A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening.

Clustered regularly interspaced short palindromic repeats (CRISPR) screening coupled with single-cell RNA sequencing has emerged as a powerful tool to characterize the effects of genetic perturbations on the whole transcriptome at a single-cell level. However, due to its sparsity and complex structure, analysis of single-cell CRISPR screening data is challenging. In particular, standard differential expression analysis methods are often underpowered to detect genes affected by CRISPR perturbations. We developed a statistical method for such data, called guided sparse factor analysis (GSFA). GSFA infers latent factors that represent coregulated genes or gene modules; by borrowing information from these factors, it infers the effects of genetic perturbations on individual genes. We demonstrated through extensive simulation studies that GSFA detects perturbation effects with much higher power than state-of-the-art methods. Using single-cell CRISPR data from human CD8+ T cells and neural progenitor cells, we showed that GSFA identified biologically relevant gene modules and specific genes affected by CRISPR perturbations, many of which were missed by existing methods, providing new insights into the functions of genes involved in T cell activation and neurodevelopment.

Quantification of extracellular proteins, protein complexes and mRNAs in single cells by proximity sequencing.

We present proximity sequencing (Prox-seq) for simultaneous measurement of proteins, protein complexes and mRNAs in thousands of single cells. Prox-seq combines proximity ligation assay with single-cell sequencing to measure proteins and their complexes from all pairwise combinations of targeted proteins, providing quadratically scaled multiplexing. We validate Prox-seq and analyze a mixture of T cells and B cells to show that it accurately identifies these cell types and detects well-known protein complexes. Next, by studying human peripheral blood mononuclear cells, we discover that naïve CD8+ T cells display the protein complex CD8-CD9. Finally, we study protein interactions during Toll-like receptor (TLR) signaling in human macrophages. We observe the formation of signal-specific protein complexes, find CD36 co-receptor activity and additive signal integration under lipopolysaccharide (TLR4) and Pam2CSK4 (TLR2) stimulation, and show that quantification of protein complexes identifies signaling inputs received by macrophages. Prox-seq provides access to an untapped measurement modality for single-cell phenotyping and can discover uncharacterized protein interactions in different cell types.

Computational prediction of protein interactions in single cells by proximity sequencing.

Proximity sequencing (Prox-seq) simultaneously measures gene expression, protein expression and protein complexes on single cells. Using information from dual-antibody binding events, Prox-seq infers surface protein dimers at the single-cell level. Prox-seq provides multi-dimensional phenotyping of single cells in high throughput, and was recently used to track the formation of receptor complexes during cell signaling and discovered a novel interaction between CD9 and CD8 in naïve T cells. The distribution of protein abundance can affect identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model of Prox-seq and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq data, which resulted in more accurate and robust quantification of protein complexes. Finally, our Prox-seq model offers a simple way to investigate the behavior of Prox-seq data under various biological conditions and guide users toward selecting the best analysis method for their data.

Hydrodynamics-Controlled Single-Particle Electrocatalysis.

Electrocatalysis is considered promising in renewable energy conversion and storage, yet numerous efforts rely on catalyst design to advance catalytic activity. Herein, a hydrodynamic single-particle electrocatalysis methodology is developed by integrating collision electrochemistry and microfluidics to improve the activity of an electrocatalysis system. As a proof-of-concept, hydrogen evolution reaction (HER) is electrocatalyzed by individual palladium nanoparticles (Pd NPs), with the development of microchannel-based ultramicroelectrodes. The controlled laminar flow enables the precise delivery of Pd NPs to the electrode-electrolyte interface one by one. Compared to the diffusion condition, hydrodynamic collision improves the number of active sites on a given electrode by 2 orders of magnitude. Furthermore, forced convection enables the enhancement of proton mass transport, thereby increasing the electrocatalytic activity of each single Pd NP. It turns out that the improvement in mass transport increases the reaction rate of HER at individual Pd NPs, thus a phase transition without requiring a high overpotential. This study provides new avenues for enhancing electrocatalytic activity by altering operating conditions, beyond material design limitations.

Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching.

Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for many biological and medical applications as it allows users to measure gene expression levels in a cell type-specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq data sets with cell types that may overlap only partially and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering and in terms of avoiding overcorrection. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell type-specific differential gene expression comparisons across biopsy sites and disease conditions and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online.

MAMs and Mitochondrial Quality Control: Overview and Their Role in Alzheimer's Disease.

