Biology subjects

Mizutani, Y.

Publications and source records attributed to Mizutani, Y..

7 recordsLinked to original sources

bioRxiv2026

Integration of UAS-based spatial surveys and bio-logging tracking enhances precision in population size estimation

Accurately estimating wildlife population sizes, essential for ecological theory and conservation management, yet remains challenging. Although unmanned aerial systems (UASs) combined with machine learning, have revolutionized population estimation, they face limitations in addressing the hierarchical population processes from individual behavior to colony-and population-level dynamics. To overcome this limitation, we developed a data integration framework that jointly analyzes multiple datasets, representing different scales of the same underlying process, were jointly analyzed. Using a seabird colony as a model system, we integrated UAS-based count data in the colony with bio-logging-based tracking data to estimate population size by quantifying both the number of individuals present and the proportion absent from the surveyed area. These complementary datasets were linked using state-space models allowing accurate population estimates with explicit uncertainty quantification. Furthermore, we evaluated the robustness of the estimations with respect to sample size. Sub-sampling simulations revealed that estimation uncertainty was more sensitive to sample size in bio-logging-based tracking data than in UAS-based count data. This finding highlights the importance of understanding dataset-specific properties when designing effective investigations. Overall, our resource-efficient framework is broadly applicable across species and populations and demonstrates how integrating complementary observation methods can improve population estimates and inform conservation practice.

ecology↗

bioRxiv2025

A Structure-guided, Indel-aware Framework for Ancestral Reconstruction of Full-length Seven-transmembrane Proteins

Microbial rhodopsins exhibit diverse functions ranging from ion pumps and channels to light sensors, despite sharing a common seven-transmembrane (7TM) architecture. Understanding how this functional diversity evolved is a long-standing problem, and ancestral sequence reconstruction (ASR) offers a direct route to inferring and experimentally testing plausible ancestral rhodopsins. However, ASR of 7TM proteins is often limited by alignment ambiguity and insertion-deletion (indel) uncertainty, especially in extra-membrane (EM) loops and termini. As a result, many studies focus on trimmed transmembrane (TM) cores and treat EM regions by manual curation, leaving the evolutionary history of full-length architecture difficult to test experimentally. Here we reconstruct and resurrect full-length ancestral schizorhodopsins (Anc-SzR) and heliorhodopsins (Anc-HeR), two microbial rhodopsin families that share a retinal-binding 7TM core but differ in membrane topology and EM secondary-structure elements. Starting from untrimmed alignments, we combine structure-consistent multiple sequence alignments and profile-based evolutionary models with an explicit indel-aware refinement that merges amino-acid ancestral states with binary ancestral gap inference on a fixed topology. Indel-aware refinement prevents artificially overextended ancestors and yields compact full-length sequences. AlphaFold3 predictions for the indel-corrected ancestors support high-confidence 7TM folds and recover lineage-specific EM features, including characteristic {beta}-strands and short helices. Both Anc-SzR and Anc-HeR can be ex-pressed in Escherichia coli and recovered as stable, colored, retinal-binding holoproteins. In a whole-cell pH assay, Anc-SzR shows light-driven proton-transport activity, whereas Anc-HeR shows no detectable ion-pumping signal, consistent with extant heliorhodopsins. Together, these results show that full-length, indel-aware ASR can produce experimentally tractable ancestral microbial rhodopsins and enable direct tests of how EM architecture evolves alongside the 7TM core.

biochemistry↗

bioRxiv2022

Meta-analysis of metagenomes via machine learning and assembly graphs reveals strain switches in Crohn's disease

Microbial strains have closely related genomes but may have different phenotypes in the same environment. Shotgun metagenomic sequencing can capture the genomes of all strains present in a community but strain-resolved analysis from shotgun sequencing alone remains difficult. We developed an approach to identify and interrogate strain-level differences in groups of metagenomes. We use this approach to perform a meta-analysis of stool microbiomes from individuals with and without inflammatory bowel disease (IBD; Crohns disease, ulcerative colitis; n = 605), a disease for which there are not specific microbial biomarkers but some evidence that microbial strain variation may stratify by disease state. We first developed a machine learning classifier based on compressed representations of complete metagenomes (FracMinHash sketches) and identified genomes that correlate with IBD subtype. To rescue variation that may not have been present in the genomes, we then used assembly graph genome queries to recover strain variation for correlated genomes. Lastly, we developed a novel differential abundance framework that works directly on the assembly graph to uncover all sequence variants correlated with IBD. We refer to this approach as dominating set differential abundance analysis and have implemented it in the spacegraphcats software package. Using this approach, we identified five bacterial strains that are associated with Crohns disease. Our method captures variation within the entire sequencing data set, allowing for discovery of previously hidden disease associations.

