Biology subjects

Kashima, Y.

Publications and source records attributed to Kashima, Y..

13 recordsLinked to original sources

bioRxiv2026

Spatial profiling of CAR protein organization reveals in vivo remodeling during CAR-T therapy

Chimeric antigen receptor (CAR) T cell therapy utilizes genetically engineered patient-derived T cells to target cancer cells. Despite its clinical successes in multiple cancer types, the underlying molecular mechanisms by which molecules on CAR-T cells and surrounding cells interact with other proteins and collectively determine treatment efficacy remain elusive. Most previous studies have relied on transcriptome profiling, which does not fully reflect protein-level organization and interactions. In this study, we developed an antibody-oligonucleotide conjugate targeting the FMC63 region of CAR and integrated it into molecular pixelation (MPX). This approach enabled profiling of the dynamics of CAR molecules on cell surfaces as well as their colocalization with other proteins at the single-cell level. By applying MPX to longitudinal samples from three patients undergoing CAR-T cell therapy, we characterized the dynamic changes in CAR-associated protein organization in both pre-infusion CAR products and post-infusion peripheral blood. While CAR protein abundance and polarization showed limited variation across clinical courses, remodeling of a CAR-centered co-localization network was observed over time, including different retentions of specific molecular associations between patients with different clinical outcomes. Although derived from a limited cohort, our study identifies insights from this methodological framework beyond those gained by conventional omics analyses and offers results of a systematic investigation to predict and enhance CAR therapeutic outcomes. Key pointsO_LIMolecular pixelation was applied for chimeric antigen receptor (CAR) profiling at single-molecule and single-cell resolutions. C_LIO_LIProtein and transcriptome analyses of the CAR molecule showed dynamic remodeling during CAR-T therapy in patients with non-Hodgkin lymphoma. C_LI

genomics↗

bioRxiv2026

Experimental SARS-CoV-2 infection using horseshoe bats

Horseshoe bats are known as the natural reservoir of sarbecoviruses. To understand how horseshoe bats coexist with sarbecoviruses in nature, experimental infection can provide direct evidence. However, in vivo infection studies using horseshoe bats have been lacking because of the difficulty of maintaining insectivorous bats in a laboratory setting. Here, we established a stable husbandry system for greater horseshoe bats (Rhinolophus ferrumequinum) and performed experimental infection with SARS-CoV-2. In contrast to Syrian hamsters which showed substantial viral replication, infected horseshoe bats exhibited low-level but persistent viral replication in the lung without overt disease. Histological analyses revealed that inflammatory lesions in the bat lungs were spatially restricted and temporally delayed compared with those in hamsters. Transcriptomic analyses further showed preferential activation of tissue repair pathways but limited inflammatory responses following infection. Notably, bats expressed several interferon-stimulated genes prior to infection. Our results suggest that a host strategy combining constitutive antiviral state, limited inflammation and enhanced tissue repair may result in controlled viral replication without overt disease, likely enabling horseshoe bats to coexist with sarbecoviruses.

microbiology↗

bioRxiv2024

Giant extrachromosomal element "Inocle" potentially expands the adaptive capacity of the human oral microbiome

Survival strategy of bacteria is expanded by extrachromosomal elements (ECEs). However, their genetic diversity and functional roles for adaptability are largely unknown. Here, we discovered a novel family of intracellular ECEs using 56 saliva samples by developing an efficient microbial DNA extraction method coupled with long-read metagenomics assembly. Even though this ECE family was not hitherto unidentified, our global prevalence analysis using 476 salivary metagenomic datasets elucidated that these ECEs reside in 74% of the population. These ECEs, which we named, "Inocles", are giant plasmid-like circular genomic elements of 395 kb in length, having Streptococcus as a host bacterium. Inocles encode a series of genes that contribute to intracellular stress tolerance, such as oxidative stress and DNA damage, and cell wall biosynthesis and modification involved in the interactions with oral epithelial cells. Moreover, Inocles exhibited significant positive correlations with immune cells and proteins responding to microbial infection in peripheral blood. Intriguingly, we examined and found their marked reductions among 68 patients of head and neck cancers and colorectal cancers, suggesting its potential usage for a novel biomarker of gastrointestinal cancers. Our results suggest that Inocles potentially boost the adaptive capacity of host bacteria against various stressors in the oral environment.