Alzheimer's disease (AD) represents the most widespread neurodegenerative disorder, distinguished by a gradual onset and slow progression, presenting a substantial challenge to global public health. The mitochondrial-associated membrane (MAMs) functions as a crucial center for signal transduction and material transport between mitochondria and the endoplasmic reticulum, playing a pivotal role in various pathological mechanisms of AD. The dysregulation of mitochondrial quality control systems is considered a fundamental factor in the development of AD, leading to mitochondrial dysfunction and subsequent neurodegenerative events. Recent studies have emphasized the role of MAMs in regulating mitochondrial quality control. This review will delve into the molecular mechanisms underlying the imbalance in mitochondrial quality control in AD and provide a comprehensive overview of the role of MAMs in regulating mitochondrial quality control.

Electroacupuncture Attenuates Ventilator-Induced Lung Injury by Modulating the Nrf2/HO-1 Pathway.

INTRODUCTION: Ventilator-induced lung injury (VILI) is the most common complication associated with mechanical ventilation. Electroacupuncture (EA) has shown potent anti-inflammatory effects. This study aimed to investigate the effects of EA on VILI and explore the underlying mechanisms. METHODS: Male C57BL/6 mice were subjected to high tidal volume ventilation to induce VILI. Prior to mechanical ventilation, mice received treatment with EA, nonacupoint EA, or EA combined with zinc protoporphyrin. RESULTS: EA treatment significantly improved oxygenation, as indicated by increased PaO2 levels in VILI mice. Moreover, EA reduced lung injury score, lung wet/dry weight ratio, and protein concentration in bronchoalveolar lavage fluid. EA also decreased the expression of pro-inflammatory cytokines including interleukin (IL)-1ß, IL-6, tumor necrosis factor-α, IL-18, chemokine keratinocyte chemoattractant, macrophage inflammatory protein 2, and malondialdehyde. Furthermore, EA increased the activities of antioxidant enzymes superoxide dismutase, catalase, and glutathione peroxidase in VILI mice. At the molecular level, EA upregulated the expression of Nrf2 (nucleus) and heme oxygenase -1, while down-regulating the expression of p-NF-κB p65, NLR Family Pyrin Domain Containing 3, Cleaved Caspase-1, and ASC in VILI mice. Notably, the effects of EA were reversed by zinc protoporphyrin treatment, nonacupoint EA did not affect the aforementioned indicators of VILI. CONCLUSIONS: EA alleviates VILI by inhibiting the NLR Family Pyrin Domain Containing three inflammasome through activation of the Nrf2/HO-1 pathway.

Role of Akkermansia muciniphila in insulin resistance.

Insulin resistance (IR) is a pathogenic factor in numerous metabolic diseases. The gut microbiota plays a crucial role in maintaining the function of the intestinal barrier and overall human health, thereby influencing IR. Dysbiosis of the gut microbiota can contribute to the development of IR. Therefore, it is essential to maintain a balanced and diverse gut microbiota for optimal health. Akkermansia muciniphila, a widely present microorganism in the human intestine, has been shown to regulate gastrointestinal mucosal barrier integrity, reduce endotoxin penetration, decrease systemic inflammation levels, and improve insulin sensitivity. Reduced abundance of A. muciniphila is associated with an increased risk of IR and other metabolic diseases, highlighting its correlation with IR. Understanding the role and regulatory mechanism of A. muciniphila is crucial for comprehending IR pathogenesis and developing novel strategies for preventing and treating related metabolic disorders. Individual variations may exist in both the gut microbiota composition and its impact on IR among different individuals. Further investigation into individual differences between A. muciniphila and IR will facilitate advancements in personalized medicine by promoting tailored interventions based on the gut microbiota composition, which is a potential future direction that would optimize insulin sensitivity while preventing metabolic disease occurrence. In this review, we describe the physiological characteristics of A. muciniphila, emphasize its roles in underlying mechanisms contributing to IR pathology, and summarize how alterations in its abundance affect IR development, thereby providing valuable insights for further research on A. muciniphila, as well as new drug development targeting diabetes.

Preoperative prediction model of lymph node metastasis in the inguinal and femoral region based on radiomics and artificial intelligence.

