Antigen 43 (Ag43) is a cell-surface exposed protein of Escherichia coli secreted by the Type V, subtype a, secretion system (T5aSS) and belonging to the family of self-associating autotransporters (SAATs). These modular proteins, comprising a cleavable N-terminal signal peptide, a surface-exposed central passenger and an outer membrane C-terminal translocator, self-recognise in a Velcro-like handshake mechanism. A phylogenetic network analysis focusing on the passenger revealed for the first time that they actually distribute into four distinct classes, namely C1, C2, C3 and C4. Structural alignment and modelling analyses demonstrated these classes arose from shuffling of two different subdomains within the Ag43 passengers. Functional analyses revealed that homotypic interactions occur for all Ag43 classes but significant differences in the sedimentation kinetics and aggregation state were present when Ag43 C3 was expressed. In contrast, heterotypic interaction occurred in a very limited number of cases. Single cell-force spectroscopy demonstrated the importance of specific as well as nonspecific interactions in mediating Ag43-Ag43 recognition. We propose that structural differences in the subdomains of the Ag43 classes account for different autoaggregation dynamics and propensities to co-interact.

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We present a Bayesian hierarchical model to estimate the abundance and the biomass of brown trout ( Salmo trutta fario ) by using removal sampling and biometric data collected at several stream sections. The model accounts for (i) variability of the abundance with fish length (as a distribution mixture), (ii) spatial variability of the abundance, (iii) variability of the catchability with fish length (as a logit regression model), (iv) spatial variability of the catchability, and (v) residual variability of the catchability with fish. Model measured variables are the areas of the stream sections as well as the length and the weight of the caught fish. We first test the model by using a simulated dataset before using a 3-location, 2-removal sampling dataset collected in the field. Fifteen model alternatives are compared with an index of complexity and fit by using the field dataset. The selected model accounts for variability of the abundance with fish length and stream section and variability of the catchability with fish length. By using the selected model, 95% credible interval estimates of the abundances at the three stream sections are (0.46,0.59), (0.90,1.07), and (0.56,0.69) fish/m2. Respective biomass estimates are (9.68, 13.58), (17.22, 22.71), and (12.69, 17.31) g/m2.