genomics↗

bioRxiv2023

Low albumin status accompanies multi-layered immunosuppressive phenotypes in metastatic breast cancer patients

Low albumin status is prevalent in advanced cancer patients, but the pathophysiology associated with this anomaly remains largely unexplored. To address this, we aim to search correlations of albumin levels with the transcriptome against peripheral blood mononuclear cells and the plasma metabolome within the same patients having metastatic breast cancers. We confirm that metastatic breast cancer patients exhibit low albumin levels in varying degrees without prominent systemic inflammation. Our data demonstrate that low albumin levels correlate with transcriptome signatures indicative of "neutrophil activation and T-cell down-regulation," an immunosuppressive phenotype. We also find that immunoregulatory metabolites, such as arginine, are reduced in plasma in an albumin-correlated manner, further corroborating systemic immunosuppression. These results are verified using a mouse model of breast cancer. We conclude that low albumin status in metastatic breast cancer patients accompanies immunosuppressive phenotypes, which is likely unfavorable for anti-cancer immunotherapy and thus can be a cause of unsuccessful treatment outcomes.

cancer biology↗

bioRxiv2023

Characterization of an EG.5.1 clinical isolate in vitro and in vivo

EG.5.1 is a subvariant of the SARS-CoV-2 Omicron XBB variant that is rapidly increasing in prevalence worldwide. EG.5.1 has additional substitutions in its spike protein (namely, Q52H and F456L) compared with XBB.1.5. However, the pathogenicity, transmissibility, and immune evasion properties of clinical isolates of EG.5.1 are largely unknown. In this study, we used wild-type Syrian hamsters to investigate the replicative ability, pathogenicity, and transmissibility of a clinical EG.5.1 isolate. Our data show that there are no obvious differences in growth ability and pathogenicity between EG.5.1 and XBB.1.5, and both EG.5.1 and XBB.1.5 are attenuated compared to a Delta variant isolate. We also found that EG.5.1 is transmitted more efficiently between hamsters compared with XBB.1.5. In addition, unlike XBB.1.5, we detected EG.5.1 virus in the lungs of four of six exposed hamsters, suggesting that the virus tropism of EG.5.1 is different from that of XBB.1.5 after airborne transmission. Finally, we assessed the neutralizing ability of plasma from convalescent individuals and found that the neutralizing activity against EG.5.1 was slightly, but significantly, lower than that against XBB.1.5 or XBB.1.9.2. This suggests that EG.5.1 effectively evades humoral immunity and that the amino acid differences in the S protein of EG.5.1 compared with that of XBB.1.5 or XBB.1.9.2 (i.e., Q52H, R158G, and F456L) alter the antigenicity of EG.5.1. Our data suggest that the increased transmissibility and altered antigenicity of EG.5.1 may be driving its increasing prevalence over XBB.1.5 in the human population.

microbiology↗

bioRxiv2022

Single-cell transcriptome analysis illuminating the characteristics of species-specific innate immune responses against viral infections

Bats harbor various viruses without severe symptoms and act as their natural reservoirs. The tolerance of bats against viral infections is assumed to originate from the uniqueness of their immune system. However, how immune responses vary between primates and bats remains unclear. Here, we characterized differences in the immune responses by peripheral blood mononuclear cells to various pathogenic stimuli between primates (humans, chimpanzees, and macaques) and bats (Egyptian fruit bats) using single-cell RNA sequencing. We show that the induction patterns of key cytosolic DNA/RNA sensors and antiviral genes differed between primates and bats. A novel subset of monocytes induced by pathogenic stimuli specifically in bats was identified. Furthermore, bats robustly respond to DNA virus infection even though major DNA sensors are dampened in bats. Overall, our data suggest that immune responses are substantially different between primates and bats, presumably underlying the difference in viral pathogenicity among the mammalian species tested.

