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The cryo-electron microscopy structure of the ER membrane complex provides insight into its overall architecture, evolution and function in co-translational protein insertion.

The endoplasmic reticulum (ER) membrane complex (EMC) cooperates with the Sec61 translocon to co-translationally insert a transmembrane helix (TMH) of many multi-pass integral membrane proteins into the ER membrane, and it is also responsible for inserting the TMH of some tail-anchored proteins(1-3). How EMC accomplishes this feat has been unclear. Here we report the first, to our knowledge, cryo-electron microscopy structure of the eukaryotic EMC. We found that the Saccharomyces cerevisiae EMC contains eight subunits (Emc1-6, Emc7 and Emc10), has a large lumenal region and a smaller cytosolic region, and has a transmembrane region formed by Emc4, Emc5 and Emc6 plus the transmembrane domains of Emc1 and Emc3. We identified a five-TMH fold centred around Emc3 that resembles the prokaryotic YidC insertase and that delineates a largely hydrophilic client protein pocket. The transmembrane domain of Emc4 tilts away from the main transmembrane region of EMC and is partially mobile. Mutational studies demonstrated that the flexibility of Emc4 and the hydrophilicity of the client pocket are required for EMC function. The EMC structure reveals notable evolutionary conservation with the prokaryotic insertases(4,5), suggests that eukaryotic TMH insertion involves a similar mechanism, and provides a framework for detailed understanding of membrane insertion for numerous eukaryotic integral membrane proteins and tail-anchored proteins.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses(1). The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset(2-5). Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.