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.     

A comparative Proteomics Analysis Identified Differentially Expressed Proteins in Pancreatic Cancer–Associated Stellate Cell Small Extracellular Vesicles

Human pancreatic stellate cells (HPSCs) are an essential stromal component and mediators of pancreatic ductal adenocarcinoma (PDAC) progression. Small extracellular vesicles (sEVs) are membrane-enclosed nanoparticles involved in cell-to-cell communications and are released from stromal cells within PDAC. A detailed comparison of sEVs from normal pancreatic stellate cells (HPaStec) and from PDAC-associated stellate cells (HPSCs) remains a gap in our current knowledge regarding stellate cells and PDAC. We hypothesized there would be differences in sEVs secretion and protein expression that might contribute to PDAC biology. To test this hypothesis, we isolated sEVs using ultracentrifugation followed by characterization by electron microscopy and Nanoparticle Tracking Analysis. We report here our initial observations. First, HPSC cells derived from PDAC tumors secrete a higher volume of sEVs when compared to normal pancreatic stellate cells (HPaStec). Although our data revealed that both normal and tumor-derived sEVs demonstrated no significant biological effect on cancer cells, we observed efficient uptake of sEVs by both normal and cancer epithelial cells. Additionally, intact membrane-associated proteins on sEVs were essential for efficient uptake. We then compared sEV proteins isolated from HPSCs and HPaStecs cells using liquid chromatography–tandem mass spectrometry. Most of the 1481 protein groups identified were shared with the exosome database, ExoCarta. Eighty-seven protein groups were differentially expressed (selected by 2-fold difference and adjusted p value ≤0.05) between HPSC and HPaStec sEVs. Of note, HPSC sEVs contained dramatically more CSE1L (chromosome segregation 1–like protein), a described marker of poor prognosis in patients with pancreatic cancer. Based on our results, we have demonstrated unique populations of sEVs originating from stromal cells with PDAC and suggest that these are significant to cancer biology. Further studies should be undertaken to gain a deeper understanding that could drive novel therapy.     

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.     

An order-specific monoclonal antibody to Diptera reveals the impact of alternative prey on spider feeding behavior in a complex food web

Generalist predators have the capacity to restrict pest population growth, especially early in the season before densities increase. However, their polyphagous feeding habits sometimes translate into reduced pest consumption when they target alternative prey. An order-specific monoclonal antibody was developed to examine the strength of trophic connections between Diptera, a major category of non-pest prey, and linyphiid spiders in alfalfa. We report the development and characterization of a monoclonal antibody with order-level specificity to Diptera. This antibody elicited strong absorbance to 22 Diptera from 13 families, no false-positive reactivity to non-dipteran invertebrates, and antigen detection periods following prey consumption that were comparable between spiders. Over 900 field-collected females of the linyphiid spiders Erigone autumnalis and Bathyphantes pallidus were screened for Diptera antigen. Significantly more B. pallidus screened positive for Diptera (40%) compared to E. autumnalis (16%), indicating differential reliance on these prey. In parallel with the collection of spiders for gut-content analysis, prey availability was estimated at web sites. The two spiders exhibited different feeding responses to prey availability. Consumption of Diptera by B. pallidus was strongly correlated with Diptera abundance whilst the availability of other potential prey did not influence predation rates. Conversely, E. autumnalis did not prey upon Diptera in proportion to availability, but increased Collembola activity-density reduced dipteran consumption. Integration of molecular gut-content analysis with precise sampling of prey demonstrated how two closely related linyphiid spiders exhibit different feeding responses to the availability of prey under natural field conditions. Elucidating the feeding preferences of natural enemies is critical to effective incorporation of biological control by generalist predators in the management of agricultural pests.     

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.