OBJECTIVE: To predict preoperative inguinal lymph node metastasis in vulvar cancer patients using a machine learning model based on imaging features and clinical data from pelvic magnetic resonance imaging (MRI). METHODS: 52 vulvar cancer patients were divided into a training set (n=37) and validation set (n=15). Clinical data and MRI images were collected, and regions of interest were delineated by experienced radiologists. A total of 1688 quantitative imaging features were extracted using the Radcloud platform. Dimensionality reduction and feature selection were applied, resulting in a radiomics signature. Clinical characteristics were screened, and a combined model integrating the radiomics signature and significant clinical features was constructed using logistic regression. Four machine learning classifiers (K nearest neighbor, random forest, adaptive boosting, and latent dirichlet allocation) were trained and validated. Model performance was evaluated using the receiver operating characteristic curve and the area under the curve (AUC), as well as decision curve analysis. RESULTS: The radiomics score significantly differentiated between lymph node metastasis positive and negative patients in both the training and validation sets. The combined model demonstrated excellent discrimination, with AUC values of 0.941 and 0.933 in the training and validation sets, respectively. The calibration curve and decision curve analysis confirmed the model's high predictive accuracy and clinical utility. Among the machine learning classifiers, latent dirichlet allocation and random forest models achieved AUC values >0.7 in the validation set. Integrating all four classifiers resulted in a total model with an AUC of 0.717 in the validation set. CONCLUSION: Radiomics combined with artificial intelligence can provide a new method for prediction of inguinal lymph node metastasis of vulvar cancer before surgery.

New strategy for intraoperative phonosurgical management of recurrent laryngeal nerve infiltrated by thyroid carcinoma.

PURPOSE: Treating an infiltration of the recurrent laryngeal nerve (RLN) by thyroid carcinoma remains a subject of ongoing debate. Therefore, this study aims to provide a novel strategy for intraoperative phenosurgical management of RLN infiltrated by thyroid carcinoma. METHODS: Forty-two patients with thyroid carcinoma infiltrating the RLN were recruited for this study and divided into three groups. Group A comprised six individuals with medullary thyroid cancer who underwent RLN resection and arytenoid adduction. Group B consisted of 29 differentiated thyroid cancer (DTC)patients who underwent RLN resection and ansa cervicalis (ACN)-to-RLN anastomosis. Group C included seven patients whose RLN was preserved. RESULTS: The videostroboscopic analysis and voice assessment collectively indicated substantial improvements in voice quality for patients in Groups A and B one year post-surgery. Additionally, the shaving technique maintained a normal or near-normal voice in Group C one year post-surgery. CONCLUSION: The new intraoperative phonosurgical strategy is as follows: Resection of the affected RLN and arytenoid adduction is required in cases of medullary or anaplastic carcinoma, regardless of preoperative RLN function. Suppose RLN is found infiltrated by well-differentiated thyroid cancer (WDTC) during surgery, and the RLN is preoperatively paralyzed, we recommend performing resection the involved RLN and ACN-to-RLN anastomosis immediately during surgery. If vocal folds exhibit normal mobility preoperatively, the MACIS scoring system is used to assess patient risk stratification. When the MACIS score > 6.99, resection of the involved RLN and immediate ACN-to-RLN anastomosis were performed. RLN preservation was limited to patients with MACIS scores ≤ 6.99.

Effective and scalable single-cell data alignment with non-linear canonical correlation analysis.

Data alignment is one of the first key steps in single cell analysis for integrating multiple datasets and performing joint analysis across studies. Data alignment is challenging in extremely large datasets, however, as the major of the current single cell data alignment methods are not computationally efficient. Here, we present VIPCCA, a computational framework based on non-linear canonical correlation analysis for effective and scalable single cell data alignment. VIPCCA leverages both deep learning for effective single cell data modeling and variational inference for scalable computation, thus enabling powerful data alignment across multiple samples, multiple data platforms, and multiple data types. VIPCCA is accurate for a range of alignment tasks including alignment between single cell RNAseq and ATACseq datasets and can easily accommodate millions of cells, thereby providing researchers unique opportunities to tackle challenges emerging from large-scale single-cell atlas.

Effect of continuous hypertonic saline infusion on clinical outcomes in patients with traumatic brain injury.

