Uncovering brain functional connectivity disruption patterns of lung cancer-related pain.

Pain is a pervasive symptom in lung cancer patients during the onset of the disease. This study aims to investigate the connectivity disruption patterns of the whole-brain functional network in lung cancer patients with cancer pain (CP+). We constructed individual whole-brain, region of interest (ROI)-level functional connectivity (FC) networks for 50 CP+ patients, 34 lung cancer patients without pain-related complaints (CP-), and 31 matched healthy controls (HC). Then, a ROI-based FC analysis was used to determine the disruptions of FC among the three groups. The relationships between aberrant FCs and clinical parameters were also characterized. The ROI-based FC analysis demonstrated that hypo-connectivity was present both in CP+ and CP- patients compared to HC, which were particularly clustered in the somatomotor and ventral attention, frontoparietal control, and default mode modules. Notably, compared to CP- patients, CP+ patients had hyper-connectivity in several brain regions mainly distributed in the somatomotor and visual modules, suggesting these abnormal FC patterns may be significant for cancer pain. Moreover, CP+ patients also showed increased intramodular and intermodular connectivity strength of the functional network, which could be replicated in cancer stage IV and lung adenocarcinoma. Finally, abnormal FCs within the prefrontal cortex and somatomotor cortex were positively correlated with pain intensity and pain duration, respectively. These findings suggested that lung cancer patients with cancer pain had disrupted connectivity in the intrinsic brain functional network, which may be the underlying neuroimaging mechanisms.

Meta-model of Human Recognition-behavioral Adaptation System.

Scientific modeling is a syllogistic system of definitive premise, sound inference and consistent explanation to understand, define, quantify, visualize or simulate feature of the target. Single-model is defined to an informative representation for identifying a property of a target object/phenomenon, and meta-model integrates the relevant single-models to explain phenomenological realities. Human recognition-behavioral adaptation is an information-metabolism system to maintain homeostasis of human-self, and that has been investigated in neurological, psychiatric and psychological aspects. I analyzed human recognition-behavioral adaptation-system via scientific modeling. Neurological meta-model of human recognition-behavioral adaptation system was synthesized as complex-network of the functional neuronal modules, and the meta-model was integrated to Mentality-model in the psychiatric aspect, and to Personality-model in the psychological aspect. The integrated meta-models successfully explained phenomenological realities in the aspects. From the above, I comprehended that the meta-model of human recognition-behavioral adaptation-system has been developed to Biopsychosocial model integrating the biological, psychological and socio-environmental factors.

Urease-Powered Micromotors with Spatially Selective Distribution of Enzymes for Capturing and Sensing Exosomes.

Enzyme-catalyzed micro/nanomotors (MNMs) exhibit tremendous potential for biological isolation and sensing, because of their biocompatibility, versatility, and ready access to biofuel. However, flow field generated by enzyme-catalyzed reactions might significantly hinder performance of surface-linked functional moieties, e.g., the binding interaction between MNMs and target cargos. Herein, we develop enzymatic micromotors with spatially selective distribution of urease to enable the independent operation of various modules and facilitate the capture and sensing of exosomes. When urease is modified into the motors' cavity, the flow field from enzyme catalysis has little effect on the exterior surface of the motors. The active motion and encapsulating urease internally result in enhancement of ∼35% and 18% in binding efficiency of target cargos, e.g., exosomes as an example here, compared to their static counterparts and moving micromotors with urease modified externally, respectively. Once exosomes are trapped, they can be transferred to a clean environment by the motors for Raman signal detection and/or identification using the surface Raman enhancement scattering (SERS) effect of coated gold nanoshell. The biocatalytic micromotors, achieving spatial separation between driving module and function module, offer considerable promise for future design of multifunctional MNMs in biomedicine and diagnostics.

Carbon, nitrogen, and phosphorus metabolic relationships and reaction mechanisms in SBBR processes in the plateau habitat.

