Network Construction Research Articles

Investigating the Role of Hub Calcification Proteins in Atherosclerosis via Integrated Transcriptomics and Network-Based Approach

Atherosclerosis (AS) is a chronic inflammatory condition of the arteries, characterized by plaque formation that can restrict blood flow and lead to potentially fatal cardiovascular events. Given that AS is responsible for a quarter of global deaths, this study aimed to develop a systematic bioinformatics approach to identify biomarkers and regulatory targets involved in plaque development, with the goal of reducing cardiovascular disease risk. AS-specific mRNA expression profiles were retrieved from a publicly accessible database, followed by differentially expressed genes (DEGs) identification and AS-specific weighted gene co-expression network (WGCN) construction. Thereafter, calcification and atherosclerosis-specific (CASS) DEGs were utilized for protein–protein interaction network (PPIN) formation, followed by gene ontology (GO) term and pathway enrichment analyses. Lastly, AS-specific 3-node miRNA feed-forward loop (FFL) construction and analysis was performed. Microarray datasets GSE43292 and GSE28829 were obtained from gene expression omnibus (GEO). A total of 3785 and 6176 DEGs were obtained in case of GSE28829 and GSE43292; 3256 and 5962 module DEGs corresponding to GSE28829 and GSE43292 were obtained from WGCN. From a total of 54 vascular calcification (VC) genes, 20 and 29 CASS-DEGs corresponding to GSE28829 and GSE43292 were overlapped. As observed from FFL centrality measures, the highest-order subnetwork motif comprised one TF (SOX7), one miRNA (miR-484), and one mRNA (SPARC) in the case of GSE28829. Also, in the case of GSE43292, the highest-order subnetwork motif comprised one TF (ESR2), one miRNA (miR-214-3p), and one mRNA (MEF2C). These findings have important implications for developing new therapeutic strategies for AS. The identified TFs and miRNAs may serve as potential therapeutic targets for treating atherosclerotic plaques, offering insights into the molecular mechanisms underlying the pathogenesis and highlighting new avenues for research and treatment.

Neural mechanisms of fear responses to emotional stimuli: a preliminary study combining early posterior negativity and electroencephalogram source network analysis

Fear emotion is a typical negative emotion that is commonly present in daily life and significantly influences human behavior. A deeper understanding of the mechanisms underlying negative emotions contributes to the improvement of diagnosing and treating disorders related to negative emotions. However, the neural mechanisms of the brain when faced with fearful emotional stimuli remain unclear. To this end, this study further combined electroencephalogram (EEG) source analysis and cortical brain network construction based on early posterior negativity (EPN) analysis to explore the differences in brain information processing mechanisms under fearful and neutral emotional picture stimuli from a spatiotemporal perspective. The results revealed that neutral emotional stimuli could elicit higher EPN amplitudes compared to fearful stimuli. Further source analysis of EEG data containing EPN components revealed significant differences in brain cortical activation areas between fearful and neutral emotional stimuli. Subsequently, more functional connections were observed in the brain network in the alpha frequency band for fearful emotions compared to neutral emotions. By quantifying brain network properties, we found that the average node degree and average clustering coefficient under fearful emotional stimuli were significantly larger compared to neutral emotions. These results indicate that combining EPN analysis with EEG source component and brain network analysis helps to explore brain functional modulation in the processing of fearful emotions with higher spatiotemporal resolution, providing a new perspective on the neural mechanisms of negative emotions.

Construction and Application of a Private 5G Standalone Medical Network in a Smart Health Environment: Exploratory Practice From China.

