Correlation Network Research Articles

Chlormequat inhibits Vallisneria natans growth and shapes the epiphytic biofilm microbial community.

Submerged macrophytes can overgrow and negatively affect freshwater ecosystems. This study aimed to investigate the use of chlormequat (CQ) to regulate submerged Vallisneria natans growth as well as its impact on the microbial community of epiphytic biofilms. V.natans height under CQ dosages of 20, 100, and 200 mg/L decreased within 21 days by 12.57%, 30.07%, and 44.62%, respectively, while chlorophyll content increased by 1.94%, 20.39%, and 38.83%. At 100 mg/L, CQ reduced the diversity of bacteria in the biofilm attached to V.natans leaves but increased the diversity of the eukaryotic microbial community. CQ strongly inhibited Cyanobacteria; compared with the control group, the treatment group experienced a significant reduction from 36.54% to 2.61%. Treatment significantly inhibited Gastrotricha and Rotifera, two dominant phyla of eukaryotes in the leaf biofilm, reducing their relative abundances by 17.41% and 6.48%, respectively. CQ significantly changed the leaf biofilm microbial community correlation network. The treatment group exhibited lower modularity (2.012) compared with the control group (2.249); however, the central network of the treated group contained a higher number of microbial genera (13) than the control group (4), highlighting the significance of eukaryotic genera in the network. The results obtained from this study provide invaluable scientific context and technical understanding pertinent to the restoration of submerged macrophytes within aquatic ecosystems. PRACTITIONER POINTS: Chlormequat reduced the plant height but increased leaf chlorophyll content. Chlormequat reduced biofilm bacterial diversity but increased eukaryotic diversity. Chlormequat affected the bacterial-fungal association networks in biofilms.

Enhancing Understanding of Eating Psychopathology: A Network Analysis Study

Compassion Focused Therapy for eating disorders (CFT-E) has been shown to be effective in treating eating psychopathology. Exploring which processes identified by the CFT-E model are most linked with eating disorder symptoms could help develop targeted treatments. However, the relationship between these variables requires further investigation. This study explores the interconnections between eating disorder symptoms and processes identified by the CFT-E using a network analysis approach. Participants were 497 individuals (77.3% female) from a community sample who completed measures to assess body dissatisfaction, shape and weight overvaluation, cognitive restraint, bingeing, emotional eating, purging, self-compassion, social comparison through appearance, self-criticism, appearance-related shame, guilt and pride, and positive and negative affect. A regularized partial correlation network was estimated. Appearance-related shame, inadequate self and body dissatisfaction emerged as the central nodes in the network. These findings support the use of interventions targeting shame and self-criticism for the treatment of eating disorder symptoms. Studies with diverse samples are needed to further examine the interconnections between eating psychopathology and the key processes identified by the CFT-E theoretical model.

Exploring synergistic effects of Achyranthes bidentata Blume and Paeonia lactiflora Pall. on hypertension with liver yang hyperactivity using the multidisciplinary integrative strategy

Traditional Chinese Medicine (TCM) formulations serve as a multi-component pharmacological combination therapy with various potential targets and have collected extensive knowledge regarding the in vivo efficacy of treating cardiovascular disorders in clinical practice for thousands of years. However, the obscurity of the chemicals and the molecular mechanisms are impediments to their continued growth and globalization. Therefore, new modern medications based on the combination of beneficial TCM components with precise clinical efficacy are required. The goal of this study was to find the best combination of Achyranthes bidentata Blume (AB) and Paeonia lactiflora Pall. (PL) for hypertension with liver yang hyperactivity (HLYH). The integrated research consisting of principal component analysis (PCA), metabolomics, microbiology, and weighted correlation network analysis (WGCNA) were used to find the optimal combination of AB-PL combinations and reveal the mechanism of action. The result showed AB-PL (2:3) had a substantial protective impact on HLYH, as shown by lower blood pressure, improved liver yang hyperactivity, reduced cardiac remodeling and malondialdehyde (MDA), and increased NO content. Furthermore, the essential elements for AB-PL reducing hypertension may be related to 135 metabolites and 23 microorganisms. In conclusion, our findings support the efficacy of herbal remedies in the treatment of hypertension and provide some pharmacological evidence for the ongoing development of novel modern Chinese drugs for cardiovascular disorders.

Nutrient removal efficacy and microbial dynamics in constructed wetlands using Fe(III)-mineral substrates for low carbon-nitrogen ratio sewage treatment.

