Functional Network Analysis Research Articles

Developing cognitive workload and performance evaluation models using functional brain network analysis

Cognition, defined as the ability to learn, remember, sustain attention, make decisions, and solve problems, is essential in daily activities and in learning new skills. The purpose of this study was to develop cognitive workload and performance evaluation models using features that were extracted from Electroencephalogram (EEG) data through functional brain network and spectral analyses. The EEG data were recorded from 124 brain areas of 26 healthy participants conducting two cognitive tasks on a robot simulator. The functional brain network and Power Spectral Density features were extracted from EEG data using coherence and spectral analyses, respectively. Participants reported their perceived cognitive workload using the SURG-TLX questionnaire after each exercise, and the simulator generated actual performance scores. The extracted features, actual performance scores, and subjectively assessed cognitive workload values were used to develop linear models for evaluating performance and cognitive workload. Furthermore, the Pearson correlation was used to find the correlation between participants’ age, performance, and cognitive workload. The findings demonstrated that combined EEG features retrieved from spectral analysis and functional brain networks can be used to evaluate cognitive workload and performance. The cognitive workload in conducting only Matchboard level 3, which is more challenging than Matchboard level 2, was correlated with age (0.54, p-value = 0.01). This finding may suggest playing more challenging computer games are more helpful in identifying changes in cognitive workload caused by aging. The findings could open the door for a new era of objective evaluation and monitoring of cognitive workload and performance.

Analysis of Weight-Directed Functional Brain Networks in the Deception State Based on EEG Signal.

Although analyzing the brain's functional and structural network has revealed that numerous brain networks are necessary to collaborate during deception, the directionality of these functional networks is still unknown. This study investigated the effective connectivity of the brain networks during deception and uncovers the information-interaction patterns of lying neural oscillations. The electroencephalography (EEG) data of 40 lying persons and 40 honest persons were used to create the weight- directed functional brain networks (WDFBN). Specifically, the connecting edge weight was defined based on the normalized phase transfer entropy (dPTE) between each electrode pair, where the network nodes involved 30 electrode channels. Additionally, the signal connectivity matrices were constructed in four frequency bands: delta, theta, alpha, and beta and were subjected to a difference analysis of entropy values between the groups. Statistical analysis of the classification results revealed that all frequency bands correctly detect deception and innocence with an accuracy of 92.83%, 94.17%, 85.93%, and 92.25%, respectively. Therefore, dPTE can be considered a valuable feature for identifying lying. According to WDFBN analysis, deception has stronger information flow in the frontoparietal, frontotemporal and temporoparietal networks compare to honest people. Furthermore, the prefrontal cortex was also found to be activated in all frequency ranges. This study examined the critical pathways of brain information interaction during deception, providing new insights into the underlying neural mechanisms. Our analysis offers significant evidence for the development of brain networks that could potentially be used for lie detection.

Endocrine and epigenetic regulation as common pathways underlying the genetic basis of sleep traits and longevity.

The amount of sleep needed over one's lifespan is age dependent and not sleeping enough or sleeping in excess is associated with increased morbidity and mortality. Yet the convergent molecular mechanisms that link longevity and sleep are largely unknown. We performed a gene enrichment study that (1) identified genes associated with both longevity and sleep traits and (2) determined molecular pathways enriched among these shared genes. We manually curated two sets of genes, one associated with longevity and aging and the other with sleep traits (e.g. insomnia, narcolepsy, sleep duration, chronotype, among others), with both gene lists heavily driven by hits from recent large-scale genome-wide association studies. There were 47 overlapping genes between the gene list associated with sleep traits (1,064 genes total) and the genes associated with longevity (367 genes total), indicating significantly more overlap than expected by chance. An over-representation analysis identified enriched pathways that suggest endocrine and epigenetic regulation as potential shared mechanisms between sleep traits and longevity. Concordantly, functional network analysis retrieved two clusters, being one associated with proteins of nuclear functions and the other, with extracellular proteins. This overlapping gene set, and the highlighted biological pathways may serve as preliminary findings for new functional investigations of sleep and longevity shared genetic mechanisms.

Structural and functional network analysis of twins using fMRI data.

