Network Clustering Research Articles

Driving Thailand healthcare communication policy on e-cigarettes through social network analysis

E-cigarettes pose a significant public health concern, particularly for youth and young adults. Policymaking in this area is complicated by changing consumption patterns, diverse user demographics, and dynamic online and offline communities. This study uses social network analytics to examine the social dynamics and communication patterns related to e-cigarette use. We analyzed data from various social media platforms, forums, and online communities, which included both advocacy for e-cigarettes as a safer smoking alternative and opposition due to health risks. Our findings inform targeted healthcare policy interventions, such as educational campaigns tailored to specific network clusters, regulations based on user interaction and influence patterns, and collaborations with key influencers to spread accurate health information.

Organic Management and Intercropping of Fruit Perennials Increase Soil Microbial Diversity and Activity in Arid Zone Orchard Cropping Systems

Organic farming encourages soil management practices that can improve soil health and fertility by increasing soil organic matter inputs and system sustainability. This study evaluated the effect of three years of continuous organic farming and intercropping orchard treatments on soil microbial diversity, microbial enumeration, respiration, soil fertility and fruit yields. Organic management resulted in higher soil organic matter content, Olsen P, and water holding capacity, but did not affect soil pH, electrical conductivity (EC), K, or Na levels. Growth parameters measured on all fruit trees were not significantly different among treatments. The enumeration of bacteria was significantly higher in organic plots when compared to conventionally managed plots. Soil respiration and substrate-induced respiration were significantly higher in the organic diverse plots in comparison to both conventional systems. The genomic analysis of prokaryotes (16S rRNA) and eukaryotes/fungi (ITS) revealed a significantly higher number of taxa, Shannon H index, and Equitability index in the organic systems for both prokaryotes and eukaryotes, in comparison to conventional farming, all of which are indicators of system sustainability. The relative abundance of Operational Taxonomic Units (OTUs) previously reported as diazotrophs, denitrifiers, or involved in the sulfur cycle, as well as Arbuscular Mychorrizae Fungi (AMF)/glomeromycotan, were highest in the organically managed soils than in the conventional plots. A multivariate correlation network clustering revealed that the microbial communities within the organic and conventional soils had strong dissimilarities regarding soil microbial niches. Our work provides evidence that organic management can be used for increasing soil microbial diversity and soil health, leading to higher levels of sustainability in fruit orchard systems.

OSC-GOR: Optimal Security-Aware Cluster-Based Hybrid Geographical and Opportunistic Routing for Vehicular ad hoc Networks

Vehicular ad hoc networks (VANETs) are crucial for infrastructure-less vehicular communication. The dynamic and complex nature of VANETs necessitates efficient routing strategies. This paper introduces an optimal security-aware cluster-based hybrid geographical and opportunistic routing (OSC-GOR) for VANETs. By utilizing the improved bald eagle optimization (IBEO) algorithm, we achieve optimal clustering that enhances energy efficiency. Cluster heads (CHs) are elected based on trust, calculated from metrics like energy consumption and signal strength. A Poplar optimization algorithm (POA) further refines these metrics. Subsequently, an optimal min–max recurrent neural network (OMM-RNN) determines the next forwarder nodes, facilitating data transfer in VANETs. Implemented using the NS-2 tool and SUMO traffic generator, OSC-GOR showcases optimized clustering and superior network efficiency. Comparative simulations reveal OSC-GOR’s advantages in energy use, latency, delivery ratio, goodput, and routing overhead, outperforming existing methods. Notably, OSC-GOR effectively counters VANET attacks such as GPS spoofing and Sybil threats.

High expression of PLA2G2A in fibroblasts plays a crucial role in the early progression of carotid atherosclerosis

