Network Analysis Research Articles

Fat Phagocytosis Promotes Anti-Inflammatory Responses of Macrophages in a Mouse Model of Osteonecrosis

Osteonecrosis (ON) of the femoral head (ONFH) is a devastating bone disease affecting over 20 million people worldwide. ONFH is caused by a disruption of the blood supply, leading to necrotic cell death and increased inflammation. Macrophages are the key cells mediating the inflammatory responses in ON. It is unclear what the dynamic phenotypes of macrophages are and what mechanisms may affect macrophage polarization and, therefore, the healing process. In our preliminary study, we found that there is an invasion of macrophages into the repair tissue during ON healing. Interestingly, in both ONFH patients and a mouse ON model, fat was co-labeled within macrophages using immunofluorescence staining, indicating the phagocytosis of fat by macrophages. To study the effects of fat phagocytosis on the macrophage phenotype, we set up an in vitro macrophage and fat co-culture system. We found that fat phagocytosis significantly decreased M1 marker expression, such as IL1β and iNOS, in macrophages, whereas the expression of the M2 marker Arg1 was significantly increased with fat phagocytosis. To investigate whether the polarization change is indeed mediated by phagocytosis, we treated the cells with Latrunculin A (LA, which inhibits actin polymerization and phagocytosis). LA supplementation significantly reversed the polarization marker gene changes induced by fat phagocytosis. To provide an unbiased transcriptional gene analysis, we submitted the RNA for bulk RNA sequencing. Differential gene expression (DGE) analysis revealed that the top upregulated genes were related to anti-inflammatory responses, while proinflammatory genes were significantly downregulated. Additionally, using pathway enrichment and network analyses (Metascape), we confirmed that gene-enriched categories related to proinflammatory responses were significantly downregulated in macrophages with fat phagocytosis. Finally, we validated the similar macrophage phenotype changes in vivo. To summarize, we discovered that fat phagocytosis occurs in both ONFH patients and an ON mouse model, which inhibits proinflammatory responses with increased anabolic gene expression in macrophages. This fat-phagocytosis-induced macrophage phenotype is consistent with the in vivo changes shown in the ON mouse model. Our study reveals a novel phagocytosis-mediated macrophage polarization mechanism in ON, which fills in our knowledge gaps of macrophage functions and provides new concepts in macrophage immunomodulation as a promising treatment for ON.

Comparison of toxic effects and underlying mechanisms of carbon quantum dots and CdSe quantum dots on Chromochloris zofingiensis from the chemical composition perspective

Quantum dots (QDs) are widely utilized semiconductor nanocrystal materials with both nanotoxicity and composition-related toxicity. To determine the toxicological impacts and underlying mechanisms of QDs with different compositions on microalgae, carbon QDs (CQDs) and CdSe QDs were used in the present study. Results showed that QDs composed of CdSe were more toxic than QDs composed of carbon, which inhibited cell growth, with reductions in chl b content, chlorophyll fluorescence parameters, and increases in lipids and starch (two major storage substances). In addition, CdSe QDs elevated reactive oxygen species (ROS), resulting in oxidative damage, while CQDs had little effect on antioxidants. Comparative transcriptome analysis showed that gene expression was accelerated by CdSe QDs, and there was a compensatory upregulation of porphyrin metabolism, potentially to support chlorophyll synthesis. In addition, an MYB transcription factor was predicted by weighted gene co-expression network analysis (WGCNA) to serve as regulator in nanoparticle toxicity, while glutathione peroxidase (GPX) and dual-specificity tyrosine phosphorylation regulated kinases 2/3/4 (DYRK2/3/4) may be key mediators of the composition-related toxicity of CdSe QDs. This study highlights the critical role of QDs’ composition in determining their impacts on aquatic microalgae, providing a theoretical reference for selecting appropriate QDs materials for various industrial applications.