In this study, two laboratory-scale SBBR reactors were established in a plateau habitat. Using high flux sequencing, the SBBR process was compared by natural sediment and autotrophic sludge to characterize the functional modules and functional genes of carbon, nitrogen, and phosphorus metabolism under different working conditions and to analyze the reaction mechanism. The results showed that all the functional modules of carbon metabolism and nitrogen metabolism were found in the SBBR process, except for methane metabolism, which occurred at 25 °C in tank 2, the functional modules related to methane metabolism are enhanced at all working conditions. Except for methane metabolism, all functional genes in tank 2 are inhibited by different working conditions, whereas tank 1 shows a slight enhancement. The different working conditions in nitrogen metabolism demonstrate inhibition of functional modules and functional genes in both tanks. Oxidative phosphorylation was missing five functional modules, except for M00153, where only two genes, K00424 and K22501, are missing, all of the required genes are missing in the other four functional modules. Overall the different conditions demonstrated some inhibition in both reaction tanks of the SBBR process. It is preferable to use self-cultivated sludge for membrane acclimation when operating the SBBR process in a plateau habitat. The findings of this study can be used to further research microbial carbon, nitrogen, and phosphorus metabolism mechanisms in SBBR processes in plateau habitats.

Construction of a Biomimetic Receptor Based on Hydrophilic Multifunctional Monomer Covalent Organic Framework Molecularly Imprinted Polymers for Molecular Recognition of Cyanidin-3-O-Glucoside.

Anthocyanins (AOCs) are phenols that are readily soluble in water and are commonly present in plants. The chemical instability of AOC, however, causes it to be severely limited in terms of extraction and purification. Hence, in order to obtain efficient and stable extraction of AOC, we designed hydrophilic multifunctional monomer covalent organic framework molecularly imprinted polymers (HMCMIPs) as adsorbents. The functional reagent, p-aminobenzenesulfonic acid (ASA), was added to this material during synthesis to facilitate the sulfonation modification of covalent organic frameworks (COFs), which enhanced its affinity for hydrophilic guests (cyanidin-3-O-glucoside, the representative nutritional and functional ingredient in AOC). With ASA serving as a terminator, overextension of the material to form micron-level cross-linked structures is prevented, thereby increasing its surface area and mass transfer efficiency. The biomimetic receptors were then created by integrating MIPs into sulfonated COFs in order to create multiple binding sites specific for C3G recognition. HMCMIPs exhibited excellent adsorption capacity (1566 mg/g) and superior selectivity (selectivity coefficient >12) for C3G. It has been demonstrated that high purity (93.72%) C3G can be obtained rapidly and efficiently by utilizing HMCMIPs. There may be a potential benefit to the synthesis strategy of HMCMIPs for the extraction of specific active ingredients in the future.

Functional Modules in the Meristems: "Tinkering" in Action.

BACKGROUND: A feature of higher plants is the modular principle of body organisation. One of these conservative morphological modules that regulate plant growth, histogenesis and organogenesis is meristems-structures that contain pools of stem cells and are generally organised according to a common principle. Basic content: The development of meristems is under the regulation of molecular modules that contain conservative interacting components and modulate the expression of target genes depending on the developmental context. In this review, we focus on two molecular modules that act in different types of meristems. The WOX-CLAVATA module, which includes the peptide ligand, its receptor and the target transcription factor, is responsible for the formation and control of the activity of all meristem types studied, but it has its own peculiarities in different meristems. Another regulatory module is the so-called florigen-activated complex, which is responsible for the phase transition in the shoot vegetative meristem (e.g., from the vegetative shoot apical meristem to the inflorescence meristem). CONCLUSIONS: The review considers the composition and functions of these two functional modules in different developmental programmes, as well as their appearance, evolution and use in plant breeding.

AdaPPI: identification of novel protein functional modules via adaptive graph convolution networks in a protein-protein interaction network.

Identifying unknown protein functional modules, such as protein complexes and biological pathways, from protein-protein interaction (PPI) networks, provides biologists with an opportunity to efficiently understand cellular function and organization. Finding complex nonlinear relationships in underlying functional modules may involve a long-chain of PPI and pose great challenges in a PPI network with an unevenly sparse and dense node distribution. To overcome these challenges, we propose AdaPPI, an adaptive convolution graph network in PPI networks to predict protein functional modules. We first suggest an attributed graph node presentation algorithm. It can effectively integrate protein gene ontology attributes and network topology, and adaptively aggregates low- or high-order graph structural information according to the node distribution by considering graph node smoothness. Based on the obtained node representations, core cliques and expansion algorithms are applied to find functional modules in PPI networks. Comprehensive performance evaluations and case studies indicate that the framework significantly outperforms state-of-the-art methods. We also presented potential functional modules based on their confidence.

Metagenomic study of the gut microbiota associated with cow milk consumption in Chinese peri-/postmenopausal women.

