Mechanistic study of acupuncture on the pterygopalatine ganglion to improve allergic rhinitis: analysis of multi-target effects based on bioinformatics/network topology strategie.

One of the prevalent chronic inflammatory disorders of the nasal mucosa, allergic rhinitis (AR) has become more widespread in recent years. Acupuncture pterygopalatine ganglion (aPPG) is an emerging alternative therapy that is used to treat AR, but the molecular mechanisms underlying its anti-inflammatory effects are unclear. This work methodically demonstrated the multi-target mechanisms of aPPG in treating AR based on bioinformatics/topology using techniques including text mining, bioinformatics, and network topology, among others. A total of 16 active biomarkers and 108 protein targets related to aPPG treatment of AR were obtained. A total of 345 Gene Ontology terms related to aPPG of AR were identified, and 135 pathways were screened based on Kyoto Encyclopedia of Genes and Genomes analysis. Our study revealed for the first time the multi-targeted mechanism of action of aPPG in the treatment of AR. In animal experiments, aPPG ameliorated rhinitis symptoms in OVA-induced AR rats; decreased serum immunoglobulin E, OVA-sIgE, and substance P levels; elevated serum neuropeptide Y levels; and modulated serum Th1/Th2/Treg/Th17 cytokine expression by a mechanism that may be related to the inhibition of activation of the TLR4/NF-κB/NLRP3 signaling pathway. In vivo animal experiments once again validated the results of the bioinformatics analysis. This study revealed a possible multi-target mechanism of action between aPPG and AR, provided new insights into the potential pathogenesis of AR, and proved that aPPG was a promising complementary alternative therapy for the treatment of AR.

Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes.

Protein-protein interactions (PPIs) are the basis of many important biological processes, with protein complexes being the key forms implementing these interactions. Understanding protein complexes and their functions is critical for elucidating mechanisms of life processes, disease diagnosis and treatment and drug development. However, experimental methods for identifying protein complexes have many limitations. Therefore, it is necessary to use computational methods to predict protein complexes. Protein sequences can indicate the structure and biological functions of proteins, while also determining their binding abilities with other proteins, influencing the formation of protein complexes. Integrating these characteristics to predict protein complexes is very promising, but currently there is no effective framework that can utilize both protein sequence and PPI network topology for complex prediction. To address this challenge, we have developed HyperGraphComplex, a method based on hypergraph variational autoencoder that can capture expressive features from protein sequences without feature engineering, while also considering topological properties in PPI networks, to predict protein complexes. Experiment results demonstrated that HyperGraphComplex achieves satisfactory predictive performance when compared with state-of-art methods. Further bioinformatics analysis shows that the predicted protein complexes have similar attributes to known ones. Moreover, case studies corroborated the remarkable predictive capability of our model in identifying protein complexes, including 3 that were not only experimentally validated by recent studies but also exhibited high-confidence structural predictions from AlphaFold-Multimer. We believe that the HyperGraphComplex algorithm and our provided proteome-wide high-confidence protein complex prediction dataset will help elucidate how proteins regulate cellular processes in the form of complexes, and facilitate disease diagnosis and treatment and drug development. Source codes are available at https://github.com/LiDlab/HyperGraphComplex.

Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes.

Developments in biotechnologies enable multi-platform data collection for functional genomic units apart from the gene. Profiling of non-coding microRNAs (miRNAs) is a valuable tool for understanding the molecular profile of the cell, both for canonical functions and malignant behavior due to complex diseases. We propose a graphical mixed-effects statistical model incorporating miRNA-gene target relationships. We implement an integrative pathway analysis that leverages measurements of miRNA activity for joint analysis with multimodal observations of gene activity including gene expression, methylation, and copy number variation. We apply our analysis to a breast cancer dataset, and consider differential activity in signaling pathways across breast tumor subtypes. We offer discussion of specific signaling pathways and the effect of miRNA integration, as well as publish an interactive data visualization to give public access to the results of our analysis.

The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.

Although much is known about how single neurons in the hippocampus represent an animal's position, how circuit interactions contribute to spatial coding is less well understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured CA1 cell-cell interactions in male rats during open field exploration. The statistics of these interactions depend on whether the animal is in a familiar or novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the informativeness of their spatial inputs. This structure facilitates linear decodability, making the information easy to read out by downstream circuits. Overall, our findings suggest that the efficient coding hypothesis is not only applicable to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.SIGNIFICANCE STATEMENT Local circuit interactions play a key role in neural computation and are dynamically shaped by experience. However, measuring and assessing their effects during behavior remains a challenge. Here, we combine techniques from statistical physics and machine learning to develop new tools for determining the effects of local network interactions on neural population activity. This approach reveals highly structured local interactions between hippocampal neurons, which make the neural code more precise and easier to read out by downstream circuits, across different levels of experience. More generally, the novel combination of theory and data analysis in the framework of maximum entropy models enables traditional neural coding questions to be asked in naturalistic settings.

