed Guitar2. Guitar2 converts genome-based coordinates and RNA-based coordinates with a more accurate strategy. Besides, Guitar2 supports the sketching of three different transcriptional views based on the overall transcripts, messenger RNA, as well as long non-coding RNA. The analysis of m6A modification using Guitar2 shows that m6A modification is significantly enriched near the stop codon in the mRNA, which is consistent with the known results. In conclusion, Guitar2's improvement of coordinate system structure and the provision of full transcriptional view will contribute to the further analysis of RNA-related biological features. Guitar2 is now publicly available from R/Bioconductor: https://bioconductor.org/packages/release/bioc/html/Guitar.html.

FBCwPlaid: A Functional Biclustering Analysis of Epi-Transcriptome Profiling Data Via a Weighted Plaid Model.

Recent studies have shown that in-depth studies on epi-transcriptomic patterns of N6-methyladenosine (m6A) may help understand its complex functions and co-regulatory mechanisms. Since most biclustering algorithms are developed in scenarios of gene expression analysis, which does not share the same characteristics with m6A methylation profile, we propose a weighted Plaid biclustering model (FBCwPlaid) based on the Lagrange multiplier method to discover the potential functional patterns. Each pattern is achieved by minimizing approximation error between FBCwPlaid predicted value and real data. To address the issue that site expression level determines methylation level confidence, it uses RNA expression levels of each site as weights to make lower expressed sites less confident. FBCwPlaid also allows overlapping biclusters, indicating some sites may participate in multiple biological functions. FBCwPlaid was then applied on MeRIP-Seq data of 69,446 methylation sites under 32 experimental conditions, each of which represented a stimulus to a particular cell line or environment. Finally, three patterns were discovered, and further pathway analysis and enzyme specificity test showed that sites involved in each pattern are highly relevant to m6A methyltransferases. Further detailed analyses showed that some patterns are condition-specific, indicating that some specific sites' methylation profiles may occur in specific cell lines or conditions.

A brief review of RNA modification related database resources.

To date, over 150 different RNA modifications have been identified, playing crucial roles in biological processes and disease pathogenesis. Thanks to the advancement of high-throughput sequencing technologies employed for transcriptome-wide mapping, a bunch of RNA modification databases have emerged as an exciting area, which promotes further investigation of the mechanisms and functions of these modified ribonucleotides. This article introduces the high-throughput sequencing technique developed for transcriptome-wide mapping of RNA modifications, as well as the procedures and main techniques of building these databases from the developers' perspective. It also reviews existing RNA modification databases in terms of their main functions, species, the number of sites they collected, the annotations, and the tools they provided. From the view of users, we further analyze and compare these databases in terms of their functions. For instance, these databases can be applied to record chemical structures and biosynthetic pathways, or unravel the epi-transcriptome comprehensively, or only investigate specific features of RNA modifications. Additionally, the limitations of the existing approaches are discussed, and some future suggestions are offered.

BDBB: A Novel Beta-Distribution-Based Biclustering Algorithm for Revealing Local Co-Methylation Patterns in Epi-Transcriptome Profiling Data.

N6-methyladenosine (m6A) has been shown to play crucial roles in RNA metabolism, physiology, and pathological processes. However, the specific regulatory mechanisms of most methylation sites remain uncharted due to the complexity of life processes. Biological experimental methods are costly to solve this problem, and computational methods are relatively lacking. The discovery of local co-methylation patterns (LCPs) of m6A epi-transcriptome data can benefit to solve the above problems. Based on this, we propose a novel biclustering algorithm based on the beta distribution (BDBB), which realizes the mining of LCPs of m6A epi-transcriptome data. BDBB employs the Gibbs sampling method to complete parameter estimation. In the process of modeling, LCPs are recognized as sharp beta distributions compared to the background distribution. Simulation study showed BDBB can extract all the three actual LCPs implanted in the background data and the overlap conditions between them with considerable accuracy (almost close to 100%). On MeRIP-Seq data of 69,446 methylation sites under 32 experimental conditions from 10 human cell lines, BDBB unveiled two LCPs, and Gene Ontology (GO) enrichment analysis showed that they were enriched in histone modification and embryo development, etc. important biological processes respectively. The GOE_Score scoring indicated that the biclustering results of BDBB in the m6A epi-transcriptome data are more biologically meaningful than the results of other biclustering algorithms.

REW-ISA V2: A Biclustering Method Fusing Homologous Information for Analyzing and Mining Epi-Transcriptome Data.

Background: Previous studies have shown that N6-methyladenosine (m6A) is related to many life processes and physiological and pathological phenomena. However, the specific regulatory mechanism of m6A sites at the systematic level is not clear. Therefore, mining the RNA co-methylation patterns in the epi-transcriptome data is expected to explain the specific regulation mechanism of m6A. Methods: Considering that the epi-transcriptome data contains homologous information (the genes corresponding to the m6A sites and the cell lines corresponding to the experimental conditions), rational use of this information will help reveal the regulatory mechanism of m6A. Therefore, based on the RNA expression weighted iterative signature algorithm (REW-ISA), we have fused homologous information and developed the REW-ISA V2 algorithm. Results: Then, REW-ISA V2 was applied in the MERIP-seq data to find potential local function blocks (LFBs), where sites are hyper-methylated simultaneously across the specific conditions. Finally, REW-ISA V2 obtained fifteen LFBs. Compared with the most advanced biclustering algorithm, the LFBs obtained by REW-ISA V2 have more significant biological significance. Further biological analysis showed that these LFBs were highly correlated with some signal pathways and m6A methyltransferase. Conclusion: REW-ISA V2 fuses homologous information to mine co-methylation patterns in the epi-transcriptome data, in which sites are co-methylated under specific conditions.