Cow milk consumption (CMC) and alterations of gut bacterial composition are proposed to be closely related to human health and disease. Our research aims to investigate the changes in human gut microbial composition in Chinese peri-/postmenopausal women with different CMC habits. A total of 517 subjects were recruited and questionnaires about their CMC status were collected; 394 subjects were included in the final analyses. Fecal samples were used for studying gut bacterial composition. All the subjects were divided into a control group (n = 248) and a CMC group (n = 146) according to their CMC status. Non-parametric tests and LEfSe at different taxonomic levels were used to reveal differentially abundant taxa and functional categories across different CMC groups. Relative abundance (RA) of one phylum (p_Actinobacteria), three genera (g_Bifidobacterium, g_Anaerostipes, and g_Bacteroides), and 28 species diversified significantly across groups. Specifically, taxa g_Anaerostipes (p < 0.01), g_Bacteroides (p < 0.05), s_Anaerostipes_hadrus (p < 0.01), and s_Bifidobacterium_pseudocatenulatum (p < 0.01) were positively correlated with CMC levels, but p_Actinobacteria (p < 0.01) and g_Bifidobacterium (p < 0.01) were negatively associated with CMC levels. KEGG module analysis revealed 48 gut microbiome functional modules significantly (p < 0.05) associated with CMC, including Vibrio cholerae pathogenicity signature, cholera toxins (p = 9.52e-04), and cephamycin C biosynthesis module (p = 0.0057), among others. In conclusion, CMC was associated with changes in gut microbiome patterns including beta diversity and richness of some gut microbiota. The alterations of certain bacteria including g_Anaerostipes and s_Bifidobacterium_pseudocatenulatum in the CMC group should be important for human health. This study further supports the biological value of habitual cow milk consumption.

Non-swarm-based computational approach for mining cancer drug target modules in protein interaction network.

Cancer is a lethal disease that drew the entire world over the past decades. Currently, numerous researches focused on these cancer treatments. Most familiar among them is the targeted therapy; a customized treatment type depends on the cancer drug targets. Further, the selection of targets is a quite sensitive task. The computational approaches are lagging in this field. This paper is intended to propose an optimized multi-functional score-based co-clustering with MapReduce (MR-CoCopt) approach for drug target module mining with optimal functional score set selection. This approach uses biological functional measures for co-clustering, MapReduce framework for handling redundant modules and complex protein interaction network (PIN), and non-swarm intelligence algorithm-bladderworts suction for optimal functional score set selection. It extracts the cancer-specific drug target modules in protein interaction networks. The protein complex coverage of the results is compared with the existing approach. The biological significance of the results is analyzed for the presence of cancer drug targets and drug target characteristics. From these results, novel cancer drug target modules are suggested for the targeted therapy and the active pharmaceutical drugs for these modules are also highlighted.

Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model.

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.

Identifying potential novel insights for COVID-19 pathogenesis and therapeutics using an integrated bioinformatics analysis of host transcriptome.

The molecular mechanisms underlying the pathogenesis of COVID-19 have not been fully discovered. This study aims to decipher potentially hidden parts of the pathogenesis of COVID-19, potential novel drug targets, and identify potential drug candidates. Two gene expression profiles were analyzed, and overlapping differentially expressed genes (DEGs) were selected for which top enriched transcription factors and kinases were identified, and pathway analysis was performed. Protein-protein interaction (PPI) of DEGs was constructed, hub genes were identified, and module analysis was also performed. DGIdb database was used to identify drugs for the potential targets (hub genes and the most enriched transcription factors and kinases for DEGs). A drug-potential target network was constructed, and drugs were ranked according to the degree. L1000FDW was used to identify drugs that can reverse transcriptional profiles of COVID-19. We identified drugs currently in clinical trials, others predicted by different methods, and novel potential drug candidates Entrectinib, Omeprazole, and Exemestane for combating COVID-19. Besides the well-known pathogenic pathways, it was found that axon guidance is a potential pathogenic pathway. Sema7A, which may exacerbate hypercytokinemia, is considered a potential novel drug target. Another potential novel pathway is related to TINF2 overexpression, which may induce potential telomere dysfunction and damage DNA that may exacerbate lung fibrosis. This study identified new potential insights regarding COVID-19 pathogenesis and treatment, which might help us improve our understanding of the mechanisms of COVID-19.

Network pharmacology to unveil the mechanism of Moluodan in the treatment of chronic atrophic gastritis.

