Excitatory amino acid transporters (EAAT/SLC1) mediate Na+-dependent uptake of extracellular glutamate and are potential drug targets for neurological disorders. Conventional methods to assess glutamate transport in vitro are based on radiolabels, fluorescent dyes or electrophysiology, which potentially compromise the cell’s physiology and are generally less suited for primary drug screens. Here, we describe a novel label-free method to assess human EAAT function in living cells, i.e., without the use of chemical modifications to the substrate or cellular environment. In adherent HEK293 cells overexpressing EAAT1, stimulation with glutamate or aspartate induced cell spreading, which was detected in real-time using an impedance-based biosensor. This change in cell morphology was prevented in the presence of the Na+/K+-ATPase inhibitor ouabain and EAAT inhibitors, which suggests the substrate-induced response was ion-dependent and transporter-specific. A mechanistic explanation for the phenotypic response was substantiated by actin cytoskeleton remodeling and changes in the intracellular levels of the osmolyte taurine, which suggests that the response involves cell swelling. In addition, substrate-induced cellular responses were observed for cells expressing other EAAT subtypes, as well as in a breast cancer cell line (MDA-MB-468) with endogenous EAAT1 expression. These findings allowed the development of a label-free high-throughput screening assay, which could be beneficial in early drug discovery for EAATs and holds potential for the study of other transport proteins that modulate cell shape.

1Polygenic adaptation is frequently associated with small allele frequency changes of many loci. 2 Recent works suggest, that large allele frequency changes can be also expected. Laboratory 3 natural selection (LNS) experiments provide an excellent experimental framework to study the 4 adaptive architecture under controlled laboratory conditions: time series data in replicate 5 populations evolving independently to the same trait optimum can be used to identify selected 6 loci. Nevertheless, the choice of the new trait optimum in the laboratory is typically an ad hoc 7 decision without consideration of the distance of the starting population to the new optimum.8Here, we used forward-simulations to study the selection signatures of polygenic adaptation in 9 populations evolving to different trait optima. Mimicking LNS experiments we analyzed allele 10 frequencies of the selected alleles and population fitness at multiple time points. We 11 demonstrate that the inferred adaptive architecture strongly depends on the choice of the new 12 trait optimum in the laboratory and the significance cut-off used for identification of selected loci. 13Our results not only have a major impact on the design of future Evolve and Resequence (E&R) 14 studies, but also on the interpretation of current E&R data sets. 15 16 Polymorphic populations are subjected to different types of stressors (e.g. temperature, 20 3 desiccation, etc) and monitored for changes in the phenotype of one or several traits that are 21 usually controlled by a large number of loci (i.e polygenic traits). Recently, the analysis of 22 phenotypic change is combined with the analysis of allele frequency changes by contrasting the 23 evolved and ancestral populations (Evolve & Resequence, E&R). The primary goal of such 24 experiments is to uncover the adaptive architecture of the focal trait(s) in populations subjected 25 to certain conditions based on the allele frequency changes during the experiment. 26 The analysis of different LNS studies revealed contrasting genomic signatures. The most 27 apparent discrepancies between studies are related to the number of putative selection targets 28 and the extent of parallelism across replicates. While some studies detected only a small number 29 of selection targets (Magwire et al. 2012; Mallard et al. 2018), others suggested a polygenic 30 response (Barghi et al., 2019; Jha et al., 2015). While some E&R studies found highly parallel 31 selection response among replicates (Burke et al. 2010; Burke et al. 2016; Graves et al. 2017; 32 Fragata et al. 2018; Mallard et al. 2018; Rebolleda-Gómez and Travisano 2018; Michalak et al. 33 2019) in other studies selection signatures were much less concordant (Cohan and Hoffmann 34 1986; Teotónio et al. 2004; Simões et al. 2008; Griffin et al. 2017; Barghi et al. 2019). A particularly 35 striking difference has been observed for natural Drosophila simulans populations exposed to the 36 same hot environment. A Portuguese population showed parallel strong selection response at 37 few g...

Background Pleiotropy describes the phenomenon in which a gene affects multiple phenotypes. The extent of pleiotropy is still disputed, mainly because of issues of inadequate power of analyses. A further challenge is that empirical tests of pleiotropy are restricted to a small subset of all possible phenotypes. To overcome these limitations, we propose a new measurement of pleiotropy that integrates across many phenotypes and multiple generations to improve power. Results We infer pleiotropy from the fitness cost imposed by frequency changes of pleiotropic loci. Mixing Drosophila simulans populations, which adapted independently to the same new environment using different sets of genes, we show that the adaptive frequency changes have been accompanied by measurable fitness costs. Conclusions Unlike previous studies characterizing the molecular basis of pleiotropy, we show that many loci, each of weak effect, contribute to genome-wide pleiotropy. We propose that the costs of pleiotropy are reduced by the modular architecture of gene expression, which facilitates adaptive gene expression changes with low impact on other functions.