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BackgroundThe Atribacterota are widely distributed in the subsurface biosphere. Recently, the first Atribacterota isolate was described and the number of Atribacterota genome sequences retrieved from environmental samples has increased significantly; however, their diversity, physiology, ecology, and evolution remain poorly understood.ResultsWe report the isolation of the second member of Atribacterota, Thermatribacter velox gen. nov., sp. nov., within a new family Thermatribacteraceae fam. nov., and the short-term laboratory cultivation of a member of the JS1 lineage, Phoenicimicrobium oleiphilum HX-OS.bin.34TS, both from a terrestrial oil reservoir. Physiological and metatranscriptomics analyses showed that Thermatribacter velox B11T and Phoenicimicrobium oleiphilum HX-OS.bin.34TS ferment sugars and n-alkanes, respectively, producing H2, CO2, and acetate as common products. Comparative genomics showed that all members of the Atribacterota lack a complete Wood-Ljungdahl Pathway (WLP), but that the Reductive Glycine Pathway (RGP) is widespread, indicating that the RGP, rather than WLP, is a central hub in Atribacterota metabolism. Ancestral character state reconstructions and phylogenetic analyses showed that key genes encoding the RGP (fdhA, fhs, folD, glyA, gcvT, gcvPAB, pdhD) and other central functions were gained independently in the two classes, Atribacteria (OP9) and Phoenicimicrobiia (JS1), after which they were inherited vertically; these genes included fumarate-adding enzymes (faeA; Phoenicimicrobiia only), the CODH/ACS complex (acsABCDE), and diverse hydrogenases (NiFe group 3b, 4b and FeFe group A3, C). Finally, we present genome-resolved community metabolic models showing the central roles of Atribacteria (OP9) and Phoenicimicrobiia (JS1) in acetate- and hydrocarbon-rich environments.ConclusionOur findings expand the knowledge of the diversity, physiology, ecology, and evolution of the phylum Atribacterota. This study is a starting point for promoting more incisive studies of their syntrophic biology and may guide the rational design of strategies to cultivate them in the laboratory. Video Abstract.

Abstract Glaucoma is the leading cause of irreversible blindness and is characterized by progressive retinal ganglion cell (RGC) loss and retinal nerve fiber layer thinning. Currently, no existing treatment is effective for the preservation of RGCs. MicroRNA-22-3p (miR22) and small extracellular vesicles derived from mesenchymal stem cells (MSC-sEVs) have neuroprotective effects. In this study, we apply miR22-overexpressing MSC-sEVs in an N-methyl-D-aspartic acid (NMDA)-induced RGC injury model to assess their short-term therapeutic effects and explore the underlying mechanisms. We find that mice in the miR22-sEVs-treated group have thicker retinas, fewer apoptotic cells, more reserved RGCs, better retinal function, and lower expression levels of Bax and caspase-3. MiR22-sEVs treatment promotes viability, inhibits apoptosis and inhibits Bax and caspase-3 expression in RGC-5 cells. MiR22 targets mitogen-activated protein kinase kinase kinase 12 to inhibit apoptosis by regulating the mitogen-activated protein kinase (MAPK) signaling pathway. Collectively, our results suggest that miR22-sEVs ameliorate NMDA-induced RGC injury through the inhibition of MAPK signaling pathway-mediated apoptosis, providing a potential therapy for glaucoma and other diseases that involve RGC damage.

Regulatory T cells (Tregs), characterized by the expression of Forkhead Box P3 (FOXP3), constitute a distinct subset of T cells crucial for immune regulation. Tregs can exert direct and indirect control over immune homeostasis by releasing inhibitory factors or differentiating into Th-like Treg (Th-Treg), thereby actively contributing to the prevention and treatment of autoimmune diseases. The epigenetic regulation of FOXP3, encompassing DNA methylation, histone modifications, and post-translational modifications, governs the development and optimal suppressive function of Tregs. In addition, Tregs can also possess the ability to maintain homeostasis in diverse microenvironments through non-suppressive mechanisms. In this review, we primarily focus on elucidating the epigenetic regulation of Tregs as well as their multifaceted roles within diverse physiological contexts while looking forward to potential strategies involving augmentation or suppression of Tregs activity for disease management, particularly in light of the ongoing global COVID-19 pandemic.