This study evaluated the roles of two common sources of Fe(III)-minerals-volcanic rock (VR) and synthetic banded iron formations from waste iron tailings (BIF-W)-in vertical flow-constructed wetlands (VFCWs). The evaluation was conducted in the absence of critical environmental factors, including Fe(II), Fe(III), and soil organic matter (SOM), using metagenomic analysis and integrated correlation networks to predict nitrogen removal pathways. Our findings revealed that Fe(III)-minerals enhanced metabolic activities and cellular processes related to carbohydrate decomposition, thereby increasing the average COD removal rates by 10.7% for VR and 5.90% for BIF-W. Notably, VR improved nitrogen removal by 1.70% and 5.40% compared to BIF-W and the control, respectively. Fe(III)-mineral amendment in bioreactors also improved the retention of denitrification and nitrification bacteria (phylum Proteobacteria) and anammox bacteria (phylum Planctomycetes), with increases of 3.60% and 3.20% using VR compared to BIF-W. Metagenomic functional predictionindicated that the nitrogen removal mechanisms in VFCWs with low C/N ratios involve simultaneous partial nitrification, ANAMMOX, and denitrification (SNAD). Network-based analyses and correlation pathways further suggest that the advantages of Fe(III)-minerals are manifested in the enhancement of denitrification microorganisms. Microbial communities may be activated by the functional dissolution of Fe(III)-minerals, which improves the stability of SOM or the conversion of Fe(III)/Fe(II). This study provides new insights into the functional roles of Fe(III)-minerals in VFCWs at the microbial community level, and provides a foundation for developing Fe-based SNAD enhancement technologies.

Effects of volcanic environment on Setaria viridis rhizospheric soil microbial keystone taxa and ecosystem multifunctionality

Keystone taxa are significant within ecosystem multifunctionality, as certain species fulfil essential functions such as recycling soil nutrients, promoting plant growth, influencing biogeochemical processes, and contributing to human health maintenance. However, there are still gaps regarding the relationship between microbial communities in volcanic rhizospheric soil and ecosystem multifunctionality. As a result, in this research, we employed Illumina MiSeq high-throughput sequencing to analyse the microbial community composition of rhizospheric soil from volcanic S. viridis. Compared with non-volcanic areas, volcanic soils have higher fungal alpha diversity and the absolute abundance of bacteria (16S gene copies) showed significant variation between the two successions (P < 0.0001). The network analysis further demonstrated that the microbial diversity in non-volcanic regions surpassed that of the volcanic area. The volcanic fungi network has more nodes and edges, is more complex than non-volcanic areas (Nodes: 425 vs. 770; Edges: 21844 vs. 74532), and more rhizosphere growth-promoting bacteria are enriched. Regression analysis and correlation networks showed that fungal communities were more closely associated with ecosystem multifunctionality than bacteria. This study lays the groundwork for examining the microbial keystone taxa in the rhizosphere of volcanic plants and offers valuable insights into the multifaceted functions of plant rhizospheric soil ecosystems.

Recognition of differently expressed genes and DNA methylation markers in patients with Lupus nephritis

Abstract Background and Objectives Systemic lupus erythematosus (SLE) is distinguished by dysregulated immune system activity, resulting in a spectrum of clinical manifestations, with lupus nephritis being particularly prominent. This study endeavors to discern novel targets as potential therapeutic markers for this condition. Methods Weighted correlation network analysis (WGCNA) was used to construct the network and select the key hub genes in the co-expression module based on the gene expression dataset GSE81622. Subsequently, functional enrichment and pathway analysis were performed for SLE and lupus nephritis. In addition, also identify genes and differences in SLE with lupus nephritis and methylation site. Finally, qRT-PCR and western blot were used to verify the up-regulated expression levels of the selected key genes. Results Within the co-expression modules constructed by WGCNA, the MElightcyan module exhibited the strongest positive correlation with lupus nephritis (0.4, P = 0.003), while showing a weaker correlation with the control group SLE (0.058) and a negative correlation with the control group (-0.41, P = 0.002). Additionally, the MEgreenyellow module displayed the highest positive correlation with SLE (0.25), but its P value was 0.06, which did not reach statistical significance(P &gt; 0.05). Furthermore, it had a negative correlation with the control group was (-0.38, P = 0.004). The module associated with lupus nephritis was characterized by processes such as neutrophil activation (neutrophil_activation), neutrophil degranulation (neutrophil_degranulation), neutrophil activation involved in immune response (neutrophil_activation_involved_in_immune_response), neutrophils mediated immune (neutrophil_mediated_immunity) and white blood cells degranulation (leukocyte_degranulation) and so on the adjustment of the process. Secondly, in the analysis of SLE samples, the identification of differentially expressed genes revealed 125 genes, with 49 being up-regulated and 76 down-regulated. In the case of lupus nephritis samples, 156 differentially expressed genes were discerned, include in 70 up-regulated and 86 down-regulated genes. When examining differential methylation sites, we observed 12432 such sites in the SLE sample analysis, encompassing 2260 hypermethylation sites and 10172 hypomethylation sites. In the lupus nephritis samples analysis, 9613 differential methylation sites were identified, comprising 4542 hypermethylation sites and 5071 hypomethylation sites. Substantiating our findings, experimental validation of the up-regulated genes in lupus nephritis confirmed increased levels of gene expression and protein expression for CEACAM1 and SLC2A5. Conclusions We have identified several genes, notably CEACAM1 and SLC2A5, as potential markers for lupus nephritis. Their elevated expression levels and reduced DNA methylation in lupus nephritis contribute to a more comprehensive understanding of the aberrant epigenetic regulation of expression in this condition. These findings hold significant implications for the diagnosis and therapeutic strategies of lupus nephritis.