Transcriptome mechanisms of dandelion under stress of polystyrene and dibutyl phthalate and quantitative tracing of nanoplastics

Polystyrene nanoplastics (PS NPs) and dibutyl phthalate (DBP) pollution pose significant risks to ecosystems and contribute to bioaccumulation in plants, yet uptake mechanisms and combined toxicity are poorly understood. We used fluorescent labeling and europium-doped PS NPs to reveal the absorption and translocation of NPs by dandelions and conducted a transcriptomic analysis under PS NPs and DBP exposure. The results indicated that NPs are transported horizontally through the intercellular gaps at the root tips and primary root-lateral root junctions via the apoplastic pathway, followed by longitudinal transport through the xylem vessels under the transpiration stream. Co-exposure significantly reduced the bioconcentration factors of dandelion seedlings by 113 % but increased the NP transfer factors by 33.8 %. Transcriptomic analysis confirmed that exposure to PS NPs and DBP activated gene expression in dandelion shoots and roots. The differentially expressed genes were primarily involved in the photosynthesis, plant hormone signal transduction, and phenylpropanoid biosynthesis pathways. Weighted gene co-expression network analysis identified key genes and hub transcription factors playing crucial roles in regulating dandelion's response to combined stress. Our study provides new insights into the plant toxicity mechanism underlying the interaction between PS NPs and DBP, highlighting the adverse effects of the combined pollution on plant health.

Mitigating risks in central line-associated bloodstream infection: a comprehensive insight into critical care nurses’ knowledge, attitudes, barriers, and compliance

BackgroundCentral venous catheter-related bloodstream infections (CLABSIs) are a significant concern in intensive care units (ICUs) as they lead to increased morbidity, mortality, and healthcare costs. Fortunately, these infections are largely preventable through strict adherence to CLABSI prevention guidelines. Nurses play a critical role in preventing CLABSIs.AimThis study aimed to investigate factors affecting critical care nurses' knowledge, attitudes, and perceived barriers related to implementing CLABSI prevention guidelines, and to predict factors influencing compliance with these guidelines.MethodsThis cross-sectional study was conducted from April to May 30, 2023, with a convenience sample of 470 critical care nurses from ICUs across eight hospitals in Sana’a, Yemen. Data were collected using an observational checklist and self-administered questionnaire. Descriptive statistics, Independent Student’s t-test, one-way ANOVA, Pearson’s correlation coefficient, multiple linear regression, and multilayer perceptron neural networks were performed.ResultsCritical care nurses exhibited low knowledge of CLABSI prevention guidelines, with compliance reaching an acceptable level. Despite the higher perceived barriers, the nurses demonstrated a positive attitude. Nurses with greater knowledge and positive attitudes displayed higher compliance levels. However, perceived barriers were negatively associated with knowledge and compliance. Notably, multilayer neural network analysis identified knowledge and perceived barriers as the strongest predictors of nurses' compliance.ConclusionThe current findings emphasize the need for multifaceted strategies to implement the CLABSI prevention guidelines. These strategies should address knowledge gaps, support positive attitudes, and address practical barriers faced by nurses to ensure successful implementation of CLABSI prevention.

Integration Policy Networks and the Potential for Local Integration Policy Diffusion in European Small- and Medium-Sized Towns and Rural Areas: A Comparative Analysis

This article aims to open a debate about the diffusion of local integration policies in European small- and medium-sized towns and rural areas (SMsTRAs), shedding light on local policymakers’ relational environment and key features of policy networks that influence policy diffusion. Elaborating on data collected through 343 interviews across 34 SMsTRAs, analysed through social network analysis and qualitative content analysis, it concludes that the relational environment in SMsTRAs is unfavourable for integration policy diffusion due to specific characteristics of integration policy networks and the growing politicisation of local integration policymaking. Nonetheless, relevant variation is identified across different types of SMsTRAs.