To date, the differentiated requirements for network performance in various health care service scenarios-within, outside, and between hospitals-remain a key challenge that restricts the development and implementation of digital medical services. This study aims to construct and implement a private 5G (the 5th generation mobile communication technology) standalone (SA) medical network in a smart health environment to meet the diverse needs of various medical services. Based on an analysis of network differentiation requirements in medical applications, the system architecture and functional positioning of the proposed private 5G SA medical network are designed and implemented. The system architecture includes the development of exclusive and preferential channels for medical use, as well as an ordinary user channel. A 3-layer network function architecture is designed, encompassing resource, control, and intelligent operation layers to facilitate management arrangements and provide network open services. Core technologies, including edge cloud collaboration; service awareness; and slicing of access, bearer, and core networks, are employed in the construction and application of the 5G SA network. The construction of the private 5G SA medical network primarily involves system architecture, standards, and security measures. The system, featuring exclusive, preferential, and common channels, supports a variety of medical applications. Relevant standards are adhered to in order to ensure the interaction and sharing of medical service information. Security is achieved through mechanisms such as authentication, abnormal behavior analysis, and dynamic access control. Three typical medical applications that rely on the 5G network in intrahospital, interhospital, and out-of-hospital scenarios-namely, mobile ward rounds, remote first aid, and remote ultrasound-were conducted. Testing of the 5G-enabled mobile ward rounds showed an average download rate of 790 Mbps and an average upload rate of 91 Mbps. Compared with 4G, the 5G network more effectively meets the diverse requirements of various business applications in prehospital emergency scenarios. For remote ultrasound, the average downlink rate of the 5G network is 4.82 Mbps, and the average uplink rate is 2 Mbps, with an average fluctuation of approximately 8 ms. The bandwidth, performance, and delay of the 5G SA network were also examined and confirmed to be effective. The proposed 5G SA medical network demonstrates strong performance in typical medical applications. Its construction and application could lead to the development of new medical service models and provide valuable references for the further advancement and implementation of 5G networks in other industries, both in China and globally.

A flexible porous polyimide/copper composite film toward high-mass-loading anodes in lithium-ion batteries

Conventional metal foil current collectors are essential for electronic conduction in lithium-ion batteries, but the planar structure limits the fast transporting of electron and the power density. Herein, a light, conductive, self-extinguishing, and flexible porous polyimide/copper composite film (PIF/Cu) derived from a polyimide foam was fabricated via hot pressing and electroless plating methods. The honeycomb structure was illustrated in PIF/Cu under a 3D X-ray tomography microscope, revealing the successful construction of a 3D conductive network. PIF/Cu-30 possessed an electronic conductivity of as high as 3.6 × 105 S m−1 at a bulk density of 0.26 g cm−3 and exhibited exceptional structural stability even after successive and harsh mechanical loads. Graphite with a mass load of 12 mg cm−2 was loaded into the PIF/Cu-30 to prepare the anode for cell assembly. The cells using PIF/Cu-30 achieved a better performance in capacity retention (98.2 %@60 cycles) than those using traditional Cu foils (55.1 %@60 cycles), which resulted from the fast transport of electrons and Li-ion through the 3D conductive networks in the porous PIF/Cu-30.

Asynchronous Functional Brain Network Construction with Spatiotemporal Transformer for MCI Classification.

Construction and analysis of functional brain networks (FBNs) with resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method to diagnose functional brain diseases. Nevertheless, the existing methods suffer from several limitations. First, the functional connectivities (FCs) of the FBN are usually measured by the temporal co-activation level between rs-fMRI time series from regions of interest (ROIs). While enjoying simplicity, the existing approach implicitly assumes simultaneous co-activation of all the ROIs, and models only their synchronous dependencies. However, the FCs are not necessarily always synchronous due to the time lag of information flow and cross-time interactions between ROIs. Therefore, it is desirable to model asynchronous FCs. Second, the traditional methods usually construct FBNs at individual level, leading to large variability and degraded diagnosis accuracy when modeling asynchronous FBN. Third, the FBN construction and analysis are conducted in two independent steps without joint alignment for the target diagnosis task. To address the first limitation, this paper proposes an effective sliding-window-based method to model spatiotemporal FCs in Transformer. Regarding the second limitation, we propose to learn common and individual FBNs adaptively with the common FBN as prior knowledge, thus alleviating the variability and enabling the network to focus on the individual disease-specific asynchronous FCs. To address the third limitation, the common and individual asynchronous FBNs are built and analyzed by an integrated network, enabling end-to-end training and improving the flexibility and discriminativity. The effectiveness of the proposed method is consistently demonstrated on three data sets for mild cognitive impairment (MCI) diagnosis.