BACKGROUND: Moluodan (MLD) is a traditional Chinese patent medicine for the treatment of chronic atrophic gastritis (CAG). However, the mechanism of action (MoA) of MLD for treating CAG still remain unclear. PURPOSE: Elucidate the MoA of MLD for treating CAG based on network pharmacology. STUDY DESIGN: Integrate computational prediction and experimental validation based on network pharmacology. METHODS: Computationally, compounds of MLD were scanned by LC-MS/MS and the target profiles of compounds were identified based on network-based target prediction method. Compounds in MLD were compared with western drugs used for gastritis by hierarchical clustering of target profile. Key biological functional modules of MLD were analyzed, and herb-biological functional module network was constructed to elucidate combinatorial rules of MLD herbs for CAG. Experimentally, MLD's effect on different biological functional modules were validated from both phenotypic level and molecular level in 1- Methyl-3-nitro-1-nitrosoguanidine (MNNG)-induced GES-1 cells. RESULTS: Computational results show that the target profiles of compounds in MLD can cover most of the biomolecules reported in literature. The MoA of MLD can cover most types of MoA of western drugs for CAG. The treatment of CAG by MLD involved the regulation of various biological functional modules, e.g., inflammation/immune, cell proliferation, cell apoptosis, cell differentiation, digestion and metabolism. Experimental results show that MLD can inhibit cell proliferation, promote cell apoptosis and differentiation, reduce the inflammation level and promote lipid droplet accumulation in MNNG-induced GES-1 cells. CONCLUSION: The network pharmacology framework integrating computational prediction and experimental validation provides a novel way for exploring the MoA of MLD.

Genomic Characterization of Two Novel RCA Phages Reveals New Insights into the Diversity and Evolution of Marine Viruses.

Viruses are the most abundant living entities in marine ecosystems, playing critical roles in altering the structure and function of microbial communities and driving ocean biogeochemistry. Phages that infect Roseobacter clade-affiliated (RCA) cluster strains are an important component of marine viral communities. Here, we characterize the genome sequences of two new RCA phages, CRP-9 and CRP-13, which infect RCA strain FZCC0023. Genomic analysis reveals that CRP-9 and CRP-13 represent a novel evolutionary lineage of marine phages. They both have a DNA replication module most similar to those in Cobavirus group phages. In contrast, their morphogenesis and packaging modules are distinct from those in cobaviruses but homologous to those in HMO-2011-type phages. The genomic architecture of CRP-9 and CRP-13 suggests a genomic recombination event between distinct phage groups. Metagenomic data sets were examined for metagenome-assembled viral genomes (MAVGs) with similar recombinant genome architectures. Fifteen CRP-9-type MAVGs were identified from marine viromes. Additionally, 158 MAVGs were identified containing HMO-2011-type morphogenesis and packaging modules with other types of DNA replication genes, providing more evidence that recombination between different phage groups is a major driver of phage evolution. Altogether, this study significantly expands the understanding of diversity and evolution of marine roseophages. Meanwhile, the analysis of these novel RCA phages and MAVGs highlights the critical role of recombination in shaping phage diversity. These phage sequences are valuable resources for inferring the evolutionary connection of distinct phage groups. IMPORTANCE Diversity and evolution of phages that infect the relatively slow-growing but dominant Roseobacter lineages are largely unknown. In this study, RCA phages CRP-9 and CRP-13 have been isolated on a Roseobacter RCA strain and shown to have a unique genomic architecture, which appears to be the result of a recombination event. CRP-9 and CRP-13 have a DNA replication module most similar to those in Cobavirus group phages and morphogenesis and packaging modules most similar to those in HMO-2011-type phages. HMO-2011-type morphogenesis and packaging modules are found in combination with distinct types of DNA replication genes, suggesting compatibility with various DNA replication modules. Altogether, this study contributes toward a better understanding of marine viral diversity and evolution.

Nitrospina-like Bacteria Are Dominant Potential Mercury Methylators in Both the Oyashio and Kuroshio Regions of the Western North Pacific.