From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis.

BACKGROUND: Cystic Fibrosis (CF) is a monogenic disease caused by mutations in the gene coding the Cystic Fibrosis Transmembrane Regulator (CFTR) protein, but its overall physio-pathology cannot be solely explained by the loss of the CFTR chloride channel function. Indeed, CFTR belongs to a yet not fully deciphered network of proteins participating in various signalling pathways. METHODS: We propose a systems biology approach to study how the absence of the CFTR protein at the membrane leads to perturbation of these pathways, resulting in a panel of deleterious CF cellular phenotypes. RESULTS: Based on publicly available transcriptomic datasets, we built and analyzed a CF network that recapitulates signalling dysregulations. The CF network topology and its resulting phenotypes were found to be consistent with CF pathology. CONCLUSION: Analysis of the network topology highlighted a few proteins that may initiate the propagation of dysregulations, those that trigger CF cellular phenotypes, and suggested several candidate therapeutic targets. Although our research is focused on CF, the global approach proposed in the present paper could also be followed to study other rare monogenic diseases.

Cortical timescales and the modular organization of structural and functional brain networks.

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Bioinformatics/network topology analysis of acupuncture in the treatment of COVID-19: response to methodological issues.

Coronavirus disease 2019 (COVID-19) is a global pandemic and there is an urgent need to discover the therapy for COVID-19. In our original article, we first obtained the target proteins of acupuncture and related target genes of COVID-19 by searching English and Chinese databases, then Gene Ontology biological processes and enrichment analysis were performed on the overlapping targets of acupuncture with COVID-19. Moreover, the compound-target and compound-disease-target network was constructed. This is an innovative attempt to predict the potential benefits of acupuncture treatment for COVID-19. In this letter, we answered reader Zheng's comments.

Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data.

Coronavirus disease 2019 (COVID-19) has attracted research interests from all fields. Phylogenetic and social network analyses based on connectivity between either COVID-19 patients or geographic regions and similarity between syndrome coronavirus 2 (SARS-CoV-2) sequences provide unique angles to answer public health and pharmaco-biological questions such as relationships between various SARS-CoV-2 mutants, the transmission pathways in a community and the effectiveness of prevention policies. This paper serves as a systematic review of current phylogenetic and social network analyses with applications in COVID-19 research. Challenges in current phylogenetic network analysis on SARS-CoV-2 such as unreliable inferences, sampling bias and batch effects are discussed as well as potential solutions. Social network analysis combined with epidemiology models helps to identify key transmission characteristics and measure the effectiveness of prevention and control strategies. Finally, future new directions of network analysis motivated by COVID-19 data are summarized.

Abnormal Structural-Functional Coupling and MRI Alterations of Brain Network Topology in Progressive Supranuclear Palsy.

BACKGROUND: Progressive supranuclear palsy (PSP) can cause structural and functional brain reconstruction. There is a lack of knowledge about the consistency between structural-functional (S-F) connection networks in PSP, despite growing evidence of anomalies in various single brain network parameters. PURPOSE: To study the changes in the structural and functional networks of PSP, network's topological properties including degree, and the consistency of S-F coupling. The relationship with clinical scales was examined including the assessment of PSP severity, and so on. STUDY TYPE: Retrospective. SUBJECTS: A total of 51 PSP patients (70.04 ± 7.46, 25 females) and 101 healthy controls (64.58 ± 8.84, 58 females). FIELD STRENGTH/SEQUENCE: 3-T, resting-state functional MRI, diffusion tensor imaging, and T1-weighted images. ASSESSMENT: A graph-theoretic approach was used to evaluate structural and functional network topology metrics. We used the S-F coupling changes to explore the consistency of structural and functional networks. STATISTICAL TESTS: Independent samples t tests were employed for continuous variables, χ2 tests were used for categorical variables. For network analysis, two-sample t tests was used and implied an false discovery rate (FDR) correction. Pearson correlation analysis was used to explore the correlations. A P-value <0.05 was considered statistically significant. RESULTS: PSP showed variations within and between modules. Specifically, PSP had decreased network properties changes (t = -2.0136; t = 2.5409; t = -2.5338; t = -2.4296; t = -2.5338; t = 2.8079). PSP showed a lower coupling in the thalamus and left putamen and a higher coupling in the visual, somatomotor, dorsal attention, and ventral attention network. S-F coupling was related to the number of network connections (r = 0.32, r = 0.22) and information transmission efficiency (r = 0.55, r = 0.28). S-F coupling was related to basic academic ability (r = 0.39) and disinhibition (r = 0.49). DATA CONCLUSION: PSP may show abnormal S-F coupling and intramodular and intermodular connectome in the structural and functional networks. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Persistent biological invasions alter ecological network topology, impacting disease transmission during community assembly.

