Journal articles on the topic 'Co-Occurence network'

Abstract Background Papillary renal cell carcinoma (PRCC) is the second most common adult kidney cancer histology, constituting 15-20% of cases. While some patients may have indolent PRCC tumors that grow slowly, other tumors rapidly metastasize. Some PRCC tumors respond to immune checkpoint inhibitors. Thus, understanding the immune tumor microenvironment and how it correlates with patient outcomes in this relatively rare disease is a critical need. Spatial interrogation of patient samples has the potential to offer novel insights into the tumor-immune axis and provide avenues for enhanced diagnosis and treatment. Addressing questions of spatial arrangement within tumors has remained a technical and biological challenge, but emerging spatial biology technologies provide molecular data at single cell resolution. We hypothesized that the spatial interaction of immune, stromal and tumor cells would be prognostic for PRCC patients. Methods A tissue microarray was assembled from an archive of ~100 patients presenting with PRCC. This dataset was assayed with PhenoCycler/CODEX (Akoya Biosciences) using a 31-antibody panel with immune- and cancer-related proteins. We have developed methodology and novel algorithms to perform signal normalization, cell segmentation, and cell typing. We computed neighborhoods for each of 2.5 million cells and performed network analysis to identify spatial clusters. This method allowed us to identify clusters consistently present across multiple TMA spots from the same patient and across multiple patients. Results Using spatial neighborhood analysis, we have identified diverse spatial clusters of potential clinical relevance, including five distinct M2 macrophage spatial clusters. We have described one of the clusters as being physically associated with helper T cells, which is visualized on a PRCC spot and shows co-occurence of M2-macrophages (CD163) and helper T cells (CD4). This pattern was replicated in additional TMA spots from the same patient. In contrast, other clusters of M2-macrophages (M2-M2 spatial cluster and M2-M1 macrophages) are visualized to show vastly different cellular neighborhoods. Clinical associations show that the patients with presence of the M2-T helper cluster have a poor cancer-associated survival (p=0.005). In comparison, the total proportion of M2 macrophages is not associated with survival (p=0.4), highlighting the importance of characterizing spatial interactions beyond cell type quantitation. Conclusions In summary, we highlight the utility of spatial biology to explain the heterogeneity in patients’ tumors and to uncover novel correlates of clinical phenotypes, establishing a platform for future discovery in this field and the identification of additional spatial correlates of patient outcome and clinical response. CDMRP DOD Funding: yes

Introduction: Black cumin (Nigella sativa) is widely used to treat various diseases. It is also believed to relief skin conditions accompanied by itching symptom, such as atopic dermatitis (AD) or eczema. However, the anti-eczema mechanism of action is still unclear. The aims of this syudy was to identify anti-eczema mechanism of action of N. sativa for AD using computer aided prediction and pathway analysis based on protein-chemical networks. Methods: We utilized dataset consisting chemical compounds of N. sativa from KNApSAcK. It is a comprehensive species-metabolite relationship database. Using canonical SMILES strings that encode molecular structures of each compound, we predicted the probabilities of activity (Pa) for anti-eczema effect based on PASS algorithms. The compounds with Pa >0.7 were included for pathway analysis based on protein-chemical interaction networks in STITCH database. We selected interactomes built by experimental data, gene co-expression, closest gene position, fusion, co-occurence, computational prediction, and other secondary data. Results: Thirty-five active compounds of N. sativa have been utilized and 19 of them have potential anti-eczema effects. Oleic acid and lauric acid were predicted with Pa-value of 0.947 and 0.920 for anti-eczema effect, respectively. However, only lauric acid was confirmed having a plausible mechanism of action via LY96-TLR4- PIK3R1 pathway for lipopolysaccharide receptor activity (false discovery rate [FDR] = 0.0243) and low-density lipoprotein particle receptor binding (FDR = 0.0118). Conclusion: Lauric acid in N. sativa has potential antieczema effect to prevent relaps in AD patients by controlling opportunistic bacterial infection that aggravates itching symptom in this condition.

