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High-Fat Diet and a High Amyloid Load Interact to Induce PKC-α Dependent Synaptic Insulin Resistance.

A plethora of studies suggest that a high-fat diet in combination with a high amyloid load causes synaptic insulin resistance and is a risk factor for Alzheimer's disease. Our understanding of the underlying mechanisms is still fragmented. To gain new insights, we conducted integrated proteomic and phosphoproteomic profiling of hippocampal synaptosomes from WT and a transgenic mouse line with a high amyloid load (heterozygous TBA2.1 mice) that show no overt signs of neurodegeneration and dementia. Mice were fed with a regular or high-fat diet. Data-independent acquisition quantified over 5400 proteins, revealing a stable synaptic proteome across conditions. However, the combination of high amyloid load and high-fat diet triggered coordinated remodeling of lipid metabolism pathways, particularly mitochondrial and peroxisomal fatty acid catabolism. Phosphoproteomic analysis showed pronounced activation of lipid- and stress-responsive kinases, including protein kinase C-α, along with increased inhibitory phosphorylation of insulin receptor substrates (IRS1/2). In vitro experiments indicate that blocking protein kinase C-α indeed prevents synaptic insulin resistance in primary neurons. The findings suggest that this proteomic workflow, combined with kinase pathway analysis, can reveal nodal points for interventions in a complex disease state with a trajectory to Alzheimer's disease.

Animals↗

Expansion of signal transduction networks.

Signal transduction networks of different cell types show a large variety in their structural design. In this paper, basic structural properties of signal transduction networks are investigated. For this, such networks with the recently developed method of network expansion are analysed. This method allows for a structural analysis of networks by calculating signal expansion profiles when provided with certain compounds, for example growth factors, inactive kinases and so on (seed compounds), to initiate such an expansion. The presented results may put forth valuable hints on the evolution of signalling networks.

Algorithms↗

Sexual network structure as an indicator of epidemic phase.

Ascertaining epidemic phase for a sexually transmitted disease (STD) has depended on secular trend data which often contain significant artefacts. The usefulness of sexual network structure as an indicator of STD epidemic phase is explored in an analysis of community wide genital chlamydia reports, with network analysis of interviewed cases and linked sexual partners, in Colorado Springs, USA, 1996 to 1999. In this period, the chlamydia case rate per 100,000 increased by 46%. Three quarters of cases (n=4953) were interviewed, nominating 7365 partners; these, combined with index cases, made up the 9114 persons in the network. Epidemiologic analysis of cases suggests that secular trend increases are artefactual. Network analysis supports this view: overall network structure is fragmented and dendritic, notably lacking the cyclic (closed loops) structures associated with network cohesion and thus with efficient STD transmission. Comparison of network structure with that of an intense STD outbreak (characterised by numerous cyclic structures) suggests low level or declining endemic rather than epidemic chlamydia transmission during the study interval. These observations accord with intuitive and stochastic predictions.

Adolescent↗

Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae.

We describe the use of model-driven analysis of multiple data types relevant to transcriptional regulation of metabolism to discover novel regulatory mechanisms in Saccharomyces cerevisiae. We have reconstructed the nutrient-controlled transcriptional regulatory network controlling metabolism in S. cerevisiae consisting of 55 transcription factors regulating 750 metabolic genes, based on information in the primary literature. This reconstructed regulatory network coupled with an existing genome-scale metabolic network model allows in silico prediction of growth phenotypes of regulatory gene deletions as well as gene expression profiles. We compared model predictions of gene expression changes in response to genetic and environmental perturbations to experimental data to identify potential novel targets for transcription factors. We then identified regulatory cascades connecting transcription factors to the potential targets through a systematic model expansion strategy using published genome-wide chromatin immunoprecipitation and binding-site-motif data sets. Finally, we show the ability of an integrated metabolic and regulatory network model to predict growth phenotypes of transcription factor knockout strains. These studies illustrate the potential of model-driven data integration to systematically discover novel components and interactions in regulatory and metabolic networks in eukaryotic cells.

Chromatin Immunoprecipitation↗

Genome-wide Association Studies of the Pathogenic Sphingosine-1-Phosphate Gene in Ulcerative Colitis.

