Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways

A wide variety of 1) parametric regression models and 2) co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. While both methodological schemes have their own well-known benefits, little is known about their synergistic potential. Our study introduces their methodological fusion that cross-exploits the strengths of individual approaches via a built-in information-sharing mechanism. This fusion is theoretically based on certain trait-conditioned dependency patterns between two genes depending on their role in the underlying parametric model. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a parametric sense, and 2) exploits the underlying parametric model itself in the estimation process. To also account for the substantial amount of intrinsic noise and collinearities, often entailed by expression data, a tailored co-expression measure is introduced along with this framework to alleviate related computational problems. A remarkable advance over the reference methods in simulated scenarios substantiate the method’s high-efficiency. As proof-of-concept, this synergistic approach is successfully applied in survival analysis, with acute myeloid leukemia data, further highlighting the framework’s versatility and broad practical relevance.     

Validation of a physical activity questionnaire in the elderly

The purpose of this study was to validate a physical activity (PA) questionnaire, Questionnaire d'Activité Physique Saint-Etienne (QAPSE), in an homogenous population of elderly subjects and to estimate its potential for application in routine PA assessments in that age group. A group of 65 (31 men and 34 women) community dwelling, healthy people aged 65–84 years volunteered to participate in a validation substudy comparing maximal oxygen uptake ( ) and anthropometric data. correlated positively with mean habitual daily energy expenditure (MHDEE) (r=0.56,P<0.0001), greater than 3MET (metabolic equivalent) daily energy expenditure (DEE) activity (r=0.371,P=0.002), leisure activity (r=0.368,P=0.003), sports activity (r=0.461,P<0.0001), basic daily activity (r=0.325,P=0.008) and moving DEE activity (r=0.273,P=0.028) in both sexes, with MHDEE (r=0.366,P=0.043) and moving DEE activity (r=0.388,P=0.031) in the men and with MHDEE (r=0.624;P<0.001), greater than 3MET DEE activity (r=0.513,P=0.002), leisure activity (r=0.388,P=0.024) and sports activity (r=0.683,P<0.001) in the women. The MHDEE was positively correlated with body mass (r=0.464) and with fat free mass (r=0.639) and negatively correlated with percentage body fat (r=−0.501). In a reproducibility substudy (n=44) a paired Student'st-test, based on mean differences between the two administrations of the questionnaire did not reach statistical significance for any of the QAPSE activity scores studied. Test-retest correlation coefficients ranged from 0.648 for moving score to 0.967 for MHDEE with correlation coefficientP values being less than 0.001 for all of the QAPSE activity scores. We concluded that QAPSE demonstrated excellent repeatability and good validity in relation to physical fitness and anthropometric data in the population of these healthy elderly volunteers.     

Inferring network interactions within a cell

The continuing growth in high-throughput data acquisition has led to a proliferation of network models to represent and analyse biological systems. These networks involve distinct interaction types detected by a combination of methods, ranging from directly observed physical interactions based in biochemistry to interactions inferred from phenotype measurements, genomic expression and comparative genomics. The discovery of interactions increasingly requires a blend of experimental and computational methods. Considering yeast as a model system, recent analytical methods are reviewed here and specific aims are proposed to improve network interaction inference and facilitate predictive biological modelling.     

Validation of protein models by a neural network approach

Background  

The development and improvement of reliable computational methods designed to evaluate the quality of protein models is relevant in the context of protein structure refinement, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction.     

Functional topology in a network of protein interactions

MOTIVATION: The building blocks of biological networks are individual protein-protein interactions (PPIs). The cumulative PPI data set in Saccharomyces cerevisiae now exceeds 78 000. Studying the network of these interactions will provide valuable insight into the inner workings of cells. RESULTS: We performed a systematic graph theory-based analysis of this PPI network to construct computational models for describing and predicting the properties of lethal mutations and proteins participating in genetic interactions, functional groups, protein complexes and signaling pathways. Our analysis suggests that lethal mutations are not only highly connected within the network, but they also satisfy an additional property: their removal causes a disruption in network structure. We also provide evidence for the existence of alternate paths that bypass viable proteins in PPI networks, while such paths do not exist for lethal mutations. In addition, we show that distinct functional classes of proteins have differing network properties. We also demonstrate a way to extract and iteratively predict protein complexes and signaling pathways. We evaluate the power of predictions by comparing them with a random model, and assess accuracy of predictions by analyzing their overlap with MIPS database. CONCLUSIONS: Our models provide a means for understanding the complex wiring underlying cellular function, and enable us to predict essentiality, genetic interaction, function, protein complexes and cellular pathways. This analysis uncovers structure-function relationships observable in a large PPI network.     

