Biology subjects

Ise, T.

Publications and source records attributed to Ise, T..

4 recordsLinked to original sources

bioRxiv2024

Generation of antagonistic biparatopic anti-CD30 antibody from an agonistic antibody by precise epitope determination and utilization of structural characteristics of CD30 molecule

CD30 is a type I membrane protein that has been successfully targeted for lymphoma therapy using Brentuximab vedotin, an antibody-drug conjugate. Recently, the potential of blocking CD30-dependent NF-{kappa}B intracellular signaling has gained attention for treating inflammatory disorders. Development of antibody-based CD30 antagonists would broaden therapeutic strategies. A challenge in developing antagonistic antibodies is that the bivalent form of natural antibody format inevitably cross-links trace amounts of CD30 molecules, leading to signal transduction. In this study, we developed a series of biparatopic antibodies with each pair of antibody variable domains (Fvs) binding to distinct epitopes on CD30, and evaluated their biological activities and binding modes. Initially, we precisely identified epitope sites of the nine antibodies precisely by assessing binding to multiple orthologous CD30 proteins and mutants. We then produced 36 biparatopic antibodies covering all possible combinations of the nine Fvs, and analyzed their biological activities. Among these, we identified both potent agonists and antagonists. Notably, a significant proportion of the biparatopic antibodies displayed reduced agonistic activities, including 1:1-binding antagonists derived from a Fv of a strong agonist previously developed for lymphoma therapy, AC10. The mechanism of signaling activity induction is discussed using epitope information, which leads to the strategies of the development of biparatopic antibodies.

biochemistry↗

bioRxiv2024

Discovery of anti-SARS-CoV-2 S2 protein antibody CV804 with broad-spectrum reactivity with various beta coronaviruses and analysis of its pharmacological properties in vitro and in vivo

SARS-CoV-2 pandemic alerts us that spillovers of various animal coronaviruses to human in the future may bring us enormous damages. Thus, there is a significant need of antibody-based drugs to treat patients infected with previously unseen coronaviruses.CV804 against the S2 domain of the spike protein, which is less prone to mutations. CV804 shows not only broad cross-reactivities with representative 20 animal-origin coronaviruses but also with diseases-associated human beta coronaviruses including SARS-CoV, MERS-CoV, HCoV-OC43, HCoV-HKU1 and mutant strains of SARS-CoV-2. Other than that, the main characteristics of CV804 are that it has strong antibody-dependent cellular cytotoxicity (ADCC) activity to SARS-CoV2 spike protein-expressed cells in vitro and completely lacks virus-neutralization activity. Comprehensively in animal models, CV804 suppressed disease progression by SARS-CoV-2 infection. Structural studies using HDX-MS and point mutations of recombinant spike proteins revealed that CV804 binds to a unique epitope within the highly conserved S2 domain of the spike proteins of various coronaviruses. Based on the overall data, we suggest that the non-neutralizing CV804 antibody recognizes the conformational structure of the spike protein expressed on the surface of the infected cells and weakens the viral virulence by supporting host immune cells attack through ADCC activity in vivo. CV804 epitope identified in this study is not only useful for the design of pan-corona antibody therapeutics but also to design next-generation coronavirus vaccines and antiviral drugs.

microbiology↗

bioRxiv2022

Development of a 1:1-binding biparatopic anti-TNFR2 antagonist by epitope selection

Conventional bivalent antibodies against cell surface receptors often initiate unwanted signal transduction by crosslinking two antigen molecules. Biparatopic antibodies (BpAbs) bind to two different epitopes on the same antigen, thus altering crosslinking ability. In this study, we developed BpAbs against tumor necrosis factor receptor 2 (TNFR2), which is an attractive immune checkpoint target. Using different pairs of variable regions specific to topographically distinct TNFR2 epitopes, we successfully regulated the size of BpAb-TNFR2 immunocomplexes to result in controlled agonistic activities. One particular antagonist BpAb bound TNFR2 in 1:1 ratio without unwanted signal transduction, with its structural basis revealed by cryo-electron microscopy. This antagonist suppressed the proliferation of regulatory T cells expressing TNFR2. Therefore, the BpAb format would be useful in designing specific and distinct antibody functions.

biochemistry↗

bioRxiv2018

Using deep learning for bamboo forest detection from Google Earth images.

Classifying and mapping vegetation are very important tasks in environmental science and natural resource management. However, these tasks are not easy because conventional methods such as field surveys are highly labor intensive. Automatic identification of target objects from visual data is one of the most promising ways to reduce the costs for vegetation mapping. Although deep learning has become a new solution for image recognition and classification recently, in general, detection of ambiguous objects such as vegetation still is considered difficult. In this paper, we investigated the potential for adapting the chopped picture method, a recently described protocol for deep learning, to detect plant communities in Google Earth images. We selected bamboo forests as the target. We obtained Google Earth images from three regions in Japan. By applying the deep convolutional neural network, the model successfully learned the features of bamboo forests in Google Earth images, and the best trained model correctly detected 97% of the targets. Our results show that identification accuracy strongly depends on the image resolution and the quality of training data. Our results also highlight that deep learning and the chopped picture method can potentially become a powerful tool for high accuracy automated detection and mapping of vegetation.

ecology↗