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At least 127 records · Page 7Linked to original sources
PubMed2001

Nomenclature of major antimicrobial-resistant clones of Streptococcus pneumoniae defined by the pneumococcal molecular epidemiology network.

The emergence of disease caused by penicillin-resistant and multidrug-resistant pneumococci has become a global concern, necessitating the identification of the epidemiological spread of such strains. The Pneumococcal Molecular Epidemiology Network was established in 1997 under the auspices of the International Union of Microbiological Societies with the aim of characterizing, standardizing, naming, and classifying antimicrobial agent-resistant pneumococcal clones. Here we describe the nomenclature for 16 pneumococcal clones that have contributed to the increase in antimicrobial resistance worldwide. Guidelines for the recognition of these clones using molecular typing procedures (pulsed-field gel electrophoresis, BOX-PCR, and multilocus sequence typing) are presented, as are the penicillin-binding profiles and macrolide resistance determinants for the 16 clones. This network can serve as a prototype for the collaboration of scientists in identifying clones of important human pathogens and as a model for the development of other networks.

Anti-Bacterial Agents↗
PubMed2026

Integrated network pharmacology, molecular docking, and experimental validation to reveal the potential mechanism of Ginsenoside Rg1 on chronic obstructive pulmonary disease.

Ginsenoside Rg1 (GS Rg1), a natural flavonoid exhibiting anti-inflammatory and antioxidant properties, holds significant potential for treatment chronic obstructive pulmonary disease (COPD). Nevertheless, the precise mechanisms underlying its therapeutic effects remain to be fully elucidated. This study aimed to explore the role and potential mechanism of GS Rg1 in the treatment of COPD using network pharmacology, molecular docking, and experimental validation.Targets related to GS Rg1 and COPD were screened from public databases, and the potential common targets were then imported into the STRING database to construct a protein-protein interaction (PPI) network. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis were performed to identify key signaling pathways. Molecular docking was employed to predict the binding interactions between GS Rg1 and core targets. A BEAS-2B cell model induced by lipopolysaccharide(LPS) and cigarette smoke extract(CSE) was used to explore the protective mechanisms of GS Rg1. Western blot analysis was conducted to validate the critical targets and pathways involved in the anti-COPD effects of GS Rg1. Network pharmacology analysis revealed 105 common targets between GS Rg1 and COPD. The EGFR/PI3K/AKT and EGFR/STAT3 signaling pathways were selected for further validation. GS Rg1 was demonstrated to effectively inhibit inflammation and mucus hypersecretion in vitro models of COPD. Western blot results showed that GS Rg1 treatment significantly downregulated the expression of proteins involved in the EGFR/PI3K/AKT and EGFR/STAT3 signaling pathway, consistent with the network pharmacology findings. CSE/LPS exposure induces inflammation and oxidative stress in COPD by disrupting the EGFR/PI3K/AKT and EGFR/STAT3 signaling pathways, and GS Rg1 significantly alleviates these effects, which may be partially through regulating the EGFR/PI3K/AKT and EGFR/STAT3 signaling pathway.

Ginsenosides↗
PubMed2006

Dynamic memory networks: dissecting molecular mechanisms underlying associative memory in the temporal domain.

The molecular mechanisms underlying the induction and maintenance of memory are highly dynamic and comprise distinct phases covering a time window from seconds to even a lifetime. Neuronal networks, which contribute to these processes, have been extensively characterized on various levels of analysis, and imaging techniques allow monitoring of both gross brain activity as well as functional changes in defined brain areas during the time course of memory formation. New techniques developed in honeybees and fruit flies even allow for manipulation of neuronal networks and molecular cascades in a short temporal domain while a living animal under observation acquires new associative memories. These advantages make honeybees and flies ideal organisms to study transient molecular events underlying dynamic memory processing in vivo. In this review we will focus on the temporal features of molecular processes in learning and memory formation, summarize recent knowledge and present an outlook on future developments.

Animals↗
PubMed2005

Molecular tectonics: self-complementary supramolecular Se...N synthons directing assembly of 1D silver chains into 3D porous molecular architectures.

Reaction of 2,1,3-benzoselenadiazole (bsd) with AgNO3 results in the formation of a novel model example of a Se...N synthon directed molecular network of different polymorphs at different temperatures. Alpha-[Ag(bsd)2(NO3)] x 0.5bsd formed at ambient temperature, has a 3D porous molecular network constructed with monomeric [Ag(bsd)2(NO3)] motif, and has 1D channels that are encapsulated with 1D arrays of two-fold-disordered dimeric (bsd)2 guests aggregated by the self-complementary nonbonded Se...N interactions. This is the first molecular net directed by supramolecular Se...N synthons. The second polymorph, beta-[Ag(bsd)2(NO3)] x 0.5bsd, formed from an analogous reaction at 50 degrees C, contains a similar 3D molecular network constructed with tetrameric [Ag4(bsd)8(NO3)4] motif and 1D arrays of well-ordered dimeric (bsd)2 guests are encapsulated in the channels. Such ordered (bsd)2 dimers provide an excellent simplified dimeric model for MO calculations of intermolecular nonbonded Se...N interactions.