Ecological networks experiencing persistent biological invasions may exhibit distinct topological properties, complicating the understanding of how network topology affects disease transmission during invasion-driven community assembly. We developed a trait-based network model to assess the impact of network topology on disease transmission, measured as community- and species-level disease prevalence. We found that trait-based feeding interactions between host species determine the frequency distribution of the niche of co-occurring species in steady-state communities, being either bimodal or multimodal. The width of the growth kernel influences the degree-biomass relationship of species, being either weakly positive or strongly negative. When this relationship is weakly positive, species-level disease prevalence is primarily correlated with biomass. However, when the degree-biomass relationship is strongly negative, species-level disease prevalence is determined by the difference between a host species' in-degree and out-degree closeness centrality. At the community level, disease prevalence is generally amplified by increasing host richness, community biomass, and the standard deviation of interaction generality, while it is diluted by higher network connectance. Our framework verifies the amplification effects of host richness during invasion-driven community assembly and offers valuable insights for estimating disease prevalence based on host network topology.

Menstrually-related migraine shapes the structural similarity network integration of brain.

Menstrually-related migraine (MM) is a primary migraine in women of reproductive age. The underlying neural mechanism of MM was still unclear. In this study, we aimed to reveal the case-control differences in network integration and segregation for the morphometric similarity network of MM. Thirty-six patients with MM and 29 healthy females were recruited and underwent MRI scanning. The morphometric features were extracted in each region to construct the single-subject interareal cortical connection using morphometric similarity. The network topology characteristics, in terms of integration and segregation, were analyzed. Our results revealed that, in the absence of morphology differences, disrupted cortical network integration was found in MM patients compared to controls. The patients with MM showed a decreased global efficiency and increased characteristic path length compared to healthy controls. Regional efficiency analysis revealed the decreased efficiency in the left precentral gyrus and bilateral superior temporal gyrus contributed to the decreased network integration. The increased nodal degree centrality in the right pars triangularis was positively associated with the attack frequency in MM. Our results suggested MM would reorganize the morphology in the pain-related brain regions and reduce the parallel information processing capacity of the brain.

Temporal dynamics of electroencephalographic microstates during sustained pain.

Brain dynamics can be modeled by a sequence of transient, nonoverlapping patterns of quasi-stable electrical potentials named "microstates." While electroencephalographic (EEG) microstates among patients with chronic pain remained inconsistent in the literature, this study characterizes the temporal dynamics of EEG microstates among healthy individuals during experimental sustained pain. We applied capsaicin (pain condition) or control (no-pain condition) cream to 58 healthy participants in different sessions and recorded resting-state EEG 15 min after application. We identified 4 canonical microstates (A-D) that are related to auditory, visual, salience, and attentional networks. Microstate C had less occurrence, as were bidirectional transitions between microstate C and microstates A and B during sustained pain. In contrast, sustained pain was associated with more frequent and longer duration of microsite D, as well as more bidirectional transitions between microstate D and microstates A and B. Microstate D duration positively correlated with intensity of ongoing pain. Sustained pain improved global integration within microstate C functional network, but weakened global integration and efficiency within microstate D functional network. These results suggest that sustained pain leads to an imbalance between processes that load on saliency (microstate C) and processes related to switching and reorientation of attention (microstate D).

Time-evolving controllability of effective connectivity networks during seizure progression.

Over one third of the estimated 3 million people with epilepsy in the United States are medication resistant. Responsive neurostimulation from chronically implanted electrodes provides a promising treatment alternative to resective surgery. However, determining optimal personalized stimulation parameters, including when and where to intervene to guarantee a positive patient outcome, is a major open challenge. Network neuroscience and control theory offer useful tools that may guide improvements in parameter selection for control of anomalous neural activity. Here we use a method to characterize dynamic controllability across consecutive effective connectivity (EC) networks based on regularized partial correlations between implanted electrodes during the onset, propagation, and termination regimes of 34 seizures. We estimate regularized partial correlation adjacency matrices from 1-s time windows of intracranial electrocorticography recordings using the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO). Average and modal controllability metrics calculated from each resulting EC network track the time-varying controllability of the brain on an evolving landscape of conditionally dependent network interactions. We show that average controllability increases throughout a seizure and is negatively correlated with modal controllability throughout. Our results support the hypothesis that the energy required to drive the brain to a seizure-free state from an ictal state is smallest during seizure onset, yet we find that applying control energy at electrodes in the seizure onset zone may not always be energetically favorable. Our work suggests that a low-complexity model of time-evolving controllability may offer insights for developing and improving control strategies targeting seizure suppression.