Excitotoxicity is a prevalent pathological event in neurodegenerative diseases. The involvement of ferroptosis in the pathogenesis of excitotoxicity remains elusive. Transcriptome analysis has revealed that cytoplasmic reduced nicotinamide adenine dinucleotide phosphate (NADPH) levels are associated with susceptibility to ferroptosis-inducing compounds. Here we show that exogenous NADPH, besides being reductant, interacts with N-myristoyltransferase 2 (NMT2) and upregulates the N-myristoylated ferroptosis suppressor protein 1 (FSP1). NADPH increases membrane-localized FSP1 and strengthens resistance to ferroptosis. Arg-291 of NMT2 is critical for the NADPH-NMT2-FSP1 axis-mediated suppression of ferroptosis. This study suggests that NMT2 plays a pivotal role by bridging NADPH levels and neuronal susceptibility to ferroptosis. We propose a mechanism by which the NADPH regulates N-myristoylation, which has important implications for ferroptosis and disease treatment.

Abstract Background In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species. Results We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein–protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions. We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset. Conclusions Our proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification.

Abstract As an essential intracellular immune activation pathway, the cGAS‐STING pathway has attracted broad attention in cancer treatment. However, low bioavailability, nonspecificity, and adverse effects of small molecule STING agonists severely limit their therapeutic efficacy and in vivo application. In this study, a peptide‐based STING agonist is first proposed, and KLA is screened out to activate the cGAS‐STING pathway by promoting mitochondrial DNA (mtDNA) leakage. To precisely activate the cGAS‐STING pathway and block the PD‐1/PD‐L1 pathway, a multi‐stimuli activatable peptide nanodrug (MAPN) is developed for the effective delivery of KLA and PD‐L1 antagonist peptide (CVR). With rational design, MAPN achieved the site‐specific release of KLA and CVR in response to multiple endogenous stimuli, simultaneously activating the cGAS‐STING pathway and blocking PD‐1/PD‐L1 pathway, ultimately initiating robust and durable T cell anti‐tumor immunity with a tumor growth inhibition rate of 78% and extending the median survival time of B16F10 tumor‐bearing mice to 40 days. Overall, antimicrobial peptides, which can promote mtDNA leakage through damaging mitochondrial membranes, may be potential alternatives for small molecule STING agonists and giving a new insight for the design of novel STING agonists. Furthermore, MAPN presents a universal delivery platform for the effective synergy of multiple peptides.

Hydraulic pumps are the core components that provide power for hydraulic transmission systems, which are widely used in aerospace, marine engineering, and mechanical engineering, and their failure affects the normal operation of the entire system. This paper takes a single axial piston pump as the research object and proposes a small-sample fault diagnosis method based on the model migration strategy for the situation in which only a small number of training samples are available for axial piston pump fault diagnosis. To achieve end-to-end fault diagnosis, a 1D Squeeze-and-Excitation Networks (1D-SENets) model was constructed based on a one-dimensional convolutional neural network and combined with the channel domain attention mechanism. The model was first pre-trained with sufficient labeled fault data from the source conditions, and then, based on the model migration strategy, some of the underlying network parameters were fixed, and a small amount of labeled fault data from the target conditions was used to fine-tune the rest of the parameters of the pre-trained model. In this paper, the proposed method was validated using an axial piston pump fault dataset, and the experimental results show that the method can effectively improve the overfitting problem in the small sample fault diagnosis of axial piston pumps and improve the recognition accuracy.

Abstract Background and Aims Extrahepatic recurrence (EHR) is one of the major reasons for the poor prognosis of hepatocellular carcinoma (HCC). The present study aimed to develop and assess the performance of predictive models by using a combination of presurgical circulating tumor cell (CTCs) data and clinicopathological features to screen patients at high risk of EHR to achieve precise decision‐making. Patients and Methods A total of 227 patients with recurrent HCC and preoperative CTC data from January 2014 to August 2019 were enrolled. All patients were randomly assigned to one of two cohorts: development or validation. Two preoperative and postoperative nomogram models for EHR prediction were developed and multi‐dimensionally validated. Results Patients with EHR had generally lower recurrence‐free survival (p

AbstractBackground We aimed to assess differences in intestinal microflora between patients with operable hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) with microvascular invasion (MVI) and those without MVI. Additionally, we investigated the potential of the microbiome as a non-invasive biomarker for patients with MVI.Methods We analyzed the preoperative gut microbiomes (GMs) of two groups, the MVI (n = 46) and non-MVI (n = 56) groups, using 16S ribosomal RNA gene sequencing data. At the operational taxonomic unit level, we employed random forest models to predict MVI risk and validated the results in independent validation cohorts [MVI group (n = 17) and non-MVI group (n = 15)].Results β diversity analysis, utilizing weighted UniFrac distances, revealed a significant difference between the MVI and non-MVI groups, as indicated by non-metric multidimensional scaling and principal coordinate analysis. We also observed a significant correlation between the characteristic intestinal microbial communities at the genus level and their main functions. Nine optimal microbial markers were identified, with an area under the curve of 79.76% between 46 MVI and 56 non-MVI samples and 79.80% in the independent verification group.Conclusion This pioneering analysis of the GM in patients with operable HBV-HCC with and without MVI opens new avenues for treating HBV-HCC with MVI. We successfully established a diagnostic model and independently verified microbial markers for patients with MVI. As preoperative targeted biomarkers, GM holds potential as a non-invasive tool for patients with HBV-HCC with MVI.