Journal Article↗
PubMed2006

Fast and accurate method for identifying high-quality protein-interaction modules by clique merging and its application to yeast.

Molecular networks in cells are organized into functional modules, where genes in the same module interact densely with each other and participate in the same biological process. Thus, identification of modules from molecular networks is an important step toward a better understanding of how cells function through the molecular networks. Here, we propose a simple, automatic method, called MC(2), to identify functional modules by enumerating and merging cliques in the protein-interaction data from large-scale experiments. Application of MC(2) to the S. cerevisiae protein-interaction data produces 84 modules, whose sizes range from 4 to 69 genes. The majority of the discovered modules are significantly enriched with a highly specific process term (at least 4 levels below root) and a specific cellular component in Gene Ontology (GO) tree. The average fraction of genes with the most enriched GO term for all modules is 82% for specific biological processes and 78% for specific cellular components. In addition, the predicted modules are enriched with coexpressed proteins. These modules are found to be useful for annotating unknown genes and uncovering novel functions of known genes. MC(2) is efficient, and takes only about 5 min to identify modules from the current yeast gene interaction network with a typical PC (Intel Xeon 2.5 GHz CPU and 512 MB memory). The CPU time of MC(2) is affordable (12 h) even when the number of interactions is increased by a factor of 10. MC(2) and its results are publicly available on http://theory.med.buffalo.edu/MC2.

Algorithms↗
PubMed2025

Computational network biology analysis revealed COVID-19 severity markers: Molecular interplay between HLA-II with CIITA.

COVID-19, severe acute respiratory syndrome coronavirus 2, rapidly spread worldwide. Severe and critical patients are expected to rapidly deteriorate. Although several studies have attempted to uncover the mechanisms underlying COVID-19 severity, most have focused on the perturbations of single genes. However, the complex mechanism of COVID-19 involves numerous perturbed genes in a molecular network rather than a single abnormal gene. Thus, we aimed to identify COVID-19 severity-specific markers in the Japanese population using gene network analysis. In order to reveal the severity-specific molecular interplays, we developed a novel computational network biology strategy that measures dissimilarity between networks based on the comprehensive information of gene network (i.e., expression levels of genes and network structure) by using Kullback-Leibler divergence. Monte Carlo simulations demonstrated the effectiveness of our strategy for differential gene network analysis. We applied this method to publicly available whole blood RNA-seq data from the Japan coronavirus disease 2019 Task Force and identified differentially regulated molecular interplays between 368 severe and 105 non-severe samples. Our analysis suggests the gene network between HLA class II, CIITA, and CD74 as a COVID-19 severity specific molecular marker. Although the association between HLA class II and COVID-19 has been demonstrated, our data analysis revealed that the molecular interplay of HLA class II with its target and/or regulator is a crucial marker for COVID-19 severity. Our findings from computational network biology analysis suggest that suppression and activation of the molecular interplay between HLA class II, CIITA, and CD74 provide crucial clues to uncover the mechanisms of COVID-19 severity.

Humans↗
PubMed2006

Create and assess protein networks through molecular characteristics of individual proteins.

MOTIVATION: The study of biological systems, pathways and processes relies increasingly on analyses of networks. Most often, such analyses focus on network topology, thereby treating all proteins or genes as identical, featureless nodes. Integrating molecular data and insights about the qualities of individual proteins into the analysis may enhance our ability to decipher biological pathways and processes. RESULTS: Here, we introduce a novel platform for data integration that generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristics to assess networks. It also annotates the function and subcellular localization of each protein and displays the process on an image of a cell, rendering each protein in its respective cellular compartment. By thus visualizing the network in a cellular context we are able to analyze pathways and processes in a novel way. As an example, we use the system to analyze proteins implicated with Alzheimers disease and show how the integrated view corroborates previous observations and how it helps in the formulation of new hypotheses regarding the molecular underpinnings of the disease. AVAILABILITY: http://www.rostlab.org/services/pinat.

Cell Physiological Phenomena↗
PubMed2002

Discovering regulatory and signalling circuits in molecular interaction networks.