At the core of salinity: Divergent transcriptomic responses to neutral and alkaline salinity in Arabidopsis thaliana

In the context of current climate change, alkaline salinity is increasingly challenging crop yields, especially in arid and semiarid regions. Alkaline salinity is more detrimental to plant performance than neutral salinity and tolerance to neutral salinity may not confer tolerance to alkaline salinity. The mechanisms behind are still poorly understood. This study aims to identify physiological and genetic traits underlying this differential tolerance to neutral and alkaline salinity by exploiting the variation present in natural populations (demes) of Arabidopsis thaliana. Growth, photosynthesis, phytohormone and mineral nutrient profiles, plant water status and transcriptomic changes were analyzed in four demes with contrasting tolerance to neutral and alkaline salinity. Results of this novel holistic approach suggest low internal Fe use efficiency caused by bicarbonate as a driver of enhanced sensitivity to alkaline salinity in plants adapted to neutral salinity prompting photosynthesis inhibition and alteration of the plant’s carbon budget for primary and secondary metabolism. Moreover, alkaline salinity specifically altered the auxin and jasmonic acid signaling pathways, while sustained ABA biosynthesis was an adaptive trait under neutral salinity. Exploring the genes with non-shared expression trends between salinity types, we identified sequence variation at the BGAL4 locus associated with advantageous responses to each type of salinity. Weighted correlation network analysis (WGCNA) validated the significant involvement of gene co-expression modules targeted by the enrichment analyses, highlighting the hubs correlated with favorable responses to both salinity types. Overall, the present study points out the complex physiological and genetic mechanisms responsible for plant tolerance to alkaline salinity and proposes target genes for breeding strategies under alkaline saline soils.

CIRCULATING HEPARAN SULFATE PROFILES IN PEDIATRIC ACUTE RESPIRATORY DISTRESS SYNDROME.

Introduction: Sepsis-induced degradation of endothelial glycocalyx heparan sulfate (HS) contributes to the pulmonary microvascular endothelial injury characteristic of acute respiratory distress syndrome (ARDS) pathogenesis. Our objectives were to (1) examine relationships between plasma indices of HS degradation and protein biomarkers of endothelial injury and (2) identify patient subgroups characterized by distinct profiles of HS degradation in children with ARDS. Methods: We analyzed prospectively collected plasma (2018-2020) from a cohort of invasively mechanically ventilated children (aged >1 month to <18 years) with ARDS. Mass spectrometry characterized and quantified patterns of HS disaccharide sulfation. Protein biomarkers reflective of endothelial injury (e.g., angiopoietin-2, vascular cell adhesion molecule-1, soluble thrombomodulin) were measured with a multiplex immunoassay. Pearson correlation coefficients were used to construct a biomarker correlation network. Centrality metrics detected influential biomarkers (i.e., network hubs). K-means clustering identified unique patient subgroups based on HS disaccharide profiles. Results: We evaluated 36 patients with pediatric ARDS. HS disaccharide sulfation patterns, 6S, NS, and NS2S, positively correlated with all biomarkers of endothelial injury (all P < 0.05) and were classified as network hubs. We identified three patient subgroups, with cluster 3 (n = 5) demonstrating elevated levels of 6S and N-sulfated HS disaccharides. In cluster 3, 60% of children were female and nonpulmonary sepsis accounted for 60% of cases. Relative to cluster 1 (n = 12), cluster 3 was associated with higher oxygen saturation index (P = 0.029) and fewer 28-day ventilator-free days (P = 0.016). Conclusions: Circulating highly sulfated HS fragments may represent emerging mechanistic biomarkers of endothelial injury and disease severity in pediatric ARDS.

