Development of a method for the imputation of the multi-allelic serotonin-transporter-linked polymorphic region (5-HTTLPR) in the Japanese population
Related Articles
Coding principles and mechanisms of serotonergic transmission modes
Serotonin-mediated intercellular communication has been implicated in myriad human behaviors and diseases, yet how serotonin communicates and how the communication is regulated remain unclear due to limitations of available monitoring tools. Here, we report a method multiplexing genetically encoded sensor-based imaging and fast-scan cyclic voltammetry, enabling simultaneous recordings of synaptic, perisynaptic, proximate and distal extrasynaptic serotonergic transmission. Employing this method alongside a genetically encoded sensor-based image analysis program (GESIAP), we discovered that heterogeneous firing patterns of serotonergic neurons create various transmission modes in the mouse raphe nucleus and amygdala, encoding information of firing pulse frequency, number, and synchrony using neurotransmitter quantity, releasing synapse count, and synaptic and/or volume transmission. During tonic and low-frequency phasic activities, serotonin is confined within synaptic clefts due to efficient retrieval by perisynaptic transporters, mediating synaptic transmission modes. Conversely, during high-frequency, especially synchronized phasic activities, or when transporter inhibition, serotonin may surpass transporter capacity, and escape synaptic clefts through 1‒3 outlet channels, leading to volume transmission modes. Our results elucidate a mechanism of how channeled synaptic enclosures, synaptic properties, and transporters collaborate to define the coding principles of activity pattern-dependent serotonergic transmission modes.
Serotonin attenuates tumor necrosis factor-induced intestinal inflammation by interacting with human mucosal tissue
The intestine hosts the largest immune system and peripheral nervous system in the human body. The gut‒brain axis orchestrates communication between the central and enteric nervous systems, playing a pivotal role in regulating overall body function and intestinal homeostasis. Here, using a human three-dimensional in vitro culture model, we investigated the effects of serotonin, a neuromodulator produced in the gut, on immune cell and intestinal tissue interactions. Serotonin attenuated the tumor necrosis factor-induced proinflammatory response, mostly by affecting the expression of chemokines. Serotonin affected the phenotype and distribution of tissue-migrating monocytes, without direct contact with the cells, by remodeling the intestinal tissue. Collectively, our results show that serotonin plays a crucial role in communication among gut–brain axis components and regulates monocyte migration and plasticity, thereby contributing to gut homeostasis and the progression of inflammation. In vivo studies focused on the role of neuromodulators in gut inflammation have shown controversial results, highlighting the importance of human experimental models. Moreover, our results emphasize the importance of human health research in human cell-based models and suggest that the serotonin signaling pathway is a new therapeutic target for inflammatory bowel disease.
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.
Genetic analysis of a Yayoi individual from the Doigahama site provides insights into the origins of immigrants to the Japanese Archipelago
Mainland Japanese have been recognized as having dual ancestry, originating from indigenous Jomon people and immigrants from continental East Eurasia. Although migration from the continent to the Japanese Archipelago continued from the Yayoi to the Kofun period, our understanding of these immigrants, particularly their origins, remains insufficient due to the lack of high-quality genome samples from the Yayoi period, complicating predictions about the admixture process. To address this, we sequenced the whole nuclear genome of a Yayoi individual from the Doigahama site in Yamaguchi prefecture, Japan. A comprehensive population genetic analysis of the Doigahama Yayoi individual, along with ancient and modern populations in East Asia and Northeastern Eurasia, revealed that the Doigahama Yayoi individual, similar to Kofun individuals and modern Mainland Japanese, had three distinct genetic ancestries: Jomon-related, East Asian-related, and Northeastern Siberian-related. Among non-Japanese populations, the Korean population, possessing both East Asian-related and Northeastern Siberian-related ancestries, exhibited the highest degree of genetic similarity to the Doigahama Yayoi individual. The analysis of admixture modeling for Yayoi individuals, Kofun individuals, and modern Japanese respectively supported a two-way admixture model assuming Jomon-related and Korean-related ancestries. These results suggest that between the Yayoi and Kofun periods, the majority of immigrants to the Japanese Archipelago originated primarily from the Korean Peninsula.
Responses