Similarities between twins have been widely demonstrated, underscoring the remarkable influence of genetics across numerous traits. In this study, we explore the genetic underpinnings of the human brain by examining MRI data from the Queensland Twin Imaging study. Specifically, this study seeks to compare brain structure and function between twins and unrelated subjects, with an emphasis on describing the effects of genetic factors. To achieve these goals, we employed the source-based morphometry method to extract intrinsic components and elucidate recognizable patterns. Our results show that twins exhibit a higher degree of similarity in gray and white matter density compared with unrelated individuals. In addition, four distinct states of brain activity were identified using coactivation patterns analysis. Furthermore, twins demonstrated a greater degree of similarity in the temporal and spatial features of each state compared with unrelated subjects. Taken together, these results support the hypothesis that twins show greater similarity in both brain structure and dynamic functional brain activity. Further exploration of these methods may advance our understanding of the complex interplay between genes, environment, and brain networks.

Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient

Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system. Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model. Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals’ local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices. Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.

Analysis of shared ceRNA networks and related-hub genes in rats with primary and secondary photoreceptor degeneration.

Photoreceptor degenerative diseases are characterized by the progressive death of photoreceptor cells, resulting in irreversible visual impairment. However, the role of competing endogenous RNA (ceRNA) in photoreceptor degeneration is unclear. We aimed to explore the shared ceRNA regulation network and potential molecular mechanisms between primary and secondary photoreceptor degenerations. We established animal models for both types of photoreceptor degenerations and conducted retina RNA sequencing to identify shared differentially expressed long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs). Using ceRNA regulatory principles, we constructed a shared ceRNA network and performed function enrichment and protein-protein interaction (PPI) analyses to identify hub genes and key pathways. Immune cell infiltration and drug-gene interaction analyses were conducted, and hub gene expression was validated by quantitative real-time polymerase chain reaction (qRT-PCR). We identified 37 shared differentially expressed lncRNAs, 34 miRNAs, and 247 mRNAs and constructed a ceRNA network consisting of 3 lncRNAs, 5 miRNAs, and 109 mRNAs. Furthermore, we examined 109 common differentially expressed genes (DEGs) through functional annotation, PPI analysis, and regulatory network analysis. We discovered that these diseases shared the complement and coagulation cascades pathway. Eight hub genes were identified and enriched in the immune system process. Immune infiltration analysis revealed increased T cells and decreased B cells in both photoreceptor degenerations. The expression of hub genes was closely associated with the quantities of immune cell types. Additionally, we identified 7 immune therapeutical drugs that target the hub genes. Our findings provide new insights and directions for understanding the common mechanisms underlying the development of photoreceptor degeneration. The hub genes and related ceRNA networks we identified may offer new perspectives for elucidating the mechanisms and hold promise for the development of innovative treatment strategies.

Functional connectivity and network analysis in adolescents with major depressive disorder showing suicidal behavior

BackgroundThis study aimed to gather a homogeneous sample of adolescent patients to analyze the differences in functional connectivity and brain network parameters between suicidal and non-suicidal major depressive disorder (MDD) patients using a data-driven whole-brain approach. MethodsPatients recruited at the psychiatry department of Korea University Guro Hospital from November 2014 to March 2020 were diagnosed with MDD, were 13–18 years old, had IQ scores >80, had no family history of psychotic or personality disorders, had no smoking or alcohol consumption history, and were drug-naïve to psychotropic medication. Depressive symptoms were assessed using the Hamilton Depression Rating Scale and the Children's Depression Inventory. Structural and functional MRI scans were conducted and analyzed using the CONN toolbox. ResultsOf 74 enrolled patients, 62 were analyzed. Regions of interest (ROIs) showing higher betweenness centrality in non-suicidal patients were the left superior temporal gyrus and left supramarginal gyrus. ROIs showing higher betweenness centrality in suicidal patients were the right hippocampus, left intracalcarine cortex, right inferior temporal gyrus, and the lateral visual network. Suicidal patients also showed different resting state functional connectivity profiles from non-suicidal patients. LimitationsSmall sample size. ConclusionSuicidal patients may overthink and overvalue future risks while having a more negatively biased autobiographical memory. Social cognition and the ability to overcome egocentricity bias seem to weaken. Such features can disrupt cognitive recovery and resilience, leading to more suicidal behaviors. Therefore, increased suicidality is not acquired, but is an innate trait.

Metabolic syndrome-related cognitive impairment with white matter hyperintensities and functional network analysis.

