FdC1 and Leaf-Type Ferredoxins Channel Electrons From Photosystem I to Different Downstream Electron Acceptors

Plant-type ferredoxins in Arabidopsis transfer electrons from the photosystem I to multiple redox-driven enzymes involved in the assimilation of carbon, nitrogen, and sulfur. Leaf-type ferredoxins also modulate the switch between the linear and cyclic electron routes of the photosystems. Recently, two novel ferredoxin homologs with extra C-termini were identified in the Arabidopsis genome (AtFdC1, AT4G14890; AtFdC2, AT1G32550). FdC1 was considered as an alternative electron acceptor of PSI under extreme ferredoxin-deficient conditions. Here, we showed that FdC1 could interact with some, but not all, electron acceptors of leaf-type Fds, including the ferredoxin-thioredoxin reductase (FTR), sulfite reductase (SiR), and nitrite reductase (NiR). Photoreduction assay on cytochrome c and enzyme assays confirmed its capability to receive electrons from PSI and donate electrons to the Fd-dependent SiR and NiR but not to the ferredoxin-NADP C oxidoreductase (FNR). Hence, FdC1 and leaf-type Fds may play differential roles by channeling electrons from photosystem I to different downstream electron acceptors in photosynthetic tissues. In addition, the median redox potential of FdC1 may allow it to receive electrons from FNR in non-photosynthetic plastids

Functional study of two ferredoxin homologs with extended C-termini in Arabidopsis thaliana

Ferredoxins, the small ancient [2Fe-2S] cluster-containing proteins at the stroma of thylakoid membrane, transfer electrons from PSI to multiple downstream redox-driven enzymes. They support the assimilation of carbon, nitrogen, and sulfur; the synthesis of amino acids and fatty acids; and the modulation of the ATP/NADPH ratio by switching between photosynthetic cyclic and linear electron routes. In the genome of Arabidopsis thaliana, four functionally diverse ferredoxins (Fds) have been classified as leaf-type (AtFd1, At1g10960; AtFd2, At1g60950) and root-type (AtFd3, At2g27510; AtFd4, At5g10000). Recently, two novel ferredoxin homologs with extended C-termini have been found and termed ferredoxin C 1 and 2 (FdC1, At4g14890; FdC2, At1g32550). FdC1 was presumed to be a substitute electron acceptor of PSI in extremely ferredoxin-deficient conditions (Voss et al. 2011), and FdC2 and its homologs in rice, were essential for the assimilation of copper and the synthesis of chloroplast grana stacks and chlorophyll (Goss 2014, Li et al. 2015, Zhao et al. 2015). However, the functional differences between FdCs and well-identified leaf-type ferredoxins in electron transfer remain obscure. Here, to explore the functions of these two novel Fd homologs with C-termini, a study adopting biochemical, histological, biomolecular, and genetic methodologies was performed. The phylogenic analysis has distinguished the divergent evolutionary distances between two FdC isoforms. FdC2 appeared in cyanobacteria while FdC1 appeared later in green algae. They were identified as chloroplast proteins by the eYFP approach in tobacco leaves. The spatial-temporal expression patterns of both FdCs in Arabidopsis were investigated by in silico expression profiling, immunoblotting, and promoter-GUS analysis. Both FdCs were detected in chlorenchyma, flower, and siliques throughout the life cycles. Additionally, FdC1 was detected in roots and anthers. The consistent results of yeast two-hybrid and bimolecular fluorescence complementation showed that two FdC isoforms can differentially interact with the PSI “stromal bridge” subunits (PsaC, D, and E), which was confirmed by their ability to receive electrons from PSI to support the photoreduction of cytochrome c. FdCs also shared some but not all of the electron receptors of both leaf-type Fds, including ferredoxin-thioredoxin reductase A/B, sulfite reductase, and nitrite reductase but not leaf-type and root-type FNRs, which was in agreement with the in vitro enzymatic assays on nitrite reductase, sulfite reductase, and the photoreduction of NADP^+. FdC1 exhibited a wider range of downstream electron acceptors than FdC2. This could be explained by the conserved negatively-charged residues and specific hydrophobic residues in the proposed three-dimensional complexes containing FdCs and their interacting partners based on the well-resolved crystal structures. Though no morphological changes were observed, the overexpression or suppression of FdC has significantly affected the chlorophyll fluorescence parameters in transgenic plants. To summarize, the heterogeneity of PsaD and PsaE which evolved in higher plants might function as branching points of electron flows to different acceptors (Fd, FdC1, and FdC2) with varied binding affinities, to regulate the routes of photosynthetic electron transfer to downstream biological processes in plastids, such as the assimilation of sulfate and nitrate, and the regulation of light-dependent enzymes in the Calvin cycle.published_or_final_versionBiological SciencesDoctoralDoctor of Philosoph