Patent status of biodegradable polymers and identification of new application areas by IPC network analysis

Biodegradable polymers (BDPs), due to their degradability, have great merits for applications in the environmental fields compared to conventional polymers. Furthermore, they are recently receiving renewed attention with the rise of globally issued environmental problems caused by plastic wastes. However, BDPs are still produced and utilized with limited amounts and applications in comparison to conventional plastics. To help expand the application area of BDPs, we mainly focus on the emerging and exotic technologies of BDPs by analyzing International Patent Classification (IPC) co-classification networks constructed from 2,862 patents on BDPs filed with the US Patent and Trademark Office from 2006 to 2022. As a result, by detecting both recently appearing and isolated IPCs, new and relatively unknown application areas of BDPs such as animal trap, polishing composition, rope lubricant, underground structure, ammunition, digital data carrier are identified. Our IPC analysis methodology studied in the field of BDPs would be also useful for researchers and entrepreneurs searching new and emerging applications in various technology areas.

Understanding shoppers’ cross-channel analysis of influencing factors of online and offline channels: Evidence from clothing product

As the online to offline (O2O) retail market develops rapidly, most consumers become O2O shoppers who shop online and offline. However, researchers seldom focus on O2O retailers collaborating online and offline channels to better satisfy consumers' needs. To fill the gap, this study explores the neglected problem from the shoppers’ cross-channel analysis perspective. In this paper, we used text data mining to capture shoppers' online comments on Suning.com, where we selected T-shirts, skirts and trousers as products to be analyzed. After determining the influencing factors, we used social network analysis to construct the two relation networks based on the semantic set of shoppers' comments. This study reveals the relations between the influencing factors of online and offline channels and finds shoppers' cross-channel analysis behavior characteristics by analyzing the density and centrality indicators of the rebuilt relation network. The research results demonstrate that the impacts of the influencing factors on shoppers' purchase intentions are different. Offline product experience, online product comments, online product price, and offline product price significantly influence and control their purchase intention more than the other factors when shoppers cross-channel analyze the influencing factors. Moreover, this study uses the Shapley model to quantitatively analyze the impact of influencing factors on shoppers' purchase intention by building collaborative alliances. The Shapley values confirm that shoppers attach the highest importance to offline product experience, followed by online product comments, online product price and so on. The findings can improve O2O retailers' collaborative marketing strategies.

Modelling land and water based movement corridors in the Western Mediterranean: a least cost path analysis from chalcolithic and early bronze age ivory records

The transportation of Chalcolithic and Early Bronze Age ivory raw materials and artefacts across the Mediterranean has been in the focus of archaeological research for over a century now. However, tracing the flow of ivory has mostly been restricted to traditional theoretical models of raw materials distribution deriving from socio-culturally centred considerations. Environmental conditions, potential transportation networks and dissemination routes have not yet been considered decisive for the spread of ivory raw material from the African shores and the eastern Mediterranean towards the Iberian Peninsula. Implementing computational environmental and archaeological modelling, we present a fully reproducible quantitative approach to estimate potential communication and transportation networks based on environmental covariates. We deploy a Network Analysis model and a predictive model based on Least Cost Path density to propose a potential land- and sea-based movement corridor for the western Mediterranean Basin that could have enabled the cultural spread of resources during the 3rd and 2nd millennia BC. Using the presented model and the open-source data underlying the analyses, distribution patterns of multiple material resources from different chronological subsets or regions can be developed, which will contribute to understanding prehistoric human patterns across the Mediterranean.

Machine learning and experimental analyses identified miRNA expression models associated with metastatic osteosarcoma