EDLm6APred: ensemble deep learning approach for mRNA m6A site prediction.

BACKGROUND: As a common and abundant RNA methylation modification, N6-methyladenosine (m6A) is widely spread in various species' transcriptomes, and it is closely related to the occurrence and development of various life processes and diseases. Thus, accurate identification of m6A methylation sites has become a hot topic. Most biological methods rely on high-throughput sequencing technology, which places great demands on the sequencing library preparation and data analysis. Thus, various machine learning methods have been proposed to extract various types of features based on sequences, then occupied conventional classifiers, such as SVM, RF, etc., for m6A methylation site identification. However, the identification performance relies heavily on the extracted features, which still need to be improved. RESULTS: This paper mainly studies feature extraction and classification of m6A methylation sites in a natural language processing way, which manages to organically integrate the feature extraction and classification simultaneously, with consideration of upstream and downstream information of m6A sites. One-hot, RNA word embedding, and Word2vec are adopted to depict sites from the perspectives of the base as well as its upstream and downstream sequence. The BiLSTM model, a well-known sequence model, was then constructed to discriminate the sequences with potential m6A sites. Since the above-mentioned three feature extraction methods focus on different perspectives of m6A sites, an ensemble deep learning predictor (EDLm6APred) was finally constructed for m6A site prediction. Experimental results on human and mouse data sets show that EDLm6APred outperforms the other single ones, indicating that base, upstream, and downstream information are all essential for m6A site detection. Compared with the existing m6A methylation site prediction models without genomic features, EDLm6APred obtains 86.6% of the area under receiver operating curve on the human data sets, indicating the effectiveness of sequential modeling on RNA. To maximize user convenience, a webserver was developed as an implementation of EDLm6APred and made publicly available at www.xjtlu.edu.cn/biologicalsciences/EDLm6APred . CONCLUSIONS: Our proposed EDLm6APred method is a reliable predictor for m6A methylation sites.

The process of methanogenesis in paddy fields under different elevated CO2 concentrations.

Understanding the process of methanogenesis in paddy fields under the scenarios of future climate change is of great significance for reducing greenhouse gas emissions and regulating the soil carbon cycle. Methyl Coenzyme M Reductase subunit A (mcrA) of methanogens is a rate-limiting enzyme that catalyzes the final step of CH4 production. However, the mechanism of methanogenesis change in the paddy fields under different elevated CO2 concentrations (e[CO2]) is rarely explored in earlier studies. In this research, we explored how the methanogens affect CH4 flux in paddy fields under various (e[CO2]). CH4 flux and CH4 production potential (MPP), and mcrA gene abundance were quantitatively analyzed under C (ambient CO2 concentration), C1 (C + 160 ppm CO2), and C2 (C + 200 ppm CO2) treatments. Additionally, the community composition and structure of methanogens were also compared with Illumina MiSeq sequencing. The results showed that C2 treatment significantly increased CH4 flux and MPP at the tillering stage. E[CO2] had a positive effect on the abundance of methanogens, but the effect was insignificant. We detected four known dominant orders of methanogenesis in this study, such as Methanosarcinales, Methanobacteriales, Methanocellales, and Methanomicrobiales. Although e[CO2] did not significantly change the overall community structure and diversity of methanogens, C2 treatment significantly reduced the relative abundance of two uncultured genera compared to C treatment. A linear regression model of DOC, methanogenic abundance, and MPP can explain 67.2% of the variation of CH4 flux under e[CO2]. Overall, our results demonstrated that CH4 flux in paddy fields under e[CO2] was mainly controlled by soil unstable C substrate and the abundance and activity of methanogens in rhizosphere soil.

Relationships between soil respiration and hyperspectral vegetation indexes and crop characteristics under different warming and straw application modes.

Examining the relationship between seasonal variations in soil respiration and abiotic factors and vegetation indexes is crucial for modeling soil respiration using upscaled remote sensing satellite data. A field experiment including control (CK), warming (WA), straw application (SA), and warming and straw application (WASA) treatments was performed in a winter wheat-soybean rotation cropland on the north shore of the lower reaches of the Yangtze River. Soil respiration, abiotic factors, crop hyperspectral vegetation indexes, leaf area index (LAI), and chlorophyll content (represented as the SPAD value) were measured during the 2018-2020 rotation growing seasons. The results indicated that the mean annual soil respiration was 2.27 ± 0.04, 3.08 ± 0.06, 3.64 ± 0.08, and 3.95 ± 0.20 µmol m-2 s-1 in the CK, WA, SA, and WASA plots, respectively, during the 2-year experimental period. Soil respiration was significantly (P < 0.05) correlated with soil temperature, soil moisture, hyperspectral vegetation indexes, LAI, and SPAD value in all plots. Models that included temperature, moisture, hyperspectral vegetation indexes, LAI, and SPAD value explained 50.5-74.7% of the seasonal variation in soil respiration in the CK, WA, SA, and WASA plots during the 2-year experimental period. A model including the seasonal mean NDVI, DVI, EVI, PRI, and LAI explained 72.4% of the interseasonal and intertreatment variations in seasonal mean soil respiration in the different plots across the four different crop-growing seasons. Our study indicated the potential applicability of hyperspectral vegetation indexes, LAI, and SPAD value to the estimation of soil respiration at a regional scale.