Comprehensive analysis revealed the immunoinflammatory targets of rheumatoid arthritis based on intestinal flora, miRNA, transcription factors, and RNA-binding proteins databases, GSEA and GSVA pathway observations, and immunoinfiltration typing

Abstract Objective Rheumatoid arthritis (RA) is a chronic inflammatory arthritis. This study aimed to identify potential biomarkers and possible pathogenesis of RA using various bioinformatics analysis tools. Methods The GMrepo database provided a visual representation of the analysis of intestinal flora. We selected the GSE55235 and GSE55457 datasets from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) separately. With the intersection of these DEGs with the target genes associated with RA found in the GeneCards database, we obtained the DEGs targeted by RA (DERATGs). Subsequently, Disease Ontology, Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes were used to analyze DERATGs functionally. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on the data from the gene expression matrix. Additionally, the protein-protein interaction network, transcription factor (TF)-targets, target-drug, microRNA (miRNA)-mRNA networks, and RNA-binding proteins (RBPs)-DERATGs correlation analyses were built. The CIBERSORT was used to evaluate the inflammatory immune state. The single-sample GSEA (ssGSEA) algorithm and differential analysis of DERATGs were used among the infiltration degree subtypes. Results There were some correlations between the abundance of gut flora and the prevalence of RA. A total of 54 DERATGs were identified, mainly related to immune and inflammatory responses and immunodeficiency diseases. Through GSEA and GSVA analysis, we found pathway alterations related to metabolic regulations, autoimmune diseases, and immunodeficiency-related disorders. We obtained 20 hub genes and 2 subnetworks. Additionally, we found that 39 TFs, 174 drugs, 2310 miRNAs, and several RBPs were related to DERATGs. Mast, plasma, and naive B cells differed during immune infiltration. We discovered DERATGs’ differences among subtypes using the ssGSEA algorithm and subtype grouping. Conclusions The findings of this study could help with RA diagnosis, prognosis, and targeted molecular treatment

Detection and Classification of Maritime Target with Micro-motion Based on CNNs

In this paper, Convolutional Neural Networks (CNN) are used to detect and classify micro-Doppler effects of maritime targets by using generalized learning ability for high-dimensional features. Based on the micro-motion model of maritime targets, two-dimensional time-frequency maps of four types of micro-motion signals are constructed in the measured sea clutter background. These maps were used as training and test datasets. Furthermore, three types of CNN models, i.e., LeNet, AlexNet, and GoogleNet, are used in binary detection and multiple micro-motion classifications. The effects of signal-to-noise ratio on detection and classification performance are also studied. Compared with the traditional support vector machine method, the proposed method can learn the micro-motion features intelligently, and has performed better in detection and classification. Thus, this study can provide a new technical approach for radar target detection and recognition under a cluttered background

Microstructure and corrosion resistance of commercial purity aluminum sheet manufactured by continuous casting direct rolling after ultrasonic melt pre-treatment

During the new continuous casting direct rolling (CCDR) process for manufacturing commercially pure aluminum sheet for foils, casting defects tend to arise as the raw material with huge mass flow rapidly solidifies under a high cooling rate, which generates negative effects on the formability of aluminum sheets and the performance of final foil products. Therefore, melt pre-treatment during continuous casting is very essential to improve the quality of the billet. In this study, multi-source ultrasonic field was employed to pretreat an Al melt during the continuous casting stage in a Hazelett CCDR strip production line. The Ti content distribution in the melt, the microstructure and corrosion behaviors of the rolled sheet with and without ultrasonic melt pre-treatment (UMPT) were investigated. The microstructure of the rolled sheet was refined due to the increased Ti content and uniformly distributed Al3Ti particles under UMPT. In addition, the morphology of the Fe-containing secondary phase was modified from an aggregated to a scattered structure, with reduced continuity. As a result, under the combined effects of the refined α-Al grains and secondary phase, the corrosion resistance of the rolled sheet was improved. The underlying mechanisms for the modifications of the microstructure and corrosion resistance after UMPT are also interpreted, which gives theoretical guidance on the practical applications of the ultrasound-assisted continuous casting in industrial production