BackgroundIn mouse models of atherosclerosis, knockout of the PLA2G2A gene has been shown to reduce the volume of atherosclerotic plaques. Clinical trials have demonstrated the potential of using the sPLA2 inhibitor Varespladib in combination with statins to reduce lipid levels. However, this approach has not yielded the expected results in reducing the risk of cardiovascular events. Therefore, it is necessary to further investigate the mechanisms of PLA2G2A.MethodsSingle-cell transcriptome data from two sets of carotid plaques, combined with clinical patient information. were used to describe the expression characteristics of PLA2G2A in carotid plaques at different stages. In order to explore the mechanisms of PLA2G2A, we conducted enrichment analysis, cell–cell communication analysis and single-cell regulatory network inference and clustering analyses. We validated the above findings at the cellular level.ResultsOur findings indicate that PLA2G2A is primarily expressed in vascular fibroblasts and shows significant cell interactions with macrophages in the early-stage, especially in complement and inflammation-related pathways. We also found that serum sPLA2 levels have stronger diagnostic value in patients with mild carotid artery stenosis. Subsequent comparisons of single-cell transcriptomic data from early and late-stage carotid artery plaques corroborated these findings and predicted transcription factors that might regulate the progression of early carotid atherosclerosis (CA) and the expression of PLA2G2A.ConclusionsOur study discovered and validated that PLA2G2A is highly expressed by vascular fibroblasts and promotes plaque progression through the activation of macrophage complement and coagulation cascade pathways in the early-stage of CA.

Measuring the dynamic balance of integration and segregation underlying consciousness, anesthesia, and sleep in humans

Consciousness requires a dynamic balance of integration and segregation in brain networks. We report an fMRI-based metric, the integration-segregation difference (ISD), which captures two key network properties: network efficiency (integration) and clustering (segregation). With this metric, we quantify brain state transitions from conscious wakefulness to unresponsiveness induced by the anesthetic propofol. The observed changes in ISD suggest a profound shift towards the segregation of brain networks during anesthesia. A common unimodal-transmodal sequence of disintegration and reintegration occurs in brain networks during, respectively, loss and return of responsiveness. Machine learning models using integration and segregation data accurately identify awake vs. unresponsive states and their transitions. Metastability (dynamic recurrence of non-equilibrium transient states) is more effectively explained by integration, while complexity (diversity of neural activity) is more closely linked with segregation. A parallel analysis of sleep states produces similar findings. Our results demonstrate that the ISD reliably indexes states of consciousness.

Effective xanthine oxidase inhibitor urate lowering therapy in gout is linked to an emergent serum protein interactome of complement and inflammation modulators

Urate-lowering treatment (ULT) to target with xanthine oxidase inhibitors (XOIs) paradoxically causes early increase in gouty arthritis flares. Because delayed reduction in flare burden is mechanistically unclear, we tested for ULT inflammation responsiveness markers. Unbiased proteomics analyzed blood samples (baseline, 48 weeks ULT) in two, independent ULT out trial cohorts (n = 19, n = 30). STRING-db and multivariate analyses supplemented determinations of altered proteins via Wilcoxon matched pairs signed rank testing in XOI ULT responders. Mechanistic studies characterized proteomes of cultured XOI-treated murine bone marrow macrophages (BMDMs). At 48 weeks ULT, serum urate normalized in all gout patients, and flares declined in association with significantly altered proteins (p < 0.05) in clustering and proteome networks in sera and peripheral blood mononuclear cells. Sera demonstrated altered complement activation and regulatory gene ontology biologic processes. In both cohorts, a treatment-emergent serum interactome included key gouty inflammation mediators (C5, IL-1B, CXCL8, IL6). Last, febuxostat treatment decreased complement activation biologic process proteins in cultured BMDMs. Reduced gout flares are linked with a XOI treatment-emergent serum protein interactome that includes inflammation regulators, associated with altered complement activation and regulatory biologic processes. Serum and leukocyte proteomics could help identify when gouty inflammatory processes begin to subside in response to ULT.Trial Registration: ClinicalTrials.gov Identifier NCT02579096, posted October 19, 2015.

Exclusive enteral nutrition initiates individual protective microbiome changes to induce remission in pediatric Crohn's disease.

Exclusive enteral nutrition (EEN) is a first-line therapy for pediatric Crohn's disease (CD), but protective mechanisms remain unknown. We established a prospective pediatric cohort to characterize the function of fecal microbiota and metabolite changes of treatment-naive CD patients in response to EEN (German Clinical Trials DRKS00013306). Integrated multi-omics analysis identified network clusters from individually variable microbiome profiles, with Lachnospiraceae and medium-chain fatty acids as protective features. Bioorthogonal non-canonical amino acid tagging selectively identified bacterial species in response to medium-chain fatty acids. Metagenomic analysis identified high strain-level dynamics in response to EEN. Functional changes in diet-exposed fecal microbiota were further validated using gut chemostat cultures and microbiota transfer into germ-free Il10-deficient mice. Dietary model conditions induced individual patient-specific strain signatures to prevent or cause inflammatory bowel disease (IBD)-like inflammation in gnotobiotic mice. Hence, we provide evidence that EEN therapy operates through explicit functional changes of temporally and individually variable microbiome profiles.