Sugar and phytohormone response pathways: navigating a signalling network

Many plant developmental, physiological and metabolic processes are regulated, at least in part, by nutrient availability. In particular, alterations in the availability of soluble sugars, such as glucose and sucrose, help regulate a diverse array of processes. Multiple lines of evidence indicate that many of these processes are also regulated in response to other signalling molecules, such as phytohormones. This review draws examples from a variety of plant systems, including bean, Arabidopsis, potato, and cereals. Five of the most interesting and best developed examples of processes regulated via 'interactions' or 'crosstalk' between sugars and phytohormones are described, including embryogenesis, seed germination, early seedling development, tuberization, and the regulation of alpha-amylase activity. The types of mechanisms by which different response pathways are known or postulated to interact are also described. These mechanisms include regulation of the metabolism and/or transport of a signalling molecule by a different response pathway. For example, sugars have been postulated to help regulate the synthesis, conjugation and/or transport of phytohormones, such as gibberellins and abscisic acid. Conversely, phytohormones, such as abscisic acid, gibberellins and cytokinins have been shown to help regulate sugar metabolism and/or transport. Similarly, sugars have been shown to regulate the expression of components of phytohormone-response pathways and phytohormones regulate the expression of some genes encoding possible components of sugar-response pathways. Examples of proteins and second messengers that appear to act in multiple response pathways are also described.     

Prediction of physical protein-protein interactions

Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.     

Protein interactions and fluctuations in a proteomic network using an elastic network model

A set of protein conformations are analyzed by normal mode analysis. An elastic network model is used to obtain fluctuation and cooperativity of residues with low amplitude fluctuations across different species. Slow modes that are associated with the function of proteins have common features among different protein structures. We show that the degree of flexibility of the protein is important for proteins to interact with other proteins and as the species gets more complex its proteins become more flexible. In the complex organism, higher cooperativity arises due to protein structure and connectivity.     

From individual Wnt pathways towards a Wnt signalling network

Wnt proteins play important roles during vertebrate and invertebrate development. They obviously have the ability to activate different intracellular signalling pathways. Based on the characteristic intracellular mediators used, these are commonly described as the Wnt/beta-catenin, the Wnt/calcium and the Wnt/Jun N-terminal kinase pathways (also called planar cell polarity pathway). In the past, these different signalling events were mainly described as individual and independent signalling branches. Here, we discuss the possibility that Wnt proteins activate a complex intracellular signalling network rather than individual pathways and suggest a graph representation of this network. Furthermore, we discuss different ways of how to predict the specific outcome of an activation of this network in a particular cell type, which will require the use of mathematical models. We point out that the use of deterministic approaches via the application of differential equations is suitable to model only small aspects of the whole network and that more qualitative approaches are possibly a suitable starting point for the prediction of the global behaviour of such large protein interaction networks.     

Ubiquitin receptors and ERAD: a network of pathways to the proteasome

The elimination of misfolded proteins, known as protein quality control, is an essential cellular process. Removal of misfolded proteins from the secretory pathway depends on their recognition in the endoplasmic reticulum (ER) followed by their retrograde transport into the cytosol for degradation. The AAA-ATPase Cdc48/p97 facilitates the translocation of misfolded ER-proteins into the cytosol. Cdc48/p97 can dock onto the ER-membrane via direct interaction with ER-membrane proteins and/or indirectly via its substrate-recruiting cofactors, which interact with the ubiquitylated substrates at the membrane. This tight interaction in conjunction with the conformational changes induced upon ATP hydrolysis within Cdc48/p97 is thought to provide the driving force for the translocation reaction. Subsequently, a series of protein-protein interactions between the Cdc48/p97 complex, its cofactors, and the ubiquitylated substrates is instrumental for the proper delivery of the ER substrates to the proteasome. These protein-protein interactions are governed mainly by ubiquitin-fold and ubiquitin-binding domains.     

