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Lymphocyte activation gene (Lag)-3 is an inhibitory co-receptor and target of immune checkpoint inhibitor (ICI) therapy for cancer. The dynamic behavior of Lag-3 was analyzed at the immune synapse upon T-cell activation to elucidate the Lag-3 inhibitory mechanism. Lag-3 formed clusters and co-localized with T-cell receptor microcluster (TCR-MC) upon T-cell activation similar to PD-1. Lag-3 blocking antibodies (Abs) inhibited the co-localization between Lag-3 and TCR-MC without inhibiting Lag-3 cluster formation. Lag-3 also inhibited MHC-II-independent stimulation and Lag-3 Ab, which did not block MHC-II binding could still block Lag-3's inhibitory function, suggesting that the Lag-3 Ab blocks the Lag-3 inhibitory signal by dissociating the co-assembly of TCR-MC and Lag-3 clusters. Consistent with the combination benefit of PD-1 and Lag-3 Abs to augment T-cell responses, bispecific Lag-3/PD-1 antagonists effectively inhibited both cluster formation and co-localization of PD-1 and Lag-3 with TCR-MC. Therefore, Lag-3 inhibits T-cell activation at TCR-MC, and the target of Lag-3 ICI is to dissociate the co-localization of Lag-3 with TCR-MC., Competing Interests: EB and RWM were employed by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA, during the course of this research work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hashimoto-Tane, Bowman, Sakuma, Yoneda, Yugi, de Waal Malefyt and Saito.)

Dysregulation of liver metabolism associated with obesity during feeding and fasting leads to the breakdown of metabolic homeostasis. However, the underlying mechanism remains unknown. Here, we measured multi-omics data in the liver of wild-type and leptin-deficient obese ( ob / ob ) mice at ad libitum feeding and constructed a differential regulatory trans-omic network of metabolic reactions. We compared the trans-omic network at feeding with that at 16 h fasting constructed in our previous study. Intermediate metabolites in glycolytic and nucleotide metabolism decreased in ob / ob mice at feeding but increased at fasting. Allosteric regulation reversely shifted between feeding and fasting, generally showing activation at feeding while inhibition at fasting in ob / ob mice. Transcriptional regulation was similar between feeding and fasting, generally showing inhibiting transcription factor regulations and activating enzyme protein regulations in ob / ob mice. The opposite metabolic dysregulation between feeding and fasting characterizes breakdown of metabolic homeostasis associated with obesity., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Biochemical network visualization is one of the essential technologies for mechanistic interpretation of omics data. In particular, recent advances in multi-omics measurement and analysis require the development of visualization methods that encompass multiple omics data. Visualization in 2.5 dimension (2.5D visualization), which is an isometric view of stacked X-Y planes, is a convenient way to interpret multi-omics/trans-omics data in the context of the conventional layouts of biochemical networks drawn on each of the stacked omics layers. However, 2.5D visualization of trans-omics networks is a state-of-the-art method that primarily relies on time-consuming human efforts involving manual drawing. Here, we present an R Bioconductor package 'transomics2cytoscape' for automated visualization of 2.5D trans-omics networks. We confirmed that transomics2cytoscape could be used for rapid visualization of trans-omics networks presented in published papers within a few minutes. Transomics2cytoscape allows for frequent update/redrawing of trans-omics networks in line with the progress in multi-omics/trans-omics data analysis, thereby enabling network-based interpretation of multi-omics data at each research step. The transomics2cytoscape source code is available at https://github.com/ecell/transomics2cytoscape ., (© 2024. The Author(s).)