Highly neurotoxic methylmercury (MeHg) accumulates in marine organisms, thereby negatively affecting human and environmental health. Recent studies have revealed that oceanic prokaryotes harboring the hgcAB gene pair are involved in Hg methylation. Presently, little is known about the distribution and phylogeny of these genes in distinct oceanic regions of the western North Pacific. In this study, we used metagenomics to survey the distribution of hgcAB genes in the seawater columns of the subarctic Oyashio region and the subtropical Kuroshio region. The hgcAB genes were detected in the MeHg-rich offshore mesopelagic layers of both the Oyashio region, which is a highly productive area in the western North Pacific, and the Kuroshio region, which has low productivity. Comparative analysis revealed that hgcAB genes belonging to the Nitrospina-like lineage were dominant in the MeHg-rich mesopelagic layers of both regions. These results indicate that Nitrospina-like bacteria are the dominant Hg methylators in the mesopelagic layers throughout the western North Pacific. IMPORTANCE MeHg is highly neurotoxic and accumulates in marine organisms. Thus, understanding MeHg production in seawater is critical for environmental and human health. Recent studies have shown that microorganisms harboring mercury-methylating genes (hgcA and hgcB) are involved in MeHg production in several marine environments. Knowing the distribution and phylogeny of hgcAB genes in seawater columns can facilitate assessment of microbial MeHg production in the ocean. We report that hgcAB genes affiliated with the microaerophilic Nitrospina lineage were detected in the MeHg-rich mesopelagic layers of two hydrologically distinct oceanic regions of the western North Pacific. This finding facilitates understanding of the microbial Hg methylation and accumulation in seawater columns of the western North Pacific.

Identification of HCC-Related Genes Based on Differential Partial Correlation Network.

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death, but its pathogenesis is still unclear. As the disease is involved in multiple biological processes, systematic identification of disease genes and module biomarkers can provide a better understanding of disease mechanisms. In this study, we provided a network-based approach to integrate multi-omics data and discover disease-related genes. We applied our method to HCC data from The Cancer Genome Atlas (TCGA) database and obtained a functional module with 15 disease-related genes as network biomarkers. The results of classification and hierarchical clustering demonstrate that the identified functional module can effectively distinguish between the disease and the control group in both supervised and unsupervised methods. In brief, this computational method to identify potential functional disease modules could be useful to disease diagnosis and further mechanism study of complex diseases.

Fluorine-Driven Enhancement of Birefringence in the Fluorooxosulfate: A Deep Evaluation from a Joint Experimental and Computational Study.

Understanding and exploring the functional modules (FMs) consisting of local atomic groups can promote the development of the materials with functional performances. Oxygen-containing tetrahedral modules are popular in deep-ultraviolet (DUV) optical materials, but their weak optical anisotropy is adverse to birefringence. Here, the fluorooxosulfate group is proved as a new birefringence-enhanced FM for the first time. The birefringence of fluorooxosulfates can be 4.8-15.5 times that of sulfates with the same metal cations while maintaining a DUV band gap. The polarizing microscope measurement confirms the birefringence enhancement by using the millimeter crystals experimentally. The theoretical studies from micro and macro levels further reveal a novel universal strategy that the fluorine induced anisotropic electronic distribution in fluorooxo-tetrahedral group is responsible for the enhancement of birefringence. This study will guide the future discovery of DUV optical materials with enlarged birefringence.

Network pharmacology to explore the anti-inflammatory mechanism of Xuebijing in the treatment of sepsis.

BACKGROUND: Xuebijing (XBJ) is a traditional Chinese patent medicine for sepsis. However, the mechanism of action (MoA) of XBJ on sepsis remain unclear. PURPOSE: Elucidate the MoA of XBJ for treating sepsis based on network pharmacology. STUDY DESIGN: Integrate computational prediction, experimental validation and literature reported clinical results analysis based on network pharmacology. METHODS: Computationally, representative compounds of XBJ were characterized by LC-MS/MS and the target profiles of each compound were identified using network-based method. Compounds from XBJ were compared with FDA approved drugs or experimental agents for sepsis by hierarchical clustering of target profile. Key biological functional modules of XBJ for treating sepsis were identified by enrichment analysis. Differential expressed analysis for each biological functional module was conducted from sepsis related public omics datasets. Herb-biological functional module network was constructed to reveal part of the traditional combinatorial rules of herbs for modules. Experimentally, the action of XBJ compounds on genes in biological functional module was validated by detecting quantitative transcriptional profiling and sepsis animal model. Clinically, combined with the clinical results recorded in literature, computational and experimental results were used to interpret the anti-inflammatory effect of XBJ for treating sepsis. RESULTS: The target profiles of compound cover most of the compound's related biomolecules reported in literature, which can characterize the comprehensive function of compound. XBJ has similar pharmacological effect as FDA approved drugs or experimental agents. Four key biological functional modules including inflammation, immune, cell apoptosis and coagulation of XBJ for treating sepsis were identified. Cell line experimental results show that part of ingredients in XBJ regulate the expression of genes in inflammation modules as predicted. Animal experiments show that compounds from XBJ could reduce the expression level of IL-1ß. Combined with literature reported clinical results, XBJ was found to exert anti-inflammatory effect through regulating the NF-kappa B signaling pathway. CONCLUSION: The network pharmacology framework integrating computational prediction, experimental validation and literature reported clinical results analysis provides a novel approach for analyzing MoA of XBJ for treating sepsis.