bioinformatics↗

bioRxiv2022

The role of wingbeat frequency and amplitude in flight power

Body-mounted accelerometers provide a new prospect for estimating power use in flying birds, as the signal varies with the two major kinematic determinants of aerodynamic power: wingbeat frequency and amplitude. Yet wingbeat frequency is sometimes used as a proxy for power output in isolation. There is therefore a need to understand which kinematic parameter birds vary and whether this is predicted by flight mode (e.g., accelerating, ascending/descending flight), speed or morphology. We investigate this using high-frequency acceleration data from (i) 14 species flying in the wild, (ii) two species flying in controlled conditions in a wind tunnel and (iii) a review of experimental and field studies. While wingbeat frequency and amplitude were positively correlated, R2 values were generally low, supporting the idea that parameters can vary independently. Indeed, birds were more likely to modulate wingbeat amplitude for more energy-demanding flight modes, including climbing and take-off. Nonetheless, the striking variability even within species and flight types, highlights the complexity of describing the kinematic relationships, which appear sensitive to both the biological and physical context. Notwithstanding this acceleration metrics that incorporate both kinematic parameters should be more robust proxies for power than wingbeat frequency alone.

biophysics↗

bioRxiv2022

Diversity in DNA sequence, structure, and heterozygosity of nuclear rRNA gene region in Neopyropia yezoensis

DNA sequence reads of Neopyropia yezoensis (susabi-nori), its relative N. tenera (asakusa-nori), and their hybrids available in the sequence read archive produced assembled sequences of the nuclear rRNA gene region for 82 samples. Analysis of the assembled sequences revealed structural differences in the region of nuclear rRNA genes, with 17 forms depending on the presence or absence of introns and their lengths. The samples were divided into three groups based on differences in DNA sequences: Japanese N. yezoensis, Chinese N. yezoensis, and N. tenera/hybrids of N. tenera and N. yezoensis that exist in both countries. Despite genetic differentiation, the Japanese and Chinese N. yezoensis exhibit common structural forms. One sample of Chinese N. yezoensis presented almost 1:1 heterozygosity, whereas five other samples of Chinese N. yezoensis showed non-1:1 heterozygosity. In the latter case, neither the ratio of alleles nor the ratio of the number of introns was 1:1, suggesting the existence of an ongoing mechanism to eliminate the nuclear rRNA gene region on one of the homologous chromosomes in N. yezoensis.

plant biology↗

bioRxiv2021

Conversion of cancer-associated fibroblasts from pro- to antitumor improves the sensitivity of pancreatic cancer to chemotherapeutics

Previous therapeutic attempts to deplete cancer-associated fibroblasts (CAFs) or inhibit their proliferation in pancreatic ductal adenocarcinoma (PDAC) were not successful in mice or patients. Thus, CAFs may be tumor suppressive or heterogeneous, with distinct cancer-restraining and -promoting CAFs (rCAFs and pCAFs, respectively). Here, we show that induced expression of the glycosylphosphatidylinositol-anchored protein Meflin, a rCAF-specific marker, in CAFs by genetic and pharmacological approaches improved the chemosensitivity of mouse PDAC. A chemical library screen identified Am80, a synthetic, non-natural retinoid, as a reagent that effectively induced Meflin expression in CAFs. Am80 administration improved the sensitivity of PDAC to chemotherapeutics, accompanied by increases in tumor vessel area and intratumoral drug delivery. Mechanistically, Meflin was involved in the suppression of tissue stiffening by interacting with lysyl oxidase to inhibit its collagen crosslinking activity. These data suggested that modulation of CAF heterogeneity may represent a strategy for PDAC treatment.

cancer biology↗

bioRxiv2019

AI on animals: AI-assisted animal-borne logger never misses the moments that biologists want

Animal-borne data loggers, i.e., biologgers, allow researchers to record a variety of sensor data from animals in their natural environments (Hussey et al. 2015; Kays et al. 2015). This data allows biologists to observe many aspects of the animals lives, including their behavior, physiology, social interactions, and external environment. However, the need to limit the size of these devices to a small fraction of the animals size imposes strict limits on the devices hardware and battery capacities (Kays et al. 2015). Here we show how AI can be leveraged on board these devices to intelligently control their activation of costly sensors, e.g., video cameras, allowing them to make the most of their limited resources during long deployment periods. Our method goes beyond previous works that have proposed controlling such costly sensors using simple threshold-based triggers, e.g., depth-based (Watanuki et al. 2007; Volpov et al. 2015) and acceleration-based (Nishiumi et al. 2018; Brown et al. 2012) triggers. Using AI-assisted biologgers, biologists can focus their data collection on specific complex target behaviors such as foraging activities, allowing them to automatically record video that captures only the moments they want to see. By doing so, the biologger can reserve its battery power for recording only those target activities. We anticipate our work will provide motivation for more widespread adoption of AI techniques on biologgers, both for intelligent sensor control and intelligent onboard data processing. Such techniques can not only be used to control what is collected by such devices, but also what is transmitted off the devices, such as is done by satellite relay tags (Cox et al. 2018).

ecology↗