Symptom trajectories in infancy for the prediction of subsequent wheeze and asthma in the BILD and PASTURE cohorts: a dynamic network analysis

Host and environment early-life risk factors are associated with progression of wheezing symptoms over time; however, their individual contribution is relatively small. We hypothesised that the dynamic interactions of these factors with an infant's developing respiratory system are the dominant factor for subsequent wheeze and asthma. In this dynamic network analysis we used data from term healthy infants from the Basel-Bern Infant Lung Development (BILD) cohort (435 neonates aged 0-4 weeks recruited in Switzerland between Jan 1, 1999, and Dec 31, 2012) and replicated the findings in the Protection Against Allergy Study in Rural Environments (PASTURE) cohort (498 infants aged 0-12 months recruited in Germany, Switzerland, Austria, France, and Finland between Jan 1, 2002, and Oct 31, 2006). BILD exclusion criteria for the current study were prematurity (<37 weeks), major birth defects, perinatal disease of the neonate, and incomplete follow-up period. PASTURE exclusion criteria were women younger than 18 years, a multiple pregnancy, the sibling of a child was already included in the study, the family intended to move away from the area where the study was conducted, and the family had no telephone connection. Outcome groups were subsequent wheeze, asthma, and healthy. The first outcome was defined as ever wheezed between the age of 2 years and 6 years. Week-by-week correlations of the determining factors with cumulative symptom scores (CSS) were calculated from weeks 2 to 52 (BILD) and weeks 8 to 52 (PASTURE). The complex dynamic interaction between the determining factors and the CSS was assessed via dynamic host-environment correlation network, quantified by a simple descriptor: trajectory function G(t). Wheeze outcomes at age 2-6 years were compared in 335 infants from BILD and 437 infants from PASTURE, and asthma outcomes were analysed at age 6 years in a merged cohort of 783 infants. CSS was significantly different for wheeze and asthma outcomes and became increasingly important during infancy in direct comparison with all determining factors. Weekly symptoms were tracked for groups of infants, showing a non-linear increase with time. Using logistic regression classification, G(t) distinguished between the healthy group and wheeze or asthma groups (area under the curve>0·97, p<0·0001; sensitivity analysis confirmed significant CSS association with wheeze [BILD p=0·0002 and PASTURE p=0·068]) and G(t) was also able to distinguish between the farming and non-farming exposure groups (p<0·0001). Similarly to other risk factors, CSS had weak sensitivity and specificity to identify risks at the individual level. At group level however, the dynamic host-environment correlation network properties (G(t)) showed excellent discriminative ability for identifying groups of infants with subsequent wheeze and asthma. Results from this study are consistent with the 2018 Lancet Commission on asthma, which emphasised the importance of dynamic interactions between risk factors during development and not the risk factors per se. The Swiss National Science Foundation, the Kühne Foundation, the EFRAIM study EU research grant, the FORALLVENT study EU research grant, and the Leibniz Prize.

Network Analyses Applied to the Dimensions of Cancer-Related Fatigue in Women With Breast Cancer.

Understanding cancer symptom cluster through network analyses is a new approach in oncology, revealing interconnected and influential relationships among reported symptoms. We aimed to assess these relationships using network analysis in posttreatment breast cancer patients, focusing on the five dimensions of cancer-related fatigue (CRF), and on other common difficulties encountered by oncological patients (i.e., pain, anxiety, depression, sleep difficulties, cognitive impairments, and emotion regulation and mental adaptation difficulties). This study involved a complementary analysis of data from two interventional studies. Participants completed questionnaires before and after the intervention, with baseline scores being used in this article. Partial correlation network analysis modeled the relationships between symptoms in five distinct networks, each of them including one specific dimension of CRF. The core symptom in each network was identified based on the highest centrality indices. Depression emerged as the core symptom in all networks, strongly associated with all fatigue dimensions (partial correlations ranging from 0.183 to 0.269) except mental fatigue. These findings indicate robust connections between symptoms, as variations in depression scores directly or indirectly influence fatigue and other symptoms within the cluster. Our results support the multidimensional aspect of CRF, and its links with other common symptoms. To effectively reduce patient CRF, interventions should address not only fatigue but also the closely related symptoms from the cluster, such as depression, given its centrality in the cluster. ClinicalTrials.gov (NCT03144154 and NCT04873661). Retrospectively registered on May 1, 2017 and April 29, 2021, respectively.