Osteosarcoma (OS), as the most common primary bone cancer, has a high invasiveness and metastatic potential, therefore, it has a poor prognosis. This study identified early diagnostic biomarkers using miRNA expression profiles associated with osteosarcoma metastasis. In the first step, we used RNA-seq and online microarray data from osteosarcoma tissues and cell lines to identify differentially expressed miRNAs. Then, using seven feature selection algorithms for ranking, the first-ranked miRNAs were selected as input for five machine learning systems. Using network analysis and machine learning algorithms, we developed new diagnostic models that successfully differentiated metastatic osteosarcoma from non-metastatic samples based on newly discovered miRNA signatures. The results showed that miR-34c-3p and miR-154-3p act as the most promising models in the diagnosis of metastatic osteosarcoma. Validation for this model by RT-qPCR in benign tissue and osteosarcoma biopsies confirmed the lower expression of miR-34c-3p and miR-154-3p in OS samples. In addition, a direct correlation between miR-34c-3p expression, miR-154-3p expression and tumor grade was discovered. The combined values of miR-34c-3p and miR-154-3p showed 90 % diagnostic power (AUC = 0.90) for osteosarcoma samples and 85 % (AUC = 0.85) for metastatic osteosarcoma. Adhesion junction and focal adhesion pathways, as well as epithelial-to-mesenchymal transition (EMT) GO terms, were identified as the most significant KEGG and GO terms for the top miRNAs. The findings of this study highlight the potential use of novel miRNA expression signatures for early detection of metastatic osteosarcoma. These findings may help in determining therapeutic approaches with a quantitative and faster method of metastasis detection and also be used in the development of targeted molecular therapy for this aggressive cancer. Further research is needed to confirm the clinical utility of miR-34c-3p and miR-154-3p as diagnostic biomarkers for metastatic osteosarcoma.

Reaction rebalancing: a novel approach to curating reaction databases

PurposeReaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks. The full potential of these resources can only be realized if datasets are accurate and complete. Missing co-reactants and co-products, i.e., unbalanced reactions, however, are the rule rather than the exception. The curation and correction of such incomplete entries is thus an urgent need.MethodsThe SynRBL framework addresses this issue with a dual-strategy: a rule-based method for non-carbon compounds, using atomic symbols and counts for prediction, alongside a Maximum Common Subgraph (MCS)-based technique for carbon compounds, aimed at aligning reactants and products to infer missing entities.ResultsThe rule-based method exceeded 99% accuracy, while MCS-based accuracy varied from 81.19 to 99.33%, depending on reaction properties. Furthermore, an applicability domain and a machine learning scoring function were devised to quantify prediction confidence. The overall efficacy of this framework was delineated through its success rate and accuracy metrics, which spanned from 89.83 to 99.75% and 90.85 to 99.05%, respectively.ConclusionThe SynRBL framework offers a novel solution for recalibrating chemical reactions, significantly enhancing reaction completeness. With rigorous validation, it achieved groundbreaking accuracy in reaction rebalancing. This sets the stage for future improvement in particular of atom-atom mapping techniques as well as of downstream tasks such as automated synthesis planning.Scientific ContributionSynRBL features a novel computational approach to correcting unbalanced entries in chemical reaction databases. By combining heuristic rules for inferring non-carbon compounds and common subgraph searches to address carbon unbalance, SynRBL successfully addresses most instances of this problem, which affects the majority of data in most large-scale resources. Compared to alternative solutions, SynRBL achieves a dramatic increase in both success rate and accurary, and provides the first freely available open source solution for this problem.

Targeted Isolation of Antiviral Labdane Diterpenes from the Bark of Neo-uvaria foetida (Annonaceae) using LC-MS/MS-Based Molecular Networking.

In the search of new inhibitors for human coronavirus (HCoV), we screened extracts of endemic Annonaceae plants on an assay using a cellular model of Huh-7 cells infected with the human alphacoronavirus HCoV-229E. The EtOAc bark extract of the rare Southeast Asian plant Neo-uvaria foetida exhibited inhibition of HCoV-229E and SARS-CoV-2 viruses with IC50 values of 3.8 and 7.8 μg/mL, respectively. Using LC-MS/MS and molecular networking analysis guided isolation, we discovered two new labdane-type diterpenoids, 8-epi-acuminolide (1) and foetidalabdane A (4), and three known labdane diterpenoids, acuminolide (2), 17-O-acetylacuminolide (3), and spiroacuminolide (5). A new norlabdane diterpene, 16-foetinorlabdoic acid (6), was also isolated and identified. Excluding compounds 5 and 6, all other metabolites were active against the virus HCoV-229E. Terpenoids 1 and 4 presented antiviral activity against SARS-CoV-2 with IC50 values of 63.3 and 93.5 μM, respectively, indicating lower potency. Additionally, virological assays demonstrated that compounds 1, 2, and 3 exert antiviral effects against Zika virus by specifically interfering with the late stage of its infectious cycle with IC50 values of 76.0, 31.9, and 14.9 μM, respectively.