microbiology↗

bioRxiv2022

Deep learning for histopathological subtyping and grading of lung adenocarcinoma

The histopathological distinction of lung adenocarcinoma (LADC) subtypes is subject to high inter-observer variability, which can compromise the optimal assessment of the patient prognosis. Therefore, this study developed convolutional neural networks (CNNs) capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the LADC tumour grades established recently by the International Association for the Study of Lung Cancer pathology committee. Consensus LADC ground truth histopathological images were obtained from seventeen expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained with EfficientNet b3 architecture to predict eight different LADC classes (lepidic, acinar, papillary, micropapillary, solid, invasive mucinous adenocarcinoma, other carcinoma types, and no carcinoma cells). Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1-scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models. Moreover, the grading prediction of one of the trained models was more accurate than those of 14 out of 15 pulmonary pathologists involved in the study (p=0.0003). Both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained CNNs improve the diagnostic accuracy of the pathologist, standardise LADC subtype recognition, and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.

pathology↗

bioRxiv2022

Collaborative workflow between pathologists and deep learning for evaluation of tumor cellularity in lung adenocarcinoma

Owing to the high demand for molecular testing, the reporting of tumor cellularity in cancer samples has become a mandatory task for pathologists. However, the pathological estimation of tumor cellularity is often inaccurate. We developed a collaborative workflow between pathologists and artificial intelligence (AI) models to evaluate tumor cellularity in lung cancer samples and prospectively applied it to routine practice. We also developed a quantitative model that we validated and tested on retrospectively analyzed cases and ran the model prospectively in a collaborative workflow where pathologists could access the AI results and apply adjustments (Adjusted-Score). The Adjusted-Scores were validated by comparing them with the ground truth established by manual annotation of hematoxylin-eosin slides with reference to immunostains with thyroid transcription factor-1 and napsin A. For training, validation, retrospective testing, and prospective application of the model, we used 40, 10, 50, and 151 whole slide images, respectively. The sensitivity and specificity of tumor segmentation were 97% and 87%, and the accuracy of nuclei recognition was 99%. Pathologists altered the initial scores in 87% of the cases after referring to the AI results and found that the scores became more precise after collaborating with AI. For validation of Adjusted-Score, we found the Adjusted-Score was significantly closer to the ground truth than non-AI-aided estimates (p<0.05). Thus, an AI-based model was successfully implemented into the routine practice of pathological investigations. The proposed model for tumor cell counting efficiently supported the pathologists to improve the prediction of tumor cellularity for genetic tests.

pathology↗

bioRxiv2021

Intensive Single Cell Analysis Reveals Immune Cell Diversity among Healthy Individuals

It is believed that immune responses are different between individuals and at different times. In addition, personal health histories and unique environmental conditions should collectively determine the present state of immune cells. However, the cellular and molecular system mechanisms underlying such heterogeneity remain largely elusive. In this study, we conducted a systematic time-lapse single-cell analysis, using 171 single-cell libraries and 30 mass cytometry datasets intensively for seven healthy individuals. We found substantial diversity in immune cell populations and their gene expression patterns between different individuals. These patterns showed daily fluctuations even within the same individual spending a usual life. Similar diversities were also observed for the T cell receptor and B cell receptor repertoires. Detailed immune cell profiles at healthy statuses should give an essential background information to understand their immune responses, when the individual is exposed to various environmental conditions. To demonstrate this idea, we conducted the similar analysis for the same individuals on the vaccination of Influenza and SARS-CoV-2, since the date and the dose of the antigens are well-defined in these cases. In fact, we found that the distinct responses to vaccines between individuals, althougth key responses are common. Single cell immune cell profile data should make fundamental data resource to understand variable immune responses, which are unique to each individual.

