Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.     

Growth-induced mass flows in fungal networks

Cord-forming fungi form extensive networks that continuously adapt to maintain an efficient transport system. As osmotically driven water uptake is often distal from the tips, and aqueous fluids are incompressible, we propose that growth induces mass flows across the mycelium, whether or not there are intrahyphal concentration gradients. We imaged the temporal evolution of networks formed by Phanerochaete velutina, and at each stage calculated the unique set of currents that account for the observed changes in cord volume, while minimizing the work required to overcome viscous drag. Predicted speeds were in reasonable agreement with experimental data, and the pressure gradients needed to produce these flows are small. Furthermore, cords that were predicted to carry fast-moving or large currents were significantly more likely to increase in size than cords with slow-moving or small currents. The incompressibility of the fluids within fungi means there is a rapid global response to local fluid movements. Hence velocity of fluid flow is a local signal that conveys quasi-global information about the role of a cord within the mycelium. We suggest that fluid incompressibility and the coupling of growth and mass flow are critical physical features that enable the development of efficient, adaptive biological transport networks.     

The role of epiphytism in architecture and evolutionary constraint within mycorrhizal networks of tropical orchids

Characterizing the architecture of bipartite networks is increasingly used as a framework to study biotic interactions within their ecological context and to assess the extent to which evolutionary constraint shape them. Orchid mycorrhizal symbioses are particularly interesting as they are viewed as more beneficial for plants than for fungi, a situation expected to result in an asymmetry of biological constraint. This study addressed the architecture and phylogenetic constraint in these associations in tropical context. We identified a bipartite network including 73 orchid species and 95 taxonomic units of mycorrhizal fungi across the natural habitats of Reunion Island. Unlike some recent evidence for nestedness in mycorrhizal symbioses, we found a highly modular architecture that largely reflected an ecological barrier between epiphytic and terrestrial subnetworks. By testing for phylogenetic signal, the overall signal was stronger for both partners in the epiphytic subnetwork. Moreover, in the subnetwork of epiphytic angraecoid orchids, the signal in orchid phylogeny was stronger than the signal in fungal phylogeny. Epiphytic associations are therefore more conservative and may co‐evolve more than terrestrial ones. We suggest that such tighter phylogenetic specialization may have been driven by stressful life conditions in the epiphytic niches. In addition to paralleling recent insights into mycorrhizal networks, this study furthermore provides support for epiphytism as a major factor affecting ecological assemblage and evolutionary constraint in tropical mycorrhizal symbioses.     

Mechanisms of madness: evolutionary psychiatry without evolutionary psychology

Delusions are currently characterised as false beliefs produced by incorrect inference about external reality (DSM IV). This inferential conception has proved hard to link to explanations pitched at the level of neurobiology and neuroanatomy. This paper provides that link via a neurocomputational theory, based on evolutionary considerations, of the role of the prefrontal cortex in regulating offline cognition. When pathologically neuromodulated the prefrontal cortex produces hypersalient experiences which monopolise offline cognition. The result is characteristic psychotic experiences and patterns of thought. This bottom-up account uses neural network theory to integrate recent theories of the role of dopamine in delusion with the insights of inferential accounts. It also provides a general model for evolutionary psychiatry which avoids theoretical problems imported from evolutionary psychology.     

Effects of stream-crossing by a pipeline on the benthic macroinvertebrate communities of a small mountain stream

Aquatic environmental impact associated with stream-crossing by a pipeline was monitored at Archibald Creek, B.C. for two years. Water chemistry and benthic macroinvertebrates were used as monitoring tools. Results indicated that impacts arising from stream-crossing were short-term and non-residual.Funded by a contract from Canadian Arctic Gas Study Limited, calgary, to Aquatic Environments Limited, Calgary.Aquatic Environments Limited, Calgary.Aquatic Environments Limited, Calgary.     

The Importance of Hydrodynamics for Protected and Endangered Biodiversity of Lowland Rivers

This paper examines the relationship between protected and endangered riverine species (target species) and hydrodynamics in river-floodplain ecosystems, combining ecological and policy-legal aspects of biodiversity conservation in river management. The importance of different hydrodynamic conditions along a lateral gradient was quantified for various taxonomic groups. Our results show that (i) target species require ecotopes along the entire hydrodynamic gradient; (ii) different parts of the hydrodynamic gradient are important to different species, belonging to different taxonomic groups; (iii) in particular low-dynamic parts are important for many species and (iv) species differ in their specificity for hydrodynamic conditions. Many species of higher plants, fish and butterflies have a narrow range for hydrodynamics and many species of birds and mammals use ecotopes along the entire gradient. Even when focussing only on target species, the entire natural hydrodynamic gradient is important. This means that the riverine species assemblage as a whole can benefit from measures focussing on target species only. River reconstruction and management should aim at re-establishing the entire hydrodynamic gradient, increasing the spatial heterogeneity of hydrodynamic conditions.     

Uncertainty associated with river health assessment in a varying environment: The case of a predictive fish-based index in France

Sensitive biological measures of river ecosystem quality are needed to assess, maintain or restore ecological conditions of water bodies. Since our understanding of these complex systems is imperfect, decision-making requires recognizing uncertainty. In this study, a new predictive multi-metric index based on fish functional traits was developed to assess French rivers. Information on fish assemblage structure, local environment and human-induced disturbances of 1654 French river sites was compiled. A Bayesian framework was used to predict theoretical metric values in absence of human pressure and to estimate the uncertainty associated with these predictions. The uncertainty associated with the index score gives the confidence associated with the evaluation of site ecological conditions.Among the 228 potential metrics tested, only 11 were retained for the index computation. The final index is independent from natural variability and sensitive to human-induced disturbances. In particular, it is affected by the accumulation of different degradations and specific degradations including hydrological perturbations. Predictive uncertainty is globally lower for IPR+ than for underlying metrics.This new methodology seems appropriate to develop bio-indication tools accounting for uncertainty related to reference condition definition and could be extended to other biological groups and areas. Our results support the use of multi-metric indexes to assess rivers and strengthen the idea that examination of uncertainty could contribute greatly to the improvement of the assessment power of bio-indicators.