Exploring the Effect of Road Network Structure on Inter-Regional Accessibility in a Diverse Road Network

Abstract. Accessibility analyses quantify the level of access to certain areas or opportunities, such as employment and healthcare facilities. Since public data is often aggregated at the level of regions, such as administrative units, it is useful to quantify accessibility between regions. Many factors influence inter-regional accessibility, most notably the accessibility metric used, and the way in which regions are chosen. This paper investigates the effects of road network structure on accessibility, using a previously developed inter-regional accessibility model that bases its accessibility metric on travel distance via the road network. This paper considers an area within the City of Tshwane municipality in South Africa. We investigate the effects of road structure in two ways. Firstly, regions are chosen based on the road network structure, which is done by extending a previously developed road network clustering algorithm for this novel use. Different spatial scales of regionalisation are considered, and the accessibility between these regions is compared to the accessibility between administrative units within the study area. Secondly, the effect of road network homogeneity on accessibility is investigated, where homogeneity corresponds to a uniform concentration of roads across a region. The results show that although road network homogeneity does not significantly correlate with accessibility, the way in which regions are chosen and their spatial scale has a strong effect on the results of the accessibility model. Our novel method of obtaining regions thus provides fresh insights into road-based accessibility within the City of Tshwane.

Interpreting the Epidemiological Characteristics of HIV-1 in Heterosexually Transmitted Population Based on Molecular Transmission Network in Kunming, Yunnan: A Retrospective Cohort Study.

Heterosexuals have become the most prevalent group of HIV-1 in Kunming, Yunnan Province. Utilizing the principle of genetic similarity between their gene sequences, we built a molecular transmission network by gathering data from earlier molecular epidemiological studies. This allowed us to analyze the epidemiological features of this group and offer fresh concepts and approaches for the prevention and management of HIV-1 epidemics. Cytoscope was used to visualize and characterize the network following the processing of the sample gene sequences by BioEdit and HyPhy. The number of possible links and the size of the clusters were investigated as influencing factors using a zero-inflated Poisson model and a logistic regression model, respectively. A scikit-learn-based prediction model was developed to account for the dynamic changes in the HIV-1 molecular network. Six noteworthy modular clusters with network scores ranging from 4 to 9 were found from 150 clusters using Molecular Complex Detection analysis at a standard genetic distance threshold of 0.01. The size of the number of possible links and the network's clustering rate were significantly impacted by sampling time, marital status, and CD4+ T lymphocytes (all p < 0.05). The gradient boosting machine (GBM) model had the highest area under the curve value, 0.884 ± 0.051, according to scikit-learn. Though not all cluster subtypes grew equally, the network clusters were relatively specific and aggregated. The largest local transmission-risk group for HIV-1CRF08_BC is now the heterosexual transmission population. The most suitable model for constructing the HIV-1 molecular network dynamics prediction model was found to be the GBM model.

High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks.

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.

The role of two major nucleoid-associated proteins in Streptomyces, HupA and HupS, in stress survival and gene expression regulation

BackgroundStreptomyces are sporulating soil bacteria with enormous potential for secondary metabolites biosynthesis. Regulatory networks governing Streptomyces coelicolor differentiation and secondary metabolites production are complex and composed of numerous regulatory proteins ranging from specific transcriptional regulators to sigma factors. Nucleoid-associated proteins (NAPs) are also believed to contribute to regulation of gene expression. Upon DNA binding, these proteins impact DNA accessibility. Among NAPs, HU proteins are the most widespread and abundant. Unlike other bacteria, the Streptomyces genomes encode two HU homologs: HupA and HupS, which differ in structure and expression profile. However, it remained unclear whether the functions of both homologs overlap. Additionally, although both proteins have been shown to bind the chromosome, their rolesin transcriptional regulation have not been studied so far.ResultsIn this study, we explore whether HupA and HupS affect S. coelicolor growth under optimal and stressful conditions and how they control global gene expression. By testing both single and double mutants, we address the question of the complementarity of both HU homologs. We show that the lack of both hup genes led to growth and sporulation inhibition, as well as increased spore fragility. We also demonstrate that both HU homologs can be considered global transcriptional regulators, influencing expression of between 2% and 6% genes encoding among others proteins linked to global regulatory networks and secondary metabolite production.ConclusionsWe identify the independent HupA and HupS regulons, as well as genes under the control of both HupA and HupS proteins. Our data indicate a partial overlap between the functions of HupA and HupS during S. coelicolor growth. HupA and HupS play important roles in Streptomyces regulatory network and impact secondary metabolite clusters.