Exploring interactions between parasites and their hosts in the Pantanal floodplain using an ecological network approach.

The study of host-parasite interactions is essential to understand the role of each host species in the parasitic transmission cycles in a given community. The use of ecological network highlights the patterns of interactions between hosts and parasites, allowing us to evaluate the underlying structural features and epidemiological roles of different species within this context. Through network analysis, we aimed to understand the epidemiological roles of mammalian hosts species (n = 67) and their parasites (n = 257) in the Pantanal biome. Our analysis revealed a modular pattern within the network, characterized by 14 distinct modules, as well as nestedness patterns within these modules. Some key nodes, such as the multi-host parasites Trypanosoma cruzi and T. evansi, connect different modules and species. These central nodes showed us that various hosts species, including those with high local abundances, contribute to parasite maintenance. Ectoparasites, such as ticks and fleas, exhibit connections that reflect their roles as vectors of certain parasites. Overall, our findings contribute to a comprehensive understanding of the structure of host-parasite interactions in the Pantanal ecosystem, highlighting the importance of network analysis as a tool to identifying the main transmission routes and maintenance of parasites pathways. Such insights are valuable for parasitic disease control and prevention strategies and shed light on the broader complexities of ecological communities.

Sediment contamination alters the submersed macrophyte Vallisneria natans and root-associated microbiome profiles during phytoremediation.

The submerged plant Vallisneria natans plays an important role in the remediation of polycyclic aromatic hydrocarbon (PAH)-contaminated sediments. In this study, V. natans and sediments were collected from different V. natans natural vegetation zones, and sediment mesocosms were set up for phytoremediation tests. In addition, commercial-grade V. natans were obtained from the Fish-Bird-Flower market for comparison with phytoremediation. Phytoremediation using V. natans from natural growth significantly increased the degradation of PAHs in Dashui Harbor (0.0148±0.0015 d-1) and Taihu Lake bay sediments (0.0082±0.0010 d-1) but not in commercial-grade V. natans. Transplanted V. natans from natural growth had a significant (p=0.002) effect on PAH degradation, especially in highly PAH-contaminated sedimentary environments. The distinct bacterial communities were strongly affected by sediment type and V. natans type, which contributed to different phytoremediation patterns. Less complex but more stable microbial co-occurrence networks play key roles in improving PAH phytoremediation potential. In addition, V. natans from natural growth in highly PAH-contaminated sediment could adapt to PAH stress by exuding tryptophan metabolites to assemble health-promoting microbiomes. This study provides novel evidence that initial microbial and physicochemical characteristics of sediment and submerged plant types should be considered in the use of bioremediation management strategies for organic pollutant-contaminated sediments.

Charging Scheduling Method for Wireless Rechargeable Sensor Networks Based on Energy Consumption Rate Prediction for Nodes.

With the development of the IoT, Wireless Rechargeable Sensor Networks (WRSNs) derive more and more application scenarios with diverse performance requirements. In scenarios where the energy consumption rate of sensor nodes changes dynamically, most existing charging scheduling methods are not applicable. The incorrect estimation of node energy requirement may lead to the death of critical nodes, resulting in missing events. To address this issue, we consider both the spatial imbalance and temporal dynamics of the energy consumption of the nodes, and minimize the Event Missing Rate (EMR) as the goal. Firstly, an Energy Consumption Balanced Tree (ECBT) construction method is proposed to prolong the lifetime of each node. Then, we transform the goal into Maximizing the value of the Evaluation function of each node's Energy Consumption Rate prediction (MEECR). Afterwards, the setting of the evaluation function is explored and the MEECR is further transformed into a variant of the knapsack problem, namely "the alternating backpack problem", and solved by dynamic programming. After predicting the energy consumption rate of the nodes, a charging scheduling scheme that meets the Dual Constraints of Nodes' energy requirements and MC's capability (DCNM) is developed. Simulations demonstrate the advantages of the proposed method. Compared to the baselines, the EMR was reduced by an average of 35.2% and 26.9%.

Tough Hydrogels with Different Toughening Mechanisms and Applications.