Urban Road Transport Infrastructure Planning and Development Implications on Socio-Economic Growth in the City of Douala, Cameroon

Cities are the major contributors to socio- economic growth with easy access to amenities which stem through road transport infrastructure development. This study was conducted to assess urban road transport infrastructure planning and examine development implications on socio-economic growth in the City of Douala. A descriptive research design was used with primary and secondary data collected and analyzed using descriptive and inferential statistical techniques. Results revealed that, transport infrastructure in Douala have been structured through urban development planning policy which influences surface spatial expansion of socio- economic activities. The planning policies have contributed to the rapid expansion and growth through the initiations of master plan, land use plan and sector plan. Infrastructural development on road transport depicts a physical expansion and construction of road networks which influences the socio-economic growth of the study area. Stakeholders’ actions on construction, reconstruction and rehabilitation, investments and maintenance of road infrastructure have helped to curb down some transportation challenges and creates easy access to facilities within different sectors of the study area. However, road transport infrastructure which is well-developed boasts socio-economic activities and growth. The state of road transport network, its diversity and characteristic features is defined by its prevalence within different areas in Douala. Contrarily, other transport infrastructures which are not functioning appropriately due to lapses in physical planning possess enormous implications on transportation in the study area. The conclusion drawn was that the manner in which urban planning policies are implemented determines the level of development in road transport infrastructure and socio-economic growth of Douala. The study recommends an effective application of planning policies to be integrated by implicating all the actors of urban managers and transportation planners for improved growth in the study area.

Acceleration of Diffusion in Ab Initio Nanoreactor Molecular Dynamics and Application to Hydrogen Sulfide Oxidation.

The computational description of chemical reactivity can become extremely complex when multiple different reaction products and intermediates come into play, forming a chemical reaction network. Therefore, computational methods for the automated construction of chemical reaction networks have been developed in the last decades. One of these methods, ab initio nanoreactor molecular dynamics (NMD), is based on external forces enhancing reactivity by e.g., periodically compressing the system and allowing it to relax. However, during the relaxation process, a significant simulation time is required to allow energy to dissipate and molecules to diffuse, making this part of the NMD simulation computationally intensive. This work aims to improve NMD by accelerating the diffusion process in the relaxation phase. We systematically investigate the speedup of reaction discovery gained by diffusion acceleration, leading to a factor of up to 28 in discovery frequency. Diffusion-accelerated nanoreactor molecular dynamics (DA-NMD) is then used to construct a reaction network of hydrogen sulfide oxidation under atmospheric conditions, where reactions are automatically detected by a change in the bond order and bond distance. A reaction network of 108 molecular species and 399 elementary reactions was constructed starting from hydrogen sulfide, hydroxy radicals, and molecular oxygen covering a broad variety of sulfur-oxygen chemistry and oxidation states of the sulfur atom ranging from -II to +VI.

Luttinger liquid tensor network: Sine versus tangent dispersion of massless Dirac fermions

To apply the powerful many-body techniques of tensor networks to massless Dirac fermions, one wants to discretize the p·σ Hamiltonian and construct a matrix-product-operator (MPO) representation. We compare two alternative discretization schemes, one with a sine dispersion, the other with a tangent dispersion, applied to a one-dimensional Luttinger liquid with Hubbard interaction. Both types of lattice fermions allow for an exact MPO representation of low bond dimension, so they are efficiently computable, but only the tangent dispersion gives a power law decay of the propagator in agreement with the continuum limit: The sine dispersion is gapped by the interactions, evidenced by an exponentially decaying propagator. Our construction of a tensor network with an unpaired Dirac cone works around the fermion-doubling obstruction by exploiting the fact that the Hamiltonian of tangent fermions permits a generalized eigenproblem. Published by the American Physical Society 2024

Heterometallic [2]Catenane-Crosslinked Supramolecular Networks with Improved Antibacterial Activity.

The construction of supramolecular networks with novel crosslinks is of great significance in expanding their chemical structures and exploring their advanced functions. Herein, we prepare a type of [2]catenane-cored supramolecular networks based on the crosslinking of polyethylene glycol (PEG) using a heterometallic [2]catenane unit. By adjusting the molecular weight of PEG, the solubility of the networks can be tuned and gels are formed using low molecular weight PEG. The introduction of heterometallic [2]catenane offers the networks good antibacterial properties owing to the synergistic antimicrobial activity of Pt(II) and Cu(I) ions in the [2]catenane. This study provides a simple and efficient strategy for constructing supramolecular networks with topological crosslinks as antibacterial materials, which will promote the structural design and biological applications of supramolecular networks.