BACKGROUND: Ulcerative colitis (UC) is a chronic inflammatory bowel disease that can lead to malignancies over time. Sphingosine-1-phosphate (S1P) receptor signaling affects lymphocyte trafficking and vascular integrity, influencing intestinal inflammation. This study aimed to identify S1P-related key genes in UC. METHODS: Differentially expressed genes (DEGs) between the UC and control groups were analyzed in the GSE87473 (training) dataset. Genes overlapping between the DEGs and S1P-related genes were considered candidate genes. These genes were incorporated into machine learning algorithms and subjected to expression analysis to identify key genes. Gene functions were determined through a gene–gene interaction network, enrichment analysis, and immune cell infiltration analysis. In addition, transcription factor–mRNA and mRNA–miRNA–lncRNA networks were constructed. Finally, reverse transcription–quantitative polymerase chain reaction (RT-qPCR) was performed to evaluate the expression of key candidate genes in UC and control tissues. RESULTS: This study identified two key genes (SPHK2 and SPNS2) associated with UC. Notably, SPHK2 expression was lower and SPNS2 expression was higher in the UC group in both training and validation datasets and in clinical UC tissues (RT-qPCR). The area under the curve values of SPHK2 and SPNS2 exceeded 0.7 in both datasets, indicating that the genes had good diagnostic efficacy for UC. Consistently, the nomogram showed that the two genes had promising diagnostic value in UC. SPHK2 and SPNS2 were found to be localized to the plasma membrane. The correlations of the two genes with different immune cells showed significantly opposite trends. In particular, SPHK2 had the strongest positive correlation with M2 macrophages (r = 0.6) and the strongest negative correlation with neutrophils. Moreover, mRNA–miRNA–lncRNA and transcription factor– mRNA networks of the key genes were constructed. CONCLUSION: This study suggests that SPHK2 and SPNS2 are key genes associated with UC, highlighting their potential as effective diagnostic biomarkers.

Humans↗

Comparison between neural networks (NN) and principal component analysis (PCA): structure activity relationships of 1,4-dihydropyridine calcium channel antagonists (nifedipine analogues).

The applicability of the neural network computer package PSDD (Perceptron Simulator for Drug Design/Perceptron-type Neural Network Simulator) in structure-activity relationship (SAR) studies was investigated. A group of 1,4-dihydropyridine derivatives was used in order to compare the PSDD results with those obtained previously with PCA. Calculated atomic and molecular descriptors using the semiempirical AM1 method were mainly used. It was shown that the predictive capability demonstrated by PSDD in SAR analysis were almost equivalent to that of PCA.

Algorithms↗

Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data.

Diverse datasets including genomic, proteomic, isotopomer, and DNA sequence variation are becoming available for human mitochondria. Thus there is a need to integrate these data within an in silico modeling framework where mitochondrial biology and related disorders can be studied and analyzed. This paper reports a reconstruction and characterization of the human mitochondrial metabolic network based on proteomic and biochemical data. The 189 reactions included in this reconstruction are both elementally and charge-balanced and are assigned to their respective cellular compartments (mitochondrial, cytosol, or extracellular). The capabilities of the reconstructed network to fulfill three metabolic functions (ATP production, heme synthesis, and mixed phospholipid synthesis) were determined. Network-based analysis of the mitochondrial energy conversion process showed that the overall ATP yield per glucose is 31.5. Network flexibility, characterized by allowable variation in reaction fluxes, was evaluated using flux variability analysis and analysis of all of the possible optimal flux distributions. Results showed that the network has high flexibility for the biosynthesis of heme and phospholipids but modest flexibility for maximal ATP production. A subset of all of the optimal network flux distributions, computed with respect to the three metabolic functions individually, was found to be highly correlated, suggesting that this set may contain physiological meaningful fluxes. Examinations of optimal flux distributions also identified correlated reaction sets that form functional modules in the network.

Amino Acids↗

Estimation of an expected caesarean section rate taking into account the case mix of a maternity hospital. Analysis from the AUDIPOG Sentinelle Network (France). Obstetricians of AUDIPOG. Association of Users of Computerised Files in Perinatalogy, Obstetrics and Gynaecology.

