ABSTRACT: B-lymphocytes recognize antigen via B-cell antigen receptors (BCRs). This binding induces signaling, leading to B-cell activation, proliferation and differentiation. Early events of BCR signaling include reorganization of actin and membrane spreading, which facilitates increased antigen gathering. We have previously shown that the gap junction protein connexin43 (Cx43; also known as GJA1) is phosphorylated upon BCR signaling, and its carboxyl tail (CT) is important for BCR-mediated spreading. Here, specific serine residues in the Cx43 CT that are phosphorylated following BCR stimulation were identified. A chimeric protein containing the extracellular and transmembrane domains of CD8 fused to the Cx43 CT was sufficient to support cell spreading. Cx43 CT truncations showed that the region between amino acids 246-307 is necessary for B-cell spreading. Site-specific serine-to-alanine mutations (S255A, S262A, S279A and S282A) resulted in differential effects on both BCR signaling and BCR-mediated spreading. These serine residues can serve as potential binding sites for actin remodeling mediators and/or BCR signaling effectors; therefore, our results may reflect unique roles for each of these serines in terms of linking the Cx43 CT to actin remodeling.
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ABSTRACT: We hypothesized that oxygen uptake (ṀO2) measured with a novel protocol of chasing rainbow trout Oncorhynchus mykiss to exhaustion inside a static respirometer while simultaneously monitoring ṀO2 (ṀO2chase) would generate the same and repeatable peak value as when peak active ṀO2 (ṀO2active) is measured in a critical swimming speed protocol. To reliably determine peak ṀO2chase, and the peak during recovery of ṀO2 following a conventional chase protocol outside the respirometer (ṀO2rec), we applied an iterative algorithm and a minimum sampling window duration (i.e., 1 min based on an analysis of the variance in background and exercise ṀO2) to account for ṀO2 dynamics. In support of our hypothesis, peak ṀO2active (707 ± 33 mg O2 h−1 kg−1) and peak ṀO2chase (663 ± 43 mg O2 h−1 kg−1) were similar (P = 0.49) and repeatable (Pearson's and Spearman's correlation test; r ≥ 0.77; P < 0.05) when measured in the same fish. Therefore, we conclude that estimates of ṀO2max can be independent of whether a fish is exhaustively chased inside a respirometer or swum to fatigue in a swim tunnel, provided ṀO2 is analyzed with an iterative algorithm and a minimum but reliable sampling window. The importance of using this analytical approach was illustrated by peak ṀO2chase being 23% higher (P < 0.05) when compared to a conventional sequential interval regression analysis, while using the conventional chase protocol (1‐min window) outside the respirometer increased this difference to 31% (P < 0.01). Moreover, because peak ṀO2chase was 18% higher (P < 0.05) than peak ṀO2rec, chasing a fish inside a static respirometer may be a better protocol for obtaining maximum ṀO2.
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ABSTRACT: Unsustainable hunting is emptying forests of large animals around the world, but current understanding of how human foraging spreads across landscapes has been stymied by data deficiencies and cryptic hunter behaviour. Unlike other global threats to biodiversity like deforestation, climate change and overfishing, maps of wild meat hunters' movements—often based on forest accessibility—typically cover small scales and are rarely validated with real-world observations. Using camera trapping data from rainforests across Malaysian Borneo, we show that while hunter movements are strongly correlated with the accessibility of different parts of the landscape, accessibility measures are most informative when they integrate fine-scale habitat features like topography and land cover. Measures of accessibility naive to fine-scale habitat complexity, like distance to the nearest road or settlement, generate poor approximations of hunters’ movements. In comparison, accessibility as measured by high-resolution movement models based on circuit theory provides vastly better reflections of real-world foraging movements. Our results highlight that simple models incorporating fine-scale landscape heterogeneity can be powerful tools for understanding and predicting widespread threats to biodiversity.