Evaluation of Water System Connectivity Based on Node Centrality in the Tarim River Basin, Xinjiang, China

Water system connectivity is an important measure to optimize the balanced spatial allocation of water resources and water security patterns. Inland river basins in arid zones are generally insufficiently connected, so the scientific evaluation of the current status of water system connectivity and the centrality of its nodes in the water system network has practical significance for the scientific construction of regional water networks. Taking the Tarim River Basin in Xinjiang, China, as an object, this study conducted a comprehensive evaluation of basin water system connectivity by constructing a water system connectivity evaluation system with a total of 12 indicators for the three aspects of pattern connectivity, structural connectivity and functional connectivity. Subsequently, the TOPSIS model, with combined weights of the analytic hierarchy process and the entropy weight method, was used to comprehensively evaluate the connectivity of the watershed’s water system. The research evaluated the node centrality of the water system network that was closely related to the basin water system connectivity by using complex network analyses. The study results indicated the following: (1) among the source streams in the Tarim River Basin, the connectivity of the Aksu Basin was the best and that of the Keriya Basin was the worst and (2) the distribution patterns of the eigenvector centrality and betweenness centrality of the basin hydrological network nodes were similar, with nodes of highest centrality concentrated in the vicinity of the mainstems and the source–mainstem intersections. This work provides a basis and reference for the construction of water system connectivity and the selection of key control sections for ecological flow in the Tarim River Basin.

Optimizing Multi-Tier Scheduling and Secure Routing in Edge-Assisted Software-Defined Wireless Sensor Network Environment Using Moving Target Defense and AI Techniques

Software Defined Wireless Sensor Networks (SDWSN) enable flexibility in Wireless Sensor Network (WSN) environments by defining the controllable functions to WSN nodes by the Software Defined Network (SDN) controller. Due to the rapid evolution of SDWSNs, adverse effects also have occurred in terms of interference, energy consumption, and security issues. Several state-of-the-art works lend their utmost best to the SDWSN environment. However, the complete picture (i.e., relatability and security in SDWSN) poses severe challenges. The state-of-the-art issues is addressed in this research by proposing interference-aware Multi-Tier Scheduling for the SDWSN environment (MTS-SDWSN). First, we perform network construction in which the proposed network is constructed in a 2D hexagonal grid structure to resolve the connectivity issue. Upon constructing the network, the SDWSN nodes are clustered and managed to reduce the energy consumption using the Divide Well To Merge Better (DWTMB) algorithm in which the optimal Cluster Leader (CL) is selected based on adequate constraint. The data from the clustered nodes are sent to the Local Base Station (LBS) via CL in which they are scheduled in multi-tier format to diminish the complexity and interference issues. The first tier involved in scheduling among Cluster Members (CMs) and CL using adequate metrics, whereas the successive tiers (i.e., second and third) involved in scheduling among CLs to LBSs and LBSs to Sink Node (SN) are done using the Non-Cooperative Fuzzy Theory (NCFT) method. Last, the scheduled nodes are routed to appropriate destinations using Secure and Optimal Routing Protocol (SORP). The proposed SORP includes the Alibaba and Forty Thieves (AFT) and Multi Criteria Decision Making (MCDM) algorithms for selecting and ranking the optimal routes. Further, the security of the routes is enabled by adopting trust and Moving Target Defense (MTD) mechanisms. The MTD includes route switching among the SDWSN devices and active switch handling using Cycle Generative Adversarial Networks (CGAN) among the switches. The proposed work is implemented using a NS-3.26 simulation tool, and performance of the proposed model and existing works shows that the proposed work outperforms the existing works.