Abstract. The Asian monsoon system has been recognised as an important tipping element in Earth's climate. In this work, we apply recurrence networks, a recently developed technique for nonlinear time series analysis of palaeoclimate data, for detecting episodes with pronounced changes in Asian monsoon dynamics during the last 10 ka in speleothem records from 10 caves covering the major branches of the Asian monsoon system. Our methodology includes multiple archives, explicit consideration of dating uncertainties with the COPRA approach and rigorous significance testing for the coexistence of monsoonal regime shifts at multiple locations to ensure a robust detection of continental-scale changes in monsoonal dynamics. This approach enables us to identify several epochs characterised by nonlinear regime shifts in Asian monsoon variability (8.5–8.0, 5.7–5.4, 4.1–3.6 and 2.8–2.2 ka BP), the timing of which suggests a connection to high-latitude Bond events and other episodes of Holocene rapid climate change (RCC). Interestingly, we also observe a previously unnoticed episode of significantly increased regularity of monsoonal variations around 7.3 ka BP, a timing that is consistent with the typical 1.0–1.5 ka return intervals of Bond events. A possible solar forcing of the detected nonlinear regime shifts in Asian monsoon dynamics is suggested by their co-occurence with pronounced minima and strong variability in solar activity. Drawing on a comprehensive review of the Holocene archeological record in the Asian monsoon realm, we find that these regime shifts partly coincide with known major periods of migration, pronounced cultural changes, and the collapse of ancient human societies. These findings indicate that also future transitions in monsoonal dynamics could induce potentially severe socio-economic impacts of climate change.

The advances in positioning techniques have generated massive trajectory data that represent the mobility of objects, e.g., pedestrians and mobile phones. It is important to integrate information from various modalities for subject tracking or trajectory prediction. Our work attempts to match a face with a corresponding mobile phone based on the heterogeneous trajectories. We propose a framework which associates face trajectories with their corresponding mobile phone trajectories using elaborate and explainable features. Our solution includes two stages: an initial selection of phone trajectories for a given face trajectory and a subsequent identification of which phone trajectory provides an exact match to the given face trajectory. In the first stage, we propose a Multi-Granularity SpatioTemporal Window Searching (MGSTWS) algorithm to select candidate mobile phones that are spatiotemporally close to a given face. In the second stage, we first build an affinity function to score face–phone trajectory point pairs selected by MGSTWS, and construct a feature set for building a face–phone trajectory matching determinator which determines whether a phone trajectory matches a given face trajectory. Our well-designed features guarantee high model simplicity and interpretability. Among the feature set, BGST intelligently leverages disassociation between a face and a mobile phone even if there exists some co-occurence for a non-matching face–phone pair. Based on the feature set, we represent the face–phone matching task as a binary classification problem and train various models, among which LightGBM achieves the best performance with 92.6% accuracy, 96.9% precision, 88.5% recall, and 92.5% F1. Our framework is acceptable in most application scenarios and may benefit some downstream tasks. The preselection-refining architecture of our framework guarantees the applicability and efficiency of the face–phone trajectory pair matching frame.

Abstract Background Dandruff is a chronic, recurring, and common scalp problem that is caused by several etiopathogeneses with complex mechanisms. Management of this condition is typically achieved via antifungal therapies. However, the precise roles played by microbiota in the development of the condition have not been elucidated. Despite their omnipresence on human scalp little is known about the co-occurrence/co-exclusion network of cutaneous microbiota. Results We characterized the scalp and hair surface bacterial and fungal communities of 95 dandruff-afflicted and healthy individuals residing in China. The degree distributions of co-occurrence/co-exclusion network in fungi-bacteria and bacteria-bacteria were higher in the healthy group (P < 0.0001), whereas the betweenness values are higher in the dandruff group (P < 0.01). Meanwhile, the co-occurrence/co-exclusion network among fungi-fungi and fungi-bacteria showed that compared to the healthy group, the dandruff group had more positive links (P < 0.0001). In addition, we observed that Malassezia slooffiae, Malassezia japonica and Malassezia furfur, were more abundant in the dandruff group than in the healthy group. These microbiota were co-exclusion by either multiple bacterial genera or Malassezia sp. in healthy group. The lactic acid bacteria on the scalp and hair surface, especially the genera Lactobacillus and Lactococcus, exhibit a negative correlation with multiple bacterial genera on the scalp and hair surface. Lactobacillus plantarum and Pediococcus lactis isolated on the healthy human scalp can inhibit the growth of Staphylococcus epidermidis in vitro. Conclusions We showed that microbial networks on scalp and hair surface with dandruff were less integrated than their healthy counterparts, with lower node degree and more positive and stronger links which were deemed to be unstable and may be more susceptible to environmental fluctuations. Lactobacillus bacteria have extensive interactions with other bacteria or fungi in the scalp and hair surface micro-ecological network and can be used as targets for improving scalp health.

