Signals of natural selection can be quickly eroded in high gene-flow systems, curtailing efforts to understand how and when genetic adaptation occurs in the ocean. This long-standing, unresolved topic in ecology and evolution has renewed importance because changing environmental conditions are driving range expansions that may necessitate rapid evolutionary responses. One example occurs in Kellet’s whelk (Kelletia kelletii), a common subtidal gastropod with a ~ 40-60 day pelagic larval duration that expanded their biogeographic range northward in the 1970s by over 300 kilometers. To test for genetic adaptation, we performed a series of experimental crosses with Kellet’s whelk adults collected from their historical (HxH) and recently expanded range (ExE), and conducted RNA-Seq on offspring that we reared in a common garden environment. We identified 2,770 differentially expressed genes (DEGs) between 54 offspring samples with either only historical-range (HxH offspring) or expanded-range (ExE offspring) ancestry. Using SNPs called directly from the DEGs, we assigned samples of known origin back to their range of origin with unprecedented accuracy for a marine species (92.6 and 94.5% for HxH and ExE offspring, respectively). The SNP with the highest predictive importance occurred on triosephosphate isomerase (TPI), an essential metabolic enzyme involved in cold stress response. TPI was significantly upregulated and contained a non-synonymous mutation in the expanded range. Our findings pave the way for accurately identifying patterns of dispersal, gene flow, and population connectivity in the ocean by demonstrating that experimental transcriptomics can reveal mechanisms for how marine organisms respond to changing environmental conditions.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homo-dimers, 3 homo-trimers, 13 hetero-dimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their 5 best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% for the targets compared to 8% two years earlier, a remarkable improvement resulting from the wide use of the AlphaFold2 and AlphaFold-Multimer software. Creative use was made of the deep learning inference engines affording the sampling of a much larger number of models and enriching the multiple sequence alignments with sequences from various sources. Wide use was also made of the AlphaFold confidence metrics to rank models, permitting top performing groups to exceed the results of the public AlphaFold-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.