Biology subjects

Kamada, M.

Publications and source records attributed to Kamada, M..

4 recordsLinked to original sources

bioRxiv2022

Three-dimensional visualization of moss rhizoid system by refraction-contrast X-ray micro-computed tomography

Land plants have two types of shoot-supporting systems, root system and rhizoid system, in vascular plants and bryophytes. However, since the evolutionary origin of the systems are different, how much they exploit common systems or distinct systems to architect their structures are largely unknown. To understand the regulatory mechanism how bryophytes architect rhizoid system responding to an environmental factor, such as gravity, and compare it with the root system of vascular plants, we have developed the methodology to visualize and quantitatively analyze the rhizoid system of the moss, Physcomitrium patens in 3D. The rhizoids having the diameter of 21.3 m on the average were visualized by refraction-contrast X-ray micro-CT using coherent X-ray optics available at synchrotron radiation facility SPring-8. Three types of shape (ring-shape, line, black circle) observed in tomographic slices of specimens embedded in paraffin were confirmed to be the rhizoids by optical and electron microscopy. Comprehensive automatic segmentation of the rhizoids which appeared in different three form types in tomograms was tested by a method using Canny edge detector or machine learning. Accuracy of output images was evaluated by comparing with the manually-segmented ground truth images using measures such as F1 score and IoU, revealing that the automatic segmentation using the machine learning was more effective than that using Canny edge detector. Thus, machine learning-based skeletonized 3D model revealed quite dense distribution of rhizoids, which was similar to root system architecture in vascular plants. We successfully visualized the moss rhizoid system in 3D for the first time.

plant biology↗

bioRxiv2022

A model for generating differences in microtubules between axonal branches depending on the distance from terminals

In the remodeling of axonal arbor, the growth and retraction of branches are differentially regulated within a single axon. Although cell-autonomously generated differences in microtubule (MT) turnover are thought to be involved in selective branch regulation, the cellular system whereby neurons generate differences of MTs between axonal branches has not been clarified. Because MT turnover tends to be slower in longer branches compared with neighboring shorter branches, feedback regulation depending on branch length is thought to be involved. In the present study, we generated a model of MT lifetime in axonal terminal branches by adapting a length-dependent model in which parameters for MT dynamics were constant in the arbor. The model predicted that differences in MT lifetime between neighboring branches could be generated depending on the distance from terminals. In addition, the following points were predicted. Firstly, destabilization of MTs throughout the arbor decreased the differences in MT lifetime between branches. Secondly, differences of MT lifetime existed even before MTs entered the branch point. In axonal MTs in primary neurons, treatment with a low concentration of nocodazole significantly decreased the differences of detyrosination (deTyr) and tyrosination (Tyr) of tubulins, indicators of MT turnover. Expansion microscopy of the axonal shaft before the branch point revealed differences in deTyr/Tyr modification on MTs. Our model recapitulates the differences in MT turnover between branches and provides a feedback mechanism for MT regulation that depends on the axonal arbor geometry.

neuroscience↗

bioRxiv2021

Distinct and interchangeable growing patterns in colorectal cancer stem-like cells are regulated by Musashi-1

The dynamic and heterogeneous features of cancer stem-like cells (CSCs) have been widely recognized, but their nongenetic cellular plasticity mechanisms remain elusive. By using colorectal cancer organoids, we phenotypically tracked their spheroid formation and growth capacity to a single-cell resolution, and we discovered that the spheroid-forming cells exhibit a heterogeneous growth pattern, consisting of slow- and fast-growing spheroids. The isolated fast-growing spheroids seem to preserve a dual-growing pattern through multiple passages, whereas the isolated slow-growing spheroids are restricted to a slow-growing pattern. Notably, the spheroids of both patterns were tumorigenic. Moreover, the expression of CSC markers varied among the subpopulations with different growth patterns. The isolated slow-growing spheroids adopted the dual-growing pattern by various extrinsic triggers, in which Musashi-1 plays a key role. The slow-growing fraction was resistant to chemotherapy, and its successful isolation can provide an in vitro platform allowing us to elucidate their role in drug resistance.

cancer biology↗

bioRxiv2021

Network-based pathogenicity prediction for variants of uncertain significance

While the number of genome sequences continues to increase, the functions of many detected gene variants remain to be identified. These variants of uncertain significance constitute a major barrier to precision medicine 1-3. Although many computational methods have been developed to predict the function of these variants, they all rely on individual gene features and do not consider complex molecular relationships. Here we develop PathoGN, a molecular network-based approach for predicting variant pathogenicity. PathoGN significantly outperforms existing methods using benchmark datasets. Moreover, PathoGN successfully predicts the pathogenicity of 3,994 variants of uncertain significance in the real-world database ClinVar and designates potential pathogenicity. This is the first computational method for the clinical interpretation of variants using biomolecular networks, and we anticipate our method to be broadly useful for the clinical interpretation of variants and for assigning biological function to unknown variants at the genomic scale.

genomics↗