This study aimed to explore the relationship between white matter hyperintensities (WMHs) and cognitive impairment related to metabolic syndrome (MetS) and the underlying neural network mechanisms. This cross-sectional study included 50participants with MetS and WMHs (MetS-WMHs), 45 with MetS without WMHs, and 50 control participants. All participants underwent resting-state functional magnetic resonance imaging and a detailed cognitive evaluation. A graph theory analysis based on resting-state functional magnetic resonance imaging was conducted to calculate functional network properties. A mediation analysis was conducted to determine the relationship between WMHs and MetS-related cognitive impairment. Compared with the control group, the participants in the MetS-WMHs group displayed lower global efficiency, local efficiency, and nodal efficiency, mainly located in the regions of the salience network. Furthermore, a significant correlation was observed between functional network efficiency and cognitive performance. Mediation analysis indicated that WMHs served as a mediating variable between MetS and cognitive decline, affecting attention/executive function, language, and global cognitive function. WMHs mediated the association between MetS and cognitive function, with a decline in the efficiency of functional brain networks being a probable neural mechanism.

Gene regulation analysis of patient-derived iPSCs and its CRISPR-corrected control provides a new tool for studying perturbations of ELMOD3 c.512A>G mutation during the development of inherited hearing loss.

The ELMOD3 gene is implicated in causing autosomal recessive/dominant non-syndromic hearing loss in humans. However, the etiology has yet to be completely elucidated. In this study, we generated a patient-derived iPSC line carrying ELMOD3 c.512A>G mutation. In addition, the patient-derived iPSC line was corrected by CRISPR/Cas9 genome editing system. Then we applied RNA sequencing profiling to compare the patient-derived iPSC line with different controls, respectively (the healthy sibling-derived iPSCs and the CRISPR/Cas9 corrected iPSCs). Functional enrichment and PPI network analysis revealed that differentially expressed genes (DEGs) were enriched in the gene ontology, such as sensory epithelial development, intermediate filament cytoskeleton organization, and the regulation of ion transmembrane transport. Our current work provided a new tool for studying how disruption of ELMOD3 mechanistically drives hearing loss.

Interfacial and Defect Polarization Enhanced Microwave Noninvasive Therapy for Staphylococcus aureus-Infected Chronic Osteomyelitis.

Chronic osteomyelitis (COM), is a long-term, constant, and intractable disease mostly induced by infection from the invasion of Staphylococcus aureus (S. aureus) into bone cells. Here, we describe a highly effective microwave (MW) therapeutic strategy for S. aureus-induced COM based on the in situ growth of interfacial oxygen vacancy-rich molybdenum disulfide (MoS2)/titanium carbide (Ti3C2Tx) MXene with oxygen-deficient titanium dioxide (TiO2-x) on Ti3C2Tx (labeled as HU-MoS2/Ti3C2Tx) by producing reactive oxygen species (ROS) and heat. HU-MoS2/Ti3C2Tx produced heat and ROS, which could effectively treat S. aureus-induced COM under MW irradiation. The underlying mechanism determined by density functional theory (DFT) and MW vector network analysis was that HU-MoS2/Ti3C2Tx formed a high-energy local electric field under MW irradiation, consequently generating more high-energy free electrons to pass and move across the interface at a high speed and accelerate by the heterointerface, which enhanced the charge accumulation on both sides of the interface. Moreover, these charges were captured by the oxygen species adsorbed at the HU-MoS2/Ti3C2Tx interface to produce ROS. MoS2 facilitated multiple reflections and scattering of electromagnetic waves as well as enhanced impedance matching. Ti3C2Tx enhanced the conduction loss of electromagnetic waves, while functional groups induced dipole polarization to enhance attenuation of MW.

Lipid metabolism transcriptomics of murine microglia in Alzheimer’s disease and neuroinflammation

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by the accumulation of amyloid-β (Aβ) plaques followed by intracellular neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau. An unrestrained immune response by microglia, the resident cells of the central nervous system (CNS), leads to neuroinflammation which can amplify AD pathology. AD pathology is also driven by metabolic dysfunction with strong correlations between dementia and metabolic disorders such as diabetes, hypercholesterolemia, and hypertriglyceridemia. Since elevated cholesterol and triglyceride levels appear to be a major risk factor for developing AD, we investigated the lipid metabolism transcriptome in an AD versus non-AD state using RNA-sequencing (RNA-seq) and microarray datasets from N9 cells and murine microglia. We identified 52 differentially expressed genes (DEG) linked to lipid metabolism in LPS-stimulated N9 microglia versus unstimulated control cells using RNA-seq, 86 lipid metabolism DEG in 5XFAD versus wild-type mice by microarray, with 16 DEG common between both datasets. Functional enrichment and network analyses identified several biological processes and molecular functions, such as cholesterol homeostasis, insulin signaling, and triglyceride metabolism. Furthermore, therapeutic drugs targeting lipid metabolism DEG found in our study were identified. Focusing on drugs that target genes associated with lipid metabolism and neuroinflammation could provide new targets for AD drug development.

MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches.

Childhood medulloblastoma is a malignant form of brain tumor that is widely classified into four subgroups based on molecular and genetic characteristics. Accurate classification of these subgroups is crucial for appropriate treatment, monitoring plans, and targeted therapies. However, misclassification between groups 3 and 4 is common. To address this issue, an AI-based R package called MBMethPred was developed based on DNA methylation and gene expression profiles of 763 medulloblastoma samples to classify subgroups using machine learning and neural network models. The developed prediction models achieved a classification accuracy of over 96% for subgroup classification by using 399 CpGs as prediction biomarkers. We also assessed the prognostic relevance of prediction biomarkers using survival analysis. Furthermore, we identified subgroup-specific drivers of medulloblastoma using functional enrichment analysis, Shapley values, and gene network analysis. In particular, the genes involved in the nervous system development process have the potential to separate medulloblastoma subgroups with 99% accuracy. Notably, our analysis identified 16 genes that were specifically significant for subgroup classification, including EP300, CXCR4, WNT4, ZIC4, MEIS1, SLC8A1, NFASC, ASCL2, KIF5C, SYNGAP1, SEMA4F, ROR1, DPYSL4, ARTN, RTN4RL1, and TLX2. Our findings contribute to enhanced survival outcomes for patients with medulloblastoma. Continued research and validation efforts are needed to further refine and expand the utility of our approach in other cancer types, advancing personalized medicine in pediatric oncology.

Effect of tDCS on corticomuscular coupling and the brain functional network of stroke patients.

Transcranial direct current stimulation (tDCS) is an emerging brain intervention technique that has gained growing attention in recent years in the rehabilitation area. In this paper, we investigated the efficacy of tDCS in the rehabilitation process of stroke patients, utilizing corticomuscular coupling (CMC) and brain functional network analysis. Specifically, we examined changes in CMC relationships between the treatment and control groups before and after rehabilitation by transfer entropy (TE), and constructed brain functional networks by TE. We further calculated features of the functional networks, including node degree, global efficiency, clustering coefficient, characteristic path length, and small world index. Our results demonstrate that CMC in patients increased significantly after treatment, with greater improvements in the tDCS group, particularly within the beta and gamma bands. In addition, the functional brain network analysis revealed enhanced connectivity between brain regions, improved information processing capacity, and increased transmission efficiency in patients as their condition improved. Notably, treatment with tDCS resulted in more significant improvements than the sham group, with a statistically significant difference observed after rehabilitation treatment (p < 0.05). These findings provide compelling evidence regarding the role of tDCS in the treatment of stroke and highlight the potential of this approach in stroke rehabilitation. The use of tDCS for therapeutic interventions in stroke rehabilitation can significantly improve the coupling of patients' functional brain networks. Also, using Transfer Entropy (TE) as a characteristic of CMC, tDCS was found to significantly enhance patients' TE, i.e. enhanced CMC.

Comments on ``Convergence Analysis of Adaptive Exponential Functional Link Network''.

This article is to comment on the derivation of the weight-update stability of in-parameter-linear nonlinear learning system with the gradient descent learning rule in the above article. Our comments are not to disqualify the commented article's whole contribution; however, the issues should be pointed out to avoid their proliferation.

Exosomal MicroRNA and Protein Profiles of Hepatitis B Virus-Related Hepatocellular Carcinoma Cells.

Infection with hepatitis B virus (HBV) is a main risk factor for hepatocellular carcinoma (HCC). Extracellular vesicles, such as exosomes, play an important role in tumor development and metastasis, including regulation of HBV-related HCC. In this study, we have characterized exosome microRNA and proteins released in vitro from hepatitis B virus (HBV)-related HCC cell lines SNU-423 and SNU-182 and immortalized normal hepatocyte cell lines (THLE2 and THLE3) using microRNA sequencing and mass spectrometry. Bioinformatics, including functional enrichment and network analysis, combined with survival analysis using data related to HCC in The Cancer Genome Atlas (TCGA) database, were applied to examine the prognostic significance of the results. More than 40 microRNAs and 200 proteins were significantly dysregulated (p < 0.05) in the exosomes released from HCC cells in comparison with the normal liver cells. The functional analysis of the differentially expressed exosomal miRNAs (i.e., mir-483, mir-133a, mir-34a, mir-155, mir-183, mir-182), their predicted targets, and exosomal differentially expressed proteins (i.e., POSTN, STAM, EXOC8, SNX9, COL1A2, IDH1, FN1) showed correlation with pathways associated with HBV, virus activity and invasion, exosome formation and adhesion, and exogenous protein binding. The results from this study may help in our understanding of the role of HBV infection in the development of HCC and in the development of new targets for treatment or non-invasive predictive biomarkers of HCC.