The complete mitochondrial genome of Anoplistes halodendri (Coleoptera: Cerambycidae)

In this study, we report the complete mitochondrial genome of Anoplistes halodendri, which covers a total of 15,697 bp in length with 28.27% GC content. The complete mitochondrial genome is composed of 12 protein-coding genes (PCGs) and also contains 22 transfer RNA genes (tRNAs) and two ribosomal RNA genes (rRNAs). Phylogenetic analysis of the A. halodendri with other 21 different species of Cerambycidae indicated that A. halodendri formed an isolated clade and belong to Cerambycinae. The results will be helpful to study the evolutionary relationship among the subfamilies of Cerambycidae

Clutter Suppression and Marine Target Detection for Radar Images Based on INet

A marine radar device is a major navigation tool for boaters and ships. The images produced by marine radars detect not only hard targets such as ships and coastlines, but also reflections from the sea surface, known as sea clutter. The strong sea clutter and the complex characteristics of marine targets result in transmission of weak echo signals of the images to the radar, which makes difficult for radars to distinguish and analyze. So, effective sea clutter suppression and robust, fast target detection mechanisms are needed for radar to detect marine targets efficiently. However, the existing marine target detection algorithms have limited performance for target detection under complex environments, and have poor adaptability to environment and target characteristics. In this paper, an Integrated Network (INet) for clutter suppression and target detection algorithm is proposed and designed to optimize the signals received from the targets. The layer normalization algorithm integrated with transfer function is used to extract key target features, and the spatial attention network is used to suppress the clutter and to enhance the target signals, and a local cross-scale residual network is built to ensure the weightlessness of the system and accuracy of the detection network. Based on the echo data collected by the navigation radar under various observation conditions, radar images with marine target dataset were constructed. INet was optimized through pre-training of the model and inter-frame accumulation of Plan Position Indicator (PPI) images to obtain the Optimized INet (O-INet). The measured data were verified, tested, and compared with data obtained through various algorithms such as YOLOv3, YOLOv4, two-parameter CFAR, and two-dimensional CA-CFAR. The results obtained prove that the proposed method has superior advantages over other methods in improving detection probability, reducing false alarm rate, and strong generalization ability under complex conditions

SLC22A1 rs622342 Polymorphism Predicts Insulin Resistance Improvement in Patients with Type 2 Diabetes Mellitus Treated with Metformin: A Cross-Sectional Study

Background. Metformin is the most widely used oral antidiabetic agent and can reduce insulin resistance (IR) effectively. Organic cation transporter 1 (encoded by SLC22A1) is responsible for the transport of metformin, and ataxia-telangiectasia-mutated (ATM) is a gene relating to the DNA repair and cell cycle control. The aim of this study was to evaluate if the genetic variants in SLC22A1 rs622342 and ATM rs11212617 could be effective predictors of islet function improvement in patients with type 2 diabetes mellitus (T2DM) on metformin treatment. Methods. This cross-sectional study included 111 patients with T2DM treated with metformin. Genotyping was performed by the dideoxy chain-termination method. The homeostatic indexes of IR (HOMA-IR) and beta-cell function (HOMA-BCF) were determined according to the homeostasis model assessment. Results. Fasting plasma glucose (FPG) levels, HbA1c levels, and HOMA-IR were significantly higher in patients with the rs622342 AA genotype than in those with C allele (P<0.05). However, these significant differences were not observed between rs11212617 genotype groups. Further data analysis revealed that the association between the rs622342 polymorphism and HOMA-IR was gender related, and so was rs11212617 polymorphism and HOMA-BCF. HOMA-IR was significantly higher in males with rs622342 AA genotype than in those with C allele (P=0.021), and HOMA-BCF value was significantly higher in females carrying rs11212617 CC genotype than in those with A allele (P=0.038). The common logarithm (Lg10) of HOMA-BCF was positively correlated with the reciprocal of HbA1c (r = 0.629, P<0.001) and negatively associated with Lg10 FPG (r = −0.708, P<0.001). Conclusions. The variant of rs622342 could be a predictor of insulin sensitivity in patients with T2DM treated with metformin. The association between the rs622342 polymorphism and HOMA-IR and the association between the rs11212617 polymorphism and HOMA-BCF were both gender related