Biology subjects

Shimamoto, K.

Publications and source records attributed to Shimamoto, K..

4 recordsLinked to original sources

bioRxiv2026

Generating E. coli 0.5 controlled by a half-sized genome

Genome synthesis is a major limitation in generative biology. Here, the half-sized genome of Escherichia coli was constructed by fleshing out an imperfect minimal genome through genome-scale debugging process. Our platform consists of integrated development environment (IDE) and runtime environment (RTE). The genome IDE supported the cell-free assembly of 200-300 kb plasmids and their in vivo fusion into a single 1.7 Mb plasmid. This imperfect genome was stably maintained in E. coli as a guest genome. The RTE relies on the restriction enzyme-mediated self-digestion of the host genome in the presence and absence of the RecA recombinase. The guest genome was tested, debugged, and partially replaced by the host genome to establish E. coli controlled by a 2.3-Mb genome. This is less than half in size of the wildtype and the smallest ever reported. Enfleshing a guest genome will facilitate genome printing that transforms AI-designed genomes into physical ones.

synthetic biology↗

bioRxiv2026

Training Strategy Optimization to Mitigate Shortcut Learning in Pan-Cancer Drug Response Prediction

BackgroundPrediction of in vivo drug response is a central challenge in precision medicine, but the scarcity of labeled clinical data still necessitates the use of large-scale cancer cell line resources for model training. Domain adaptation methods, which aim to transfer knowledge learned from a source domain (cell lines) to a target domain (patients) by aligning feature distributions across domains, are a promising approach to bridge the gap between in vitro models and in vivo patients. However, we observed that these methods can exhibit a significant discrepancy between pan-cancer evaluation metrics and cancer type-specific prediction accuracy. This performance gap warrants a detailed investigation into their underlying predictive characteristics. ResultsWe discovered that cancer-type-specific class imbalances in training data can lead domain adaptation models to engage in shortcut learning, where they primarily discriminate between cancer types rather than capturing the actual biological determinants of drug sensitivity. To address this, we propose a strategy of combining two approaches: (1) excluding cancer types causing imbalance from the training data, and (2) adjusting class balance through oversampling and class weighting while retaining cancer types causing the imbalance. Among all configurations tested in conjunction with the CODE-AE (Context-aware Deconfounding AutoEncoder) framework, the combination of moderate oversampling (30% non-responder ratio) with class weighting achieved the best performance, significantly improving prediction accuracy in 5 out of 11 external patient cohorts from TCGA and GEO. ConclusionsOur findings demonstrate that appropriate class imbalance correction--rather than wholesale exclusion of imbalanced cancer subtypes--enables effective utilization of biologically relevant information shared across cancer types for drug response prediction. This study highlights the critical importance of jointly optimizing training data composition and class balance adjustment strategies in developing robust pan-cancer drug response prediction models for precision medicine applications. HighlightsO_LIIdentified a critical discrepancy in current domain adaptation models for drug response prediction: high pan-cancer accuracy often masks poor performance within specific cancer types. C_LIO_LIRevealed the root cause as "shortcut learning," where models tend to distinguish between cancer tissue types (hematological vs. solid) rather than learning individual drug sensitivity. C_LIO_LIDiscovered severe class imbalance in training data, with hematological cell lines being disproportionately drug-responsive across multiple chemotherapeutics. C_LIO_LIProposed an architecture-agnostic fix using the CODE-AE framework: moderate oversampling (30% minority ratio) combined with class weighting. C_LIO_LIDemonstrated significant improvements in 5 of 11 external patient cohorts, showing that correcting class bias is more effective than simply excluding problematic data. C_LI

bioinformatics↗

bioRxiv2020

The Small GTPase OsRac1 forms two distinct immune receptor complexes containing the PRR OsCERK1 and the NLR Pit

Plants employ two different types of immune receptors, cell surface pattern recognition receptors (PRRs) and intracellular nucleotide-binding and Leucine-rich repeat-containing proteins (NLRs), to cope with pathogen invasion. Both immune receptors often share similar downstream components and responses but it remains unknown whether a PRR and an NLR assemble into the same protein complex or two distinct receptor complexes. We have previously found that the small GTPase OsRac1 plays key roles in the signaling of OsCERK1, a PRR for fungal chitin, and of Pit, an NLR for rice blast fungus, and associates directly and indirectly with both of these immune receptors. In this study, using biochemical and bioimaging approaches, we reveal that OsRac1 formed two distinct receptor complexes with OsCERK1 and with Pit. Supporting this result, OsCERK1 and Pit utilized different transport systems for anchorage to the plasma membrane. Activation of OsCERK1 and Pit led to OsRac1 activation and, concomitantly, OsRac1 shifted from a small to a large protein complex fraction. We also found that the chaperone Hsp90 contributed to the proper transport of Pit to the plasma membrane and the immune induction of Pit. These findings illuminate how the PRR OsCERK1 and the NLR Pit orchestrate rice immunity through the small GTPase OsRac1.Competing Interest StatementThe authors have declared no competing interest.View Full Text

plant biology↗

bioRxiv2018

In vivo monitoring of plant small GTPase activation using a Förster resonance energy transfer biosensor

Small GTPases act as molecular switches that regulate various plant responses such as disease resistance, pollen tube growth, root hair development, cell wall patterning and hormone responses. Thus, to monitor their activation status within plant cells is believed to be the key step in understanding their roles. We have established a plant version of a Forster resonance energy transfer (FRET) probe called Ras and interacting protein chimeric unit (Raichu) that can successfully monitor activation of the rice small GTPase OsRac1 during various defence responses in rice cells. Here, we describe the protocol for visualizing spatiotemporal activity of plant Rac/ROP GTPase in living plant cells, transfection of rice protoplasts with Raichu-OsRac1 and acquisition of FRET images. Our protocol should be widely adaptable for monitoring activation for other plant small GTPases and for other FRET sensors in various plant cells.