We propose a model of growing networks based on cliques formations. A clique is used to illustrate for example co-authorship in co-publication networks, co-occurence of words or collaboration between actors of the same movie. Our model is iterative and at each step, a clique of λη existing vertices and (1 − λ)η new vertices is created and added in the network; η is the mean number of vertices per clique and λ is the proportion of old vertices per clique. The old vertices are selected according to preferential attachment. We show that the degree distribution of the generated networks follows the Power Law of parameter 1 + 1/ λ and thus they are ultra small-world networks with high clustering coefficient and low density. Moreover, the networks generated by the proposed model match with some real co-publication networks such as CARI, EGC and HepTh. Nous proposons un modèle de croissance de graphe basé sur la formation de clique. Une clique peut par exemple illustrer la collaboration entre auteurs dans un réseau de co-publication, les relations de co-occurrence des mots dans une phrase ou les relations entre acteurs d'un film. C'est un modèle itératif qui à chaque étape crée une clique de λη anciens sommets et (1 − λ)η nouveaux sommets et l'insère dans le graphe. η est le nombre moyen de sommets dans une clique et λ la proportion moyenne d'anciens sommets dans une clique. La distribution des degrés des réseaux générés suit la Loi de Puissance de paramètre 1 + 1/λ et par conséquent ce sont des réseaux petit-mondes qui présentent un coefficient de clustering élevé et une faible densité. En outre, les réseaux générés par le modèle proposé reproduisent la structure des réseaux de terrains à l'instar des réseaux de co-publication du CARI, de EGC et de HepTh.

AbstractIt is well established that epilepsy and autism spectrum disorder (ASD) commonly co-occur; however, the underlying biological mechanisms of the co-occurence from their genetic susceptibility are not well understood. Our aim in this study is to characterize genetic modules of subgroups of epilepsy and autism genes that have similar phenotypic manifestations and biological functions. We first integrate a large number of expert-compiled and well-established epilepsy- and ASD-associated genes in a multiplex network, where one layer is connected through protein–protein interaction (PPI) and the other layer through gene-phenotype associations. We identify two modules in the multiplex network, which are significantly enriched in genes associated with both epilepsy and autism as well as genes highly expressed in brain tissues. We find that the first module, which represents the Gene Ontology category of ion transmembrane transport, is more epilepsy-focused, while the second module, representing synaptic signaling, is more ASD-focused. However, because of their enrichment in common genes and association with both epilepsy and ASD phenotypes, these modules point to genetic etiologies and biological processes shared between specific subtypes of epilepsy and ASD. Finally, we use our analysis to prioritize new candidate genes for epilepsy (i.e. ANK2, CACNA1E, CACNA2D3, GRIA2, DLG4) for further validation. The analytical approaches in our study can be applied to similar studies in the future to investigate the genetic connections between different human diseases.

IntroductionOver the past decades, immune dysregulation has been consistently demonstrated being common charactoristics of endometriosis (EM) and Inflammatory Bowel Disease (IBD) in numerous studies. However, the underlying pathological mechanisms remain unknown. In this study, bioinformatics techniques were used to screen large-scale gene expression data for plausible correlations at the molecular level in order to identify common pathogenic pathways between EM and IBD.MethodsBased on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects.Results113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD.DiscussionOur research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.

AbstractSelection for bacteria which are K-strategists instead of r-strategists has been shown to improve fish health and survival in aquaculture. We considered an experiment where microcosms were inoculated with natural seawater and the selection regime was switched from K-selection (by continuous feeding) to r-selection (by pulse feeding) and vice versa. We found the networks of significant co-occurrences to contain clusters of taxonomically related bacteria having positive associations. Comparing this with the time dynamics, we found that the clusters most likely were results of similar niche preferences of the involved bacteria. In particular, the distinction between r- or K-strategists was evident. Each selection regime seemed to give rise to a specific pattern, to which the community converges regardless of its prehistory. Furthermore, the results proved robust to parameter choices in the analysis, such as the filtering threshold, level of random noise, replacing absolute abundances with relative abundances, and the choice of similarity measure. Even though our data and approaches cannot directly predict ecological interactions, our approach provides insights on how the selection regime affects the composition of the microbial community, providing a basis for aquaculture experiments targeted at eliminating opportunistic fish pathogens.