The role of SEC14L4 in esophageal squamous cell cancer: insights into clinical relevance and molecular pathways.

Esophageal squamous cell cancer (ESCC) is the most common type of esophageal cancer. This study aimed to elucidate the role of Saccharomyces cerevisiae-like 4 (SEC14L4) in ESCC. To elucidate the role of SEC14L4 in ESCC, this study analyzed the clinical data, gene sequencing data, and other relevant data retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information. The methodology involved several analytical approaches, including nomogram model analysis, co-expression analysis, gene set enrichment and variation analysis, weighted correlation network analysis, drug susceptibility analysis, and single-cell analysis. These methods were employed to evaluate the significance of SEC14L4 in ESCC. The expression of SEC14L4 was evaluated via quantitative real-time polymerase chain reaction (qRT-PCR). SEC14L4 expression (P<0.001) was significantly elevated in those with ESCC, especially in patients with locally advanced disease (P=0.005), and indicated a poor prognosis (P=0.045). Findings from the nomogram model analysis identified the contribution of clinical indicators to survival prediction with good efficacy. Subsequently, the single-nucleotide polymorphisms and co-expressed genes of SEC14L4 were identified. Furthermore, pathways associated with SEC14L4, including DNA metabolic process, transcription factor binding, apoptosis, and others, were examined. Notably, SEC14L4 expression was predominantly observed in monocytes. Drug sensitivity analysis indicated the association of SEC14L4 expression with sensitivity of ESCC to the common chemotherapy drugs AICAR, BMS.708163, GNF.2, Nutlin.3a, PD.0325901, and RDEA119. Verification of the high expression of SEC14L4 in KYSE520 and KYSE150 was conducted, thereby confirming the study's findings. High expression of SEC14L4 is associated with poorer clinical outcomes, highlighting its potential as a therapeutic target and suggesting its involvement in the molecular mechanisms underlying ESCC.

Cardiac Molecular Analysis Reveals Aging-Associated Metabolic Alterations Promoting Glycosaminoglycans Accumulation via Hexosamine Biosynthetic Pathway.

Age is a prominent risk factor for cardiometabolic disease, often leading to heart structural and functional changes. However, precise molecular mechanisms underlying cardiac remodeling and dysfunction exclusively resulting from physiological aging remain elusive. Previous research demonstrated age-related functional alterations in baboons, analogous to humans. The goal of this study is to identify early cardiac molecular alterations preceding functional adaptations, shedding light on the regulation of age-associated changes. Unbiased transcriptomics of left ventricle samples are performed from female baboons aged 7.5-22.1 years (human equivalent ≈30-88 years). Weighted-gene correlation network and pathway enrichment analyses are performed, with histological validation. Modules of transcripts negatively correlated with age implicated declined metabolism-oxidative phosphorylation, tricarboxylic acid cycle, glycolysis, and fatty-acid β-oxidation. Transcripts positively correlated with age suggested a metabolic shift toward glucose-dependent anabolic pathways, including hexosamine biosynthetic pathway (HBP). This shift is associated with increased glycosaminoglycan synthesis, modification, precursor synthesis via HBP, and extracellular matrix accumulation, verified histologically. Upregulated extracellular matrix-induced signaling coincided with glycosaminoglycan accumulation, followed by cardiac hypertrophy-related pathways. Overall, these findings revealed a transcriptional shift in metabolism favoring glycosaminoglycan accumulation through HBP before cardiac hypertrophy. Unveiling this metabolic shift provides potential targets for age-related cardiac diseases, offering novel insights into early age-related mechanisms.