Osmotic therapy has been recognized as an important treatment option for patients with traumatic brain injury (TBI). Nevertheless, the effect of hypertonic saline (HTS) remains unknown, as findings are primarily based on a large database. This study aimed to elucidate the effect of HTS on the clinical outcomes of patients with TBI admitted to the intensive care unit (ICU). We retrospectively identified patients with moderate-to-severe TBI from two public databases: Medical Information Mart for Intensive Care (MIMIC)-IV and eICU Collaborative Research Database (eICU-CRD). A marginal structural Cox model (MSCM) was used, with time-dependent variates designed to reflect exposure over time during ICU stay. Trajectory modeling based on the intracranial pressure evolution pattern allowed for the identification of subgroups. Overall, 130 (6.65%) of 1955 eligible patients underwent HTS. MSCM indicated that the HTS significantly associated with higher infection complications (e.g., urinary tract infection (HR 1.88, 95% CI 1.26-2.81, p = 0.002)) and increased ICU LOS (HR 2.02, 95% CI 1.71-2.40, p < 0.001). A protective effect of HTS on GCS was found in subgroups with medium and low intracranial pressure. Our study revealed no significant difference in mortality between patients who underwent HTS and those who did not. Increased occurrence rates of infection and electrolyte imbalance are inevitable outcomes of continuous HTS infusion. Although the study suggests slight beneficial effects, including better neurological outcomes, these results warrant further validation.

Texture-Based Classification of Fetal Growth Restriction From Intrauterine Neurosonographic Image.

OBJECTIVE: Fetal growth restriction (FGR) is a condition where fetuses fail to reach their genetic potential for growth, posing a significant health challenge for newborns. The aim of this research was to explore the efficacy of texture-based analysis of neurosonographic images in identifying FGR in fetuses, which may provide a promising tool for early assessment of FGR. METHODS: A retrospective analysis collected 100 intrauterine neurosonographic images from 50 FGR and 50 gestational age-appropriate fetuses. Using MaZda software, approximately 300 texture features were extracted from occipital white matter (OWM) and cerebellum of intrauterine neurosonographic images, respectively. Then 10 optimal features were separately selected by 3 algorithms, including the Fisher coefficient method, the method of minimizing classification error probability and average correlation coefficients, and the mutual information coefficient method. Further, the 10 statistically most significant features were selected from these sets to form the mixed feature set. After nonlinear discriminant analysis was performed to reduce feature dimensionality, the artificial neural network (ANN) classifier was conducted, respectively. RESULTS: For OWM and cerebellum, a total of 11 and 14 statistically significant features were selected. When the mixed feature sets of OWM and cerebellum were applied to ANN classifier, classification accuracy were 90.00% (κ = 0.800; P < .001) and 93.00% (κ = 0.860; P < .001), and the receiver operating characteristic curve for identifying FGR showed an area under the curve of 0.82 and 0.87. CONCLUSIONS: Texture analysis of fetal intrauterine neurosonographic images is a feasible and noninvasive strategy for evaluating FGR fetuses.

Factors associated with the core dimensions of spiritual health among older adults with chronic obstructive pulmonary disease: A cross-sectional study.

AIMS: To investigate the level of spiritual health in older patients with chronic obstructive pulmonary disease (COPD) from the core dimensions and to explore its associated factors. DESIGN: A cross-sectional study. METHODS: Participants were recruited from four hospitals between September 2020 and June 2021, using a convenience sampling. Older patients with COPD (n = 162) completed the demographic and disease-related information questionnaires, Function Assessment of Chronic Illness Therapy Spiritual Scale, 10-item Connor-Davidson Resilience Scale, General Self-efficacy Scale, Social Support Rating Scale, COPD Assessment Test, 15-item Geriatric Depression Scale and modified Medical Research Council Dyspnea Scale. Descriptive statistics, Pearson and Spearman correlation analyses, t-tests, one-way ANOVA and multiple linear regression models were used. RESULTS: Older patients with COPD have a moderate level of spiritual health. The multiple linear regression analysis showed that psychological resilience, general self-efficacy, social support, symptom burden and monthly income were associated with the core dimensions of spiritual health. CONCLUSION: Chinese older patients with COPD have a moderate level of spiritual health. Psychological resilience, general self-efficacy, social support, monthly income and symptom burden were associated with the core dimensions of spiritual health. IMPACT: This study is the first to investigate the level of spiritual health in older patients with COPD from the core dimensions and to explore its associated factors, providing a basis for developing spiritual intervention programs. Our findings can help us realize that intervention strategies of psychological resilience, general self-efficacy and social support can all be used to enhance spiritual health. Nurses should focus on the spiritual health of older COPD patients with high symptom burden and low monthly income. PATIENT OR PUBLIC CONTRIBUTION: Although we did not directly involve patients and the public because of the COVID-19 pandemic, the results of the study will be disseminated to patients and the public through WeChat and seminars.