Functional network analysis identifies multiple virulence and antibiotic resistance targets in Stenotrophomonas maltophilia

Stenotrophomonas maltophilia, an emerging multidrug-resistant opportunistic bacterium in humans is of major concern for immunocompromised individuals for causing pneumonia and bloodborne infections. This bacterial pathogen is associated with a considerable fatality/case ratio, with up to 100%, when presented as hemorrhagic fever. It is resistant to commonly used drugs as well as to antibiotic combinations. In-silico based functional network analysis is a key approach to get novel insights into virulence and resistance in pathogenic organisms. This study included the protein-protein interaction (PPI) network analysis of 150 specific genes identified for antibiotic resistance mechanism and virulence pathways. Eight proteins, namely, PilL, FliA, Smlt2260, Smlt2267, CheW, Smlt2318, CheZ, and FliM were identified as hub proteins. Further docking studies of 58 selected phytochemicals were performed against the identified hub proteins. Deoxytubulosine and corosolic acid were found to be potent inhibitors of hub proteins of pathogenic S. maltophilia based on protein-ligand interactive study. Further pharmacophore studies are warranted with these molecules to develop them as novel antibiotics against S. maltophilia.

RAB14 promotes epithelial-mesenchymal transition in bladder cancer through autophagy‑dependent AKT signaling pathway

Bladder cancer (BLCA) is the 9th most common cancer of mortality. Autophagy and epithelial to mesenchymal transition (EMT) have an essential role in cancer invasion and metastasis. However, the relationship between autophagy and EMT is still poorly understood in BLCA. Functional enrichment and pathway network analysis were carried out. Comprehensive protein-protein interactions (PPI) networks were proposed to prioritize candidate autophagy-related genes. Furthermore, an autophagy-related signature and a nomogram model were established by integrating clinical information and this signature risk score to evaluate candidate autophagy-related genes. RAB14 expression and its association with pathological information and survival were evaluated in samples from TCGA dataset. Knocking down RAB14 in T24 cells was constructed, and immunofluorescence staining, transmission electron microscopy, immunohistochemistry and western blotting and a series of functional assays were performed to evaluate the migration, invasion, EMT and autophagy abilities of BLCA cells. The autophagy-related gene RAB14 was the only candidate gene identified by three kinds of analytic approaches. RAB14 was highly upregulated in BLCA and correlated with clinical outcomes based on TCGA BLCA datasets. Knocking down RAB14 was found to inhibit EMT and autophagy in T24 cells. RAB14 levels were positively related to those of LC3B and Beclin1, two genes with critical roles in the autophagy process, and the correlation was further confirmed in clinical tissue specimens by IHC and western blot analysis. In addition, RAB14-promoted EMT, migration, and invasion in T24 cells could be partially reversed by autophagy activator, rapamycin. The effects of RAB14 on autophagy was associated with level of p-Akt, indicating that they were possibly mediated via PI3K/AKT signaling. These findings indicated that autophagy-related gene RAB14-promoted EMT, migration and invasion of bladder cancer via the Akt-associated autophagic pathway.

Association of NOTCH4 and ACHE gene polymorphism in Alzheimer’s disease of Gujarat cohort

BackgroundAlzheimer's Disease (AD) is the most common form of dementia, affecting cognitive and behavioral functions. AD is a complex disease resulting from the modest effect of gene interaction and environmental factors, as a result of which the exact pathogenesis is still unknown. AimThe aim of the present study was to investigate the association between variants of 98 targeted genes with Alzheimer's disease phenotype. MethodA total of 98 genes from 32 AD cases and 11 controls were genotyped using the Haloplex target enrichment method and the PCR-RFLP approach.Association analysis was performed using the PLINK tool to identify the variant significantly associated with AD. Functional enrichment analysis and network analysis was performed using ClueGo and String database respectively. The Expression Quantitative Trait Loci (eQTL) analysis using the Genotype Tissue Expression (GTEx) dataset to explore the possible implication of the variant on the expression of one or more genes in different brain regions and whole blood. ResultAssociation analysis showed significant association of 19 variant assigned to 16 genes with Alzheimer’s with p-value < 0.05 with rs367398/NOTCH4 only variant that passed multiple test corrections. Functional enrichment analysis showed association of these genes with AD. ClueGo and network analysis utilizing the String database suggested that genes are directly and indirectly linked to the AD pathogenesis. eQTL analysis revealed that the rs367398/NOTCH4 and rs1799806/ACHE variant showed significant eQTL for the neighbouring genes. ConclusionThe present study showed the possible role of 16 genes in AD pathogenesis, especially highlighting the role of rs367398/NOTCH4 and rs1799806/ACHE. However further investigation with large cohort is required to study and validate the implication of these variants in the AD pathogenesis.