Screening lncRNAs essential for cardiomyocyte proliferation by integrative profiling of lncRNAs and mRNAs associated with heart development

BackgroundThe proliferation potential of mammalian cardiomyocytes declines markedly shortly after birth. Both long non-coding RNAs (lncRNAs) and mRNAs demonstrate altered expression patterns during cardiac development. However, the role of lncRNAs in the cell cycle arrest of cardiomyocytes remains inadequately understood. MethodThe expression pattern of lncRNAs and mRNAs was analyzed in mouse hearts exhibiting varying regenerative potentials on postnatal days (P) 1, 7, and 28. Weighted correlation network analysis (WGCNA) was employed to elucidate the co-expression relationship between lncRNAs and mRNAs. Protein-protein interaction (PPI) network was built using the STRING database, and hub lncRNAs were identified by CytoHubba. Molecular Complex Detection (MCODE) was used to screen core modules of the PPI network in Cytoscape. Upstream lncRNAs and miRNAs which may regulate mRNAs were predicted using miRTarBase and AnnoLnc2, respectively. Myocardial infarction (MI) was induced by ligation of the left anterior descending coronary artery. ResultsCompared with the P1 heart, 618 mRNAs and 414 lncRNAs displayed.transcriptional changes in the P7 heart, while 2358 mRNAs and 1290 lncRNAs showed from P7 to P28. Gene Ontology (GO) analysis revealed that module 1 in the both comparisons was enriched in the mitotic cell cycle process. 2810408I11Rik and 2010110K18Rik were identified as hub lncRNAs and their effects on the proliferation of cardiomyocytes were verified in vitro. Additionally, four lncRNA-miRNA-mRNA regulatory axes were predicted to explain the mechanism by which 2810408I11Rik and 2010110K18Rik regulate cardiomyocyte proliferation. Notably, the overexpression of 2810408I11Rik enhances cardiomyocyte proliferation and heart regeneration in the adult heart following MI. ConclusionThis study systematically analyzed the landscape of lncRNAs and mRNAs at P1, P7, and P28. These findings may enhance our understanding of the framework for heart development and could have significant implications for heart regeneration.

Metabolomic Alterations Associated with Phthalate Exposures among Pregnant Women in Puerto Rico.

Although phthalate exposure has been linked with multiple adverse pregnancy outcomes, their underlying biological mechanisms are not fully understood. We examined associations between biomarkers of phthalate exposures and metabolic alterations using untargeted metabolomics in 99 pregnant women and 86 newborns [mean (SD) gestational age = 39.5 (1.5) weeks] in the PROTECT cohort. Maternal urinary phthalate metabolites were quantified using isotope dilution high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS), while metabolic profiles in maternal and cord blood plasma were characterized via reversed-phase LC-MS. Multivariable linear regression was used in metabolome-wide association studies (MWAS) to identify individual metabolic features associated with elevated phthalate levels, while clustering and correlation network analyses were used to discern the interconnectedness of biologically relevant features. In the MWAS adjusted for maternal age and prepregnancy BMI, we observed significant associations between specific phthalates, namely, di(2-ethylhexyl) phthalate (DEHP) and mono(3-carboxypropyl) phthalate (MCPP), and 34 maternal plasma metabolic features. These associations predominantly included upregulation of fatty acids, amino acids, purines, or their derivatives and downregulation of ceramides and sphingomyelins. In contrast, fewer significant associations were observed with metabolic features in cord blood. Correlation network analysis highlighted the overlap of features associated with phthalates and those identified as differentiating markers for preterm birth in a previous study. Overall, our findings underscore the complex impact of phthalate exposures on maternal and fetal metabolism, highlighting metabolomics as a tool for understanding associated biological processes. Future research should focus on expanding the sample size, exploring the effects of phthalate mixtures, and validating identified metabolic features in larger, more diverse populations.

Research of multi-label text classification based on label attention and correlation networks.

Multi-Label Text Classification (MLTC) is a crucial task in natural language processing. Compared to single-label text classification, MLTC is more challenging due to its vast collection of labels which include extracting local semantic information, learning label correlations, and solving label data imbalance problems. This paper proposes a model of Label Attention and Correlation Networks (LACN) to address the challenges of classifying multi-label text and enhance classification performance. The proposed model employs the label attention mechanism for a more discriminative text representation and uses the correlation network based on label distribution to enhance the classification results. Also, a weight factor based on the number of samples and a modulation function based on prediction probability are combined to alleviate the label data imbalance effectively. Extensive experiments are conducted on the widely-used conventional datasets AAPD and RCV1-v2, and extreme datasets EUR-LEX and AmazonCat-13K. The results indicate that the proposed model can be used to deal with extreme multi-label data and achieve optimal or suboptimal results versus state-of-the-art methods. For the AAPD dataset, compared with the suboptimal method, it outperforms the second-best method by 2.05% ∼ 5.07% in precision@k and by 2.10% ∼ 3.24% in NDCG@k for k = 1, 3, 5. The superior outcomes demonstrate the effectiveness of LACN and its competitiveness in dealing with MLTC tasks.