MOTIVATION: In model organisms such as yeast, large databases of protein-protein and protein-DNA interactions have become an extremely important resource for the study of protein function, evolution, and gene regulatory dynamics. In this paper we demonstrate that by integrating these interactions with widely-available mRNA expression data, it is possible to generate concrete hypotheses for the underlying mechanisms governing the observed changes in gene expression. To perform this integration systematically and at large scale, we introduce an approach for screening a molecular interaction network to identify active subnetworks, i.e., connected regions of the network that show significant changes in expression over particular subsets of conditions. The method we present here combines a rigorous statistical measure for scoring subnetworks with a search algorithm for identifying subnetworks with high score. RESULTS: We evaluated our procedure on a small network of 332 genes and 362 interactions and a large network of 4160 genes containing all 7462 protein-protein and protein-DNA interactions in the yeast public databases. In the case of the small network, we identified five significant subnetworks that covered 41 out of 77 (53%) of all significant changes in expression. Both network analyses returned several top-scoring subnetworks with good correspondence to known regulatory mechanisms in the literature. These results demonstrate how large-scale genomic approaches may be used to uncover signalling and regulatory pathways in a systematic, integrative fashion.

Algorithms↗
PubMed2006

Exploring the cell's network with molecular imaging.

Molecular imaging is already a powerful tool for investigating molecular interactions within the cell. Interpreting molecular imaging findings will, however, take us into the more unfamiliar, nonlinear realm of networks. The network class of interest is the "scale-free" network, which characterizes not only the cell, but also surprisingly, other real work networks such as the world wide web. This network topology yields insights in how the cell is functionally organized via motifs, modules, and different types of hubs. Additional organizational information is gained from the cell's evolutionary history. Interpretation of molecular images will be deepened by a both qualitative and quantitative knowledge of the cell's network. Importantly, cell network behavior can be independent of molecular detail. For this reason, the same molecule can serve different functions in different cells or even within the same cell. Since a scale-free network's behavior is likely to be nonlinear and exhibit emergent behavior, a degree of caution is prudent in assigning cause and effect to molecular imaging findings in our effort to reengineer some of the cell's functions. Molecular imagers will need to be cognizant of the level of organization in the cell's network they are interrogating.

Amino Acid Motifs↗
PubMed2007

Molecular interaction network of the Hsp90 chaperone system.

Hsp90 is an essential and ubiquitous molecular chaperone that is required for the proper folding of a set of client proteins at a late stage in their folding process. In eukaryotes, cytoplasmic Hsp90 is absolutely essential for cell viability under all growth conditions. The functional cycle of the Hsp90 system requires a cohort of cochaperones and cofactors that regulate the activity of this chaperone. Hence, Hsp90 function is highly complex; in order to understand that complexity, several groups have attempted to map out the interaction network of this chaperone in yeast and mammalian systems using the latest available proteomic and genomic tools. Interaction networks emerging from these large scale efforts clearly demonstrate that Hsp90 plays a central role effecting multiple pathways and cellular processes. In yeast Saccharomyces cerevisiae, Hsp90 was shown to interact directly or indirectly with at least 10% of the yeast ORFs. The systematic application of large scale approaches to map out the Hsp90 chaperone network should allow the determination of the mechanisms employed by this chaperone system to maintain protein homeostasis in the cell.

HSP90 Heat-Shock Proteins↗
PubMed1996

Molecular basis for protein S hereditary deficiency: genetic defects observed in 118 patients with type I and type IIa deficiencies. The French Network on Molecular Abnormalities Responsible for Protein C and Protein S Deficiencies.

Circulating protein S (PS) is partly bound to C4b-binding protein, and only free PS can act as a cofactor for protein C (PC), a natural anticoagulant. Two types of PS deficiencies are commonly observed in patients with unexplained thrombosis, and they are characterized by having both a low total PS level and a low free PS level (type I) or by having only a low free PS level (type IIa). To elucidate the genetic mechanisms responsible for these two plasma phenotypes, we screened 118 symptomatic patients with type I or type IIa PS deficiency for a PS gene coding sequence variation. A total of 34 mutations, 17 of which were novel, were identified in 65 propositi (70% in type I and 44% in type IIa). In type I deficiency, 29 different mutations were distributed throughout the coding sequence. In type IIa deficiency, five different missense mutations were clustered in exons XII and XIII, with a Ser 460 to Pro mutation accounting for most cases (82%). This points to a role of the domain encoded by exons XII and XIII in the distribution between bound and free PS. The Ser 460 to Pro mutation was associated with the factor V Arg 506 to Gin mutation or a PC gene mutation in about half the patients, suggesting a cooperative effect on clinical expression.

DNA Mutational Analysis↗
PubMedDate not supplied

Automatic generation of complementary descriptors with molecular graph networks.

We describe a method for the automatic generation of weakly correlated descriptors for molecular data sets. The method can be regarded as a statistical learning procedure that turns the molecular graph, representing the 2D formula of the compound, into an adaptive whole molecule composite descriptor. By translating the molecular graph structure into a dynamical system, the algorithm can compute an output value that is highly sensitive to the molecular topology. This system can be trained by gradient descent techniques, which rely on the efficient calculation of the gradient by back-propagation. We present computational experiments concerning the classification of the Developmental Therapeutics Program AIDS antiviral screen data set on which the performance of the method compares with that of approaches based on substructure comparison.