Constructing and Validating a Network of Potential Olfactory Sheathing Cell Transplants Regulating Spinal Cord Injury Progression.

The pathology of spinal cord injury (SCI), including primary and secondary injuries, primarily involves hemorrhage, ischemia, edema, and inflammatory responses. Cell transplantation has been the most promising treatment for SCI in recent years; however, its specific molecular mechanism remains unclear. In this study, bioinformatics analysis verified by experiment was used to elucidate the hub genes associated with SCI and to discover the underlying molecular mechanisms of cell intervention. GSE46988 data were downloaded from the Gene Expression Omnibus dataset. In our study, differentially expressed genes (DEGs) were reanalyzed using the "R" software (R v4.2.1). Functional enrichment and protein-protein interaction network analyses were performed, and key modules and hub genes were identified. Network construction was performed for the hub genes and their associated miRNAs. Finally, a semi-quantitative analysis of hub genes and pathways was performed using quantitative real-time polymerase chain reaction. In total, 718 DEGs were identified, mainly enriched in immune and inflammation-related functions. We found that Cd4, Tp53, Rac2, and Akt3 differed between vehicle and transplanted groups, suggesting that these genes may play an essential role in the transplantation of olfactory ensheathing cells, while a toll-like receptor signaling pathway was significantly enriched in Gene set enrichment analysis, and then, the differences were statistically significant by experimentally verifying the expression of their associated molecules (Tlr4, Nf-κb, Ikkβ, Cxcl2, and Tnf-α). In addition, we searched for upstream regulatory molecules of these four central genes and constructed a regulatory network. This study is the first to construct a regulatory network for olfactory ensheathing cell transplantation in treating SCI, providing a new idea for SCI cell therapy.

MiR-21 Targets ASPP2 to Inhibit Apoptosis via CHOP-Mediated Signaling in Helicobacter pylori-Infected Gastric Cancer Cells.

Helicobacter pylori (H. pylori) infection affects cell survival pathways, including apoptosis and proliferation in host cells, and disruption of this balance is the key event in the development of H. pylori-induced gastric cancer (HPGC). H. pylori infection induces alterations in microRNAs expression that may be involved in GC development. Bioinformatic analysis showed that microRNA-21 (miR-21) is significantly upregulated in HPGC. Furthermore, quantitative proteomics and in silico prediction were employed to identify potential targets of miR-21. Following functional enrichment and clustered interaction network analyses, five candidates of miR-21 targets, PDCD4, ASPP2, DAXX, PIK3R1, and MAP3K1, were found across three functional clusters in association with cell death and survival, cellular movement, and cellular growth and proliferation. ASPP2 is inhibited by H. pylori-induced miR-21 overexpression. Moreover, ASPP2 levels are inversely correlated with miR-21 levels in HPGC tumor tissues. Thus, ASPP2 was identified as a miR-21 target in HPGC. Here, we observed that H. pylori-induced ASPP2 suppression enhances resistance to apoptosis in GC cells using apoptosis assays. Using protein interaction network and coimmunoprecipitation assay, we identified CHOP as a direct mediator of the ASPP2 proapoptotic activity in H. pylori-infected GC cells. Mechanistically, ASPP2 suppression promotes p300-mediated CHOP degradation, in turn inhibiting CHOP-mediated transcription of Noxa, Bak, and suppression of Bcl-2 to enact antiapoptosis in the GC cells after H. pylori infection. Clinicopathological analysis revealed correlations between decreased ASPP2 expression and higher HPGC risk and poor prognosis. In summary, the discovery of H. pylori-induced antiapoptosis via miR-21-mediated suppression of ASPP2/CHOP-mediated signaling provides a novel perspective for developing HPGC management and treatment.