Higher Microbial Abundance and Diversity in Bronchus-Associated Lymphoid Tissue (BALT) Lymphomas than in Non-Cancerous Lung Tissues.

It is well known that the majority of the extranodal marginal zone lymphomas of mucosa-associated lymphoid tissues (MALT lymphomas) are associated with microbiota, e.g., gastric MALT lymphoma with Helicobacter pylori. In general, they are very sensitive to low-dose radiotherapy and chemotherapeutic agents. The microbiota profile is not clearly elucidated in bronchus-associated lymphoid tissue (BALT) lymphoma, a rare type of MALT lymphoma in the lung. Thus, this study aimed to clarify the intratumor microbiome in BALT lymphoma using the third-generation NGS method. DNAs were extracted from 12 formalin-fixed paraffin-embedded (FFPE) tumor tissues obtained from BALT lymphoma patients diagnosed between 1990 and 2016. 16S rRNA gene was amplified by polymerase chain reaction. Amplicons were sequenced using a Nanopore platform. Next-generation sequencing analysis was performed to assess microbial profiles. For comparison, FFPE specimens from nine non-cancerous lung tissues were also analyzed. Specific bacterial families including Burkholderiaceae, Bacillaceae, and Microbacteriaceae were associated with BALT lymphoma by a linear discriminant analysis effect size approach. Although the number of specimens was limited, BALT lymphomas exhibited significantly higher microbial abundance and diversity with distinct microbial composition patterns and correlation networks than non-cancerous lung tissues. This study provides the first insight into intratumor microbiome in BALT lymphoma using the third-generation NGS method. A distinct microbial composition suggests the presence of a unique tumor microenvironment of BALT lymphoma.

A host immune-related LncRNA and mRNA signature as a discriminant classifier for bacterial from non-bacterial sepsis in children

BackgroundThe variations in non-coding RNA alterations and the host immune response between patients with bacterial and non-bacterial sepsis, along with their clinical characteristics, are largely unknown. MethodsThe landscape of long non-coding RNA (lncRNA) and mRNA in whole blood cells from pediatric patients with bacterial sepsis or non-bacterial sepsis were characterized using an Arraystar human LncRNA microarray. Weighted correlation network analysis (WGCNA) were conducted to identify immune-related LncRNA-mRNA signatures. Least absolute shrinkage and selection operator (Lasso) regression and Ridge regression analysis were employed to develop a specific LncRNA-mRNA signature, serving as a discriminant classifier for bacterial and non-bacterial sepsis in children. ResultsA total of 33 differentially expressed lncRNAs and 52 mRNAs were identified in pediatric patients with either bacterial sepsis or non-bacterial sepsis. Among these, 69 lncRNAs and mRNAs were pinpointed using WGCNA and found to be significantly correlated with clinical parameters. Further intersection analysis identified 12 lncRNAs and 16 mRNAs as immune-related signature for discerning bacterial infections in children with sepsis. Additionaly, the lncRNA-mRNA co-expression network highlighted the key lncRNAs (AC090159.1 and AC080129.2) and mRNAs (S100A8 and TCF7L2) as an infection score model. Lasso regression analysis revealed that this infection score model achieved an area under the received operating curve (AUROC) of 0.96 in the training set and 0.86 in the validation set. Ultimately, the expression levels of these 4 key lncRNAs and mRNAs showed significant correlation with CRP or PCT levels. ConclusionThe machine learning model, developed utilizing key lncRNAs (AC090159.1 and AC080129.2) and mRNAs (S100A8 and TCF7L2), demonstrates robust discrimination and calibration capabilities for distinguishing between bacterial and non-bacterial sepsis in children.

Exploring a new signature for lung adenocarcinoma: analyzing cuproptosis-related genes through Integrated single-cell and bulk RNA sequencing.