genomics↗

bioRxiv2021

Decoding explicit and implicit representations of health and taste attributes of foods in the human brain

Obesity has become a significant problem word-wide and is strongly linked to poor food choices. Even in healthy individuals, taste perceptions often drive dietary decisions more strongly than healthiness. This study tested whether health and taste representations can be directly decoded from brain activity, both when explicitly considered, and when implicitly processed for decision-making. We used multivariate support vector regression for event-related potentials (as measured by the electroencephalogram) occurring in the first second of food cue processing to predict ratings of tastiness and healthiness. In Experiment 1, 37 healthy participants viewed images of various foods and explicitly rated their tastiness and healthiness, whereas in Experiment 2, 89 healthy participants indicated their desire to consume snack foods, with no explicit instruction to consider tastiness or healthiness. In Experiment 1 both attributes could be decoded, with taste information being available earlier than health. In Experiment 2, both dimensions were also decodable, and their significant decoding preceded the decoding of decisions (i.e., desire to consume the food). However, in Experiment 2, health representations were decodable earlier than taste representations. These results suggest that health information is activated in the brain during the early stages of dietary decisions, which is promising for designing obesity interventions aimed at quickly activating health awareness.

neuroscience↗

bioRxiv2019

Evolutionary stories told by one protein family: ERM phylogeny in metazoans

Ezrin, radixin, moesin, and merlin are the cytoskeletal proteins which functions are specific to metazoans. They participate in cell cortex rearrangements, including cell-cell contact formation, and play important role in cancer progression. Here we perform a comprehensive phylogenetic analysis of the proteins spanning 87 species. The results describe a possible mechanism of the protein family origin in the root of Metazoa, paralogs diversification in vertebrates and acquisition of novel functions, including tumor suppression. In addition, a merlin paralog, present in most of vertebrates, but lost in mammals, has been described. We also highlight the set of amino acid variations within the conserved motifs as the candidates for determining physiological differences between the ERM paralogs.

evolutionary biology↗

bioRxiv2019

Long read sequencing reveals a novel class of structural aberrations in cancers: identification and characterization of cancerous local amplifications

Here we report identification of a new class of local structural aberrations in lung cancers. The whole-genome sequencing of cell lines using a long read sequencer, PromethION, demonstrated that typical cancerous mutations, such as point mutations, large deletions and gene fusions can be detected also on this platform. Unexpectedly, we revealed unique structural aberrations consisting of complex combinations of local duplications, inversions and micro deletions. We further analyzed and found that these mutations also occur in vivo, even in key cancer-related genes. These mutations may elucidate the molecular etiology of patients for whom causative cancerous events and therapeutic strategies remain elusive.

genomics↗

bioRxiv2018

The economy and environmental attitudes: Does a good economy make citizens care more about the environment?

The relationship between economic conditions and environmental attitudes has been hotly debated in academic and public discourse. Some contend that economic prosperity strengthens environmental attitudes, while others argue that no such relationship exists. We shed light on the economy-environmental importance relationship by incorporating both nonlinear effects and public perceptions of the economy. Two countries with comparable economic performance and cultural heritage, Australia and Canada, are investigated. Using opinion survey data in a time-series analysis over a 20-year period, we find that GDP per capita has a nonlinear relationship with environmental attitudes in both countries, and the inclusion of perceptions of the economy significantly improves the prediction of environmental attitudes. In both Australia and Canada, a U-shaped relationship is observed; initial improvements in economic conditions tend to worsen environmental attitudes, but past a certain threshold, further economic improvements result in increased environmental concern. Perceptions of the economy too showed analogous trends. In both countries, positive perceptions of the economy were associated with strong environmental attitudes; at least in Canada, negative perceptions of the economy were also positively associated with environmental attitudes. The nonlinear effects of the economy as well as perceptions of the economy helps shed light on the current inconclusive literature on the relationship between the economy and citizens pro-environmental support. This is an important consideration given the critical importance to the future sustainability of the natural environment.

scientific communication and education↗