To explore the presence of extrachromosomal circular DNA (eccDNA) in the anterior capsule of the lens in the eyes of patients with cataract and with high myopia. Circle-Seq was performed to identify differences in the eccDNA and gene expression between the anterior capsule of the lens of patients with simple nuclear cataract (C, n = 6 cases) and patients with nuclear cataract along with high myopia (HM, n = 6 cases). The expression of eccDNA was confirmed using routine quantitative polymerase chain reaction. The eccDNA ranked in C and HM ranged in length from 0.017 kb − 9.9 Mb with two distinctive peaks detected at 0.2 kb and 0.5 kb, while eccDNA that were differentially expressed ranged in size from 0.05 kb − 57.8 kb with two distinctive peaks observed at 0.1 kb and 0.5 kb. Only 2.5% of the eccDNA in C and 2% in HM were>25 kb in size. The gene-rich chromosomes contributed to more number of eccDNA/Mb, while several well-known high myopia candidate genes, including catenin delta 2 (CTNND2) and ubiquitin-like with PHD, exhibited significantly increased levels of eccDNA in the anterior capsule of the lens in patients with high myopia. This study highlighted the topologic analysis of the anterior capsule of eyes with high myopia, which is an emerging direction for research and clinical applications. These findings suggested that eccDNA was commonly detected in eyes with high myopia and cataracts, and the candidate genes for high myopia identified in previous studies were also observed in the eccDNA.

Abstract The world is currently undergoing profound changes which have never happened within the past century. Global competition in the technology and industry fields is becoming increasingly fierce. The strategic competition of the major powers further focuses on the manufacturing industry. Developed countries such as the United States, Germany, and Japan have successively put forward strategic plans such as “re-industrialization” and “return of manufacturing industry”, aiming to seize the commanding heights of a new round of global high-end technology competition and expand international market share. Standing at the historic intersection of a new round of scientific and technological revolution and China’s accelerated high-quality development, the “14th Five-Year Plan” clearly pointed out that intelligent manufacturing is the main development trend to promote China’s manufacturing to the medium-high end of the global value chain. This reflects the importance of advanced manufacturing for national strategic layout. To better grasp the development direction of advanced manufacturing equipment, the development process and current application status of manufacturing equipment are summarized, and thereafter the characteristics of manufacturing equipment in different development stages of the manufacturing industry are analyzed. Finally, the development trend of advanced milling equipment is prospected.

Inherited and age-related retinal degenerations are the commonest causes of blindness without effective treatments. Retinal progenitor cells (RPCs), which have the multipotency to differentiate into various retinal cell types, are regarded as a promising source of cell transplantation therapy for retinal degenerative diseases. However, the self-limited expansion of RPCs causes difficulty in cell source supply and restrict its clinical treatment. In this work, we found that inhibition of microRNA-449a (miR-449a) in RPCs can promote proliferation and inhibit apoptosis of RPCs, partially through upregulating Notch signaling. Further optimization of transduction miR-449a inhibitor into RPCs by endothelial cell-derived exosomes can promote the survival of RPCs transplanted in vivo and reduce cell apoptosis in retinal degeneration mouse models. In summary, these studies have shown that exosome-miR-449a inhibitor can effectively promote the expansion of RPCs in vitro and enhance transplanted RPCs survival in vivo, which might provide a novel intervention strategy for retinal degenerations in the future.

Abstract Background Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein–protein interaction (PPI) networks. Machine learning approaches based on high-throughput data lack the exploitation of the temporal and spatial dimensions of biological information. Results We put forward a deep learning framework to predict essential proteins by integrating features obtained from the PPI network, subcellular localization, and gene expression profiles. In our model, the node2vec method is applied to learn continuous feature representations for proteins in the PPI network, which capture the diversity of connectivity patterns in the network. The concept of depthwise separable convolution is employed on gene expression profiles to extract properties and observe the trends of gene expression over time under different experimental conditions. Subcellular localization information is mapped into a long one-dimensional vector to capture its characteristics. Additionally, we use a sampling method to mitigate the impact of imbalanced learning when training the model. With experiments carried out on the data of Saccharomyces cerevisiae, results show that our model outperforms traditional centrality methods and machine learning methods. Likewise, the comparative experiments have manifested that our process of various biological information is preferable. Conclusions Our proposed deep learning framework effectively identifies essential proteins by integrating multiple biological data, proving a broader selection of subcellular localization information significantly improves the results of prediction and depthwise separable convolution implemented on gene expression profiles enhances the performance.