Visual stimulation is required for refinement of ON and OFF pathways in postnatal retina

ON and OFF pathways separately relay increment and decrement luminance signals from retinal bipolar cells to cortex. ON-OFF retinal ganglion cells (RGCs) are activated via synaptic inputs onto bistratified dendrites localized in the ON and OFF regions of the inner plexiform layer. Postnatal maturational processes convert bistratifying ON-OFF RGCs to monostratifying ON and OFF RGCs. Although visual deprivation influences refinement of higher visual centers, no previous studies suggest that light regulates either the development of the visual-evoked signaling in retinal ON and OFF pathways, nor pruning of bistratified RGC dendrites. We find that dark rearing blocks both the maturational loss of ON-OFF responsive RGCs and the pruning of dendrites. Thus, in retina, there is a previously unrecognized, pathway-specific maturation that is profoundly affected by visual deprivation.     

Effects of physical refuges on fish–plankton interactions

1.Refuges that reduce fish-induced mortality of zooplankton are considered to be key factors in controlling phytoplankton growth in lake ecosystems. In order to better understand the role of physical refuges for zooplankton on zooplanktivorous fish-plankton relationships, an enclosure experiment was run in a mesotrophic lake. Even-link systems (zooplankton and phytoplankton) and odd-link systems (zooplanktivorous fish, zooplankton and phytoplankton) were established. We also established an odd-link system with a physical refuge for zooplankton where fish predation was limited in the upper half of the enclosure.
2.Fish negatively affected density and mean body length of herbivorous zooplankton and total zooplankton, filtering rates with some intermediate effects in the presence of the refuge. A clear refuge effect was observed for the dominant herbivore, Ceriodaphnia . On the other hand, the refuge seemed to increase the vulnerability of those taxa that aggregated in upper layers of the water column. Grazing was thus reduced in both odd-link systems.
3.The lack of significant correlation between nutrient availability and phytoplankton biomass in enclosures suggested a top-down control of algal growth in our experimental systems. In both odd-link systems ('fish' and 'refuge') phytoplankton biomass was significantly enhanced, and transparency was reduced in comparison with the even-link system.     

Light- and carbon-signaling pathways. Modeling circuits of interactions

Here, we report the systematic exploration and modeling of interactions between light and sugar signaling. The data set analyzed explores the interactions of sugar (sucrose) with distinct light qualities (white, blue, red, and far-red) used at different fluence rates (low or high) in etiolated seedlings and mature green plants. Boolean logic was used to model the effect of these carbon/light interactions on three target genes involved in nitrogen assimilation: asparagine synthetase (ASN1 and ASN2) and glutamine synthetase (GLN2). This analysis enabled us to assess the effects of carbon on light-induced genes (GLN2/ASN2) versus light-repressed genes (ASN1) in this pathway. New interactions between carbon and blue-light signaling were discovered, and further connections between red/far-red light and carbon were modeled. Overall, light was able to override carbon as a major regulator of ASN1 and GLN2 in etiolated seedlings. By contrast, carbon overrides light as the major regulator of GLN2 and ASN2 in light-grown plants. Specific examples include the following: Carbon attenuated the blue-light induction of GLN2 in etiolated seedlings and also attenuated the white-, blue-, and red-light induction of GLN2 and ASN2 in light-grown plants. By contrast, carbon potentiated far-red-light induction of GLN2 and ASN2 in light-grown plants. Depending on the fluence rate of far-red light, carbon either attenuated or potentiated light repression of ASN1 in light-grown plants. These studies indicate the interaction of carbon with blue, red, and far-red-light signaling and set the stage for further investigation into modeling this complex web of interacting pathways using systems biology approaches.