Insulin resistance is the primary pathophysiology underlying metabolic syndrome and type 2 diabetes 1,2 . Previous metagenomic studies have described the characteristics of gut microbiota and their roles in metabolizing major nutrients in insulin resistance 3-9 . In particular, carbohydrate metabolism of commensals has been proposed to contribute up to 10% of the host's overall energy extraction 10 , thereby playing a role in the pathogenesis of obesity and prediabetes 3,4,6 . Nevertheless, the underlying mechanism remains unclear. Here we investigate this relationship using a comprehensive multi-omics strategy in humans. We combine unbiased faecal metabolomics with metagenomics, host metabolomics and transcriptomics data to profile the involvement of the microbiome in insulin resistance. These data reveal that faecal carbohydrates, particularly host-accessible monosaccharides, are increased in individuals with insulin resistance and are associated with microbial carbohydrate metabolisms and host inflammatory cytokines. We identify gut bacteria associated with insulin resistance and insulin sensitivity that show a distinct pattern of carbohydrate metabolism, and demonstrate that insulin-sensitivity-associated bacteria ameliorate host phenotypes of insulin resistance in a mouse model. Our study, which provides a comprehensive view of the host-microorganism relationships in insulin resistance, reveals the impact of carbohydrate metabolism by microbiota, suggesting a potential therapeutic target for ameliorating insulin resistance., (© 2023. The Author(s).)

Metabolic regulation in skeletal muscle is essential for blood glucose homeostasis. Obesity causes insulin resistance in skeletal muscle, leading to hyperglycemia and type 2 diabetes. In this study, we performed multiomic analysis of the skeletal muscle of wild-type (WT) and leptin-deficient obese (ob/ob) mice, and constructed regulatory transomic networks for metabolism after oral glucose administration. Our network revealed that metabolic regulation by glucose-responsive metabolites had a major effect on WT mice, especially carbohydrate metabolic pathways. By contrast, in ob/ob mice, much of the metabolic regulation by glucose-responsive metabolites was lost and metabolic regulation by glucose-responsive genes was largely increased, especially in carbohydrate and lipid metabolic pathways. We present some characteristic metabolic regulatory pathways found in central carbon, branched amino acids, and ketone body metabolism. Our transomic analysis will provide insights into how skeletal muscle responds to changes in blood glucose and how it fails to respond in obesity., (© 2022. The Author(s).)

Insulin signaling promotes anabolic metabolism to regulate cell growth through multi-omic interactions. To obtain a comprehensive view of the cellular responses to insulin, we constructed a trans-omic network of insulin action in Drosophila cells that involves the integration of multi-omic data sets. In this network, 14 transcription factors, including Myc, coordinately upregulate the gene expression of anabolic processes such as nucleotide synthesis, transcription, and translation, consistent with decreases in metabolites such as nucleotide triphosphates and proteinogenic amino acids required for transcription and translation. Next, as cell growth is required for cell proliferation and insulin can stimulate proliferation in a context-dependent manner, we integrated the trans-omic network with results from a CRISPR functional screen for cell proliferation. This analysis validates the role of a Myc-mediated subnetwork that coordinates the activation of genes involved in anabolic processes required for cell growth., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

There have been a number of reports about the transcriptional regulatory networks in schizophrenia. However, most of these studies were based on a specific transcription factor or a single dataset, an approach that is inadequate to understand the diverse etiology and underlying common characteristics of schizophrenia. Here we reconstructed and compared the transcriptional regulatory network for lipid metabolism enzymes using 15 public transcriptome datasets of neural cells from schizophrenia patients. Since many of the well-known schizophrenia-related SNPs are in enhancers, we reconstructed a network including enhancer-dependent regulation and found that 53.3 % of the total number of edges (7,577 pairs) involved regulation via enhancers. By examining multiple datasets, we found common and unique transcriptional modes of regulation. Furthermore, enrichment analysis of SNPs that were connected with genes in the transcriptional regulatory networks by eQTL suggested an association with hematological cell counts and some other traits/diseases, whose relationship to schizophrenia was either not or insufficiently reported in previous studies. Based on these results, we suggest that in future studies on schizophrenia, information on genotype, comorbidities and hematological cell counts should be included, along with the transcriptome, for a more detailed genetic stratification and mechanistic exploration of schizophrenia., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)