OBJECTIVE: To provide maternity unit with an expected caesarean section rate, according to its case mix (i.e. women's characteristics associated with caesarean section risk). DESIGN: Cohort study. SETTING: 149 maternity units in France. SAMPLE: 40,512 single births collected by the French Sentinelle Network, in January every year, from 1994 until 1998. METHODS: Univariate analysis was used to identify caesarean section risk factors, and multivariate analysis to adjust for the role of the maternity units' characteristics, after taking into account the women's characteristics. A two-level logistic model was used to show that the caesarean section rate varied according to maternity units' characteristics and to estimate therefore expected caesarean section rates (before and during labour), for each maternity unit, according to its case mix. MAIN OUTCOME: Caesarean section rates (before and during labour). RESULTS: Within the Sentinelle Network the caesarean section rate was 15.0% (7.6% were before labour). The joint effect of the size and juridical status on caesarean section risk was studied. The reference hospital was university maternity units with more than 2000 deliveries/year. Community or private maternity units with more than 2000 deliveries/year carried out fewer prophylactic caesarean sections than the reference hospital (ORadj = 0.7 and 0.6, respectively). Conversely, private maternity units with fewer than 2000 deliveries/year performed more prophylactic caesarean sections than the reference hospital (ORadj = 1.7). The two-level logistic model showed that a maternity unit effect still existed after taking into consideration both women's characteristics and those of the maternity unit, and estimated expected caesarean section rates. CONCLUSION: Knowledge of the expected caesarean section rate constitutes a personal reference to which the maternity hospital can compare its observed caesarean section rate, and is thus likely to have a significant effect on delivery practices.

Adult↗

Hierarchical modularity of nested bow-ties in metabolic networks.

BACKGROUND: The exploration of the structural topology and the organizing principles of genome-based large-scale metabolic networks is essential for studying possible relations between structure and functionality of metabolic networks. Topological analysis of graph models has often been applied to study the structural characteristics of complex metabolic networks. RESULTS: In this work, metabolic networks of 75 organisms were investigated from a topological point of view. Network decomposition of three microbes (Escherichia coli, Aeropyrum pernix and Saccharomyces cerevisiae) shows that almost all of the sub-networks exhibit a highly modularized bow-tie topological pattern similar to that of the global metabolic networks. Moreover, these small bow-ties are hierarchically nested into larger ones and collectively integrated into a large metabolic network, and important features of this modularity are not observed in the random shuffled network. In addition, such a bow-tie pattern appears to be present in certain chemically isolated functional modules and spatially separated modules including carbohydrate metabolism, cytosol and mitochondrion respectively. CONCLUSION: The highly modularized bow-tie pattern is present at different levels and scales, and in different chemical and spatial modules of metabolic networks, which is likely the result of the evolutionary process rather than a random accident. Identification and analysis of such a pattern is helpful for understanding the design principles and facilitate the modelling of metabolic networks.

Algorithms↗

Application of neural networks to flow cytometry data analysis and real-time cell classification.

Conventional analysis of flow cytometric data requires that population identification be performed graphically after a sample has been run using two-parameter scatter plots. As more parameters are measured, the number of possible two-parameter plots increases geometrically, making data analysis increasingly cumbersome. Artificial Neural Systems (ANS), also known as neural networks, are a powerful and convenient method for overcoming this data bottleneck. ANS "learn" to make classifications using all of the measured parameters simultaneously. Mathematical models and programming expertise are not required. ANS are inherently parallel so that high processing speed can be achieved. Because ANS are nonlinear, curved class boundaries and other nonlinearities can emerge naturally. Here, we present biomedical and oceanographic data to demonstrate the useful properties of neural networks for processing and analyzing flow cytometry data. We show that ANS are equally useful for human leukocytes and marine plankton data. They can easily accommodate nonlinear variations in data, detect subtle changes in measurements, interpolate and classify cells they were not trained on, and analyze multiparameter cell data in real time. Real-time classification of a mixture of six cyanobacteria strains was achieved with an average accuracy of 98%.

