Biology subjects

Hase, T.

Publications and source records attributed to Hase, T..

4 recordsLinked to original sources

bioRxiv2025

Local, but not circulating, complement C3 governs immune checkpoint blockade efficacy

The roles of circulating complement C3, produced in hepatocytes, in immune defense are well defined in the literature. Interestingly, the C3 gene evolved before the circulatory system, suggesting an essential but yet unknown role of locally produced C3. Here, we investigated the effect of C3 on cancer immunotherapy and found that cancer-associated fibroblast (CAF)-derived C3, not hepatocyte-derived C3, determines the efficacy of immune checkpoint blockade (ICB). Tumors developed in CAF-specific C3 knockout mice exhibited resistance to anti-PD-1 therapy, with increased immunosuppressive M2-like macrophage infiltration. Mechanistically, the C3 degradation product iC3b suppressed myeloid cell infiltration via complement receptor 3 signaling, and the therapeutic targeting of this pathway restored ICB sensitivity of immunotherapy-resistant tumors. In human cancers, stromal C3 expression correlates with reduced M2-like macrophage infiltration and improved immunotherapeutic outcomes. These data demonstrate the role of locally produced C3 in the innate immune response, which may have been conserved across many organisms.

cancer biology↗

bioRxiv2021

Neural network embedding of functional microconnectome.

Our brain works as a complex network system. Experiential knowledge seems to be coded into the organisms network architecture rather than retaining only properties of individual neurons. In order to be better able to consider the high complexity of this network architecture, extracting simple rules through both automated as well as interpretable analysis of topological patterns will be necessary in order to allow more useful observations of interrelationships within the complex neural architecture. By combining these two types of analysis methods, we could automatically compress and naturally interpret topological patterns of functional connectivities, which produced electrical activities from many neurons simultaneously from acute slices of mice brain for 2.5 hours [Kajiwara et al. 2021]. As the first type of analysis, this study trained an artificial neural network system called Neural Network Embedding (NNE), and automatically compressed the functional connectivities into only small (25%) dimensions. As the second type of analysis, we widely compared the compressed features with ~15 representative network metrics, having clear interpretations, including > 5 centrality-type metrics and newly developed network metrics, that quantify degrees or ratio of hubs distanced by several-nodes from initially focused hubs. As the result, although we could give interpretations for only 55-60% of the extracted features, these new metrics, together with the commonly utilized network metrics, enabled interpretations for 80-100% features, using automated analysis. The result demonstrates not only the fact that the NNE method surpasses limitations of commonly used human-made variables, but also the possibility that acknowledgement of our own limitations drives us to extend interpretable possibilities by developing new analytic methodologies.

neuroscience↗

bioRxiv2020

Artificial intelligence based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease

BackgroundIdentification of novel therapeutic targets is a key for successful drug development. However, the cost to experimentally identify therapeutic targets is huge and only 400 genes are targets for FDA-approved drugs. Therefore, it is inevitable to develop powerful computational tools to identify potential novel therapeutic targets. Because proteins make their functions together with their interacting partners, a protein-protein interaction network (PIN) in human could be a useful resource to build computational tools to investigate potential targets for therapeutic drugs. Network embedding methods, especially deep-learning based methods would be useful tools to extract an informative low-dimensional latent space that contains enough information required to fully represent original high-dimensional non-linear data of PINs. ResultsIn this study, we developed a deep learning based computational framework that extracts low-dimensional latent space embedded in high-dimensional data of the human PIN and uses the features in the latent space (latent features) to infer potential novel targets for therapeutic drugs. We examined the relationships between the latent features and the representative network metrics and found that the network metrics can explain a large number of the latent features, while several latent features do not correlate with all the network metrics. The results indicate that the features are likely to capture information that the representative network metrics can not capture, while the latent features also can capture information obtained from the network metrics. Our computational framework uses the latent features together with state-of-the-art machine learning techniques to infer potential drug target genes. We applied our computational framework to prioritized novel putative target genes for Alzheimers disease and successfully identified key genes for potential novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we inferred repositionable candidate-compounds for the disease (e.g., Tamoxifen, Bosutinib, and Dasatinib) DiscussionsOur computational framework could be powerful computational tools to efficiently prioritize new therapeutic targets and drug repositioning. It is pertinent to note here that our computational platform is easily applicable to investigate novel potential targets and repositionable compounds for any diseases, especially for rare diseases.

bioinformatics↗

bioRxiv2018

A Dual Controllability Analysis of Influenza Virus-Host Protein-Protein Interaction Networks for Antiviral Drug Target Discovery

Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. This knowledge can be utilized to understand disease dynamics and isolate proteins for study as drug target candidates. Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets.\n\nImportanceSeasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year, with a constant threat of pandemic infection. Even so, the FDA has only approved four treatments, two of which are unsuited for at risk groups such as children and those with breathing complications. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. Controllability analyses identify key regulating host factors of the infected cells progression, findings which are supported by biological context. These results are beneficial to future studies of influenza virus, both experimental and computational.

systems biology↗