Network mutual information measures for graph similarity

A wide range of tasks in network analysis, such as clustering network populations or identifying anomalies in temporal graph streams, require a measure of the similarity between two graphs. To provide a meaningful data summary for downstream scientific analyses, the graph similarity measures used for these tasks must be principled, interpretable, and capable of distinguishing meaningful overlapping network structure from statistical noise at different scales of interest. Here we derive a family of graph mutual information measures that satisfy these criteria and are constructed using only fundamental information theoretic principles. Our measures capture the information shared among networks according to different encodings of their structural information, with our mesoscale mutual information measure allowing for network comparison under any specified network coarse-graining. We test our measures in a range of applications on real and synthetic network data, finding that they effectively highlight intuitive aspects of network similarity across scales in a variety of systems.

From Text to Safety: A Novel Framework for Mining Unsafe Aviation Events Using Advanced Neural Network and Feature Network

The rapid growth of the aviation industry highlights the need for strong safety management. Analyzing data on unsafe aviation events is crucial for preventing risks. This paper presents a new method that integrates the Transformer network model, clustering analysis, and feature network modeling to analyze Chinese text data on unsafe aviation events. Initially, the Transformer model is used to generate summaries of event texts, and the performance of three pre-trained Chinese models is evaluated and compared. Next, the Jieba tool is applied to segment both summarized and original texts to extract key features of unsafe events and prove the effectiveness of the pre-trained Transformer model in simplifying lengthy and redundant original texts. Then, cluster analysis based on text similarity categorizes the extracted features. By solving the correlation matrix of these features, this paper constructs a feature network for unsafe aviation events. The network’s global and individual metrics are calculated and then used to identify key feature nodes, which alert aviation professionals to focus more on the decision-making process for safety management. Based on the established network and these metrics, a data-driven hidden danger warning strategy is proposed and illustrated. Overall, the proposed method can effectively analyze Chinese texts of unsafe aviation events and provide a basis for improving aviation safety management.

Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure

Reduced functional connectivity of physiological systems is associated with poor prognosis in critically ill patients. However, physiological network analysis is not commonly used in clinical practice and awaits quantitative evidence. Acute liver failure (ALF) is associated with multiorgan failure and mortality. Prognostication in ALF is highly important for clinical management but is currently dependent on models that do not consider the interaction between organ systems. This study aims to examine whether physiological network analysis can predict survival in patients with ALF. Data from 640 adult patients admitted to the ICU for paracetamol-induced ALF were extracted from the MIMIC-III database. Parenclitic network analysis was performed on the routine biomarkers using 28-day survivors as reference population and network clusters were identified for survivors and non-survivors using k-clique percolation method. Network analysis showed that liver function biomarkers were more clustered in survivors than in non-survivors. Arterial pH was also found to cluster with serum creatinine and bicarbonate in survivors compared with non-survivors, where it clustered with respiratory nodes indicating physiologically distinctive compensatory mechanism. Deviation along the pH-bicarbonate and pH-creatinine axes significantly predicts mortality independent of current prognostic indicators. These results demonstrate that network analysis can provide pathophysiologic insight and predict survival in critically ill patients with ALF.

Combining full-length transcriptome sequencing and next generation sequencing to provide insight into the growth superiority of the hybrid grouper (Cromileptes altivelas (♀) × Epinephelus lanceolatus (♂)).