Animals↗

Generalized local-world models for weighted networks.

Based on the weighted scale-free network model proposed by Barrat, Barthélemy, and Vespignani [Phys. Rev. Lett. 92, 228701 (2004)] and enlightened by our local-world concept [Li and Chen, Physica A 328, 274 (2003)], we propose two generalized local-world (GLW) models for weighted complex networks. Theoretical analysis and numerical simulations show that the GLW models generate weighted networks as a crossover between exponential and scale-free weighted networks, and exhibit an alteration from assortative networks to disassortative networks.

Journal Article↗

Integrated methylome and transcriptome analysis provides insight into DNA methylation-mediated networks in sexual dimorphism of Vernicia montana.

BACKGROUND: Sexual dimorphism is fundamental to reproduction in dioecious plants and is regulated by both genetic and epigenetic mechanisms. DNA methylation is a central epigenetic mark known to influence phenotypic variation in plants. However, its specific role in shaping sexual dimorphism in dioecious trees remains poorly understood. To address this question, we performed integrated genome-wide DNA methylome and transcriptome analyses of four tissue types in the dioecious tung tree (Vernicia montana), including male and female flower buds and their corresponding leaves. RESULTS: Our analysis revealed distinct DNA methylation patterns between male and female tissues. Notably, the coordination between DNA methylation reprogramming and transcriptional regulation appeared to be more strongly associated with reproductive development than with vegetative growth in V. montana. We identified a set of sex-biased genes that may reflect different reproductive strategies between the sexes. Further analysis identified several key transcription factors (TFs) potentially associated with promoter differentially methylated regions (DMRs), including flowering-time regulators (e.g., FRS5, REM16, and VRN1) and TFs involved in hormone signaling pathways such as jasmonic acid, auxin, and salicylic acid signaling. Cis-regulatory element analysis showed that some promoter DMRs overlapped with hormone response elements related to abscisic acid, auxin, and gibberellin. Co-expression network analysis further revealed potential regulatory correlations among promoter DMR-mediated TFs, hormone-responsive pathways, and key floral development regulators. CONCLUSIONS: Collectively, our results suggest that interactions among DNA methylation, transcriptional regulation, and hormone-responsive pathways may contribute to the establishment of sexual dimorphism in V. montana. This study provides the first integrated view of these regulatory layers in V. montana and supports a species-specific regulatory framework for understanding the epigenetic basis of sexual dimorphism in this economically important dioecious tree. The proposed framework is based on multi-omics analyses and warrants further validation through targeted functional studies.

DNA Methylation↗

Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms.

MOTIVATION: Information from fully sequenced genomes makes it possible to reconstruct strain-specific global metabolic network for structural and functional studies. These networks are often very large and complex. To properly understand and analyze the global properties of metabolic networks, methods for rationally representing and quantitatively analyzing their structure are needed. RESULTS: In this work, the metabolic networks of 80 fully sequenced organisms are in silico reconstructed from genome data and an extensively revised bioreaction database. The networks are represented as directed graphs and analyzed by using the 'breadth first searching algorithm to identify the shortest pathway (path length) between any pair of the metabolites. The average path length of the networks are then calculated and compared for all the organisms. Different from previous studies the connections through current metabolites and cofactors are deleted to make the path length analysis physiologically more meaningful. The distribution of the connection degree of these networks is shown to follow the power law, indicating that the overall structure of all the metabolic networks has the characteristics of a small world network. However, clear differences exist in the network structure of the three domains of organisms. Eukaryotes and archaea have a longer average path length than bacteria. AVAILABILITY: The reaction database in excel format and the programs in VBA (Visual Basic for Applications) are available upon request. SUPPLEMENTARY MATERIAL: Bioinformatics Online.

Archaea↗

A query language for biological networks.

