Related Articles

Genetically encoded biosensor for fluorescence lifetime imaging of PTEN dynamics in the intact brain

The phosphatase and tensin homolog (PTEN) is a vital protein that maintains an inhibitory brake for cellular proliferation and growth. Accordingly, PTEN loss-of-function mutations are associated with a broad spectrum of human pathologies. Despite its importance, there is currently no method to directly monitor PTEN activity with cellular specificity within intact biological systems. Here we describe the development of a FRET-based biosensor using PTEN conformation as a proxy for the PTEN activity state, for two-photon fluorescence lifetime imaging microscopy. We identify a point mutation that allows the monitoring of PTEN activity with minimal interference to endogenous PTEN signaling. We demonstrate imaging of PTEN activity in cell lines, intact Caenorhabditis elegans and in the mouse brain. Finally, we develop a red-shifted sensor variant that allows us to identify cell-type-specific PTEN activity in excitatory and inhibitory cortical cells. In summary, our approach enables dynamic imaging of PTEN activity in vivo with unprecedented spatial and temporal resolution.

Evolutionary optimization of model merging recipes

Large language models (LLMs) have become increasingly capable, but their development often requires substantial computational resources. Although model merging has emerged as a cost-effective promising approach for creating new models by combining existing ones, it currently relies on human intuition and domain knowledge, limiting its potential. Here we propose an evolutionary approach that overcomes this limitation by automatically discovering effective combinations of diverse open-source models, harnessing their collective intelligence without requiring extensive additional training data or compute. Our approach operates in both parameter space and data flow space, allowing optimization beyond just the weights of the individual models. This approach even facilitates cross-domain merging, generating models such as a Japanese LLM with math reasoning capabilities. Surprisingly, our Japanese math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with substantially more parameters, despite not being explicitly trained for such tasks. Furthermore, a culturally aware Japanese vision–language model generated through our approach demonstrates its effectiveness in describing Japanese culture-specific content, outperforming previous Japanese vision–language models. This work not only contributes new state-of-the-art models back to the open-source community but also introduces a new paradigm for automated model composition, paving the way for exploring alternative, efficient approaches to foundation model development.

The genomic landscape of gene-level structural variations in Japanese and global soybean Glycine max cultivars

Japanese soybeans are traditionally bred to produce soy foods such as tofu, miso and boiled soybeans. Here, to investigate their distinctive genomic features, including genomic structural variations (SVs), we constructed 11 nanopore-based genome references for Japanese and other soybean lines. Our assembly-based comparative method, designated ‘Asm2sv’, identified gene-level SVs comprehensively, enabling pangenome analysis of 462 worldwide cultivars and varieties. Based on these, we identified selective sweeps between Japanese and US soybeans, one of which was the pod-shattering resistance gene PDH1. Genome-wide association studies further identified several quantitative trait loci that accounted for large-seed phenotypes of Japanese soybean lines, some of which were also close to regions of the selective sweeps, including PDH1. Notably, specific combinations of alleles, including SVs, were found to increase the seed size of some Japanese landraces. In addition to the differences in cultivation environments, distinct food processing usages might result in changes in Japanese soybean genomes.

Mechanisms of NLRP3 activation and inhibition elucidated by functional analysis of disease-associated variants

The NLRP3 inflammasome is a multiprotein complex that mediates caspase-1 activation and the release of proinflammatory cytokines, including interleukin (IL)-1β and IL-18. Gain-of-function variants in the gene encoding NLRP3 (also called cryopyrin) lead to constitutive inflammasome activation and excessive IL-1β production in cryopyrin-associated periodic syndromes (CAPS). Here we present functional screening and automated analysis of 534 NLRP3 variants from the international INFEVERS registry and the ClinVar database. This resource captures the effect of NLRP3 variants on ASC speck formation spontaneously, at low temperature, after inflammasome stimulation and with the specific NLRP3 inhibitor MCC950. Most notably, our analysis facilitated the updated classification of NLRP3 variants in INFEVERS. Structural analysis suggested multiple mechanisms by which CAPS variants activate NLRP3, including enhanced ATP binding, stabilizing the active NLRP3 conformation, destabilizing the inactive NLRP3 complex and promoting oligomerization of the pyrin domain. Furthermore, we identified pathogenic variants that can hypersensitize the activation of NLRP3 in response to nigericin and cold temperature exposure. We also found that most CAPS-related NLRP3 variants can be inhibited by MCC950; however, NLRP3 variants with changes to proline affecting helices near the inhibitor binding site are resistant to MCC950, as are variants in the pyrin domain, which likely trigger activation directly with the pyrin domain of ASC. Our findings could help stratify the CAPS population for NLRP3 inhibitor clinical trials and our automated methodologies can be implemented for molecules with a different mechanism of activation and in laboratories worldwide that are interested in adding new functionally validated NLRP3 variants to the resource. Overall, our study provides improved diagnosis for patients with CAPS, mechanistic insight into the activation of NLRP3 and stratification of patients for the future application of targeted therapeutics.

Responses

Your email address will not be published. Required fields are marked *