3D Printing of Ultralow-Concentration 2D Nanomaterial Inks for Multifunctional Architectures.

The direct 3D printing of ultralight architectures with ultralow-concentration 2D nanomaterial inks is necessary yet challenging. Here, we describe an emulsion-based ink for direct printing using 2D nanomaterials, i.e., MXene and graphene oxide (GO). The electrostatic interactions between the ligands in the oil phase and the 2D nanomaterials in the aqueous phase help form sheet-like surfactants at the interface. The interactions between the anchored ligands among different droplets dictate the rheological characteristics of inks, enabling a gel-like behavior ideally suitable for 3D printing at ultralow concentrations of 2D nanomaterials. The 3D printed foams possess lightweight structures with densities of 2.8 mg cm-3 (GO-based) and 4.1 mg cm-3 (MXene-based), and the latter integrates outstanding electrical conductivity, electromagnetic shielding performance, and thermal insulation comparable to air. This work describes a general approach for direct-printing ultralight porous structures that take advantage of the inherent properties of 2D building blocks.

Unlocking Single Particle Anisotropy in Real-Time for Photoelectrochemistry Processes at the Nanoscale.

The key to rationally and rapidly designing high-performance materials is the monitoring and comprehension of dynamic processes within individual particles in real-time, particularly to gain insight into the anisotropy of nanoparticles. The intrinsic property of nanoparticles typically varies from one crystal facet to the next under realistic working conditions. Here, we introduce the operando collision electrochemistry to resolve the single silver nanoprisms (Ag NPs) anisotropy in photoelectrochemistry. We directly identify the effect of anisotropy on the plasmonic-assisted electrochemistry at the single NP/electrolyte interface. The statistical collision frequency shows that heterogeneous diffusion coefficients among crystal facets facilitate Ag NPs to undergo direction-dependent mass transfer toward the gold ultramicroelectrode. Subsequently, the current amplitudes of transient events indicate that the anisotropy enables variations in dynamic interfacial electron transfer behaviors during photothermal processes. The results presented here demonstrate that the measurement precision of collision electrochemistry can be extended to the sub-nanoparticle level, highlighting the potential for high-throughput material screening with comprehensive kinetics information at the nanoscale.

SIRP-alpha-IL-6 axis induces immunosuppressive macrophages in non-small-cell lung cancer.

Signal regulatory protein-alpha (SIRPα) and IL-6 participate in the induction of tumor immune suppressive environment and facilitate tumor growth. In this study, we found that SIRPα was significantly elevated in macrophages of non-small cell lung cancer (NSCLC) tissues, which was positively correlated to the expression of CD163, PD-1, IL-6, and lung cancer progression. SIRPα in peripheral blood mononuclear cells (PBMCs) of NSCLC patients was also associated with CD163, PD-1, and plasma IL-6. Blockade of SIRPα signaling in SIRPα ± and SIRPα-/- mice attenuated lung cancer growth and reduced IL-6 expression in LLC cells-transplanted murine lung cancer model. Co-targeting SIRPα and IL-6 additively suppressed the expression of IL-6 and activation of STAT3, accompanied with a reduced population of pro-tumorigenic CD206+ M2 subtype of macrophages, PD-1+ tumor-associated macrophages (TAMs), and PD-1+CD8+ T cells in tumor tissues of anti-IL-6 antibody (aIL-6)-treated mice deficient in SIRPα. Further in vitro studies showed that blockade of SIRPα signaling by anti-SIRPα effectively improved phagocytosis of human PBMCs. IL-6 treatment improved polarization of M2 subtypes and the expression of PD-1 in bone marrow-derived macrophages (BMDMs); whereas both aIL-6 and STAT3 inhibitor C188-9 suppressed the expression of PD-1 and SIRPα in BMDMs. M2 cell-biased polarization was also reduced in aIL-6 or C188-9 treated BMDMs. Thereby, SIRPα and IL-6 form a positive feedback loop and regulate each other through STAT3 signaling in macrophages. The increased SIRPα/IL-6 axis may promote immune suppressive environment and lung cancer growth, which may be a potential target for clinical treatment.

A novel CD47-blocking peptide fused to pro-apoptotic KLA repeat inhibits lung cancer growth in mice.