Construction of urban tomographic synthetic aperture radar double-tier permanent scatterer network combined with region-growing algorithm

Construction of urban tomographic synthetic aperture radar double-tier permanent scatterer network combined with region-growing algorithm

Screening of hub genes for sepsis-induced myopathy by weighted gene co-expression network analysis and protein-protein interaction network construction

Sepsis-induced myopathy is one of the serious complications of sepsis, which severely affects the respiratory and peripheral motor systems of patients, reduces their quality of life, and jeopardizes their lives, as evidenced by muscle atrophy, loss of strength, and impaired regeneration after injury. The pathogenesis of sepsis-induced myopathy is complex, mainly including cytokine action, enhances free radical production in muscle, increases muscle protein hydrolysis, and decreases skeletal muscle protein synthesis, etc. The above mechanisms have been demonstrated in existing studies. However, it is still unclear how the overall pattern of gene co-expression affects the pathological process of sepsis-induced myopathy. Therefore, we intend to identify hub genes and signaling pathways. Weighted gene co-expression network analysis was our main approach to study gene expression profiles: skeletal muscle transcriptome in ICU patients with sepsis-induced multi-organ failure (GSE13205). After data pre-processing, about 15,181 genes were used to identify 13 co-expression modules. Then, 16 genes (FEM1B, KLHDC3, GPX3, NIFK, GNL2, EBNA1BP2, PES1, FBP2, PFKP, BYSL, HEATR1, WDR75, TBL3, and WDR43) were selected as the hub genes including 3 up-regulated genes and 13 down-regulated genes. Then, Gene Set Enrichment Analysis was performed to show that the hub genes were closely associated with skeletal muscle dysfunction, necrotic and apoptotic skeletal myoblasts, and apoptosis in sepsis-induced myopathy. Overall, 16 candidate biomarkers were certified as reliable features for more in-depth exploration of sepsis-induced myopathy in basic and clinical studies.

Identification of genes related to fatty acid metabolism in type 2 diabetes mellitus

AimFatty acid metabolism is pivotal for lipid synthesis, cellular signaling, and maintaining cell membrane integrity. However, its diagnostic significance in type 2 diabetes mellitus (T2DM) remains unclear. Materials and methodsThree datasets and fatty acid metabolism-related genes were retrieved. Differential expression analysis, WGCNA, machine learning algorithms, diagnostic analysis, and validation were employed to identify key feature genes. Functional analysis, ceRNA network construction, immune microenvironment assessment, and drug prediction were conducted to explore the underlying molecular mechanisms. ResultsSix feature genes were identified with strong diagnostic performance and were involved in processes such as ribosome function and fatty acid metabolism. Immune cells, including dendritic cells, eosinophils, and neutrophils, may play a role in the progression of T2DM. ceRNA and drug-target network analysis revealed potential interactions, such as RP11-miR-29a-YTHDF3 and BPA-MSANTD1. The expression patterns of the feature genes, except for YTHDF3, were consistently upregulated in T2DM, aligning with trends observed in the training set. ConclusionThis study investigated the potential molecular mechanisms of six fatty acid metabolism-related genes in T2DM, offering valuable insights that may guide future research and therapeutic development.

High-Performance Flexible Pressure Sensor with Micropyramid Structure for Human Motion Signal Detection and Electromagnetic Interference Shielding.

Flexible pressure sensors have a wide range of applications in the field of human motion signal detection, but the electromagnetic radiation generated during the monitoring process has become an unavoidable problem. Nowadays, it is still a challenge to develop high-performance pressure sensors with excellent electromagnetic interference (EMI) shielding performance. Herein, the Ti3C2TX MXene/carbon fiber/multiwalled carbon nanotube/polydimethylsiloxane (MXene/CMP) films with a micropyramidal structure were developed by the technology of vacuum high-temperature hot pressing and spray deposition. Benefiting from the dielectric loss of the conductive fillers and the presence of the microstructure, the MXene/CMP film exhibits excellent EMI shielding performance, and the EMI shielding efficiency (SE) can reach 49.37 dB. Besides, the introduction of CF effectively improves the mechanical properties of the MXene/CMP films, and the MXene nanosheets deposited on the surface of the film can reduce stress concentration, which in turn delays the expansion of cracks. Furthermore, the MXene/CMP sensor exhibits high sensitivity (89.76 kPa-1) and fast response/recovery time (61/60 ms). The deformation of microstructure and the construction of conductive networks enable the sensor to realize the monitoring of human motion signals (such as finger bending, knee bending, wrist bending, etc.). Meanwhile, the sensor maintains sensing stability even after 2000 pressure cycles, which can be attributed to the improvement in mechanical properties. In summary, the developed high-performance flexible pressure sensor with micropyramid structure has great application prospects in wearable devices and healthcare.