Lung adenocarcinoma (LUAD) continues to pose a significant global health challenge. This research investigates cuproptosis and its association with LUAD progression. Employing various bioinformatics techniques, the study explores the heterogeneity of LUAD cells, identifies prognostic cuproptosis-related genes (CRGs), examines cell-to-cell communication networks, and assesses their functional roles. We downloaded single-cell RNA sequencing data from TISCH2 and bulk RNA sequencing data from TCGA for exploring LUAD cell heterogeneity. Subsequently, "CellChat" package was employed for intercellular communication network analysis, while weighted correlation network analysis was applied for identification of hub CRGs. Further, A cuproptosis related prognostic signature was constructed via LASSO regression, validated through survival analysis, nomogram development, and ROC curves. We assessed immune infiltration, gene mutations, and GSEA of prognostic CRGs. Finally, in vitro experiments were applied to validate CDC25C's role in LUAD. We identified 15 clusters and nine cell type in LUAD. Malignant cells showed active communication and pathway enrichment in "oxidative phosphorylation" and "glycolysis". Meanwhile, prognostic hub CRGs including PFKP, CDC25C, F12, SIGLEC6, and NLRP7 were identified, with a robust prognostic signature. Immune infiltration, gene mutations, and functional enrichment correlated with prognostic CRGs. In vitro cell experiments have shown that CDC25C-deficient LUAD cell lines exhibited reduced activity. This research reveals the heterogeneity of LUAD cells, identifies key prognostic CRGs, and maps intercellular communication networks, providing insights into LUAD pathogenesis. These findings pave the way for developing targeted therapies and precision medicine approaches.

Bioinformatics Identification and Validation of Angiogenesis-Related Genes in Myocardial Ischemic Reperfusion Injury.

Angiogenesis plays a critical protective role in myocardial ischemia-reperfusion injury (MIRI); however, therapeutic targeting of associated genes remains constrained. To bridge this gap, we conducted bioinformatics analysis to identify pivotal angiogenesis-related genes in MIRI, potentially applicable for preventive and therapeutic interventions. We collected two mouse heart I/R expression datasets (GSE61592 and GSE83472) from Gene Expression Omnibus, utilizing the Limma package to identify differentially expressed genes (DEGs). Angiogenesis-related genes (ARGs) were extracted from GeneCards, and their overlap with DEGs produced differentially expressed ARGs (ARDEGs). Further analyses included Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and disease ontology to explore biological functions. Weighted gene correlation network analysis (WGCNA) was used to investigate molecular modules linked to MIRI. Additionally, a protein-protein interaction (PPI) network was constructed to pinpoint hub genes relevant to MIRI. Receiver operating characteristic curves were used to assess the diagnostic efficacy of these hub genes for MIRI. An ischemia-reperfusion injury model was established using human cardiac microvascular endothelial cells (HCMECs), with the expression of hub genes validated within this experimental framework. We identified 47 ARDEGs, 41 upregulated and 6 downregulated. PPI network analysis revealed suppressor of cytokine signaling 3 (Socs3), C-X-C motif chemokine ligand 1 (Cxcl1), interleukin 1 beta (Il1b), and matrix metallopeptidase 9 (Mmp9) as hub genes. Receiver operating characteristic (ROC) curve analysis demonstrated strong diagnostic potential for Socs3, Cxcl1, Il1b, and Mmp9. In vitro validation corroborated the mRNA and protein expression predictions. Our study highlights the pivotal role of Socs3, Cxcl1, Il1b, and Mmp9 in MIRI development, their significance in immune cell infiltration, and their diagnostic accuracy. These findings offer valuable insights for MIRI diagnosis and treatment, presenting potential molecular targets for future research.

Identification of Key Immune and Cell Cycle Modules and Prognostic Genes for Glioma Patients through Transcriptome Analysis.

Gliomas, the most prevalent type of primary brain tumor, stand out as one of the most aggressive and lethal types of human cancer. To uncover potential prognostic markers, we employed the weighted correlation network analysis (WGCNA) on the Chinese Glioma Genome Atlas (CGGA) 693 dataset to reveal four modules significantly associated with glioma clinical traits, primarily involved in immune function, cell cycle regulation, and ribosome biogenesis. Using the least absolute shrinkage and selection operator (LASSO) regression algorithm, we identified 11 key genes and developed a prognostic risk score model, which exhibits precise prognostic prediction in the CGGA 325 dataset. More importantly, we also validated the model in 12 glioma patients with overall survival (OS) ranging from 4 to 132 months using mRNA sequencing and immunohistochemical analysis. The analysis of immune infiltration revealed that patients with high-risk scores exhibit a heightened immune infiltration, particularly immune suppression cells, along with increased expression of immune checkpoints. Furthermore, we explored potentially effective drugs targeting 11 key genes for gliomas using the library of integrated network-based cellular signatures (LINCS) L1000 database, identifying that in vitro, both torin-1 and clofarabine exhibit promising anti-glioma activity and inhibitory effect on the cell cycle, a significant pathway enriched in the identified glioma modules. In conclusion, our study provides valuable insights into molecular mechanisms and identifying potential therapeutic targets for gliomas.