Plasmonic-Enhanced Graphene Oxide-Based Aquatic Robot for Target Cargo Delivery.

The design and fabrication of light-actuated robots that can perform selective motions and targeted cargo delivery have attracted increasing interest in various fields. However, these robots' high-speed locomotion, precise direction control, and efficient actuation ability remain big challenges because of the relatively low photothermal efficiency, especially in the aquatic environment. This work proposes a plasmonic-enhanced graphene oxide (GO)-gold nanorod (Au NR)/calcium alginate (Ca-alginate) aquatic robot. The proposed robot design includes an independent power module (GO-Au NR layer) and a microscale cargo-loaded module (Ca-alginate layer). The plasmonic effect of Au NRs greatly improves the heat transfer efficiency, which in turn increases the temperature variation up to three times during the actuating process. This situation leads to a high traveling speed of the robot up to ∼35 mm/s. Benefiting from the high light-to-work efficiency, the position and posture of the proposed robot have good control in the aquatic environment. The robot is capable of programmable trajectory following, multirobot gathering, separation, and cooperation, providing an efficient solution for cargo delivery. Moreover, after releasing the cargo-loaded module to the target location, the power module can be easily actuated for collection, avoiding the potential side effects from the residual photothermal particles in conventional methods. The plasmonic-enhanced photothermal mechanism and independent module design offer a strategy for light-actuated aquatic robot development and would bring opportunities to further develop biomedical applications.

Inferring MicroRNA-Disease Associations Based on the Identification of a Functional Module.

Inferring potential associations between microRNAs (miRNAs) and human diseases can help people understand the pathogenesis of complex human diseases. Several computational approaches have been presented to discover novel miRNA-disease associations based on a top-ranked association model. However, some top-ranked miRNAs are not easily used to reveal the association between miRNAs and diseases. This study aims to infer miRNA-disease relationship by identifying a functional module. We first construct a miRNA functional similarity network derived from a disease similarity network and a known miRNA-disease relationship network. We then present an improved K-means (i.e., IK-means) algorithm to detect miRNA functional modules and used 243 diseases to validate the performance of our proposed method. Experimental results indicate that the performance of IK-means is better compared with classical K-means algorithms. Case studies on some functional modules further demonstrate the applicability of IK-means in the identification of new miRNA-disease associations.

A network-based algorithm for the identification of moonlighting noncoding RNAs and its application in sepsis.

Moonlighting proteins provide more options for cells to execute multiple functions without increasing the genome and transcriptome complexity. Although there have long been calls for computational methods for the prediction of moonlighting proteins, no method has been designed for determining moonlighting long noncoding ribonucleicacidz (RNAs) (mlncRNAs). Previously, we developed an algorithm MoonFinder for the identification of mlncRNAs at the genome level based on the functional annotation and interactome data of lncRNAs and proteins. Here, we update MoonFinder to MoonFinder v2.0 by providing an extensive framework for the detection of protein modules and the establishment of RNA-module associations in human. A novel measure, moonlighting coefficient, was also proposed to assess the confidence of an ncRNA acting in a moonlighting manner. Moreover, we explored the expression characteristics of mlncRNAs in sepsis, in which we found that mlncRNAs tend to be upregulated and differentially expressed. Interestingly, the mlncRNAs are mutually exclusive in terms of coexpression when compared to the other lncRNAs. Overall, MoonFinder v2.0 is dedicated to the prediction of human mlncRNAs and thus bears great promise to serve as a valuable R package for worldwide research communities (https://cran.r-project.org/web/packages/MoonFinder/index.html). Also, our analyses provide the first attempt to characterize mlncRNA expression and coexpression properties in adult sepsis patients, which will facilitate the understanding of the interaction and expression patterns of mlncRNAs.