Construction of competitive endogenous RNA network and identification of potential regulatory axis in vascular calcification.

Competitive endogenous RNAs (ceRNA) theory has been proved in numerous biological processes. Nevertheless, there is a lack of research applying the ceRNA theory to the study of vascular calcification (VC) in chronic kidney diseases (CKD). In the present study, a ceRNA network was constructed after conducting transcriptome sequencing of differentially expressed genes, followed by experimental validation to identify a new target for the diagnosis and treatment of vascular calcification. Total RNA was extracted from β-glycerophosphate (β-GP) cultured vascular smooth muscle cells (VSMCs) on Day 7. Illumina HiSeq platform was utilized to build sequencing libraries. GO and KEGG analysis was conducted to identify the function of the differentially expressed genes. Protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. A ceRNA network was established based on TargetScan, miRDB, miRWALK, and miRanda database. Western blot and qRT-PCR were used to explore the expression level of protein and RNA, respectively. The direct binding sites were confirmed by dual-luciferase reporter assay. In total, 647 differentially expressed lncRNAs and 289 differentially expressed mRNAs were identified (|log2FC| ≥ 1, p < .05). The function of differentially expressed mRNAs was mainly enriched in negative regulation of osteoblast differentiation, regulation of RNA metabolic process, and other typical pathways. The ceRNA network was generated with a total of 107 interaction pairs. The lncRNA Prrc2c/miR-145-5p/Smad3 axis was considered a potential regulatory pathway within the ceRNA network. The regulatory relationship and targets of this ceRNA axis were validated via invitro experiments. For the first time, we found that lncRNA Prrc2c was highly expressed and promoted calcification of VSMCs. Luciferase reporter assay showed that lncRNA Prrc2c could bind miR-145-5p at site 1755-1761. Similarly, luciferase reporter assay showed that miR-145-5p inhibited Smad3 expression by binding to its 3'UTR. Our findings provide a comprehensive examination of the ceRNA networks in vascular smooth muscle cells (VSMCs) treated with high phosphorate. Specifically, we have identified the role of lncRNA Prrc2c in promoting VSMC calcification through the miR-145-5p/Smad3 axis.

High expression of CNOT6L contributes to the negative development of type 2 diabetes.

Type 2 diabetes (T2D) is a chronic metabolic disorder characterized by reduced responsiveness of body cells to insulin, leading to elevated blood sugar levels. CNOT6L is involved in glucose metabolism, insulin secretion regulation, pancreatic beta-cell proliferation, and apoptosis. These functions may be closely related to the pathogenesis of T2D. However, the exact molecular mechanisms linking CNOT6L to T2D remain unclear. Therefore, this study aims to elucidate the role of CNOT6L in T2D. The T2D datasets GSE163980 and GSE26168 profiles were downloaded from the Gene Expression Omnibusdatabase generated by GPL20115 and GPL6883.The R package limma was used to screen differentially expressed genes (DEGs). A weighted gene co-expression network analysis was performed. Construction and analysis of the protein-protein interaction (PPI) network, functional enrichment analysis, gene set enrichment analysis, and comparative toxicogenomics database (CTD) analysis were performed. Target Scan was used to screen miRNAs that regulate central DEGs. The results were verified by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR), western blotting (WB), and blood glucose measurements in mice. A total of 1951 DEGs were identified. GO and KEGG enrichment analysis revealed that differentially expressed genes were mainly enriched in the insulin signaling pathway, ECM-receptor interaction, and PPAR signaling pathway. Metascape analysis indicated enrichment primarily in the cAMP signaling pathway and enzyme-linked receptor protein signaling pathway. WGCNA analysis yielded 50 intersecting genes. PPI network construction and algorithm identification identified two core genes (CNOT6L and GRIN2B), among which CNOT6L gene was associated with multiple miRNAs. CTD analysis revealed associations of core genes with type 2 diabetes, diabetic complications, dyslipidemia, hyperglycemia, and inflammation. WB and RT-qPCR results showed that in different pathways, CNOT6L protein and mRNA levels were upregulated in type 2 diabetes. CNOT6L is highly expressed in type 2 diabetes mellitus, and can cause diabetes complications, inflammation and other physiological processes by regulating miRNA, PPAR and other related signaling pathways, with poor prognosis. CNOT6L can be used as a potential therapeutic target for type 2 diabetes.