Identification of hub genes associated with pyroptosis in diabetic nephropathy patients using integrated bioinformatic analysis.

To investigate the role of pyroptosis in diabetic nephropathy (DN) and identify potential biomarkers for diagnosis. We analyzed the GEO dataset GSE96804 to identify differentially expressed genes (DEGs) related to pyroptosis in DN. The CIBERSORT method was used to assess M1 macrophage infiltration in the samples. Using weighted gene co-expression network analysis (WGCNA), we identified gene modules associated with M1 macrophages. The least absolute shrinkage and selection operator (LASSO) method was then applied to screen for key genes. The intersection of key genes identified by LASSO and the gene modules obtained from WGCNA resulted in the identification of ten hub genes as potential biomarkers for DN. A total of 366 DEGs were identified, with 310 genes associated with pyroptosis. Increased M1 macrophage infiltration was observed in DN patients. Ten hub genes were identified as potential DN biomarkers: ECM1, LRP2BP, ALKBH7, CDH10, DUSP1, HSPA1A, LPL, NFIL3, PDK4, and TMEM150C. This study highlights the importance of pyroptosis in DN pathophysiology and identifies 10 hub genes as potential biomarkers. These findings may contribute to improved diagnosis and treatment of DN.

Gene expression networks in endothelial cellsfrom failing human hearts.

IntroductionChronic heart failure is associated with adverse remodeling of the heart that is typically characterized by cardiomyocyte hypertrophy. This requires the formation of new capillaries to maintain oxygen supply. Insufficient angiogenesis promotes the transition from compensated hypertrophy into heart failure. The aim of the present study was to identify angiogenesis-related gene networks and corresponding regulatory hubs in endothelial cells from failing human hearts.Methods and resultsWe isolated left ventricular endothelial cells from patients with advanced heart failure undergoing left ventricular assist device surgery (n = 15) and healthy organ donors (n = 2) and performed RNA sequencing. Subgroup analysis revealed no impact of co-morbidities on gene expression. In a weighted coexpression network analysis, we found 26 gene clusters, of which nine clusters showed a significant positive or negative correlation with the presence of heart failure. We identified the transcription factors CASZ1 (castor zinc finger 1), ZNF523 (zinc finger protein 523), and NFE2L1 (nuclear factor erythroid 2-related factor 1) as hub genes of a cluster related to angiogenesis. Knock-down of CASZ1, ZNF523, or NFE2L1 in human umbilical vein endothelial cells led to a downregulation of genes from the respective cluster, including CD34 and platelet derived growth factor β, confirming their regulatory function.ConclusionIn conclusion, we assessed gene networks in endothelial cells and identified transcription factors CASZ1, ZNF532, and NFE2L1 as potential regulators of angiogenesis in failing human hearts. Our study provides insights into the transcriptional regulation of angiogenesis beyond the classical vascular endothelial growth factor signaling pathway.

Contiguous hourly variations and relativeness of polycyclic aromatic hydrocarbons and their chlorinated derivatives in urban PM2.5

The time-resolved analysis of ambient polycyclic aromatic hydrocarbons (PAHs) has been frequently performed in various sites, whereas that of chlorinated PAHs (ClPAHs) is limited. This study performed the time-resolved analysis of PAHs and ClPAHs in hourly PM2.5 spots collected using a tape filter at three stations in urban cities. The hourly concentrations of the total PAHs and ClPAHs exhibited specific variations in each station, attributable to the different sources and origins. According to PAH diagnostic ratio analysis, the hours at which the ClPAH concentrations were elevated showed the contributions of coal, petroleum combustions, and aged particles. The network analysis of the datasets was performed to visualize the association of each compound with the sampling time and station. PAHs were prevalent in all the stations at every time, whereas the impacts of ClPAHs were limited to certain stations and times. This suggests that the ClPAHs in the air could be attributed to specific sources, dissimilar to the ubiquitous sources of PAHs.