Load-bearing biological tissues, such as cartilage and muscles, exhibit several crucial properties, including high elasticity, strength, and recoverability. These characteristics enable these tissues to endure significant mechanical stresses and swiftly recover after deformation, contributing to their exceptional durability and functionality. In contrast, while hydrogels are highly biocompatible and hold promise as synthetic biomaterials, their inherent network structure often limits their ability to simultaneously possess a diverse range of superior mechanical properties. As a result, the applications of hydrogels are significantly constrained. This article delves into the design mechanisms and mechanical properties of various tough hydrogels and investigates their applications in tissue engineering, flexible electronics, and other fields. The objective is to provide insights into the fabrication and application of hydrogels with combined high strength, stretchability, toughness, and fast recovery as well as their future development directions and challenges.

Spirobifluorene-Based Porous Organic Salts: Their Porous Network Diversification and Construction of Chiral Helical Luminescent Structures.

Porous organic salts (POSs) are organic porous materials assembled via charge-assisted hydrogen bonds between strong acids and bases such as sulfonic acids and amines. To diversify the network topology of POSs and extend its functions, this study focused on using 4,4',4'',4'''-(9,9'-spirobi[fluorene]-2,2',7,7'-tetrayl)tetrabenzenesulfonic acid (spiroBPS), which is a tetrasulfonic acid comprising a square planar skeleton. The POS consisting of spiroBPS and triphenylmethylamine (TPMA) (spiroBPS/TPMA) was constructed from the two-fold interpenetration of an orthogonal network with pts topology, which has not been reported in conventional POSs, owing to the shape of the spirobifluorene backbone. Furthermore, combining tris(4-chlorophenyl)methylamine (TPMA-Cl) and tris(4-bromophenyl)methylamine (TPMA-Br), which are bulkier than TPMA owing to the introduction of halogens at the p-position of the phenyl groups with spiroBPS allows us to construct novel POSs (spiroBPS/TPMA-Cl and spiroBPS/TPMA-Br). These POSs were constructed from a chiral helical network with pth topology, which was induced by the steric hindrance between the halogens and the curved fluorene skeleton. Moreover, spiroBPS/TPMA-Cl with pth topology exhibited circularly polarized luminescence (CPL) in the solid state, which has not been reported in hydrogen-bonded organic frameworks (HOFs).

How brain networks tic: Predicting tic severity through rs-fMRI dynamics in Tourette syndrome.

Tourette syndrome (TS) is a neuropsychiatric disorder characterized by motor and phonic tics, which several different theories, such as basal ganglia-thalamo-cortical loop dysfunction and amygdala hypersensitivity, have sought to explain. Previous research has shown dynamic changes in the brain prior to tic onset leading to tics, and this study aims to investigate the contribution of network dynamics to them. For this, we have employed three methods of functional connectivity to resting-state fMRI data - namely the static, the sliding window dynamic and the ICA based estimated dynamic; followed by an examination of the static and dynamic network topological properties. A leave-one-out (LOO-) validated regression model with LASSO regularization was used to identify the key predictors. The relevant predictors pointed to dysfunction of the primary motor cortex, the prefrontal-basal ganglia loop and amygdala-mediated visual social processing network. This is in line with a recently proposed social decision-making dysfunction hypothesis, opening new horizons in understanding tic pathophysiology.

Brain network dynamics in people with visual snow syndrome.

Visual snow syndrome (VSS) is a neurological disorder characterized by a range of continuous visual disturbances. Little is known about the functional pathological mechanisms underlying VSS and their effect on brain network topology, studied using high-resolution resting-state (RS) 7 T MRI. Forty VSS patients and 60 healthy controls underwent RS MRI. Functional connectivity matrices were calculated, and global efficiency (network integration), modularity (network segregation), local efficiency (LE, connectedness neighbors) and eigenvector centrality (significance node in network) were derived using a dynamic approach (temporal fluctuations during acquisition). Network measures were compared between groups, with regions of significant difference correlated with known aberrant ocular motor VSS metrics (shortened latencies and higher number of inhibitory errors) in VSS patients. Lastly, nodal co-modularity, a binary measure of node pairs belonging to the same module, was studied. VSS patients had lower modularity, supramarginal centrality and LE dynamics of multiple (sub)cortical regions, centered around occipital and parietal lobules. In VSS patients, lateral occipital cortex LE dynamics correlated positively with shortened prosaccade latencies (p = .041, r = .353). In VSS patients, occipital, parietal, and motor nodes belonged more often to the same module and demonstrated lower nodal co-modularity with temporal and frontal regions. This study revealed reduced dynamic variation in modularity and local efficiency strength in the VSS brain, suggesting that brain network dynamics are less variable in terms of segregation and local clustering. Further investigation of these changes could inform our understanding of the pathogenesis of the disorder and potentially lead to treatment strategies.