Algorithms↗
PubMed1999

Visualization and characterization of non-covalent networks in molecular crystals: automated assignment of graph-set descriptors for asymmetric molecules.

A method of visualizing intermolecular networks (for example, hydrogen-bonded networks) in the crystalline state has been developed, based on the concept of link atoms, i.e. those atoms deemed to be in contact with each unique molecule or ion in the crystal chemical unit (CCU). Extension of a structure using each of these primary links can be achieved, enabling the generation and investigation of extended networks. Algorithms have been developed for the automatic assignment of graph-set notation for patterns up to second level, i.e. those involving one or two crystallographically independent non-covalent bonds, in the absence of internal crystallographic symmetry in the unique molecules of the CCU. The self, ring, chain and discrete motifs may be displayed by highlighting the atoms and bonds comprising the pattern. These methodologies have been implemented in the Cambridge Structural Database program PLUTO.

Journal Article↗
PubMed2026

Lipid reprogramming of stratified squamous epithelium by the high-risk HPV E6 and E6/E7 oncoproteins.

INTRODUCTION: High risk human papillomavirus (HPV) infection and genome integration with pronounced expression of the viral E6/E7 oncogenes is the major cause of cervical cancer. Emerging evidence suggests that HPV reprograms host metabolism to support viral persistence and cellular transformation. However, global HPV oncogene-induced lipidomic reprogramming remains poorly understood, particularly at early stages of HPV-induced transformation. OBJECTIVE: We sought to define the regulation of lipid metabolism in squamous epithelia of transgenic mice expressing the HPV16 oncogene E6 alone or in conjunction with E7. METHODS: Untargeted lipidomics was used to identify novel lipid biomarkers in the skin and female reproductive tract (FRT) of HPV16 E6 and E6/E7 transgenic compared to wild-type (WT) mice. To investigate enzymatic dysregulation of lipids by HPV oncogene expression, we employed Lipid Network Explorer (LINEX2), which analyzes lipidomics data through lipid enrichment analysis. We also used the Global Natural Product Social Molecular Networking (GNPS) platform to enhance lipid identification, exploring molecular networking to improve feature annotation. RESULTS: Our lipidomic analysis produced several new observations. First, E6 expression caused a consistent alteration of glycerophospholipids, with particularly significant substrate-product shifts in the phosphatidylcholine (PC) to lysophosphatidylcholine (LPC) pathway in the skin. Second, E6/E7 expression caused a dysregulation of glucosylceramide (GlcCer) biosynthesis. Third, both E6/E7 expressing skin and FRT tissues exhibited a redox imbalance and increased levels of oxidized lipids, including oxylipins and several oxidized PCs. These findings suggest that HPV oncoproteins drive lipid reprogramming, potentially contributing to early HPV-related tumorigenesis. CONCLUSIONS: These findings provide new insights into HPV‑induced lipid reprogramming and establish a framework for future studies examining the functional and clinical relevance of lipid alterations in HPV‑associated cancers.

Animals↗
PubMed2006

Influence of chlorine substitution on the self-assembly of zinc phthalocyanine.

The adsorption and ordering of zinc phthalocyanine (ZnPc) and octachloro zinc phthalocyanine (ZnPcCl(8)) on an Ag(111) surface is studied in situ by scanning tunneling microscopy under ultrahigh vacuum. Two-dimensional self-assembled supramolecular domains are observed for these two molecules. We show how substituting chlorine atoms for half of the peripheral hydrogen atoms on ZnPc influences the self-assembly mechanisms. While intermolecular interactions are dominated by van der Waals forces in ZnPc molecular networks, ZnPcCl(8) molecular packing undergoes a sequential phase evolution driven by the creation of C-Cl...H-C hydrogen bonds between adjacent molecules. At the end of this evolution, the final molecular assembly involves all possible hydrogen bonds. Our study also reveals the influence of molecule-substrate interactions through the presence of fault lines generating a stripe structure in the molecular film.

Journal Article↗
PubMed2003

Bis(2-chloro-5-methylpyridine-kappaN)bis(nitrato-kappa(2)O,O')copper(II).

In the molecule of the title compound, [Cu(NO(3))(2)(C(6)H(6)ClN)(2)], the Cu atom lies on an inversion centre and is six-coordinated by two pyridine N atoms and four nitrate O atoms in trans positions. The nitrate acts as an unsymmetrical bidentate ligand. The coordination geometry is octahedral, with the Cu-N and the two Cu-O distances being 1.9939 (16), 2.0246 (16) and 2.4866 (19) A, respectively. There are five types of C-H.O hydrogen bonds. Two of these generate two-dimensional molecular networks in the direction of the a axis, and the others connect adjacent molecular networks.

Journal Article↗