Targeted co-expression networks for the study of traits

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks. This method identifies the transcripts that best predict the trait of interest based on gene expression using a refinement of the LASSO regression. Then, it builds the co-expression modules around those transcripts. Algorithm properties were characterized using the expression of 13 brain regions from the Genotype-Tissue Expression project. When comparing our method with WGCNA, TGCN networks lead to more precise modules that have more specific and yet rich biological meaning. Then, we illustrate its applicability by creating an APP-TGCN on The Religious Orders Study and Memory and Aging Project dataset, aiming to identify the molecular pathways specifically associated with APP role in Alzheimer’s disease. Main biological findings were further validated in two independent cohorts. In conclusion, we provide a new framework that serves to create targeted networks that are smaller, biologically relevant and useful in high throughput hypothesis driven research. The TGCN R package is available on Github: https://github.com/aliciagp/TGCN.

The use of a protein network analysis to explore the complexity of early skin inflammation after oronasal mask application- A pilot study

Medical devices, such as non-invasive ventilation masks, save lives in health care settings but can be a cause of tissue injuries due to the pressure and shear loads on skin and soft tissue. These pressure injuries could be painful for the individual and cause a significant economic impact on healthcare providers. In the etiology of device related pressure ulcers, inflammation plays an important role. However, the exact nature and timing of inflammatory biomarker upregulation is still unknown in the early stages of skin damage. This study aimed to explore the inflammatory profile of vulnerable skin sites following non-invasive mask application on a convenience sample of eleven hospital patients. Seventy-one inflammatory proteins were explored from sebum sampled at the skin surface after oronasal mask application. A multivariate analysis to investigate differences between loaded and control site was conducted, with a protein network analysis used to explore interactions in the early inflammation. The study revealed that 21 cytokines and chemokines were important for the separation between loaded and control site. These proteins were associated with remodeling of tissue, vascular wound healing and/or cell death.

Peripheral whole blood microRNA expression in relation to vascular function: a population-based study

BackgroundAs key regulators of gene expression, microRNAs affect many cardiovascular mechanisms and have been associated with several cardiovascular diseases. In this study, we aimed to investigate the relation of whole blood microRNAs with several quantitative measurements of vascular function, and explore their biological role through an integrative microRNA-gene expression analysis.MethodsPeripheral whole blood microRNA expression was assessed through RNA-Seq in 2606 participants (45.8% men, mean age: 53.93, age range: 30 to 95 years) from the Rhineland Study, an ongoing population-based cohort study in Bonn, Germany. Weighted gene co-expression network analysis was used to cluster microRNAs with highly correlated expression levels into 14 modules. Through linear regression models, we investigated the association between each module’s expression and quantitative markers of vascular health, including pulse wave velocity, total arterial compliance index, cardiac index, stroke index, systemic vascular resistance index, reactive skin hyperemia and white matter hyperintensity burden. For each module associated with at least one trait, one or more hub-microRNAs driving the association were defined. Hub-microRNAs were further characterized through mapping to putative target genes followed by gene ontology pathway analysis.ResultsFour modules, represented by hub-microRNAs miR-320 family, miR-378 family, miR-3605-3p, miR-6747-3p, miR-6786-3p, and miR-330-5p, were associated with total arterial compliance index. Importantly, the miR-320 family module was also associated with white matter hyperintensity burden, an effect partially mediated through arterial compliance. Furthermore, hub-microRNA miR-192-5p was related to cardiac index. Functional analysis corroborated the relevance of the identified microRNAs for vascular function by revealing, among others, enrichment for pathways involved in blood vessel morphogenesis and development, angiogenesis, telomere organization and maintenance, and insulin secretion.ConclusionsWe identified several microRNAs robustly associated with cardiovascular function, especially arterial compliance and cardiac output. Moreover, our results highlight miR-320 as a regulator of cerebrovascular damage, partly through modulation of vascular function. As many of these microRNAs were involved in biological processes related to vasculature development and aging, our results contribute to the understanding of vascular physiology and provide putative targets for cardiovascular disease prevention.

Identification and validation of potential genes for the diagnosis of sepsis by bioinformatics and 2-sample Mendelian randomization study.

