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Influenza A virus (IAV) is a pathogen responsible for severe seasonal epidemics threatening human and animal populations every year. During the viral assembly process in the infected cells, the plasma membrane (PM) has to bend in localized regions into a vesicle towards the extracellular side. Studies in cellular models have proposed that different viral proteins might be responsible for inducing membrane curvature in this context (including M1), but a clear consensus has not been reached. M1 is the most abundant protein in IAV particles. It plays an important role in virus assembly and budding at the PM. M1 is recruited to the host cell membrane where it associates with lipids and other viral proteins. However, the details of M1 interactions with the cellular PM, as well as M1-mediated membrane bending at the budozone, have not been clarified.
In this work, we used several experimental approaches to analyze M1-lipids and M1-M1 interactions. By performing SPR analysis, we quantified membrane association for full-length M1 and different genetically engineered M1 constructs (i.e., N- and C-terminally truncated constructs and a mutant of the polybasic region). This allowed us to obtain novel information on the protein regions mediating M1 binding to membranes. By using fluorescence microscopy, cryogenic transmission electron microscopy (cryo-TEM), and three-dimensional (3D) tomography (cryo-ET), we showed that M1 is indeed able to cause membrane deformation on vesicles containing negatively-charged lipids, in the absence of other viral components. Further, sFCS analysis proved that simple protein binding is not sufficient to induce membrane restructuring. Rather, it appears that stable M1-M1 interactions and multimer formation are required to alter the bilayer three-dimensional structure through the formation of a protein scaffold.
Finally, to mimic the budding mechanism in cells that arise by the lateral organization of the virus membrane components on lipid raft domains, we created vesicles with lipid domains. Our results showed that local binding of M1 to spatial confined acidic lipids within membrane domains of vesicles led to local M1 inward curvature.
One of the most striking features of ecological systems is their ability to undergo sudden outbreaks in the population numbers of one or a small number of species. The similarity of outbreak characteristics, which is exhibited in totally different and unrelated (ecological) systems naturally leads to the question whether there are universal mechanisms underlying outbreak dynamics in Ecology. It will be shown into two case studies (dynamics of phytoplankton blooms under variable nutrients supply and spread of epidemics in networks of cities) that one explanation for the regular recurrence of outbreaks stems from the interaction of the natural systems with periodical variations of their environment. Natural aquatic systems like lakes offer very good examples for the annual recurrence of outbreaks in Ecology. The idea whether chaos is responsible for the irregular heights of outbreaks is central in the domain of ecological modeling. This question is investigated in the context of phytoplankton blooms. The dynamics of epidemics in networks of cities is a problem which offers many ecological and theoretical aspects. The coupling between the cities is introduced through their sizes and gives rise to a weighted network which topology is generated from the distribution of the city sizes. We examine the dynamics in this network and classified the different possible regimes. It could be shown that a single epidemiological model can be reduced to a one-dimensional map. We analyze in this context the dynamics in networks of weighted maps. The coupling is a saturation function which possess a parameter which can be interpreted as an effective temperature for the network. This parameter allows to vary continously the network topology from global coupling to hierarchical network. We perform bifurcation analysis of the global dynamics and succeed to construct an effective theory explaining very well the behavior of the system.