MOTIVATION: Many areas of modern biology are concerned with the management, storage, visualization, comparison and analysis of networks, but no appropriate query language for such complex data structures yet exists. RESULTS: We have designed and implemented the pathway query language (PQL) for querying large protein interaction or pathway databases. PQL is based on a simple graph data model with extensions reflecting properties of biological objects. Queries match subgraphs in the database based on node properties and paths between nodes. The syntax is easy to learn for anybody familiar with SQL. As an important feature, a query may require a certain structure in the database to exist but return a different subgraph. We have tested PQL queries on networks of up to 16,000 nodes and found it to scale very well. AVAILABILITY: The code is available on request from the author.

Computational Biology↗

Insights into the molecular basis of thermal stability from the analysis of ion-pair networks in the glutamate dehydrogenase family.

The recent structure determination of glutamate dehydrogenase from the hyperthermophile Pyrococcus furiosus and the comparison of this structure with its counterparts from the mesophiles Clostridium symbiosum and Escherichia coli has highlighted the formation of extended networks of ion-pairs as a possible explanation for the superior thermal stability of the hyperthermostable enzyme. In the light of this, we have carried out a homology-based modelling study using sequences of a range of glutamate dehydrogenases drawn from species which span a wide spectrum of optimal growth temperatures. We have attempted to analyse the extent of the formation of ion-pair networks in these different enzymes and tried to correlate this with the observed thermal stability. The results of this analysis indicate that the ion-pair networks become more fragmented as the temperature stability of the enzyme decreases and are consistent with a role for the involvement of such networks in the adaptation of enzymes to extreme temperatures.

Amino Acid Sequence↗

Topology-based cancer classification and related pathway mining using microarray data.

Cancer classification is the critical basis for patient-tailored therapy, while pathway analysis is a promising method to discover the underlying molecular mechanisms related to cancer development by using microarray data. However, linking the molecular classification and pathway analysis with gene network approach has not been discussed yet. In this study, we developed a novel framework based on cancer class-specific gene networks for classification and pathway analysis. This framework involves a novel gene network construction, named ordering network, which exhibits the power-law node-degree distribution as seen in correlation networks. The results obtained from five public cancer datasets showed that the gene networks with ordering relationship are better than those with correlation relationship in terms of accuracy and stability of the classification performance. Furthermore, we integrated the ordering networks, classification information and pathway database to develop the topology-based pathway analysis for identifying cancer class-specific pathways, which might be essential in the biological significance of cancer. Our results suggest that the topology-based classification technology can precisely distinguish cancer subclasses and the topology-based pathway analysis can characterize the correspondent biochemical pathways even if there are subtle, but consistent, changes in gene expression, which may provide new insights into the underlying molecular mechanisms of tumorigenesis.

Gene Expression Profiling↗

Systems analysis of a quorum sensing network: design constraints imposed by the functional requirements, network topology and kinetic constants.

Understanding the relationship between the structural organization of intracellular decision networks and the observable phenotypes they control is one of the exigent problems of modern systems biology. Here we perform a systems analysis of a prototypic quorum sensing network whose operation allows bacterial populations to activate certain patterns of gene expression cooperatively. We apply structural perturbations to the model and analyze the resulting changes in the network behavior with the aim to identify the contribution of individual network elements to the functional fitness of the whole network. Specifically, we demonstrate the importance of the dimerization of the transcription factor and the presence of the auxiliary positive feedback loop on the switch-like behavior of the network and the stability of its "on" and "off" states under the influence of molecular noise.

Bacterial Proteins↗

Topological analysis of the morphogenesis of liver cirrhosis.

Cirrhotic and precirrhotic livers consist of three-dimensional parenchymal networks. Topological analysis has been carried out using the total connectivity number p1 on the livers from 6 autopsy cases of chronic liver diseases of different types. The total p1 amounts to 6,100,000 in chronic hepatitis of periportal type and 6,350,000 in portal cirrhosis, but to only 100,000 in posthepatitic cirrhosis of coarse-nodular type. In view of the similarity of values for p1 in chronic hepatitis and portal cirrhosis, the former is considered to give rise to the latter by continuous change in structure or through "piecemeal" progression of the periportal lesion. Development of posthepatitic cirrhosis from chronic hepatitis is possible only through a substantial reduction in p1, which is brought about by parenchymal necrosis sufficient in extent to cause multiple disconnection of the network.

Adult↗