This integrated study combines bioinformatics, machine learning, and Mendelian randomization (MR) to discover and validate molecular biomarkers for sepsis diagnosis. Methods include differential expression analysis, weighted gene co-expression network analysis (WGCNA) for identifying sepsis-related modules and hub genes, and functional enrichment analyses to explore the roles of hub genes. Machine learning algorithms identify 3 diagnostic genes - CD177, LDHA, and MCEMP1 - consistently highly expressed in sepsis patients. The nomogram model effectively predicts sepsis risk, supported by receiver operator characteristic (ROC) curves. Correlations between diagnostic genes and immune cell infiltration are observed. MR analysis reveals a positive causal relationship between MCEMP1 and sepsis risk. In conclusion, this study presents potential sepsis diagnostic biomarkers, highlighting the genetic association of MCEMP1 with sepsis for insights into early diagnosis.

Shared patterns of glial transcriptional dysregulation link Huntington's disease and schizophrenia.

Huntington's disease and juvenile-onset schizophrenia have long been regarded as distinct disorders. However, both manifest cell-intrinsic abnormalities in glial differentiation, with resultant astrocytic dysfunction and hypomyelination. To assess whether a common mechanism might underlie the similar glial pathology of these otherwise disparate conditions, we used comparative correlation network approaches to analyse RNA-sequencing data from human glial progenitor cells (hGPCs) produced from disease-derived pluripotent stem cells. We identified gene sets preserved between Huntington's disease and schizophrenia hGPCs yet distinct from normal controls that included 174 highly connected genes in the shared disease-associated network, focusing on genes involved in synaptic signalling. These synaptic genes were largely suppressed in both schizophrenia and Huntington's disease hGPCs, and gene regulatory network analysis identified a core set of upstream regulators of this network, of which OLIG2 and TCF7L2 were prominent. Among their downstream targets, ADGRL3, a modulator of glutamatergic synapses, was notably suppressed in both schizophrenia and Huntington's disease hGPCs. Chromatin immunoprecipitation sequencing confirmed that OLIG2 and TCF7L2 each bound to the regulatory region of ADGRL3, whose expression was then rescued by lentiviral overexpression of these transcription factors. These data suggest that the disease-associated suppression of OLIG2 and TCF7L2-dependent transcription of glutamate signalling regulators may impair glial receptivity to neuronal glutamate. The consequent loss of activity-dependent mobilization of hGPCs may yield deficient oligodendrocyte production, and hence the hypomyelination noted in these disorders, as well as the disrupted astrocytic differentiation and attendant synaptic dysfunction associated with each. Together, these data highlight the importance of convergent glial molecular pathology in both the pathogenesis and phenotypic similarities of two otherwise unrelated disorders, Huntington's disease and schizophrenia.

Difference in microbial community structure along a gradient of crater altitude: insights from the Nushan volcano.

The variation in the soil microbial community along the altitude gradient has been widely documented. However, the structure and function of the microbial communities distributed along the altitude gradient in the crater still need to be determined. We gathered soil specimens from different elevations within the Nushan volcano crater to bridge this knowledge gap. We investigated the microbial communities of bacteria and fungi in the soil. It is noteworthy that the microbial alpha diversity peaks in the middle of the crater. However, network analysis shows that bacterial (nodes 760 vs 613 vs 601) and fungal (nodes 328 vs 224 vs 400) communities are most stable at the bottom and top of the crater, respectively. Furthermore, the soil microbial network exhibited a decline, followed by an increase across varying altitudes. The core microorganisms displayed the highest correlation with pH and alkaline phosphatase (AP, as determined through redundancy analysis (RDA) and Mantel tests for correlation analysis. The fungal community has a higher number of core microorganisms, while the bacterial core microorganisms demonstrate greater susceptibility to environmental factors. In conclusion, we utilized Illumina sequencing techniques to assess the disparities in the structure and function of bacteria and fungi in the soil.IMPORTANCEThese findings serve as a foundation for future investigations on microbial communities present in volcanic soil.