The hybrid grouper (Cromileptes altivelas, ♀ × Epinephelus lanceolatus, ♂) is an economically important aquaculture species that exhibits certain growth advantages compared to its female parent, Cromileptes altivelas. However, the current understanding of the molecular mechanisms underlying the growth of hybrid groupers is lacking. Herein, we performed full-length transcriptome sequencing and next-generation sequencing on the hybrid grouper and its parents to identify growth-related genes and comprehensively analyze the regulatory mechanism of growth heterosis in the hybrid grouper. Approximately 44.70, 40.44, and 45.32 Gb of single-molecule real-time sequencing data were generated in C. altivelas (Cal), E. lanceolatus (Ela), and the hybrid (Hyb), which were combined into 204,322 non-redundant isoforms using the PacBio sequencing platform. Differentially expressed genes (DEGs) were identified between Hyb and Cal (3,494, 2,125, and 1,487 in brain, liver, and muscle tissues, respectively) and Hyb and Ela (3,415, 2,351, and 1,675 in brain, liver, and muscle tissues, respectively). Then, 27 DEGs (13 in the brain and 14 in the muscle) related to growth traits were identified using cluster and correlation network analysis. Quantitative RT-PCR validated 15 DEGs consistent with transcriptome sequencing (RNA-seq) trends. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that these 15 genes were mainly involved in regulating the actin cytoskeleton, suggesting that this pathway plays an essential role in fish growth. In addition, we found that the phosphatase and tensin homologue (PTEN) is a key regulator of growth heterosis in Hyb. These results shed light on the regulatory mechanism of growth in the Hyb, which is important for marker-assisted selection programs to improve the growth quality of groupers.

ARR1 and ARR12 modulate arsenite toxicity responses in Arabidopsis roots by transcriptionally controlling the actions of NIP1;1 and NIP6;1.

Cytokinin is central to coordinating plant adaptation to environmental stresses. Here, we first demonstrated the involvement of cytokinin in Arabidopsis responses to arsenite [As(III)] stress. As(III) treatment reduced cytokinin contents, while cytokinin treatment repressed further primary root growth in Arabidopsis plants under As(III) stress. Subsequently, we revealed that the cytokinin signaling members ARR1 and ARR12, the type-B ARABIDOPSIS RESPONSE REGULATORs, participate in cytokinin signaling-mediated As(III) responses in plants as negative regulators. A comprehensive transcriptome analysis of the arr1 and arr12 single and arr1,12 double mutants was then performed to decipher the cytokinin signaling-mediated mechanisms underlying plant As(III) stress adaptation. Results revealed important roles for ARR1 and ARR12 in ion transport, nutrient responses, and secondary metabolite accumulation. Furthermore, using hierarchical clustering and regulatory network analyses, we identified two NODULIN 26-LIKE INTRINSIC PROTEIN (NIP)-encoding genes, NIP1;1 and NIP6;1, potentially involved in ARR1/12-mediated As(III) uptake and transport in Arabidopsis. By analyzing various combinations of arr and nip mutants, including high-order triple and quadruple mutants, we demonstrated that ARR1 and ARR12 redundantly function as negative regulators of As(III) tolerance by acting upstream of NIP1;1 and NIP6;1 to modulate their function in arsenic accumulation. ChIP-qPCR, EMSA, and transient dual-LUC reporter assays revealed that ARR1 and ARR12 transcriptionally activate the expression of NIP1;1 and NIP6;1 by directly binding to their promoters and upregulating their expression, leading to increased arsenic accumulation under As(III) stress. These findings collectively provide insights into cytokinin signaling-mediated plant adaptation to excessive As(III), contributing to the development of crops with low arsenic accumulation.

Energy aware multiobjective levy flight artificial rabbits optimization based clustering and routing for mobile wireless sensor networks

Energy aware multiobjective levy flight artificial rabbits optimization based clustering and routing for mobile wireless sensor networks

Microbial inoculants using spent mushroom substrates as carriers improve soil multifunctionality and plant growth by changing soil microbial community structure