Unravelling key genes associated with ovine Brucellosis by differential gene expression analysis: A holistic bioinformatics study

Ovine Brucellosis, caused by Brucella ovis bacteria, is a pathognomonic reproductive infectious disease of sheep that causes epididymitis in rams (male sheep) and placental inflammation in ewes (female sheep) leading to reduced fertility. The specific molecular process that causes alterations in genome of sheep during brucellosis is not yet fully understood. This study aimed to identify key host genes associated with the pathogenesis of ovine brucellosis caused by B. ovis. The GSE35614 dataset containing six healthy and six Brucella ovis infected sample of rams in the chronic phase 2 was obtained from the NCBI GEO database to examine and detect any differences in gene expression (DEGs). Functional and pathway enrichment analyse of the DEGs were performed along with the construction of protein-protein interaction network. Next, functional modules and hub genes were clustered and identified respectively, using the MCODE plugin. As a result, a total of 316 differentially expresses genes were filtered according to the provided cut-off criteria. The enriched DEGs were related to extracellular matrix interaction, cell adhesion mediated by integrin, angiogenesis, and inflammatory response. Furthermore, the hub gene analysis resulted in five hub genes namely, FN1, FBN1, CDH1, CD44, and SPP1, were up-regulated during the infection which could lead to reproductive disorders in sheep. In conclusion, the DEGs, functional and pathways terms, along with hub genes identified in the current study can provide prospective targets for the early diagnosis and treatment of brucellosis and provide insight into the molecular mechanism underlying the alterations that occur during brucellosis in sheep.

Construction of an interactome network among circRNA-miRNA-mRNA reveals new biomarkers in hBMSCs osteogenic differentiation

Human bone marrow mesenchymal stem cells (hBMSCs) are adult stem cells residing in the bone marrow, characterized by their capacity for multi-directional differentiation, self-renewal, migration, and engraftment. Serving as seed cells, BMSCs play a pivotal role in the regeneration of bone defects. Hence, investigating the transcription factors and signaling pathways involved in the regulation of osteogenic differentiation in BMSCs holds significant importance. Recent research has unveiled that certain circular RNAs (circRNAs) can function as molecular sponges, influencing the osteogenic differentiation process of mesenchymal stem cells. However, many circRNAs remain undiscovered, and their precise mechanisms remain elusive. Therefore, the objective of this study is to construct an osteogenic differentiation-related circRNA-miRNA-mRNA network in hBMSCs. Subsequently, through bioinformatics analysis, we constructed a ceRNA network related to the osteogenic differentiation ability of hBMSCs, comprising 22 circRNAs, 17 miRNAs, and 15 mRNAs. The potential circRNA-miRNA-mRNA axes, including the role of hsa_circ_0001600 in promoting the osteogenic differentiation of hBMSCs through the targeted regulation of hsa-miR-542-3p, were validated through in vitro experiments.

Gene expression profile analysis of severe influenza-based modulation of idiopathic pulmonary fibrosis

BackgroundIt is known severe influenza infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other. Till now, no associated mechanism has been reported.MethodWe collected the genetic pattern of expression of severe influenza (GSE111368) and IPF (GSE70866) from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (C-DEGs) were identified from the two datasets, and using this data, we conducted three forms of analyses, functional annotation, protein–protein interaction (PPI) network and module construction, and hub gene identification and co-expression analysis.ResultsIn all, 174 C-DEGs were selected for additional analyses. Based on our functional analysis, these C-DEGs mediated inflammatory response and cell differentiation. Furthermore, using cytoHubba, we identified 15 genes, namely, MELK, HJURP, BIRC5, TPX2, TK1, CDT1, UBE2C, UHRF1, CCNA2, TYMS, CDCA5, CDCA8, RAD54L, CCNB2, and ITGAM, which served as hub genes to possibly contribute to severe influenza patients with IPF disease as comorbidity. The hub gene expressions were further confirmed using two stand-alone datasets (GSE101702 for severe influenza and GSE10667 for IPF).ConclusionHerein, we demonstrated the significance of common pathways and critical genes in severe influenza and IPF etiologies. The identified pathways and genes may be employed as possible therapeutic targets for future therapy against severe influenza patients with IPF.