CD47 is highly expressed in many tumor tissues and induces immune evasion by interaction with SIRP-alpha (signal regulatory protein-alpha) expressed on tumor-associated macrophages. In this study, we identified a novel CD47-blocking peptide VK17 by phage display technique. A pro-apoptotic VK30 peptide was obtained after VK17 was fused to KLA amino acid repeat at C-termini. The VK30 was specifically bound to CD47 on lung cancer cells, and subsequently inducing lung cancer cell apoptosis. Meanwhile, the expression of Bax was increased, whereas the expression of Bcl-2 and Ki-67 were reduced in the VK30-treated lung cancer cells. In addition, VK30 effectively improved the phagocytic activity of macrophages against VK30-pretreated lung cancer cells. Combinational treatment of lung cancer cells with blocking antibody anti-CD47 and VK30 additively enhanced VK30 binding to CD47, subsequently increasing lung cancer cell apoptosis and macrophage phagocytosis. Intraperitoneal administration of 2 mg/kg VK30 induced effective trafficking of VK30 into tumor tissues, and suppressing lung cancer cell growth in mice, associated with increased tumor cell apoptosis, macrophage activation and phagocytosis in vivo. The expression of CD47 was reduced in the VK30-treated tumor tissues and the expression level was positively correlated to tumor size. In addition, VK30 reduced the infiltration of CD11b+Ly6G+ neutrophils and CD11b+Ly6C+Ly6G+ granulocytic myeloid-derived suppressor cells (Gr-MDSCs) in tumor tissues, associated with suppressed expression of tumorigenic IL-6 and TNF-alpha from these cell types. Thereby, VK30 exerted anti-tumor effects in mice through inducing tumor cell apoptosis and macrophage phagocytosis. VK30 would be a novel therapeutic peptide in lung cancer immunotherapy.

circRNA-miRNA-mRNA network analysis to explore the pathogenesis of abnormal spermatogenesis due to aberrant m6A methylation.

Many studies have shown that circRNAs and miRNAs play important roles in many different life processes. However, the function of circRNAs in spermatogenesis remains unknown. Here, we aimed to explore the mechanisms whereby circRNA-miRNAs-mRNAs regulate abnormal m6A methylation in GC-1spg spermatogonia. We first reduced m6A methylation in GC-1spg whole cells after knocking down the m6A methyltransferase enzyme, METTL3. Then, we performed circRNA- and miRNA-seq on GC-1spg cells with low m6A methylation and identified 48 and 50 differentially expressed circRNAs and miRNAs, respectively. We also predicted the targets of the differentially expressed miRNAs by using Miranda software and further constructed the differentially expressed circRNA-differentially expressed miRNA-mRNA ceRNA network. GO analysis was performed on the differentially expressed circRNAs and miRNA-targeted mRNAs, and an interaction network between the proteins of interest was constructed using Cytoscape. The final GO analysis showed that the target mRNAs were involved in sperm formation. Therefore, a PPI network was subsequently constructed and 2 hub genes (H2afx and Dnmt3a) were identified. In this study, we constructed a ceRNA network and explored the regulatory roles of circRNAs and miRNAs in the pathogenesis of abnormal spermatogenesis caused by low levels of methylated m6A. Also, we identified two pivotal genes that may be key factors in infertility caused by abnormal m6A methylation. This may provide some ideas for the treatment of infertility resulting from abnormal spermatogenesis.

The Role of PGC-1α-Mediated Mitochondrial Biogenesis in Neurons.

Neurons are highly dependent on mitochondrial ATP production and Ca2+ buffering. Neurons have unique compartmentalized anatomy and energy requirements, and each compartment requires continuously renewed mitochondria to maintain neuronal survival and activity. Peroxisome proliferator-activated receptor-gamma coactivator-1α (PGC-1α) is a key factor in the regulation of mitochondrial biogenesis. It is widely accepted that mitochondria are synthesized in the cell body and transported via axons to the distal end. However, axonal mitochondrial biogenesis is necessary to maintain axonal bioenergy supply and mitochondrial density due to limitations in mitochondrial axonal transport rate and mitochondrial protein lifespan. In addition, impaired mitochondrial biogenesis leading to inadequate energy supply and neuronal damage has been observed in neurological disorders. In this review, we focus on the sites where mitochondrial biogenesis occurs in neurons and the mechanisms by which it maintains axonal mitochondrial density. Finally, we summarize several neurological disorders in which mitochondrial biogenesis is affected.