Peat is typically used as a carrier for microbial inoculants; however, due to its non-renewable nature alternatives need to be identified as reliable and renewable carriers for mineral-solubilizing inoculants. In pot experiments, solid microbial inoculants were comprised of peat (P), biochar (BC), and spent mushroom substrates (SMS) using Medicago sativa L. as experimental materials, and the purpose of this study is to assess the effect of solid microbial inoculants on soil multifunctionality and plant growth. The results revealed that the SMS microbial inoculant had the greatest positive impact on plant biomass and significantly stimulated soil multifunctionality which is typically managed or assessed based on various soil functions or processes that are crucial for sustaining productivity, in contrast to the peat microbial inoculant, particularly at a supply level of 100 g/pot. There was no significant correlation between soil multifunctionality and bacterial/fungal microbial diversity. However, according to the co-occurrence network of bacteria and fungi, soil multifunctionality was intimately correlated with the biodiversity of the main ecological clusters (modules) of bacteria and fungi, rather than to the entire soil microbial community structure. The keystone species of module hubs and connectors play critical roles in maintaining the stability of ecological clusters of microbial co-occurrence networks and linkages between ecological clusters. Soil pH is a major predictor of changes in plant biomass, and leads to changes therein by affecting the major ecological clusters of bacterial and fungal co-occurrence networks. These results suggested that SMS may serve as a good alternative to peat as a carrier of mineral-solubilizing microorganisms to maintain soil multifunctionality and promote plant growth.

Chronobiological Spatial Clusters of Cortical Regions in the Human Brain.

We demonstrate that different regions of the cerebral cortex have different diurnal rhythms of spontaneously occurring high-frequency oscillations (HFOs). High-frequency oscillations were assessed with standard-of-care stereotactic electroencephalography in patients with drug-resistant epilepsy. To ensure generalizability of our findings beyond patients with drug-resistant epilepsy, we excluded stereotactic electroencephalography electrode contacts lying within seizure-onset zones, epileptogenic lesions, having frequent epileptiform activity, and excessive artifact. For each patient, we evaluated twenty-four 5-minute stereotactic electroencephalography epochs, sampled hourly throughout the day, and obtained the HFO rate (number of HFOs/minute) in every stereotactic electroencephalography channel. We analyzed diurnal rhythms of the HFO rates with the cosinor model and clustered neuroanatomic parcels in a standard brain space based on similarity of their cosinor parameters. Finally, we compared overlap among resting-state networks, described in the neuroimaging literature, and chronobiological spatial clusters discovered by us. We found five clusters that localized predominantly or exclusively to the left perisylvian, left perirolandic and left temporal, right perisylvian and right parietal, right frontal, and right insular-opercular cortices, respectively. These clusters were characterized by similarity of the HFO rates according to the time of the day. Also, these chronobiological spatial clusters preferentially overlapped with specific resting-state networks, particularly default mode network (clusters 1 and 3), frontoparietal network (cluster 1), visual network (cluster 1), and mesial temporal network (cluster 2). This is probably the first human study to report clusters of cortical regions with similar diurnal rhythms of electrographic activity. Overlap with resting-state networks attests to their functional significance and has implications for understanding cognitive functions and epilepsy-related mortality.

Linking Microbiota Profiles to Disease Characterization in Common Variable Immunodeficiency: The Case of Granulomatous-Lymphocytic Interstitial Lung Disease.

Background and objectives: Common variable immunodeficiency (CVID) is a primary immunodeficiency characterized by decreased immunoglobulins and recurrent infections, with non-infectious complications such as granulomatous-lymphocytic interstitial lung disease (GLILD) affecting up to 30% of patients. Methods: Using high-throughput 16S rRNA gene sequencing, salivary, sputum, and fecal microbiome from CVID patients with GLILD, comparing them to CVID patients without GLILD-with immune dysregulation (dCVID) and only infections (iCVID)-and healthy controls was analyzed. Results: A total of 41 CVID patients, 7 with GLILD, and 15 healthy donors were included. Global fecal biodiversity was significantly lower in GLILD patients compared to CVID subgroups and controls. GLILD patients harbored different specific bacterial communities in all niches, with some keystone species common to dCVID. Conchiformibius, Micrococcales, and Capnocytophaga are more frequent in the sputum of GLILD patients. Saliva in GLILD shows higher frequencies of Conchiformibius and Haemophilusparainfluenzae. Fecal samples from GLILD patients have higher levels of Gemella morbilorum, Lacticaseibacillus, and Cellulosimicrobium. A non-assigned Conchiformibius spp. is consistently associated with GLILD across different niches and could be a potential pathobiont or relevant microbiological marker for GLILD. Cluster network and correlation analyses show profound dysbiosis in the sputum, saliva, and feces of GLILD patients. Conclusions: These findings highlight significant microbiome alterations in CVID patients with GLILD, particularly in the respiratory tract, suggesting a possible link to both local and systemic immune dysregulation.